RBI Bulletin - Sep 24, 2025

SEPTEMBER 2025 VOLUME LXXIX NUMBER 9Editorial Committee Indranil Bhattacharyya Anujit Mitra Rekha Misra Anupam Prakash Sunil Kumar Snehal Herwadkar Pankaj Kumar V. Dhanya Shweta Kumari Anirban Sanyal Sujata Kundu Editor Asish Thomas George The Reserve Bank of India Bulletin is issued monthly by the Department of Economic and Policy Research, Reserve Bank of India, under the direction of the Editorial Committee. The Central Board of the Bank is not responsible for interpretation and opinions expressed. In the case of signed articles, the responsibility is that of the author. © Reserve Bank of India 2025 All rights reserved. Reproduction is permitted provided an acknowledgement of the source is made. For subscription to Bulletin, please refer to Section ‘Recent Publications’ The Reserve Bank of India Bulletin can be accessed at https://bulletin.rbi.org.inCONTENTS Speech Balancing Innovation and Prudence- AI’s Role in India’s Financial Future 1 Shri M Rajeshwar Rao Articles State of the Economy 9 Flow of Financial Resources to Commercial Sector in India during 2024-25 43 The Untold Story of FinTech Customers’ Experience 53 Review of Performance of the NBFC Sector 67 Impact of UPI on Cash Demand – Evidence from National and Subnational Levels 79 Is Consumption Inequality Declining? – What the 2022-23 NSSO Survey Tells Us 97 Infrastructure - An Engine of India’s Growth Express 107 Current Statistics 127 Recent Publications 181SPEECH Balancing Innovation and Prudence- AI’s Role in India’s Financial Future Shri M Rajeshwar RaoBalancing Innovation and Prudence- AI’s Role in India’s Financial Future SPEECH Balancing Innovation and The Government of India has articulated an ambitious vision of “Viksit Bharat” i.e., transforming Prudence- AI’s Role in India’s India into a developed country by 2047. This sets the Financial Future* tone for the next step in our technological journey of financial sector and more specifically, in the Shri M. Rajeshwar Rao banking sector. Under Viksit Bharat, the goal is for every adult to not only have a bank account, a target Distinguished guests, participants, ladies and largely achieved under Jan Dhan Yojana1, but also have gentlemen, a very good evening. access to affordable credit, insurance, and investment options. The endeavour of banking sector should be I am delighted to address this august gathering to ensure that benefits of banking, more so of credit of distinguished persons, and key stakeholders across accessibility, is made available to customers across all the financial spectrum in the banking transformation segments on a fair, transparent and affordable basis. summit on the theme of ‘Banking That Builds Bharat: AI-Powered, Credit-Driven’, which is extremely Measures taken by RBI contextual and relevant and encapsulates the spirit In last five years, between 2019-20 to 2024-25, the of Viksit Bharat. bank credit growth has been averaging around 10.5 percent2. It is also seen that the share of retail credit Introduction has grown to 33 per cent3 while the share of credit The Indian banking system has time and again to Micro, Small & Medium Enterprises (MSME) sector exhibited its capability to embrace and adapt to has been growing steadily, forming 18 per cent of total newer technologies. Beginning from the early days bank credit as of March 2025. Before delving into the of computerization in the 1980s, to spread of ATMs role of new technologies, let me briefly first touch in the 1990s, the expansion of internet and mobile upon the steps taken by RBI over the years to increase banking in the 2000s, interoperable infrastructure for flow of credit. payments, data repositories and data sharing since The RBI has undertaken several measures to 2010s, the banking sector has adapted successfully expand both the reach and depth of credit by reducing to the requirements of the changing times and has friction, increasing access points, and lowering the cost over the period made banking more efficient and of financial service delivery. Key initiatives include inclusive. Banking has now entered into a new phase Aadhaar-based KYC, the Central KYC Registry, the of evolution driven by digital democratization. A revised Priority Sector Lending norms, Partial Credit prime example is the homegrown Unified Payments Enhancement Guidelines, and the Account Aggregator Interface (UPI), which has made India a global leader 1 Accounts have grown from 14.72 crore in 2015 to over 56.16 crore by in digital payments which reinforces our belief that August 2025, with around 67% in rural/semi-urban areas and 33% of Jan Dhan accounts were opened in urban/ metro - https://ww**w.pib.gov.in/ responsible innovation can be a powerful driver of PressNoteDetails.aspx?NoteId=155102&ModuleId=3 areas. progress. 2 Handbook of Statistics on the Indian Economy, 2024-25 - Table 44 - Scheduled Commercial Banks - Select Aggregates - Adjusted Bank Credit * Keynote Address delivered by Shri M Rajeshwar Rao, Deputy Governor, (excluding the impact of a merger of non-bank with a bank since July 28, Reserve Bank of India on September 16, 2025 at 3rd edition of the CNBC- 2023). TV18 Banking Transformation Summit on ‘Banking That Builds Bharat: AI- 3 Handbook of Statistics on the Indian Economy, 2024-25 - Table 45 Powered, Credit-Driven’ in Mumbai. Inputs provided by Chandni Trehan - Sectoral Deployment of Non-Food Gross Bank Credit - Share of Non- Saluja and Abhishek Kumar Narwal are gratefully acknowledged. Adjusted Personal Loans RBI Bulletin September 2025 1SPEECH Balancing Innovation and Prudence- AI’s Role in India’s Financial Future framework4. In response to the growing digitalisation met by formal institutions10. While the Financial of credit, the Digital Lending Guidelines5 were Inclusion Index developed by RBI has shown steady introduced to ensure transparency, fairness, data improvement, rising from 53.9 in March 2021 to 67.0 privacy, while enabling fund flow to wider sectors. in March 202511 reflecting the growth in account The RBI has also enabled formal credit access through ownership and credit access, scope remains in frameworks like Trade Receivables e-Discounting effecting improvement in the usage and quality of System (TReDS) for MSMEs, co-lending models services delivered. The gap between inclusion and between banks and NBFCs, and targeted refinancing credit access is a challenge and an opportunity to schemes. banking fraternity. The credit needs to not only grow in terms of volume and numbers but also needs to More recently, the RBI has, in collaboration with its subsidiary RBI Innovation Hub (RBIH), tested a be directed towards productive, sectors that deliver prototype of the Public Tech Platform for Frictionless higher multipliers such as MSMEs, infrastructure, Credit6 to enable seamless digital data flow to lenders. informal sectors, and rural population, to achieve Building on this, the Unified Lending Interface not just the goal of “Viksit Bharat” but to have a (ULI)7 is being developed to transform credit delivery “Samaveshi Viksit Bharat”. by integrating access to both financial and non- The new wave in banking- Artificial Intelligence financial data such as digitised land records, Goods Driving the next credit revolution will require and Services Tax Network (GSTN) data, property harnessing new technologies. Over time, the role of records, and satellite data along with services like technology in finance has shifted from improving e-KYC, the Account Aggregator framework, and Credit operational efficiency to fully automating and Guarantee Fund Trust for Micro and Small Enterprises centralizing previously manual, fragmented processes. (CGTMSE). This ecosystem aims to make lending Among emerging technologies, Artificial Intelligence faster, cheaper, and more accessible to underserved (AI) stands out for its vast potential from strengthening segments. Together, this digital public infrastructure internal operations and risk management to delivering and an enabling regulatory framework would help to faster, more seamless customer experiences. Reports drive inclusive credit growth. indicate that nearly 70% of Banking, Financial Services, Gaps in access to formal credit and Insurance (BFSI) organisations in India have an Having said that, a lot of distance still needs to enterprise level AI strategy in FY 202412. An RBI study be traversed, as the gap in credit penetration persists. of banks’ annual reports also shows a sharp rise in Even today, only around 25 per cent of India’s references to AI, underscoring its growing strategic adult population have formal access to institutional importance13. While both demand-side factors credit8 while in the MSME sector (which forms ~30 (profitability, competition, compliance efficiency) and per cent of GDP)9, only part of their credit needs is supply-side drivers (tech advances, data growth, new 4 https://rbi.org.in/scripts/NotificationUser.aspx?Mode=0&Id=10598 10 https://www.sidbi.in/uploads/Understanding_Indian_MSME_sector_ 5 https://rbi.org.in/Scripts/NotificationUser.aspx?Id=12848&Mode=0 Progress_and_Challenges_13_05_25_Final.pdf 6 https://rbi.org.in/scripts/BS_PressReleaseDisplay.aspx?prid=56200 11 https://www.rbi.org.in/scripts/FS_PressRelease.aspx?prid=60875&fn= 7 https://rbihub.in/unified-lending-interface/ 2754 dated July 22, 2025 8 https://newsroom.transunioncibil.com/more-than-160-million-indians- 12 NASSCOM AI Adoption Index – India] are-credit-underserved/ 13 RBI Bulletin – How Indian Banks are Adopting Artificial Intelligence? 9 https://www.pib.gov.in/PressReleasePage.aspx?PRID=2142170 [https://rbi.org.in/Scripts/BS_ViewBulletin.aspx?Id=22931] 2 RBI Bulletin September 2025Balancing Innovation and Prudence- AI’s Role in India’s Financial Future SPEECH business models) influence AI adoption, supply-side of “credit invisibles”, i.e., people with no formal forces remain the primary catalyst. credit history or new to credit customers. Given the availability of a variety of digital footprints which AI as a discipline has evolved over decades and customers have, AI can be leveraged to peruse has seen rise of machine learning systems which can alternative data sets, to assess their creditworthiness. learn from historical data to make decisions, often This would mean a paradigm shift from asset- with high accuracy. Indian banks and non-banks, based lending to cash-flow and alternate data-based embarked on AI journey nearly a decade ago, mainly lending, promoting a journey towards inclusive digital handling at that time, large volume of information finance. Government of India also has recognised the and supplementary analysis which added value potential of alternate data to drive financial inclusion without displacing established systems. The adoption and has started initiatives such as the “Grameen has since moved from deployment of AI for back- Credit Score”15 which aims to provide underserved office functions for efficiency enhancements to communities with formal credit access by analyzing more varied use cases such as in the areas of fraud alternative financial data, including UPI transactions, risk management, optimising IT operations, facial government subsidy receipts, and utility payments. recognition for KYC, credit scoring, claim processing, and customer focused services. As observed in the (ii) Turnaround Time surveys conducted by RBI in 2023 and 202414, more AI enables banks to process large volumes of than three-fourth of the banks have deployed AI- customer data quickly, accelerating credit decisions powered chatbots for customer service. This marks and service delivery. This is especially valuable in time- a fundamental shift in which AI is no longer just an sensitive sectors like MSME working capital, where enabler but a part of the decision-making process, AI can analyse diverse datasets like bank statements, product design, and customer engagement. payment histories, GST filings, e-invoices, TReDS AI across the credit lifecycle receivables, and public records. It also aids lenders in assessing seasonality, supply chains, inventory cycles, Though, the banks have been using AI in some customer concentration, and overall creditworthiness areas of lending, there is potential use cases for its of MSMEs. usage across other areas of the credit lifecycle. I would like to highlight a few of them: (iii) Credit Appraisals (i) Credit Inclusion The banks can embrace AI algorithms for leveraging behavioural analytics by analysing vast An important use case would be in the way amounts of transactional data for detecting patterns credit is assessed and distributed. This would indicative of creditworthiness and stable financial require supplementing existing assessment methods behaviour, leading to more accurate credit assessments with broader data based and more refined analysis, and improved decision-making processes. whereby financial institutions could gain additional perspectives on risk and capability of the potential (iv) Customized Credit Solutions borrowers. This would be a significant game changer AI’s ability to dynamically assess customer to address the credit requirement of the millions preferences and behaviour can empower banks 14 RBI Trends and Progress of banking in India – 2023-24 (Para 10 under 15 https://www.pib.gov.in/PressReleasePage.aspx?PRID=2112198 Chapter IV) RBI Bulletin September 2025 3SPEECH Balancing Innovation and Prudence- AI’s Role in India’s Financial Future to offer customized credit solutions tailored to an boosts trust in the banking system and encourages individual’s financial capacity. This not only improves deeper engagement, furthering financial inclusion. loan accessibility but also ensures that borrowers As digital access has expanded, so has customer receive fair and structured financial products that complaints. This highlights another use case for AI. align with their needs. It can efficiently categorize, prioritize, and route complaints, enabling faster, proactive resolution by (v) Early Warning Signals and Provisioning detecting patterns and addressing root causes before AI-based early warning systems can help lenders issues escalate into grievances. monitor credit portfolios more effectively through (viii) Loan Servicing dynamic risk scoring and real-time default probability tracking. By flagging early signs of financial stress, AI-powered loan servicing platforms can create these systems protect the lender’s balance sheet while personalised repayment solutions and reduce giving borrowers a chance to course-correct. The goal, operational errors across servicing and portfolio ultimately, is not just to lend more but to lend better. management. They can also provide new ways of loan servicing by supporting collections and recovery (vi) Document Management through prioritised outreach strategies. AI can help AI also plays a key role in automating document to strengthen compliance with auditable trails of verification by using techniques like Optical Character measures initiated for recovery. Recognition (OCR) to read and process information (ix) Fraud Risk Management and Cyber Security from documents – especially handwritten documents which are generally unstructured, allowing for 20. AI also holds potential in safeguarding the automated extraction of data thus reducing rejections. financial system itself. As cybercriminals increasingly It can also examine the visual characteristics of use AI for sophisticated attacks, Regulated Entities the documents to detect forgery, tampering, or (REs) can leverage AI-driven tools to protect customers modifications and accurately process large volumes and detect threats. AI can monitor large transaction of records, classifying, extracting, and validating key volumes in real time, flagging anomalies that may data, thereby improving efficiency and minimizing assist in detecting fraud or money laundering. errors in the lending process. Collaboration for Credit Revolution (vii) Customer Support and Grievance Redress With fintechs advancing rapidly, REs are One promising use case of AI is the development increasingly partnering with them across the credit of multilingual chatbots and voice assistants which lifecycle. A key model–Digital Lending, involves enable customers to interact with banks in their embedding processes like KYC, credit assessment, native language. This is a revolutionary change as by and collection directly into fintech platforms. While localizing the user experience, the AI can enable more such collaborations enhance financial inclusion and people across different languages, literacy levels, and innovation, they also bring risks such as blurred abilities to confidently use formal banking services. accountability, data misuse, inadequate grievance Banks are also experimenting with using AI redressal, and potential mis-selling. The guiding powered virtual assistants to enable staff to respond principle to address this remains the premise that to customer queries which can enhance customer regardless of the model used, accountability ultimately experience. Timely resolution of customer queries rests with the regulated entity. 4 RBI Bulletin September 2025Balancing Innovation and Prudence- AI’s Role in India’s Financial Future SPEECH Risks and challenges of AI - New dimensions and the (iii) Cyber Risk Ethical imperative The integration of AI into financial systems– Technological advancements come with especially through new interaction methods and challenges that REs must recognize, as these risks can increased reliance on specialized providers expands undermine the benefits of innovation. For example, the cyber threat landscape in unpredictable ways. the recent Supreme Court ruling16 on video-based e-KYC AI’s strengths, such as reliance on large datasets, highlighted that mandatory automated processes open interfaces, and automated decisions, also create may create barriers for people with disabilities, vulnerabilities. Malicious actors can exploit these underscoring the need for fairness and accessibility in through adversarial attacks or compromised training technology adoption to avoid exclusions. AI, without data, potentially corrupting AI outputs. Even a single proper controls, can introduce risks like algorithmic breach can disrupt critical operations across multiple bias, lack of transparency, ethical concerns, and REs and undermine trust in AI across the sector. systemic vulnerabilities. It can also amplify existing (iv) Model Risk risks such as third-party dependencies, concentration, Unlike traditional models built on clear rules model, cyber, and data privacy risks. Let me highlight and well laid out assumptions, AI models operate a few: through dense, opaque algorithms, which we also (i) Third-party dependency refer to as “black boxes” and evolve with the data they consume. This introduces the risk of prejudice within As AI adoption grows, financial institutions models where biased data, opaque design, or untested increasingly rely on complex networks of external assumptions may lead to biased outcomes of model. providers, cloud platforms, AI vendors, and data Such distortions can lead to unfair credit assessment, aggregators. These interdependencies create excluding deserving segments, or conversely vulnerabilities where a disruption or breach in one link extending credit where risks are understated. can cascade across multiple institutions, disrupting critical services. The complexity and opacity of these (v) Data Risk layers makes it difficult to identify risks, which may AI is only as strong as the data that shapes it and accumulate unnoticed and spread rapidly during this leads to a host of vulnerabilities emanating from shocks. data quality. Currently, while most of the financial data (ii) Market Correlation is structured, much of it is fragmented across systems, often in inconsistent formats, sometimes incomplete A critical vulnerability of AI is its potential to or outdated, or skewed by historical biases. When such synchronize behaviors across the financial system. data flows into AI models, it can produce results that When institutions use similar models trained on may appear authentic, but suboptimal and in some overlapping data, their decisions on asset pricing, cases may produce wrong outputs. Over-reliance on credit assessment, trading, and others may align, such models can quietly turn data gaps into large-scale creating hidden linkages. This can amplify market misjudgments and wrong business decisions. stress, spread shocks rapidly, worsen liquidity (vi) Legal Certainty and Intellectual Property Right shortages, increase asset price volatility, and trigger issues sharp, self-reinforcing market swings. AI models are often trained on publicly available 16 https://api.sci.gov.in/supremecourt/2024/17879/17879_2024_13_1501_ 61229_Judgement_30-Apr-2025.pdf data, like news stories, articles, and explainer videos, RBI Bulletin September 2025 5SPEECH Balancing Innovation and Prudence- AI’s Role in India’s Financial Future etc., and may lead to intellectual property and standards that cascade through the organisation. copyright infringements. Robust monitoring and reporting mechanisms should be put in place to ensure alignment between (vii) Concentration Risk innovation goals and institutional stability. Further, Reserve Bank’s Financial Stability Report17 has in a regulated industry like banking, it is essential to pointed out the high market concentration in critical understand how a model arrives at its decisions, making third-party providers of cloud/ AI services, noting that explainability a critical requirement. Thus, there is a heavy reliance on a small number of tech players could need for financial institutions to invest in Explainable create single points of failure. Such concentration AI frameworks that provide clear, auditable reasons risks are compounded by the vertical integration of for loan decisions. Strong governance is central to certain providers, who supply not just models but also managing AI-driven model risk. the underlying infrastructure and datasets. (ii) Human-in-the-loop (viii) Frauds and disinformation While AI can automate and recommend, the While AI is transforming many of the processes humans should be responsible for the decisions. The of financial institutions, the rise of Generative AI has financial institutions while adopting AI for business also lowered the barriers for fraud, putting powerful processes should implement the principle of human- deception tools in the hands of malicious actors. in the-loop to ensure that AI is leveraged as a tool Deepfakes can mimic voices, faces, and documents to support and enhance human decisions and not with unsettling accuracy, while AI-generated phishing replace them. lures, fake identities, and forged credentials can slip past traditional checks. (iii) Maintaining Data quality and security In light of these multi-faceted risks, some of High-quality data is the backbone of safe and which I have touched upon, it becomes crucial that effective AI in finance. While the RBI already collects adoption of AI in banking sector must be done in a data through supervisory reports, regulatory returns, responsible and measured manner. The excitement and surveys, the introduction of model risk guidelines, around AI’s benefits should not overshadow prudent aligned with global best practices, will soon extend this risk management. In this context, the following scope to include data on AI models used by regulated aspects become even more crucial. entities. Financial institutions should therefore adopt robust data strategies, incorporating diverse, reliable (i) Governance indicators that reflect both the scale of AI adoption A robust governance is indispensable for ensuring and associated vulnerabilities. the integrity of data, the reliability of models, and When AI is used for credit decisioning or mitigating the risks associated with adoption of AI. financial inclusion, especially through alternative The financial institutions should have in place a data, customer data becomes central, making privacy comprehensive strategy for AI adoption. It should and security paramount. The Digital Personal Data be accompanied by clear policies, risk appetites, Protection (DPDP) Act, 2023 provides the legal criticality, and impact assessments as well as ethical framework for responsible data use, and financial 17 RBI FSR - June 2025 (Para 1.42 under Chapter 1) https://rbidocs.rbi.org. institutions must ensure compliance through consent- in/rdocs/PublicationReport/Pdfs/0FSRJUNE20253006258AE798B4484642A D861CC35BC2CB3D8E.PDF based, privacy-first data handling practices. 6 RBI Bulletin September 2025Balancing Innovation and Prudence- AI’s Role in India’s Financial Future SPEECH (iv) Research and Development domains, the Bank is in the process of expanding the scope of these guidelines and would be issuing Continued investment in research and overarching Model Risk Management Guidelines development is critical for advancing the capabilities of applicable across all models. As technologies like AI AI in lending lifecycle for unlocking new opportunities. are generally not adopted uniformly across the sector Research efforts of the financial institutions should and owing to presence of a varied type of entities focus on improving data quality and accessibility, with different scales, the principle of proportionality developing novel AI algorithms for enhancing credit has to be also factored in. The objective would be to inclusion, and addressing key challenges related to ensure that all REs can adopt technologies best suited bias, fairness, and interpretability in credit evaluation to their business models and customer needs, while as well as enhancement of in-house capabilities to effectively managing risks such as explainability, manage concentration risk of providers. algorithmic bias, resilience, and over-automation19. (v) Industry Collaborations In continuation of this approach, the recently released report of the Committee on Framework for Collaboration and knowledge-sharing among Responsible, Efficient, and Ethical AI (FREE-AI)20, industry stakeholders, including financial institutions, has laid out seven guiding sutras for trustworthy AI, fintech companies, and academic institutions is and emphasized the need for a robust model risk essential for driving innovation and addressing management framework by REs. common challenges in AI-driven credit processes. This can foster the development of best practices, Conclusion standards, and frameworks for responsible AI use, Over the decades, Indian banking sector promoting transparency, fairness, and accountability has exhibited its ability to integrate meaningful in credit evaluation. Some of the initial areas where technological advancements. As AI transforms the industry can collaboratively work is harmonising financial services, it’s clear this is a development AI taxonomies and developing common benchmarks which is not a mere upgrade but a major shift and metrics. impacting products, processes, and operations. From the risk perspective, the long-term implications of AI Regulatory guardrails for new technologies adoption on the financial system remain uncertain As AI adoption gains traction, regulatory oversight but exhibit potentially far-reaching consequences. is crucial in ensuring an efficient, responsible and It is therefore imperative for the financial sector fair adoption. Recognising the increasing usage to approach AI adoption with foresight, investing of model-driven credit assessments and decision- not just in innovation, but also in resilience by making in REs, the RBI had issued a draft circular building strong governance structure, diversifying on model risk management in credit18, setting out dependencies, engaging in continual assessment expectations on governance, validation, monitoring, of emerging risks, and ensuring their AI strategies and accountability. Building on this foundation and align with long-term safety and sustainability of the recognising the increasing usage of models by the REs, not only for credit functions but also for wide spectrum 19 RBI Annual Report 2024-25: VI.19 under Chapter VI (Regulation, Supervision and Financial Stability) of processes across functional and operational 20 FREE-AI Committee Report [https://rbidocsrbi.org.in/ rdocs/PublicationReport/Pdfs/ 18 https://www.rbi.org.in/scripts/bs_viewcontent.aspx?Id=4479 FREEAIR130820250A24FF2D4578453F824C72ED9F5D5851.PDF] RBI Bulletin September 2025 7SPEECH Balancing Innovation and Prudence- AI’s Role in India’s Financial Future financial system. Ensuring that AI-driven decisions are system that truly builds Bharat, and not just builds, ethical, unbiased, and transparent will be paramount but transforms Bharat. in building a sustainable, AI-powered financial future. Let me sign off with the thought “Trust is the This calls for “optimistic vigilance” wherein AI and currency of banking”. Even as we broaden the credit other technologies in banking are neither feared nor coverage using algorithms and digital interfaces, embraced blindly but “navigated”. The RBI, on its maintaining the trust will be our biggest challenge part, will continue to provide an enabling regulatory and also our biggest responsibility. environment so that together we can build a banking Thank you. 8 RBI Bulletin September 2025ARTICLES State of the Economy Flow of Financial Resources to Commercial Sector in India during 2024-25 The Untold Story of FinTech Customers’ Experience Review of Performance of the NBFC Sector Impact of UPI on Cash Demand – Evidence from National and Subnational Levels Is Consumption Inequality Declining? – What the 2022-23 NSSO Survey Tells Us Infrastructure - An Engine of India’s Growth ExpressState of the Economy ARTICLE State of the Economy* commodity prices largely eased in August though a sharp pick-up in select commodities, particularly gold Global uncertainty remained elevated in the wake of and coffee, were witnessed since the second half of imposition of US trade tariffs on major trading partners August. and renewed concerns over fiscal health of advanced Bond yields across AEs continued to harden in economies. The Indian economy exhibited marked August as debt sustainability concerns assumed resilience as evident from the five-quarter high growth centre-stage. Demand for alternative safe-haven during Q1:2025-26, propelled by domestic drivers. The assets, on the other hand, propelled gold prices landmark GST reforms should progressively result in a to a record high. US bond yields declined since sustained positive impact through significant gains in ease end-August on rising expectations of Fed easing. of doing business, lower retail prices and strengthening Thereafter in September, yields declined further of consumption growth drivers. CPI headline inflation and stabilised following the rate cut by Fed. edged up but remained well below the target rate for the Reflecting these factors, the US dollar also fell for seventh consecutive month. System liquidity remained most of August and September. Equity markets in in surplus facilitating the pass through of policy rate key economies remained buoyant in August largely cuts. Indian equity markets witnessed bidirectional driven by strong Q2:2025 earnings, expectations of movements during August-September. India’s current Fed easing and optimism over BigTech stocks. Fed’s account deficit moderated in Q1 over last year, supported rate cut in September further strengthened the rally, by robust services exports and strong remittances especially in the US. Equity flows to emerging receipts. markets remained thin in August on risk-off investor sentiments. Introduction Inflation trends remained divergent across AEs Global uncertainty remained elevated in the wake and emerging markets and developing economies of lingering US trade policy uncertainties with key (EMDEs). While AEs grappled with sticky inflation, trading partners, renewed concerns on fiscal health the EMDEs experienced disinflation. In August- of advanced economies (AEs) and geopolitical risks. September, major central banks reduced their Despite uncertainties clouding the economic outlook, benchmark interest rates, prioritising concerns the global purchasing manager’s index (PMI) rose to on domestic growth and unemployment over a 14-month high in August with manufacturing PMI inflation. moving into the expansion zone accompanied by a robust expansion in services sector activity. Global The Indian economy exhibited marked resilience, as evident from the five-quarter high growth * This article has been prepared by Rekha Misra, Asish Thomas during Q1:2025-26, propelled by domestic drivers. George,Shashi Kant, Biswajeet Mohanty, Durga G., Shreya Kansal, Yamini Jhamb, Jessica Maria Anthony, Harendra Kumar Behera, Vrinda Gupta, Consumption expenditure and fixed investment Akash Raj, Amrita Basu, Sanjana Sejwal, Love Kumar Shandilya, Prashant Kumar, Sritama Ray, Pratibha Kedia, Nilava Das, Archana Dilip, Sumit Roy, activity remained strong, negating the decline in net Yogesh Rana, Avnish Kumar, Pulastya Bandyopadhyay, Supriyo Mondal, Pallak Goyal, Yuvraj Kashyap, Rasmi Ranjan Behera and Samridhi. The exports. The high-frequency indicators for economic guidance and comments provided by Dr. Poonam Gupta, Deputy Governor, activity showed steady growth in August with rural is gratefully acknowledged. Peer review by Rakhe Paluvai Balachandran, Subhadhra Sankaran and Abhinav Narayanan is also acknowledged. Views demand remaining robust, though urban demand expressed in this article are those of the authors and do not represent the views of the Reserve Bank of India. continued to show some weakness. RBI Bulletin September 2025 9ARTICLE State of the Economy An import tariff of 50 per cent is applicable on In the fixed income segment, government bond India’s exports to the US from August 27, 2025.1 Its yields softened in September, with the government immediate impact may be sector-specific, given that reiterating its commitment to fiscal consolidation. around 45 per cent of India’s merchandise exports to The pass-through of the cumulative reduction in the the US are exempted from the tariffs, including sectors repo rate by 100 bps during February to August 2025 to constituting major export products, particularly bank lending and deposit rates has been robust. Credit smartphones and pharmaceuticals. Despite the growth by banks picked up slightly to double-digits in elevated trade policy uncertainties, merchandise August, with deposit growth remaining steady. During exports have shown resilience during April-August 2025-26 so far, the flow of non-food bank credit to the 2025-26. commercial sector moderated; however, it was more than offset by the flow from non-bank sources. The decisions of the GST Council in its 3rd September meeting set in motion major structural Indian equity markets witnessed bidirectional reforms in the GST regime, simplifying rates and movements during August-September, shaped by processes. Beyond rate simplification, the reforms policy announcements and key macro-data releases. have also addressed challenges relating to inverted After recording a sharp rebound in mid-August duty structure2, and made processes business-friendly, following the S&P sovereign rating upgrade and particularly benefiting micro, small and medium announcement of GST reforms, equity markets fell by enterprises, and startups. Overall, these reforms are end-August as additional US tariffs on Indian exports expected to boost tax buoyancy, improve compliance, took effect. Thereafter, equity markets rebounded in and contribute to greater ease of living as well as ease early September on strong Q1 GDP growth numbers of doing business. and the decision of the GST council to rationalise CPI headline inflation edged up but remained rates. Although foreign portfolio investors continued well below the target rate for the seventh consecutive to sell, robust domestic institutional buying more month. Food group largely contributed to the pick- than offset these outflows. up in overall inflation. Core inflation (CPI excluding Despite elevated global trade uncertainties, food and fuel inflation) also increased at the margin, India’s external sector exhibited resilience. The driven primarily by the uptick in gold prices. current account deficit moderated in Q1:2025-26 Overall financial conditions turned as compared with the previous year, supported by accommodative since the beginning of September. strong services exports and robust remittance inflows. System liquidity remained in surplus during August These trends are expected to keep the deficit low and and September (up to September 19). The weighted rangebound throughout the year. Net foreign direct average call rate – the operating target of monetary investment (FDI) inflows reached a 38-month high in policy – broadly hovered in the lower half of the July, aided by higher gross FDI and slower repatriation corridor. and outward FDI. Foreign exchange reserves remained adequate, underscoring external sector stability. 1 On August 6, 2025, the US imposed an additional tariff of 25 per cent on India’s imports over and above the 25 per cent imposed on July 31, 2025. Set against this backdrop, the remainder of the 2 Inverted duty structure has been corrected for manmade textile and fertilizer sector. article is structured into four sections. Section II 10 RBI Bulletin September 2025State of the Economy ARTICLE covers the rapidly evolving developments in the fiscal sustainability concerns triggered increased global economy. Section III provides an assessment financial market volatility in August, especially in of domestic macroeconomic conditions. Section IV Europe (Chart II.1a and II.1b). encapsulates financial conditions in India, while Global purchasing manager’s index (PMI) rose Section V presents the concluding observations. to a 14-month high in August reflecting continued II. Global Setting expansion in output and new business. Global manufacturing PMI moved into the expansion zone in Global developments remained clouded by August after contracting in July on higher production elevated uncertainty on lingering US trade policy — partly reflecting a front-loading of production uncertainties with key trading partners and ahead of potential tariff hikes. PMI services continued geopolitical risks. Concerns over fiscal sustainability to rise at a strong pace (Table II.1). in AEs gained prominence in August, significantly Business activity, as per PMI indices, expanded impacting financial markets. In the US, rising equity in major AEs, including the US, the UK, Japan, and the markets co-existed with weakening consumer Eurozone in August. Among major EMDEs, business sentiment amidst softening labour market conditions activity in China and India expanded, with India and rising inflation concerns. recording its highest rate in a decade, whereas Brazil Global economic uncertainty remained elevated and Russia continued to contract (Chart II.2a). Major in August on account of trade policy uncertainties and economies saw a contraction in new export orders, renewed concerns on debt sustainability across AEs. whereas India continued to experience expansion The economic and trade policy uncertainty indices in (Chart II.2b). The global supply chain pressure the US eased, albeit moderately, following tariff deals index slipped below its historical average (Annex between US and several trade partners. Mounting chart A1). Chart II.1: Uncertainty Indicators a. Uncertainty Indices b. Volatility Indices (Index(Jan=2024), left scale; Index (Jan 2025=100) index (Jan=2024), right scale) 600 20000 18000 500 16000 400 14000 12000 300 10000 8000 200 6000 100 4000 2000 0 0 World Uncertainty Index US Economic Policy Uncertainty Index US Trade Policy Uncertainty Index (RHS) Notes: 1. World Uncertainty Index (WUI) is computed by counting the percent of word “uncertain” (or its variant) in the Economist Intelligence Unit country reports. 2. Economic Policy Uncertainty (EPU) index measures the level of uncertainty surrounding future economic policies, derived from the frequency of specific keywords like “economy,” “policy,” and “uncertainty” in major newspaper articles. Trade Policy Uncertainty Index measures theunpredictability of government trade policy decisions. Sources: Bloomberg; www.PolicyUncertainty.com; and World Uncertainty Index (WUI) database. RBI Bulletin September 2025 11 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 310 280 250 220 190 160 130 100 70 52-naJ-20 52-naJ-22 52-beF-11 52-raM-30 52-raM-32 52-rpA-21 52-yaM-20 52-yaM-22 52-nuJ-11 52-luJ-10 52-luJ-12 52-guA-01 52-guA-03 52-peS-91 US VIX Emerging Markets VIX EURO STOXX VIXARTICLE State of the Economy Table II.1: Global Purchasing Managers’ Index Aug-24 Sep-24 Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 Aug-25 PMI composite 52.9 51.9 52.3 52.4 52.6 51.8 51.5 52.1 50.8 51.2 51.7 52.4 52.9 PMI manufacturing 49.6 48.7 49.4 50.1 49.6 50.1 50.6 50.3 49.8 49.5 50.4 49.7 50.9 PMI services 53.9 52.9 53.1 53.1 53.8 52.2 51.5 52.7 50.8 52.0 51.8 53.4 53.4 PMI export orders 49.0 48.5 48.9 49.3 48.7 49.6 49.7 50.1 47.5 48.0 49.1 48.5 48.9 PMI export orders: 48.4 47.5 48.3 48.6 48.2 49.4 49.6 50.1 47.3 48.0 49.2 48.2 48.7 manufacturing PMI export orders: 50.8 51.6 50.7 51.3 50.3 50.2 50.2 50.1 48.2 47.9 48.7 49.4 49.3 services 50 <<<<<<Contraction---------------------------------------------------------------Expansion>>>>>> Notes: 1. The Purchasing Managers’ Index (PMI), a diffusion index, captures the change in each variable compared to the prior month, noting whether each has risen/improved, fallen/deteriorated or remained unchanged. A PMI value >50 denote expansion; <50 denote contraction; and =50 denote ‘no change’. 2. Heat map is applied on data from April 2023 till August 2025. The map is colour coded–red denotes the lowest value, yellow denotes 50 (or the no change value), and green denotes the highest value in each of the PMI series. Source: S&P Global. Commodity prices eased in August vis-à-vis July. intermediation. Crude oil prices registered a further Global food prices remained largely unchanged from dip in September on announcement of increased the previous month as an increase in prices of meat production by OPEC plus. Nevertheless, high and sugar was offset by a decline in cereal and dairy frequency commodity price indicators showed a sharp prices.3 Crude oil prices weakened in August due pick-up in select commodity prices since the second to supply glut in global markets and expectations half of August. Coffee prices recorded a sharp pick- of easing sanctions on Russian crude following US up on supply shortages in Brazil. After remaining Chart II.2: Purchasing Managers’ Index: Comparison across Jurisdictions a. S&P Global Composite PMI b. PMI Export Orders (Index) (Index) 64 60 56 52 48 44 40 Aug-25 Jul-25 Aug-25 Jul-25 Note: A level of 50 indicates no change in activity, while a reading above 50 signals expansion and below 50 suggests contraction. Source: S&P Global. 3 As per the Food and Agriculture Organization’s Food Price Index for the month of August 2025. 12 RBI Bulletin September 2025 aidnI ailartsuA setatS detinU niapS modgniK detinU labolG napaJ anihC ylatI eropagniS enozoruE ynamreG ecnarF aissuR lizarB adanaC 56 52 48 44 40 aidnI niapS ailartsuA anihC setatS detinU ylatI ynamreG enozoruE modgniK detinU aissuR ecnarF napaJ adanaCState of the Economy ARTICLE rangebound, gold prices surged since end-August to remained steady at 3.1 per cent. In the Euro area, all-time highs, buoyed by safe-haven demand (Chart headline inflation remained steady at 2 per cent in II.3a and 3b). August, with services inflation exhibiting a slight moderation alongside soft energy prices. Inflation Inflation trends remained divergent across AEs and EMDEs. While the former grappled with sticky in the UK was also steady at 3.8 per cent. Headline inflation, the latter experienced disinflation. CPI inflation in Japan eased to 2.7 per cent, marking inflation in the US edged up to 2.9 per cent in August, the lowest reading since October 2024 (Chart II.4a). the highest since January, though core inflation Among major EMDEs, inflation in Brazil continued to Chart II.4: Headline Inflation a. Select AEs b. Select EMEs (Per cent) (Per cent) 4.0 3.8 3.5 3.0 2.9 2.7 2.5 2.0 2.0 1.5 1.0 Brazil Russia China US UK Euro area Japan South Africa India Sources: Bloomberg; and OECD. RBI Bulletin September 2025 13 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 9 8.1 7 5 5.1 3 3.3 2.1 1 -0.4 -1 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA Chart II.3: Commodity and Food Prices a. Commodity and Food Indices b. Gold - Copper - Brent Crude Oil Index (Jan 2024=100) Index (Jan 2025=100) 110 105 100 95 90 Food and Agriculture Organization Food Price Index Bloomberg commodity index World Bank Commodity Price Index Gold Copper Brent Crude oil Sources: Food and Agriculture Organization; Bloomberg; and World Bank Pink Sheet. 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 130 120 110 100 90 80 70 52-naJ-20 52-naJ-22 52-beF-11 52-raM-30 52-raM-32 52-rpA-21 52-yaM-20 52-yaM-22 52-nuJ-11 52-luJ-10 52-luJ-12 52-guA-01 52-guA-03 52-peS-91ARTICLE State of the Economy soften, but remained above the target level (Annex supported by the US-Japan trade deal. Indices gained chart A2). Persistent price declines have pushed further in September, on the back of improved China into the deflationary zone again after a hiatus business confidence and better-than-expected of 2 months. In Russia, though on a moderating path, Q2:2025 GDP data. Chinese equities surged in August inflation continued to remain well above the target. supported by the 90-day extension of the US-China South Africa’s annual inflation eased in August (Chart trade truce, which were further bolstered by the II.4b). stimulus measures in the housing sector towards the end of the month. The indices, however, witnessed Equity markets in key economies remained a modest pullback in September amidst fears of buoyant in August largely driven by strong Q2:2025 regulatory tightening, weak consumer spending and earnings, expectations of Fed easing and optimism factory output numbers (Chart II.5a). over BigTech stocks. Fed’s rate cut in September further strengthened the rally, especially in the US. Bond markets in AEs grappled with a substantial Europe STOXX 600 slipped marginally in the end increase in long-dated borrowing costs in most of of August on muted earnings. In September, stocks August, which touched multi-year highs amidst were subdued as the sovereign rating downgrade renewed concerns about the fiscal health of major of France weighed on sentiment. Equity markets economies. Towards the end of August, yields softened in Japan registered strong performance in August, in the US following rising expectations of Fed easing Chart II.5: Global Financial Markets a. Equity Indices: Select Economies b. Government Bond Yields Index (April 07, 2025=100) (Per cent, left scale; Index, right scale) 145 135 125 115 105 95 S&P 500 SSE Composite Index Nikkei 225 STOXX 600 Note: Equity markets are represented by S&P 500 for US, SSE Composite Index for US Govt Bonds JPMorgan EMBI Global Spread (RHS) China, Nikkei 225 for Japan, and STOXX 600 for Europe. Source: Bloomberg. Source: Bloomberg. c. Currency Indices d. Portfolio Flows to Emerging Markets (Index, left scale; Index, right scale) (US$ billion) MSCI EME currency index Dollar index (RHS) Debt Equity Total Source: Bloomberg. Source: Institute of International Finance. 14 RBI Bulletin September 2025 52-rpA-10 52-rpA-01 52-rpA-91 52-rpA-82 52-yaM-70 52-yaM-61 52-yaM-52 52-nuJ-30 52-nuJ-21 52-nuJ-12 52-nuJ-03 52-luJ-90 52-luJ-81 52-luJ-72 52-guA-50 52-guA-41 52-guA-32 52-peS-10 52-peS-01 52-peS-91 355 4.7 335 4.5 315 4.3 295 4.1 4.1 275 266.0 3.9 255 52-naJ-20 52-naJ-22 52-beF-11 52-raM-30 52-raM-32 52-rpA-21 52-yaM-20 52-yaM-22 52-nuJ-11 52-luJ-10 52-luJ-12 52-guA-01 52-guA-03 52-peS-91 1,860 110 1848.3 1,840 108 1,820 106 1,800 104 1,780 102 1,760 1,740 100 1,720 98 97.6 1,700 96 52-naJ-20 52-naJ-22 52-beF-11 52-raM-30 52-raM-32 52-rpA-21 52-yaM-20 52-yaM-22 52-nuJ-11 52-luJ-10 52-luJ-12 52-guA-01 52-guA-03 52-peS-91 60 50 40 30 20 10 0 -10 -20 -30 22-beF 22-rpA 22-nuJ 22-guA 22-tcO 22-ceD 32-beF 32-rpA 32-nuJ 32-guA 32-tcO 32-ceD 42-beF 42-rpA 42-nuJ 42-guA 42-tcO 42-ceD 52-beF 52-rpA 52-nuJ 52-guAState of the Economy ARTICLE in September meeting due to subdued July inflation basis points. In September, the US Federal Reserve data combined with the Fed’s communication on reduced its policy rate by 25 bps. ECB kept its policy shifting balance of economic risks.4 Thereafter in rate unchanged for the second consecutive time. September, yields declined further and stabilised Bank of England also kept their interest rates steady following the rate cut by Fed (Chart II.5b). EME bond in September. Amongst the EMDEs, Indonesia, spreads over AEs narrowed until mid-August, before Mexico and Thailand cut their policy rates by 25 basis edging up later in the month. In September, spreads points each. China held its benchmark lending rate held broadly steady on account of strong appetite for steady for the third consecutive month in August. In emerging market assets. September, Malaysia’s central bank held its overnight The US dollar retreated in August and September policy rate steady while Russia reduced it citing easing so far on Fed’s pivot to easing (Chart II.5c). Equity inflationary pressures (Chart II.6). flows to emerging markets remained thin in August III. Domestic Developments on risk-off investor sentiments. Debt flows, on the other hand, picked up sharply (Chart II.5d). The Indian economy exhibited marked resilience as evident from the better-than-expected growth in In August-September, major central banks opted Q1:2025-26. The dual engines of growth – consumption to reduce their benchmark interest rates, weighing concerns of domestic growth and unemployment and investment – remained strong. Aggregate demand over inflation. In August, New Zealand, Australia and conditions remain robust characterised by strong England reduced the benchmark interest rate by 25 rural remand in a low inflation environment. Chart II.6: Policy Rates Type Countries 4 Monetary Policy and the Fed’s Framework Review- Speech by Chair Jerome H. Powell at “Labor Markets in Transition: Demographics, Productivity, and Macroeconomic Policy,” an economic symposium sponsored by the Federal Reserve Bank of Kansas City, Jackson Hole, Wyoming. RBI Bulletin September 2025 15 22-naJ 32-naJ 42-naJ 52-naJ 52-guA 5202.90.22 Australia 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Canada 0 0 0 1 0 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 -1 0 0 0 0 0 0 0 0 0 Euro area 0 0 0 0 0 0 1 0 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 Japan 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Advanced New Zealand 0 0 0 1 1 0 1 1 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 -1 0 0 -1 0 0 0 0 0 0 0 Economies South Korea 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Sweden 0 0 0 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 Switzerland 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 United Kingdom 0 0 0 0 0 0 0 1 1 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 United States 0 0 0 0 1 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 Brazil 0 2 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 -1 -1 0 -1 -1 0 -1 -1 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 0 0 0 China 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 India 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 Indonesia 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Emerging Malaysia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Market Mexico 0 1 1 0 1 1 0 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 -1 0 -1 -1 0 0 0 Economies Philippines 0 0 0 0 0 0 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Russia 0 12 0 -3 -6 -2 -2 0 0 -1 0 0 0 0 0 0 0 0 1 4 1 2 0 1 0 0 0 0 0 0 2 0 1 2 0 0 0 0 0 0 0 -1 -2 0 -1 Saudi Arabia 0 0 0 0 1 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 South Africa 0 0 0 0 1 0 1 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Thailand 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rate Change < -0.75 -0.75 to -0.50 -0.50 to -0.25 -0.25 to 0 0 to 0.25 0.25 to 0.50 0.50 to 0.75 > 0.75 Source: Bloomberg.ARTICLE State of the Economy Aggregate Demand grew at a robust pace and posted their second-highest tally, reflecting inventory stocking for the festive Real GDP growth picked up pace reaching a five- season and a surge in orders ahead of additional US quarter high in Q1:2025-26, rising to 7.8 per cent tariffs on Indian exports. While electricity demand, (year-on-year) from 7.4 per cent (year-on-year) in the and petroleum consumption picked-up pace, growth preceding quarter. Consumption and fixed investment in GST revenue and toll collection remained broadly remained the key drivers contributing 4.7 percentage steady. Digital payments recorded robust double- points and 2.7 percentage points, respectively. The digit growth in volume, while growth in value terms growth in private final consumption expenditure was exhibited moderation (Table III.I). sustained by strong rural demand and easing inflation. During August, rural demand stood strong with The government final consumption expenditure saw an robust retail tractor sales and recovery in two-wheeler accelerated growth due to higher revenue expenditure sales aided by a favourable monsoon and easing (excluding interest payments and subsidies) of both inflation. Household demand for employment under union and state governments. Net exports turned into the Mahatma Gandhi National Rural Employment a drag on growth reversing their positive contribution Guarantee Scheme (MGNREGS) declined in August, in the previous quarter as import expansion outpaced reflecting the availability of alternative avenues of export growth. (Chart III.1 and Annex table A1). In employment due to higher kharif sowing. Urban nominal terms, the GDP growth registered a three- demand continued to show some weakness as quarter low of 8.8 per cent. The narrowing of the gap indicated by a modest uptick in automobile sales and between nominal and real GDP growth resulted from a subdued domestic air passenger traffic (Table III.2). sharp moderation in GDP deflator to an all-time low of Various indicators of employment conditions 0.9 per cent (Annex chart A6). depicted a mixed picture in August. The all-India The high-frequency indicators for overall economic unemployment rate declined to 5.1 per cent, led by activity remained robust in August. GST e-way bills a sharp decline in urban unemployment. Labour Chart III.1: Weighted Contribution to GDP Growth (Percentage points) 20 15 10 7.8 5 0 -5 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 -10 2023-24 2024-25 2025-26 Private final consumption expenditure Government final consumption expenditure Gross fixed capital formation Net exports GDP (Y-o-Y growth, per cent) Others Note: Others include change in stock, valuables, and statistical discrepancies. Source: NSO. 16 RBI Bulletin September 2025State of the Economy ARTICLE Table III.1: High Frequency Indicators: Economic Activity Aug-24 Sep-24 Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 Aug-25 GST E-way bills 12.9 18.5 16.9 16.3 17.6 23.1 14.7 20.2 23.4 18.9 19.3 25.8 22.4 GST revenue 10.0 6.5 8.9 8.5 7.3 12.3 9.1 9.9 12.6 16.4 6.2 7.5 6.5 Toll collection 6.8 6.5 7.9 11.9 9.8 14.8 18.7 11.9 16.6 16.4 15.5 14.8 12.7 Electricity demand -5.0 -0.8 -0.4 3.7 5.1 1.3 2.4 5.7 2.8 -4.8 -2.3 2.6 3.9 Petroleum consumption -3.1 -4.4 4.1 10.6 2.0 3.0 -5.2 -3.1 0.2 0.7 0.5 -3.9 2.6 Of which Petrol 8.6 3.0 8.7 9.6 11.1 6.7 5.0 5.7 5.0 9.2 6.8 5.9 5.5 Diesel -2.5 -1.9 0.1 8.5 5.9 4.2 -1.3 0.9 4.2 2.1 1.5 2.4 1.2 Aviation turbine fuel 8.1 10.4 9.4 8.5 8.7 9.4 4.2 5.7 3.9 4.3 3.3 -2.3 -2.9 Digital Payments - volume 34.9 36.3 40.3 30.1 33.1 33.0 26.7 30.8 30.0 29.2 28.3 30.9 28.8 Digital Payments - value 16.7 21.5 27.5 9.5 19.6 18.6 9.5 17.3 18.4 12.6 17.4 16.6 5.6 <<Contraction --------------------------------------------------------------------------------------------- Expansion>> Notes: 1. The y-o-y growth (in per cent) has been calculated for all indicators. 2. The heatmap is applied on data from April 2023 till August 2025. Digital Payments data for August 2025 is provisional. 3. The heatmap translates the data range for each indicator into a colour gradient scheme with red denoting the lowest values and green corresponding to the highest values of the respective data series. Sources: Goods and Services Tax Network (GSTN); RBI; Central Electricity Authority (CEA); and Ministry of Petroleum and Natural Gas, GoI. force participation rate and worker population ratio During FY 2025-26 (April-July), the key deficit increased in August, driven by gains in both rural and indicators of the union government stood higher, urban areas.5 As per the Naukri JobSpeak index, the as compared to the corresponding period of the growth in white-collar job listings was modest, led previous year.6 This was primarily due to higher by hiring in AI/ML and non-IT sectors like insurance, revenue and capital expenditure alongside a travel/hospitality, BPO/ITES and real estate. PMI slowdown in revenue receipts (Chart III.2a). The employment indices for both manufacturing and moderation in revenue receipts can be mainly services remained in expansion (Table III.3). attributed to lower direct tax collections, especially Table III.2: High Frequency Indicators: Rural and Urban Demand Aug-24 Sep-24 Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 Aug-25 Urban Domestic air passenger traffic 6.7 7.4 9.6 13.8 10.8 14.1 12.1 9.9 9.7 2.6 3.7 -2.5 -1.1 demand Retail passenger vehicle sales -4.5 -18.8 32.4 -13.7 -2.0 15.5 -10.3 6.3 1.6 -3.1 2.5 -0.8 0.9 Retail automobile sales 2.9 -9.3 32.1 11.2 -12.5 6.6 -7.2 -0.7 2.9 5.4 4.8 -4.3 2.8 Rural Retail tractor sales -11.4 14.7 3.1 29.9 25.8 5.2 -14.5 -5.7 7.6 2.8 8.7 11.0 30.1 demand Retail Two-wheeler sales 6.3 -8.5 36.3 15.8 -17.6 4.2 -6.3 -1.8 2.3 7.3 4.7 -6.5 2.2 MGNREGA: work demand -16.0 -13.4 -7.6 3.9 8.2 14.4 2.8 2.2 -6.5 4.4 4.4 -12.3 -26.1 <<Contraction ----------------------------------------------------------------------------------------- Expansion>> Notes: 1. The y-o-y growth (in per cent) has been calculated for all indicators. 2. The heatmap is applied on data from April 2023 till August 2025. 3. The heatmap translates the data range for each indicator into a colour gradient scheme with red denoting the lowest values and green corresponding to the highest values of the respective data series. 4. The data on domestic air passenger traffic for August 2025 growth rate is calculated by aggregating daily data. Sources: Airports Authority of India; Federation of Automobile Dealers Associations (FADA); and Ministry of Rural Development, GoI. 5 PLFS August 2025 Monthly Bulletin released on September 15, 2025 6 As per the latest data released by the Controller General of Accounts. RBI Bulletin September 2025 17ARTICLE State of the Economy Table III.3:High Frequency Indicators: Employment Aug-24 Sep-24 Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 Aug-25 Unemployment rate (PLFS: All-India) 5.1 5.6 5.6 5.2 5.1 Unemployment rate (PLFS: Rural) 4.5 5.1 4.9 4.4 4.3 Unemployment rate (PLFS: Urban) 6.5 6.9 7.1 7.2 6.7 Naukri JobSpeak Index -3.4 6.0 10.0 2.0 8.7 3.9 4.0 -1.5 8.9 0.3 10.5 6.8 3.4 PMI employment: Manufacturing 53.5 52.1 53.3 52.9 53.4 54.8 54.5 53.4 54.2 54.9 55.1 53.3 53.1 PMI employment: Services 53.1 53.4 54.3 56.6 55.5 56.3 56.2 52.5 53.9 57.1 55.1 51.4 52.2 <<Contraction --------------------------------------------------------------------------------------------- Expansion>> Notes: 1. All PLFS indicators are in the current weekly status and for people aged 15 years and above. 2. The y-o-y growth (in per cent) has been calculated for Naukri index. 3. The heatmap is applied on data from April 2023 till August 2025. 4. The heatmap translates the data range for each indicator into a colour gradient scheme with red denoting the lowest values and green corresponding to the highest values of the respective data series. 5. All PMI values are reported in index form. A PMI value >50 denotes expansion, <50 denotes contraction and =50 denotes ‘no change’. In the PMI heatmaps, red denotes the lowest value, yellow denotes 50 (or the no change value), and green denotes the highest value in each of the PMI series. Sources: Ministry of Statistics and Program Implementation (MoSPI), GoI; Info Edge; Employees’ Provident Fund Organisation; and S&P Global. income tax.7 Growth in the indirect tax collections, collections and slackening of growth in sales tax/VAT. however, was broadly in line with last year. Revenue expenditure remained robust while capital expenditure slowed in July. Gross fiscal deficit of states during April-July 2025, as a proportion of budget estimates for the The decisions of the GST Council in its 3rd financial year, was higher than the same period last September meeting set in motion major structural year (Chart III.2b). This was largely due to moderation reforms in the GST regime, simplifying rates and in the growth of state’s goods and services tax processes. The four existing slabs (5, 12, 18 and 28 Chart III.2: Major Fiscal Indicators (Up to end-July) a. Union Government b. State Governments (Actuals as per cent of budget estimates) (Actuals as per cent of budget estimates) 35 90 28.9 29.9 81.4 80 25 70 17.2 60 15 50 7.4 40 38.4 5 3.8 30 22.7 20 18.9 18.9 -5 11.3 10 -11.3 0 -15 Revenue GrossFiscal Primary Revenue GrossFiscal Primary Deficit Deficit Deficit Deficit Deficit Deficit 2024-25 2025-26 2024-25 2025-26 Notes: 1. Negative primary deficit numbers, as per cent of budget estimates indicates primary surplus. 2. In Chart b, data pertains to 24 States/UTs. Sources: Controller General of Accounts; and Comptroller and Auditor General of India. 7 Within the direct taxes, income tax revenues contracted though corporation tax registered an expansion in comparison to the same period of last year. Income tax contracted by 9.9 per cent up to end-July 2025-26 in comparison to growth of 53.4 per cent in the corresponding period of the previous year. 18 RBI Bulletin September 2025State of the Economy ARTICLE per cent) have been streamlined mainly into two Trade (5 and 18 per cent), with rationalisation cutting Merchandise trade deficit narrowed to US$ 26.5 across sectors. The new framework is designed to billion in August 2025 from a record high deficit balance the needs of the common man with ease of US$ 35.6 billion in August 2024 on account of a of administration. Most of the essential items now narrowing of non-oil deficit (Chart III.3).8 attract either ‘nil’ or 5 per cent GST. A majority of the Merchandise exports expanded for the second electronic items and motor vehicles would be taxed consecutive month (Annex chart A3).9 Electronic at 18 per cent (Annex table A2). A new category has goods, engineering goods, gems and jewellery, also been created for luxury and sin goods, taxable at petroleum products; and drugs and pharmaceuticals 40 per cent. Beyond rate simplification, the reforms performed well, while tobacco, ready-made garments also tackle challenges relating to inverted duty of all textiles, and iron ore contributed negatively to structure. Processes have also been made business- exports. friendly: simpler registration and return filing, faster Merchandise imports contracted in August 2025 refunds, and lower compliance costs – particularly (Annex chart A4).10 Petroleum crude and products, benefiting micro, small and medium enterprises fertilisers, electronic goods, vegetable oil and and startups. Overall, these reforms are expected machinery contributed positively to import growth to boost tax buoyancy, improve compliance, and during the month. Gold, transport equipment, coal, contribute to greater ease of living as well as ease of coke and briquettes, silver, and iron and steel dragged doing business. imports down. Chart III.3: Merchandise Trade Deficit (Per cent, y-o-y, left scale; US$ billion, right scale) 30 30 20 20 6.7 10 10 0 0 -10 -10 -10.1 -20 -20 -30 -30 -40 -40 Trade balance in US$ billion (RHS) Exports (y-o-y, per cent) (LHS) Imports (y-o-y, per cent) (LHS) Sources: PIB; DGCI&S; and RBI staff estimates. 8 Oil trade deficit increased to US$ 8.8 billion in August from US$ 7.9 billion a year ago. Its share in total trade deficit increased to 33.2 per cent in August from 22.2 per cent a year ago. Non-oil deficit narrowed to US$ 17.7 billion in August as compared to US$ 27.7 billion a year ago. 9 US$ 35.1 billion in August [growth of 6.7 per cent (y-o-y)] 10 US$ 61.6 billion in August [contracted by 10.1 per cent (y-o-y)] RBI Bulletin September 2025 19 32-guA 32-peS 32-tcO 32-voN 32-ceD 42-naJ 42-beF 42-raM 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guAARTICLE State of the Economy Chart III.4: Trend in Services Exports and Imports Per cent (y-o-y) 35 25 -15 10.2 8.5 5 -5 -15 Exports Imports Source: RBI. Services trade remained robust in July 2025. The Agriculture net services export earnings expanded by 12.2 per Kharif sowing crossed normal acreage for cent (y-o-y) to US$ 16.4 billion (Chart III.4). Services the full season supported by above normal monsoon exports grew on the back of software and business rainfall (Chart III.6).11 The increase in sown area was services. At the same time, imports also rose rapidly, mainly in rice, maize, urad and sugarcane while the reflecting a rise in imports of software, business and area under oilseeds and cotton declined. The tur travel services. Chart III.5: Weighted Contribution to Real Aggregate Supply GVA Growth Per cent (y-o-y) 10 On the supply side, real gross value added (GVA) at basic prices registered a growth of 7.6 per cent 8 7.6 in Q1:2025-26 over a growth of 6.8 per cent in the preceding quarter. The quarterly momentum in real 6 GVA growth was driven by a strong services sector and 4 a pickup in industrial sector. Agriculture and allied sectors moderated sequentially from the previous 2 quarter, yet their performance remained robust on 0 a year-on-year basis, reflecting resilience (Chart III.5 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 2023-24 2024-25 2025-26 and Annex Table A3). Industry and services remained Agriculture, livestock, forestry, and fishing Industry the key drivers contributing 1.3 and 5.9 percentage GVA at basic prices (Y-o-Y growth, per cent) Services points, respectively. Manufacturing and construction Sources: NSO; and RBI staff estimates. saw robust expansion supported by higher demand 11 Kharif sowing, as on September 19, 2025, was at 1115.9 lakh hectares, and infrastructure push. which is around 101.8 per cent of the normal area. 20 RBI Bulletin September 2025 32-luJ 32-guA 32-peS 32-tcO 32-voN 32-ceD 42-naJ 42-beF 42-raM 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJState of the Economy ARTICLE acreage has been shifting to maize due to decline in At the sectoral level, manufacturing sector recorded tur prices and the rising demand for maize in ethanol robust growth reaching the highest in last six months. production12. Index of eight core industries witnessed a sharp jump in August led by double-digit expansion in steel and The cumulative rainfall during June 1 – coal. September 22, 2025 at the all-India level stood 7 per cent above the normal level with some of the major Available high-frequency indicators for August kharif producing states receiving excessive rainfall point to expansion in manufacturing activity, (Chart III.7). Reservoir levels have reached 90 per cent with its PMI surging to a near 18-year high, with of the capacity which augurs well for upcoming rabi Chart III.8: Reservoir Storage season (Chart III.8). (Per cent of full reservoir level) 100 The combined stock of rice and wheat with 90 the government continued to remain comfortable 90 87 supported by adequate procurement in kharif and 80 75 rabi marketing seasons.13 70 Industry and Services 60 Monthly Indicators of Industrial Activity 50 Growth in industrial activity, as measured by the year-on-year change in Index of Industrial Production 40 (IIP), improved to a four-month high in July 2025. Last 10 years average 2024 2025 12 Tur acreage declined by 0.5 per cent, while that of maize rose by 12.6 per Note: Data are as on September 18, 2025. cent as compared to the previous year. Source: Central Water Commission. 13 As on September 1, 2025, public stock was 2 times the buffer norm. RBI Bulletin September 2025 21 nrehtroN nretsaE nretseW lartneC nrehtuoS aidnI llA Chart III.7: Major Kharif States with Excessive Rainfall (Per cent share, left scale; per cent deviation from normal, right scale) 14 70 66 12 60 10 50 48 8 40 37 6 30 24 24 4 20 20 2 10 0 0 Full season kharif normal area Rainfall (RHS) Note: Rainfall data is as on September 22, 2025. Sources: Ministry of Agriculture and Farmers’ Welfare; Indian Meteorological Department. hsedarP ayhdaM nahtsajaR tarajuG anagnaleT bajnuP anayraH Chart III.6: Kharif Sown Area (Lakh hectares, left scale; per cent, right scale) 500 120 100 400 80 300 60 200 40 100 20 0 0 Note: Data is as on September 19. Source: Ministry of Agriculture and Farmers’ Welfare. eciR sesluP slaerec esraoC sdeesliO enacraguS nottoC 2024-25 2025-26 Per cent of full season normal area (RHS)ARTICLE State of the Economy ongoing improvements in demand conditions stayed below their historical average levels (Annex leading to increase in factory orders. Automobile chart A5). production remained robust, led by strong output Monthly Indicators of Services Activity of three-wheelers and two-wheelers. Production of India’s services sector sustained its growth passenger vehicles declined due to recalibration of momentum in August, with services PMI recording dispatches ahead of GST reforms. Production and the highest expansion since June 2010. International sales of passenger vehicles are likely to pick up in the air passenger traffic remained high and retail upcoming festive season supported by the GST rate commercial vehicles segment recorded a strong cut. Conventional electricity generation recovered growth. Port traffic expanded albeit at a slower pace as thermal coal production improved. Renewable energy generation sustained its pace (Table III.4). than in July. Growth in steel consumption picked up Supply chain pressures inched up in August 2025 but while cement production decelerated (Table III.5). Table III.4: High Frequency Indicators- Industry Aug-24 Sep-24 Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 Aug-25 IIP-Headline 0.0 3.2 3.7 5.0 3.7 5.2 2.7 3.9 2.6 1.9 1.5 3.5 IIP Manufacturing 1.2 4.0 4.4 5.5 3.7 5.8 2.8 4.0 3.1 3.2 3.7 5.4 IIP capital goods 0.0 3.5 2.9 8.9 10.5 10.2 8.2 3.6 14.0 13.3 3.0 5.0 PMI Manufacturing 57.5 56.5 57.5 56.5 56.4 57.7 56.3 58.1 58.2 57.6 58.4 59.1 59.3 PMI Export Order 54.4 52.9 53.6 54.6 54.7 58.6 56.3 54.9 57.6 56.9 60.6 57.3 56.1 PMI Manufacturing: Future Output 62.1 61.6 62.1 65.5 62.5 65.1 64.9 64.4 64.6 63.1 62.2 57.6 60.5 Eight Core Index -1.5 2.4 3.8 5.8 5.1 5.1 3.4 4.5 1.0 1.2 2.2 3.7 6.3 Electricity generation: Conventional -3.8 -1.3 0.5 2.7 4.5 -1.3 2.4 4.8 -1.8 -8.2 -6.1 -0.8 1.0 Electricity generation: Renewable -3.7 12.5 14.9 19.0 17.9 31.9 12.2 25.2 28.0 18.2 28.7 26.4 Automobile Production 4.4 10.1 10.0 8.0 1.3 9.4 2.3 6.5 -1.7 5.2 1.2 10.7 8.1 Passenger vehicle production 0.7 -3.4 -4.0 6.5 9.2 3.7 4.5 11.2 10.8 5.4 -1.8 0.1 -4.1 Tractor production -1.0 2.7 0.4 24.7 20.9 23.7 -7.8 18.5 20.5 9.1 9.8 11.5 9.4 Two-wheelers production 4.9 12.9 13.3 8.8 -0.6 10.3 1.6 5.6 -4.1 4.7 1.4 12.3 10.0 Three-wheelers production 9.0 3.9 -6.7 -5.5 7.6 16.2 6.5 6.0 4.1 16.9 8.6 24.0 15.8 Crude steel production 3.9 0.3 4.2 4.5 8.3 7.4 6.0 8.5 9.3 11.0 12.6 14.0 11.1 Finished steel production 3.0 0.7 4.0 2.8 5.3 6.7 6.7 10.0 6.6 7.0 10.9 13.8 13.0 Import of capital goods 12.3 10.9 7.0 4.7 6.1 15.5 -0.5 8.6 21.5 14.3 2.6 12.2 <<Contraction ----------------------------------------------------------------------------------------- Expansion>> Notes: 1. The y-o-y growth (in per cent) has been calculated for all indicators (except for PMI). 2. The heatmap translates the data range for each indicator into a colour gradient scheme with red denoting the lowest values and green corresponding to the highest values of the respective data series. 3. The heatmap is applied on data from April 2023 till August 2025, other than for electricity generation: renewable, where the data are till June 2025. 4. All PMI values are reported in index form. A PMI value >50 denotes expansion, <50 denotes contraction and =50 denotes ‘no change’. In the PMI heatmaps, red denotes the lowest value, yellow denotes 50 (or the no change value), and green denotes the highest value in each of the PMI series. Sources: Ministry of Statistics and Programme Implementation (MoSPI); S&P Global; Central Electricity Authority (CEA), Ministry of Power; Society of Indian Automobile Manufacturers (SIAM); Office of Economic Adviser, GoI; Joint Plant Committee; Directorate General of Commercial Intelligence & Statistics; and Tractor and Mechanisation Association. 22 RBI Bulletin September 2025State of the Economy ARTICLE Table III.5: High Frequency Indicators-Services Aug-24 Sep-24 Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 Aug-25 PMI services 60.9 57.7 58.5 58.4 59.3 56.5 59.0 58.5 58.7 58.8 60.4 60.5 62.9 International air passenger traffic 11.1 11.2 10.3 10.7 9.0 11.1 7.7 6.8 13.0 5.0 3.4 5.5 7.4 Domestic air cargo 0.6 14.0 8.9 0.3 4.3 6.9 -2.5 4.9 16.6 2.3 2.6 4.8 International air cargo 20.7 20.5 18.4 16.1 10.5 7.1 -6.3 3.3 8.6 6.8 -1.2 4.2 Port cargo traffic 6.7 5.8 -3.4 -5.0 3.4 7.6 3.6 13.3 7.0 4.3 5.6 4.0 2.5 Retail commercial vehicle sales -6.0 -10.4 6.4 -6.1 -5.2 8.2 -8.6 2.7 -1.0 -3.7 6.6 0.2 8.6 Hotel occupancy 0.7 2.1 -5.3 11.1 -0.2 1.2 0.6 1.9 7.2 -2.8 -0.3 -2.4 Tourist arrivals -4.2 0.4 -1.4 -0.1 -6.6 -0.2 -8.6 -13.7 -3.8 Steel consumption 14.1 13.5 12.7 12.3 11.4 11.4 11.3 11.5 6.0 7.1 7.9 7.7 8.2 Cement production -2.5 7.6 3.1 13.1 10.3 14.3 10.7 12.2 6.3 9.7 8.2 11.6 6.1 <<Contraction --------------------------------------------------------------------------------------------- Expansion>> Notes: 1. The y-o-y growth (in per cent) has been calculated for all indicators (except for PMI). 2. The heatmap translates the data range for each indicator into a colour gradient scheme with red denoting the lowest values and green corresponding to the highest values of the respective data series. 3. The heatmap is applied on data from April 2023 till August 2025, other than for domestic and international air cargo, and hotel occupancy, where the data are till July 2025. The latest data for tourist arrivals is till April 2025. 4. The data on international air passenger traffic for August 2025 growth rate is calculated by aggregating daily data. 5. All PMI values are reported in index form. A PMI value >50 denotes expansion, <50 denotes contraction and =50 denotes ‘no change’. In the PMI heatmaps, red denotes the lowest value, yellow denotes 50 (or the no change value), and green denotes the highest value in each of the PMI series. Sources: Federation of Automobile Dealers Associations (FADA); Indian Ports Association; Airports Authority of India; HVS Anarock; Ministry of Tourism, GoI; Joint Plant Committee; Office of Economic Adviser; and S&P Global. Inflation moderating for electricity.15 Kerosene prices continued to remain in deflation. Headline CPI inflation14 inched up in August after falling for nine consecutive months, with the pickup Core inflation edged up to 4.2 per cent in August coming largely from the food group (Chart III.9). CPI from 4.1 per cent in July. The uptick in inflation was inflation stood at 2.1 per cent in August as against 1.6 mostly driven by the ‘personal care and effects’16 per cent in July. sub-group, on account of rising gold prices. Gold contributed around 28 per cent to core inflation Food group recorded zero inflation in August in August. Inflation, however, moderated in other after being in deflation during June- July. Inflation in subgroups such as clothing and footwear, housing, sub-groups such as oils and fats, eggs, meat and fish health, education, and transport and communication. and sugar inched up. Cereals, fruits, milk, prepared meals and non-alcoholic beverages saw a moderation Both rural and urban inflation edged up to 1.7 in inflation. Deflation continued in vegetables, per cent and 2.5 per cent, respectively, in August. pulses, and spices (Chart III.10). While the state-level inflation rates varied between (-) 1.40 per cent and 9.04 per cent, inflation was Fuel and light inflation moderated in August with inflation remaining elevated for LPG, while 15 Inflation in fuel and light subgroup was 2.4 per cent in August. 16 The ‘personal care and effects’ sub-group in the CPI basket contains 14 As per the provisional data released by the National Statistical Office precious metal items like gold and silver apart from items such as soap, (NSO) on September 12, 2025. hair oil, and other cosmetics. RBI Bulletin September 2025 23ARTICLE State of the Economy Chart III.9: Trends and Drivers of CPI Inflation a. CPI Inflation b. Contribution to Inflation (Y-o-y, per cent) (Percentage points) 12 10 8 6 4 4.2 2.4 2 2.1 0 0.0 -2 -4 -6 Sources: National Statistical Office (NSO); and RBI staff estimates. contained below 4 per cent in majority of the states, in prices of tur/arhar dal and moong dal and an with only two states recording a higher inflation increase in gram dal price. Among edible oils, prices (Chart III.11). firmed up for mustard, sunflower and palm oils while High-frequency food price data for September groundnut oil prices eased. Prices of key vegetables so far (up to 19th) point towards a pick-up in cereal (potato, onion, and tomato) softened with a notable prices. Pulses recorded a mixed trend, with a decline decline in tomato prices (Chart III.12). Chart III.10: Annual Inflation across Sub-groups (Y-o-y, per cent) Sources: NSO; and RBI staff estimates. 24 RBI Bulletin September 2025 32-guA 32-tcO 32-ceD 42-beF 42-rpA 42-nuJ 42-guA 42-tcO 42-ceD 52-beF 52-rpA 52-nuJ 52-guA 8 7 6 5 4 3 2.1 2 1 0 -1 Food and beverages CPI excluding food and fuel Food and beverages CPI excluding food and fuel Fuel and light CPI Headline (y-o-y, per cent) Fuel and light CPI Headline (y-o-y, per cent) 32-guA 32-tcO 32-ceD 42-beF 42-rpA 42-nuJ 42-guA 42-tcO 42-ceD 52-beF 52-rpA 52-nuJ 52-guAState of the Economy ARTICLE Chart III.11: Spatial Distribution of Inflation: August 2025 (CPI-Combined) Table III.6: Petroleum Products Prices (Y-o-y, per cent) Item Unit Domestic Prices Month-over- month (per cent) Sep-24 Aug-25 Sep-25^ Aug-25 Sep-25^ Petrol ₹/litre 100.97 101.12 101.12 0.0 0.0 Diesel ₹/litre 90.42 90.53 90.53 0.0 0.0 Kerosene ₹/litre 45.78 44.47 44.25 3.4 -0.5 (subsidised) LPG (non- ₹/cylinder 813.25 863.25 863.25 0.0 0.0 subsidised) ^: For the period September 1-19, 2025. Note: Other than kerosene, prices represent the average Indian Oil Corporation Limited (IOCL) prices in four major metros (Delhi, Kolkata, Mumbai and Chennai). For kerosene, prices denote the average of the subsidised prices in Kolkata, Mumbai and Chennai. Inflation Number of Inflation Number of Sources: IOCL; Petroleum Planning and Analysis Cell (PPAC); and RBI staff Range States/UTs Trend States/UTs estimates. <2 18 Decline 16 2-4 17 Stable 0 Retail selling prices of petrol and diesel remained 4-6 0 Increase 21 unchanged in September (up to 19th). Kerosene 6-8 1 8-10 1 prices moderated slightly while LPG prices remained Note: Map is for illustrative purposes only. Sources: NSO; and RBI Staff estimates. unchanged (Table III.6). Chart III.12: DCA Essential Commodity Prices a. Cereals b. Pulses Index (Jan 2024 = 100) Index (Jan 2024 = 100) 115 110 105 104.2 100 100.0 95 90 Wheat Rice Gram dal Tur/ Arhar dal Moong dal c. Vegetables d. Edible Oils Index (Jan 2024 = 100) Index (Jan 2024 = 100) Groundnut oil Mustard oil Sunflower oil Sources: Department of Consumer Affairs, GoI; and RBI staff estimates. RBI Bulletin September 2025 25 42-naJ 42-raM 42-yaM 42-luJ 42-peS 42-voN 52-naJ 52-raM 52-yaM 52-luJ 52-peS 120 110 106.0 100 95.4 90 80 77.3 70 42-1-1 42-1-3 42-1-5 42-1-7 42-1-9 42-1-11 52-1-1 52-1-3 52-1-5 52-1-7 52-1-9 250 150 121.6 115.4 72.9 50 -50 Potato Onion Tomato 42-naJ 42-raM 42-yaM 42-luJ 42-peS 42-voN 52-naJ 52-raM 52-yaM 52-luJ 52-peS 130 129.5 120.5 110 100.6 90 42-naJ 42-raM 42-yaM 42-luJ 42-peS 42-voN 52-naJ 52-raM 52-yaM 52-luJ 52-peS <2 2-4 6-8 8-10ARTICLE State of the Economy The PMIs for August 2025 recorded a pick- IV. Financial Conditions up in the rate of expansion of input prices for Overall financial conditions turned manufacturing which rose due to higher prices for accommodative since the beginning of September metals, leather, and electronic parts. In services, primarily on account of easing corporate bond market too, input costs expansion picked up due to higher (Chart IV.1). salaries. Selling prices also accelerated for both services and manufacturing firms reportedly due to System liquidity remained in surplus during strengthening of demand conditions (Chart III.13). August and September (up to September 19). The Chart IV.1: Daily Financial Conditions Index for India (Standard deviation from average since 2012) 1.0 0.5 0.0 -0.5 -1.0 -1.5 Money Government securities Corporate bond Equity Foreign exchange Financial conditions index (standardised) Note: The financial conditions index provides a metric based on its historical average; in this context, a zero value corresponds to a financial system operating at the historical average level of all the financial indicators included in the index. To present the results, standardised index is used.17 Source: RBI staff estimates. 17 For detailed methodology see https://rbi.org.in/Scripts/BS_ViewBulletin.aspx?Id=23451 26 RBI Bulletin September 2025 42-rpA-71 42-yaM-70 42-yaM-72 42-nuJ-61 42-luJ-60 42-luJ-62 42-guA-51 42-peS-40 42-peS-42 42-tcO-41 42-voN-30 42-voN-32 42-ceD-31 52-naJ-20 52-naJ-22 52-beF-11 52-raM-30 52-raM-32 52-rpA-21 52-yaM-20 52-yaM-22 52-nuJ-11 52-luJ-10 52-luJ-12 52-guA-01 52-guA-03 52-peS-91 Chart III.13: PMI: Input and Output Prices a. Manufacturing b. Services Index (50=No Change) Index (50=No Change) 60 55 54.4 52.7 50 45 Input Prices Output Prices Note: A level of 50 corresponds to no change in activity, and a reading above 50 denotes expansion and vice versa. Source: S&P. Tighter conditions Easier conditions 32-guA 32-tcO 32-ceD 42-beF 42-rpA 42-nuJ 42-guA 42-tcO 42-ceD 52-beF 52-rpA 52-nuJ 52-guA 60 55.5 55 55.3 50 45 Input Prices Prices Charged 32-guA 32-tcO 32-ceD 42-beF 42-rpA 42-nuJ 42-guA 42-tcO 42-ceD 52-beF 52-rpA 52-nuJ 52-guAState of the Economy ARTICLE cash reserve ratio reduction of 25 bps effective Money Market September 6 aided banking system liquidity. The Amidst surplus liquidity, the weighted average average daily net absorption under the liquidity call rate – the operating target of monetary policy – adjustment facility stood at ₹2.4 lakh crore during generally hovered in the lower half of the corridor in August 16 to September 19, 2025, compared to ₹3.09 August and the first half of September. However, it lakh crore in the preceding one-month period (Chart hardened during September 15-19 as system liquidity IV.2). During this period, the Reserve Bank conducted moderated on account of tax outflows (Chart IV.3a).19 5 variable rate reverse repo auctions (overnight Overnight rates in the collateralised segments − the to 8-day maturity) to absorb excess liquidity and triparty and market repo – and the benchmark secured align overnight money market rates with the policy overnight rupee rate largely moved in tandem with the repo rate. Amidst surplus liquidity conditions, the uncollateralised rate. Yields on three-month treasury average balances under the standing deposit facility bills, certificates of deposit, and commercial papers continued to remain elevated and banks’ recourse to the marginal standing facility remained low.18 issued by non-banking financial companies hardened The Reserve Bank also conducted variable rate repo during this period (Chart IV.3b).20 The average risk auction on August 21 and September 16 to 19 to premium in the money market (the spread between address frictional liquidity strains on account of tax the yields on 3-month commercial paper and 91-day outflows. treasury bill) increased marginally.21 Chart IV.2: Liquidity Operations (₹ lakh crore) 4.5 3.5 2.5 1.5 0.5 -0.5 -1.5 -2.5 -3.5 -4.5 Daily standing deposit facility Variable rate reverse repo Marginal standing facility Variable rate repo Net liquidity adjustment facility Total absorption Source: RBI. 18 Balances under the standing deposit facility were ₹1.21 lakh crore crore during August 16 to September 19, 2025 as compared to ₹1.32 lakh crore in the preceding one-month period. MSF stood at an average of ₹0.03 lakh crore during this period. 19 The spread averaged (-) 8 bps during the period August 16 to September 19, 2025, as against (-) 6 bps during the period July 16 to August 15, 2025. 20 The average yields on 3-month treasury bills, 3-month commercial papers issued by NBFCs, and 3-month certificate of deposit hardened by 9 bps, 18 bps and 5 bps, respectively, during the period August 16 to September 19, 2025, as compared to the period July 16 to August 15, 2025. 21 Increased to 86 bps during the period August 16 to September 19, 2025, as compared to 76 bps in the preceding one-month period. RBI Bulletin September 2025 27 52-naJ-91 52-naJ-82 52-beF-60 52-beF-51 52-beF-42 52-raM-50 52-raM-41 52-raM-32 52-rpA-10 52-rpA-01 52-rpA-91 52-rpA-82 52-yaM-70 52-yaM-61 52-yaM-52 52-nuJ-30 52-nuJ-21 52-nuJ-12 52-nuJ-03 52-luJ-90 52-luJ-81 52-luJ-72 52-guA-50 52-guA-41 52-guA-32 52-peS-10 52-peS-01 52-peS-91ARTICLE State of the Economy Chart IV.3: Policy Corridor and Money Market Rates a. Policy Corridor and Call Rate b. Money Market Rates (Per cent) (Per cent) 7.5 7.0 6.5 6.0 5.51 5.5 5.0 Tri-party repo Market repo Repo rate Weighted average call rate 3-month treasury bill 3-month certificate of deposit Standing deposit facility Marginal standing facility 3-month commercial paper (NBFC) Sources: RBI; Clearing Corporation of India Limited; and Bloomberg. Government Securities (G-sec) Market by 7 bps during August 16 to September 19, 2025 as compared to the preceding one-month period (Charts In the fixed income segment, yields hardened IV.4a and IV.4b). in August before softening in September, as markets reacted positively to the reiteration of the government Corporate Bond Market towards the path of fiscal consolidation. The average Corporate bond yields exhibited mixed term premium (the difference between the yields movements across tenors and rating spectrum, while of 10-year G-sec and 91-day treasury bill) increased their spreads over the corresponding risk-free rates 28 RBI Bulletin September 2025 42-voN-92 42-ceD-31 42-ceD-72 52-naJ-01 52-naJ-42 52-beF-7 52-beF-12 52-raM-7 52-raM-12 52-rpA-4 52-rpA-81 52-yaM-2 52-yaM-61 52-yaM-03 52-nuJ-31 52-nuJ-72 52-luJ-11 52-luJ-52 52-guA-8 52-guA-22 52-peS-5 52-peS-91 8.5 8.0 7.5 7.0 6.43 6.5 6.0 5.79 5.45 5.5 5.0 4.5 52-naJ-02 52-beF-11 52-raM-50 52-raM-72 52-rpA-81 52-yaM-01 52-nuJ-10 52-nuJ-32 52-luJ-51 52-guA-60 52-guA-82 52-peS-91 Chart IV.4: Developments in G-sec Market a. Movement in G-sec yield b. Term Premium (Per cent) (Per cent) 7.30 7.00 6.70 6.49 6.40 6.04 6.10 5.97 5.80 5.50 3 year 5 year 10 year Note: In chart b, term premium is calculated as the difference between the 10-year G-sec yield and the 91-day treasury bill yield. Sources: Bloomberg; FIMMDA; and RBI staff estimates. 52-naJ-42 52-beF-70 52-beF-12 52-raM-70 52-raM-12 52-rpA-40 52-rpA-81 52-yaM-20 52-yaM-61 52-yaM-03 52-nuJ-31 52-nuJ-72 52-luJ-11 52-luJ-52 52-guA-80 52-guA-22 52-peS-50 52-peS-91 1.20 1.06 1.00 0.80 0.60 0.40 0.20 0.00 5202-naJ-42 5202-beF-01 5202-beF-72 5202-raM-61 5202-rpA-20 5202-rpA-91 5202-yaM-60 5202-yaM-32 5202-nuJ-90 5202-nuJ-62 5202-luJ-31 5202-luJ-03 5202-guA-61 5202-peS-20 5202-peS-91State of the Economy ARTICLE Table IV.1: Corporate Bonds - Rates and Spread Interest Rates Spread (bps) (Per cent) (Over Corresponding Risk-free Rate) Instrument July 16, 2025 – August 16, 2025 – Variation July 16, 2025 – August 16, 2025 – Variation August 15, 2025 September 18, 2025 August 15, 2025 September 18, 2025 1 2 3 (4 = 3-2) 5 6 (7 = 6-5) (i) AAA (1-year) 6.56 6.58 2 93 91 -2 (ii) AAA (3-year) 6.95 7.01 6 97 94 -3 (iii) AAA (5-year) 7.13 7.10 -3 95 81 -14 (iv) AA (3-year) 8.02 7.94 -8 204 199 -5 (v) BBB- (3-year) 11.64 11.38 -26 567 567 0 Note: Yields and spreads are computed as averages for the respective periods. Source: FIMMDA. declined (Table IV.1). Corporate bond issuances increased in September, tracking growth in currency remained higher than last year on a cumulative basis in circulation. Currency in circulation grew reflecting though there was some decline in July.22 increased demand for currency ahead of the festival season and kharif harvest activities. The growth in Money and Credit money supply (M ) remained broadly stable during Reserve money growth, adjusted for the first- 3 August (Chart IV.5).23,24 round impact of changes in the cash reserve ratio, Chart IV.5: Growth in Reserve Money and Money Supply (M ) 3 (Y-o-y, per cent) 12 11 9.5 10 9 9.4 8 7 6 5 4 Reserve money (CRR adjusted) Money Supply Source: RBI. 22 Declined to ₹0.58 lakh crore in July 2025, compared to ₹1.08 lakh crore in June 2025. On a cumulative basis (April to July), it was at ₹3.5 lakh crore in 2025-26 as compared to ₹2.5 lakh crore in corresponding period of the previous year. 23 Reserve money (adjusted for CRR) grew by 9.4 per cent (y-o-y) as on September 19, 2025 [8.7 per cent (y-o-y) as on August 22, 2025]. Currency in circulation grew by 8.9 per cent (y-o-y) as on September 19, 2025 [8.5 per cent (y-o-y) as on August 22, 2025]. 24 Money supply grew by 9.5 per cent (y-o-y) as on September 5, 2025 [9.6 per cent (y-o-y) as on August 8, 2025]. It includes the impact of the merger of a nonbank with a bank (with effect from July 1, 2023). RBI Bulletin September 2025 29 42-naJ-91 42-beF-9 42-raM-1 42-raM-22 42-rpA-21 42-yaM-3 42-yaM-42 42-nuJ-41 42-luJ-5 42-luJ-62 42-guA-61 42-peS-6 42-peS-72 42-tcO-81 42-voN-8 42-voN-92 42-ceD-02 52-naJ-01 52-naJ-13 52-beF-12 52-raM-41 52-rpA-4 52-rpA-52 52-yaM-61 52-nuJ-6 52-nuJ-72 52-luJ-81 52-guA-8 52-guA-92 52-peS-91ARTICLE State of the Economy Chart IV.6: Scheduled Commercial Banks: Credit and Deposit Growth (Y-o-y, per cent) 22 20 18 16 14 12 10.3 10 9.8 8 Credit growth Deposit growth Note: Scheduled commercial banks’ data are inclusive of regional rural banks. Data include the impact of the merger of a non-bank with a bank. Source: Fortnightly Section 42 Returns, RBI. Scheduled commercial banks’ credit to industry recorded a slight uptick on the back of growth picked up slightly to double-digits robust credit growth to MSMEs. in August, with deposit growth remaining Deposit and Lending Rates steady (Chart IV.6 and Annex Chart A7).25 The pass-through of the cumulative 100 bps During 2025-26 so far, the flow of non-food bank reduction in the repo rate during February to August credit to the commercial sector moderated; however, 2025 to lending and deposit rates has been robust. it was more than offset by the flow from non- The weighted average lending rate on fresh and bank sources. Consequently, total flow of financial outstanding rupee loans of scheduled commercial resources to the commercial sector was higher than in the corresponding period a year ago. banks declined by 53 bps and 49 bps, respectively, in the current easing phase. On the deposit side, Across key sectors, bank credit growth recorded the weighted average domestic term deposit rates a modest improvement in July (Annex Chart A8 ).26,27 on fresh and outstanding deposits also moderated Within the services sector, credit growth sustained (Table IV.2). its upward trajectory, mainly driven by trade and commercial real estate. Growth in personal loans was The decline in the weighted average lending rate largely supported by housing loans and buoyancy on fresh and outstanding rupee loans was higher in gold and other personal loan segments. Credit in the case of private banks relative to public sector 25 Credit growth of scheduled commercial banks was 10.3 per cent (y-o-y) as on September 5, 2025 [10.2 per cent (y-o-y) a month ago]. Deposit growth was 9.8 per cent (y-o-y) as on September 5, 2025 [10.0 per cent (y-o-y) a month ago]. 26 As at end-July, growth in non-food bank credit stood at 9.9 per cent (y-o-y), up from 9.3 per cent (y-o-y) recorded in June 2025. Non-food credit data are based on fortnightly Section-42 return for the last reporting Friday of the month, which covers all scheduled commercial banks (SCBs). 27 Sectoral non-food credit data are based on sector-wise and industry-wise bank credit (SIBC) return, which covers select banks accounting for about 95 per cent of total non-food credit extended by all scheduled commercial banks, pertaining to the last reporting Friday of the month. Data include the impact of the merger of a non-bank with a bank. 30 RBI Bulletin September 2025 32-luJ-41 32-guA-11 32-peS-80 32-tcO-60 32-voN-30 32-ceD-10 32-ceD-92 42-naJ-62 42-beF-32 42-raM-22 42-rpA-91 42-yaM-71 42-nuJ-41 42-luJ-21 42-guA-90 42-peS-60 42-tcO-40 42-voN-10 42-voN-92 42-ceD-72 52-naJ-42 52-beF-12 52-raM-12 52-rpA-81 52-yaM-61 52-nuJ-31 52-luJ-11 52-guA-80 52-peS-50State of the Economy ARTICLE Table IV.2: Transmission to Banks’ Deposit and Lending Rates (Variation in basis points) Term Deposit Rates Lending Rates Period Repo Rate WADTDR- WADTDR- EBLR 1-Year MCLR WALR - Fresh Rupee Loans WALR- Fresh Outstanding (Median) Outstanding Overall Interest Rate Deposits Deposits Rupee Loans Effect # (1) (2) (3) (4) (5) (6) (7) (8) (9) Tightening Period +250 259 206 250 175 181 193 115 May 2022 to Jan 2025 Easing Phase -100 -101 -17 -100 -40 -53 -60 -49 Feb 2025 to Aug* 2025 Notes: Data on EBLR pertain to 32 domestic banks. *: Data on WADTDR and WALR pertain to July 2025. #: At constant share. WALR: Weighted Average Lending Rate; WADTDR: Weighted Average Domestic Term Deposit Rate; MCLR: Marginal Cost of Funds-based Lending Rate; EBLR: External Benchmark-based Lending Rate. Source: RBI. banks (Chart IV.7). On the deposit side, transmission came into effect. Markets began on a positive note in was higher for public sector banks compared to early September on a revival of investor sentiment, private banks. buoyed by the release of higher-than-expected GDP growth data for Q1:2025-26 and the strong PMI data Equity Markets releases. Selling activity by foreign investors was Indian equity markets gained during mid-August more than offset by the sustained buying interest following the S&P sovereign rating upgrade and the from domestic investors (Chart IV.8). A consistent announcement of GST reforms. Thereafter, equity buying trend by domestic institutional investors has markets declined towards the end of the month as the resulted in their equity market holdings surpassing additional US import tariff levied on Indian products those of FPIs (Annex Chart A9). Chart IV.7: Transmission across Bank Groups (February 2025 – July 2025) a. Lending Rates b. Deposit Rate (Basis points) (Basis points) 0 0 -20 -20 -12 -16 -40 -40 -46 -44 -60 -53 -60 -62 -80 -80 -84 -80 -100 -100 -104 -100 -99 -97 -120 -120 WALR WALR WADTDR WADTDR (Fresh rupee loans) (Outstanding rupee loans) (Fresh deposits) (Outstanding deposits ) Public Private Foreign banks Public Private Foreign banks sector banks banks sector banks banks Note: Transmission during February to July 2025 is calculated by subtracting the weighted average lending and deposit rates of January 2025 from those of July 2025. Source: RBI. RBI Bulletin September 2025 31ARTICLE State of the Economy Chart IV.8: BSE Sensex and Institutional Flows (Index, left scale; ₹ thousand crores, right scale) FPI flows (RHS) DII fund flows (RHS) Sensex Note: FPI and mutual fund flows are represented on 15-days rolling sum basis. Sources: Bloomberg; and Capitaline. Balance of Payments basis) in Q1:2025-26 as net capital inflows were higher than the current account deficit. India’s current account balance improved in Q1:2025-26 over the same period last year, supported External Sources of Finance by robust services exports and strong remittances Net FDI reached a 38-month high in July, driven receipts (Chart IV.9).28 There was an accretion to the by strong gross FDI and reduced repatriation and foreign exchange reserves (on a balance of payment outward FDI investment.29 Gross inward FDI doubled 28 Current account balance as per cent of GDP in Q1:2025-26 was (-) 0.2 per cent as compared to (-) 0.9 per cent in Q1:2024-25. 29 Net FDI was at US$5 billion in July 2025. 32 RBI Bulletin September 2025 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 88000 90 82626.23 80 85000 70 82000 60 50 79000 40 30 76000 20 73000 10 0 70000 -10 -20 67000 -30 64000 -40 -50 61000 -60 -70 58000 -80 55000 -90 Chart IV.9: India’s Balance of Payments (US$ billion, left scale; per cent, right scale) 40 1.5 1.3 30 1.0 20 0.5 10 0.0 0 -0.5 -10 -0.2 -0.9 -1.0 -20 -30 -1.5 -40 -2.0 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 2023-24 2024-25 2025-26 Change in reserves on a BoP basis (- increase/+ decrease) Current account balance (-deficit/+surplus) Capital account balance (-deficit/+surplus) Current account balance to GDP ratio (RHS) Source: RBI.State of the Economy ARTICLE Chart IV:10: Foreign Direct Investment Flows a. Gross and Net FDI b. Country-Wise Outward FDI (US$ billion) (US$ miliion) 15 11.1 10 5 5.0 0 -5 -10 Net outward FDI Repatriation/Disinvestment Gross FDI Net FDI Source: RBI. from a year ago (Chart IV.10a). Singapore, followed The registrations of external commercial by the Netherlands, Mauritius, the US and the UAE, borrowings moderated during April-July 2025. together accounted for more than three-fourth of Despite the slowdown, inflows continued to outpace total inflows. Manufacturing and services including outflows, resulting in positive net inflows (Chart communication, computer and business services IV.12). Notably, 40 per cent of the total external commercial borrowings registered during this period were the top recipient sectors. Both repatriation of were intended for capital expenditure. FDI and outward FDI moderated. Outward FDI was mainly directed towards financial, insurance and business services, as well as manufacturing, with the US, Singapore, the Netherlands, Mauritius, and the UK being the major destinations (Chart IV.10b). These movements together led to an increase in net FDI. Foreign portfolio investment recorded net outflows in August, mainly due to equity outflows amidst heightened risk-off sentiment on US announcement of additional tariffs on Indian products (Chart IV.11). In contrast, the debt segment saw net inflows owing to India’s sovereign credit rating upgrade by S&P Global. During September so far (up to September 18) overall net foreign portfolio investment turned positive primarily driven by sustained debt inflows on the US Fed rate cut. RBI Bulletin September 2025 33 42-rpA 42-nuJ 42-guA 42-tcO 42-ceD 52-beF 52-rpA 52-nuJ USA Singapore Netherlands Mauritius UK UAE Russia 0 0.2 0.4 0.6 0.8 15 10 5 2.4 1.3 0 -5 -2.5 -2.5 -10 -15 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA *52-peS Chart IV.11: Foreign Portfolio Investments (US$ billion) Equity Debt Total Notes: 1. Debt also includes investments under the hybrid instruments. 2. *: Data up to September 22. Source: National Securities Depository Limited (NSDL).ARTICLE State of the Economy Chart IV.12: External Commercial Borrowings - Registrations and Flows (US$ billion) 20 14.7 15 12.5 10 6.5 5 3.3 2.8 1.5 0 -5 -10 Registrations Net inflows Source: Form ECB, RBI. India’s foreign exchange reserves remained Foreign Exchange Market adequate, providing a cover for more than 11 months The Indian rupee depreciated against the US of goods imports and for more than 95 per cent of the dollar in August amidst mixed performance of major external debt outstanding at end-March 2025 (Chart currencies. This reflected escalating India-US tariff IV.13).30 tensions, FPI outflows and a strengthening US dollar (Chart IV.14). 30 The import cover for goods and services was around nine months. 34 RBI Bulletin September 2025 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 4202 yluJ-rpA 5202 yluJ-rpA Chart IV.13: India’s Foreign Exchange Reserves (US$ billion, left scale; months, right scale) 750 14 11.5 12 650 10 8 550 703 6 4 450 2 350 0 Foreign exchange reserves Import cover (RHS) Notes: 1. As on September 12, 2025. 2. The import cover data is based on annualised merchandise imports as per the balance of payments statistics. Source: RBI. 32-raM 32-nuJ 32-peS 32-ceD 42-raM 42-nuJ 42-peS 42-ceD 52-raM 52-nuJ 52-guA *52-tpeSState of the Economy ARTICLE Chart IV.14: Movements in Major Currencies against the US Dollar in August 2025 (Per cent, m-o-m, left scale; per cent, right scale) 2.0 2 1.5 1.0 1 0.5 0.0 0 -0.5 -1.0 -1 -1.5 -2.0 --11..66 -2 Percentage change (+ appreciation/ - depreciation) Volatility (RHS) Note: 1. Appreciation/depreciation (m-o-m) calculated using monthly average exchange rates. 2. US dollar (DXY) measures the movements of the US dollar against a basket of major currencies (Euro, Japanese yen, British pound, Canadian dollar, Swedish krona, Swiss franc). 3. For each currency, volatility is measured as the coefficient of variation (100*Standard Deviation/Mean) using daily exchange rate data for August 2025. Sources: SBIL; Thomson Reuters; and RBI staff estimates. In real effective terms, the Indian rupee V. Conclusion depreciated in August (Chart IV.15a). The While the imposition of high US import tariff depreciation in real effective exchange rate was brought in some headwinds to the domestic mainly driven by depreciation in nominal effective macro-outlook, the developments since then have exchange rate with a marginal relative price effect underscored the resilience of the economy. The S&P (Chart IV.15b). sovereign rating upgrade was an acknowledgement RBI Bulletin September 2025 35 )YXD( ralloD SU xednI ycnerruC EME laer nailizarB dnar nacirfA htuoS rallod gnoK gnoH tiggnir naisyalaM thab dnaliahT nauy esenihC osep nacixeM haipur naisenodnI oruE ney esenapaJ dnuop KU osep enippilihP gnod esemanteiV now naeroK eepur naidnI Chart IV.15: Movements in the 40-Currency Real Effective Exchange Rate a. Monthly Changes b. Decomposition of Monthly Changes (Index (2015-16 = 100), left scale; per cent, right scale) (Per cent) 110 4 108 106 2 104 102 -1.4 100 0 98 96 98.8 -2 94 92 90 -4 Relative price effect Nominal exchange rate effect Change in REER (RHS) REER Change in REER Note: REER Index above (below) 100 indicates appreciation (depreciation) against 40 major trading partners compared to base year 2015-16. Source: RBI. 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 4 2 0 -1.3 -1.4 -2 -4 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guAARTICLE State of the Economy of its strong macro-fundamentals. The Q1:2025-26 doing business, lower retail prices and strengthening GDP estimates reinforced the resilience of domestic of consumption growth drivers. A higher kharif growth drivers. High frequency indicators for August sowing is expected to translate to a sustained growth momentum in the agriculture sector, while also show manufacturing and services activity at a decadal keeping food prices under check. The transmission high. of the front-loaded monetary policy easing measures In this scenario, the growth outlook for H2 is one have been robust. Coupled with income tax relief for of optimism. Healthy corporate balance sheets and households and employment augmenting measures31, the focus on structural reforms by the government are the stage is set for a sustained pick-up in consumption the bright spots of the economy. The landmark GST demand in H2 and potentially for a virtuous cycle of reforms should progressively result in a sustained higher investments and stronger growth impulses, positive impact through significant gains in ease of overcoming persistent global uncertainties. 31 Under the government’s Employment Linked Incentive Scheme. For details, please refer to https://www.pib.gov.in/PressReleasePage. aspx?PRID=2141129 36 RBI Bulletin September 2025State of the Economy ARTICLE Annex Chart A1: Global Supply Chain Pressure Index (Standard deviations from average value) 0.2 0.0 -0.08 -0.2 -0.4 -0.6 -0.8 -1.0 Source: Federal Reserve Bank of New York.- RBI Bulletin September 2025 37 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA Chart A2: Inflation Gap (Actual minus Target) (Percentage points) Chart A3: India's Merchandise Exports a. Trend in Exports (US$ billion, left scale; Growth in per cent, right scale) 45 40 40 30 35 30 6.7 20 25 10 20 15 0 10 -10 5 0 -20 b. Decomposition of Sequential Change in Export Growth (Per cent, y-o-y) Note: Inflation for the US is based on the personal consumption expenditure Note: POL: Petroleum, oil and lubricants. data. Sources: PIB; DGCI&S; and RBI staff estimates. Sources: Bloomberg; and RBI staff estimates. 32-guA 32-tcO 32-ceD 42-beF 42-rpA 42-nuJ 42-guA 42-tcO 42-ceD 52-beF 52-rpA 52-nuJ 52-guA Non-POL POL Y-o-y, growth (RHS) 25 20 15 10 5 0 -5 -10 -7 -15 -20 -25 32-guA 32-tcO 32-ceD 42-beF 42-rpA 42-nuJ 42-guA 42-tcO 42-ceD 52-beF 52-rpA 52-nuJ 52-guA Base effect Momentum in y-o-y growthARTICLE State of the Economy Chart A4: India's Merchandise Imports a. Trend in Imports b. Decomposition of Sequential Change in Import Growth (US$ billion, left scale; growth in per cent, right scale) (Per cent, y-o-y) 80 60 70 50 60 40 50 30 40 20 30 10 20 0 10 -10.1 -10 0 -20 Base effect Momentum ∆ in y-o-y growth Sources: PIB; DGCI&S; and RBI staff estimates. 38 RBI Bulletin September 2025 32-guA 32-tcO 32-ceD 42-beF 42-rpA 42-nuJ 42-guA 42-tcO 42-ceD 52-beF 52-rpA 52-nuJ 52-guA 30 20 10 0 -10 -20 -19 -30 Non-POL non-gold POL Gold Y-o-y, growth (RHS) 32-guA 32-tcO 32-ceD 42-beF 42-rpA 42-nuJ 42-guA 42-tcO 42-ceD 52-beF 52-rpA 52-nuJ 52-guA Chart A5: Index of Supply Chain Pressures for India (Standard deviations from average value) 3 2 1 0 -1 -2 -3 11-guA 21-raM 21-tcO 31-yaM 31-ceD 41-luJ 51-beF 51-peS 61-rpA 61-voN 71-nuJ 81-naJ 81-guA 91-raM 91-tcO 02-yaM 02-ceD 12-luJ 22-beF 22-peS 32-rpA 32-voN 42-nuJ 52-naJ 52-guA Chart A6: GDP Deflator (Per cent) 15 10 5 0.9 0 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 2022-23 2023-24 2024-25 2025- Note: ISPI depicts the deviation of supply chain situation in each month from 26 long period average (time series starting from March 2005). Source: RBI staff estimates. Sources: NSO; and RBI staff estimates. Chart A7: Scheduled Commercial Banks: Credit and Deposit Growth a. Credit b. Deposit (Percentage points) (Percentage points) 2 0.8 1 0 -1 -0.6 -2 -3 -4 Credit momentum effect Credit base effect Deposit momentum effect Deposit base effect Note: Scheduled commercial banks’ data are inclusive of regional rural banks. Data include the impact of the merger of a non-bank with a bank. Source: Fortnightly Section 42 Return, RBI. 42-luJ-21 42-guA-90 42-peS-60 42-tcO-40 42-voN-10 42-voN-92 42-ceD-72 52-naJ-42 52-beF-12 52-raM-12 52-rpA-81 52-yaM-61 52-nuJ-31 52-luJ-11 52-guA-80 52-peS-50 3 2 1 0.7 0 -1 -1.1 -2 -3 42-luJ-21 42-guA-90 42-peS-60 42-tcO-40 42-voN-10 42-voN-92 42-ceD-72 52-naJ-42 52-beF-12 52-raM-12 52-rpA-81 52-yaM-61 52-nuJ-31 52-luJ-11 52-guA-80 52-peS-50State of the Economy ARTICLE Chart A8: Sectoral Deployment of Bank Credit (Y-o-y, per cent) a. Credit: Agriculture b. Credit: Industry 25 20 15 10 7.3 5 0 b.1. Credit: MSMEs Industry b.2. Credit: Large Industry c. Credit: Services c.1. Credit: services - NBFCs d. Credit: Personal Loans d.1. Credit: Personal Loans - Housing Notes: 1. Sectoral non-food credit data is based on sector-wise and industry-wise bank credit (SIBC) return, which covers select banks accounting for about 95 per cent of total non-food credit extended by all SCBs, pertaining to the last reporting Friday of the month. Data are provisional. The bank groups covered under the SIBC return are - Public Sector Banks, Private Sector Banks, Foreign Banks, and Small Finance Banks. 2. Data include the impact of the merger of a non-bank with a bank. Source: RBI. RBI Bulletin September 2025 39 32-naJ 32-raM 32-yaM 32-luJ 32-peS 32-voN 42-naJ 42-raM 42-yaM 42-luJ 42-peS 42-voN 52-naJ 52-raM 52-yaM 52-luJ 12 10 8 6.0 6 4 2 0 32-naJ 32-raM 32-yaM 32-luJ 32-peS 32-voN 42-naJ 42-raM 42-yaM 42-luJ 42-peS 42-voN 52-naJ 52-raM 52-yaM 52-luJ 25 19.1 20 15 10 5 0 32-naJ 32-raM 32-yaM 32-luJ 32-peS 32-voN 42-naJ 42-raM 42-yaM 42-luJ 42-peS 42-voN 52-naJ 52-raM 52-yaM 52-luJ 10 8 6 4 2 0.9 0 32-naJ 32-raM 32-yaM 32-luJ 32-peS 32-voN 42-naJ 42-raM 42-yaM 42-luJ 42-peS 42-voN 52-naJ 52-raM 52-yaM 52-luJ 30 25 20 15 10.6 10 5 0 32-naJ 32-raM 32-yaM 32-luJ 32-peS 32-voN 42-naJ 42-raM 42-yaM 42-luJ 42-peS 42-voN 52-naJ 52-raM 52-yaM 52-luJ 40 35 30 25 20 15 10 5 2.6 0 -5 32-naJ 32-raM 32-yaM 32-luJ 32-peS 32-voN 42-naJ 42-raM 42-yaM 42-luJ 42-peS 42-voN 52-naJ 52-raM 52-yaM 52-luJ 35 30 25 20 15 10 11.9 5 0 32-naJ 32-raM 32-yaM 32-luJ 32-peS 32-voN 42-naJ 42-raM 42-yaM 42-luJ 42-peS 42-voN 52-naJ 52-raM 52-yaM 52-luJ 50 40 30 20 10 9.6 0 32-naJ 32-raM 32-yaM 32-luJ 32-peS 32-voN 42-naJ 42-raM 42-yaM 42-luJ 42-peS 42-voN 52-naJ 52-raM 52-yaM 52-luJARTICLE State of the Economy Chart A9: Share of Major Investors in Indian Equity Markets (Per cent, end of quarter) 25.0 20.0 17.8 15.0 17.0 10.0 5.0 0.0 Foreign institutional investment Domestic institutional investment Note: The Indian equity market is captured via NSE-listed companies. Source: Prime Database. 40 RBI Bulletin September 2025 51-nuJ 51-peS 51-ceD 61-raM 61-nuJ 61-peS 61-ceD 71-raM 71-nuJ 71-peS 71-ceD 81-raM 81-nuJ 81-peS 81-ceD 91-raM 91-nuJ 91-peS 91-ceD 02-raM 02-nuJ 02-peS 02-ceD 12-raM 12-nuJ 12-peS 12-ceD 22-raM 22-nuJ 22-peS 22-ceD 32-raM 32-nuJ 32-peS 32-ceD 42-raM 42-nuJ 42-peS 42-ceD 52-raM 52-nuJ Table A1: Real Gross Domestic Product (GDP) Growth (Y-o-y growth, in per cent) Components Share in Weighted 2023-24 2024-25 2025-26 2024-25 Contribution (Per cent) (percentage points) 2023-24 2024-25 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 I. Total Consumption 65.6 4.0 4.3 7.1 5.1 5.3 6.3 7.0 6.1 8.3 4.7 7.1 Expenditure Private 56.5 3.2 4.0 7.4 3.0 5.7 6.2 8.3 6.4 8.1 6.0 7.0 Government 9.1 0.8 0.2 5.3 20.1 2.3 6.6 -0.3 4.3 9.3 -1.8 7.4 II. Gross Capital Formation 36.8 3.8 2.5 8.9 11.9 12.4 9.1 6.2 7.7 4.9 7.8 6.4 Fixed Investment 33.7 3.0 2.4 8.4 11.7 9.3 6.0 6.7 6.7 5.2 9.4 7.8 III. Net Exports Exports 21.6 0.5 1.4 -7.0 4.6 3.0 7.7 8.3 3.0 10.8 3.9 6.3 Imports 22.5 3.3 -0.9 18.0 14.3 11.3 11.4 -1.6 1.0 -2.1 -12.7 10.9 GDP 100.0 9.2 6.5 9.7 9.3 9.5 8.4 6.5 5.6 6.4 7.4 7.8 Sources: NSO; and RBI staff estimates.State of the Economy ARTICLE Table A2: Key Recommendations of the 56th GST Council From (Per To (Per cent, From (Per To (Per cent, GST GST Rate) cent, GST cent, GST rate) rate) Rate) Daily Essentials Farmers and Agriculture Hair Oil, Shampoo, Toothpaste, Toilet soap 18 5 Tractors tyres and parts 18 5 bar, toothbrushes, shaving cream Butter, Ghee, Cheese, Dairy spreads 12 5 Specified bio-pesticides, micro-nutrients 12 5 Pre-packaged namkeens, Bhujia, mixtures 12 5 Drip irrigation system and sprinklers 12 5 Utensils 12 5 Agriculture, horticulture or forestry 12 5 machines for soil penetration, cultivation, harvesting and threshing Feeding bottles, clinical diapers, etc 12 5 Sewing machines and parts 12 5 Healthcare Sector Automobiles Individual health and life insurance 18 ‘nil’ Petrol and petrol hybrid, LPG, CNG cars 28 18 (certain types) Thermometer 18 5 Diesel and diesel hybrid cars (certain type) 28 18 Medical grade oxygen 12 5 3 wheeled vehicles 28 18 All diagnostic kits and reagents 12 5 Motor cycles (certain types) 28 18 Glucometer and test strips 12 5 Motor vehicles for transport of goods 28 18 Corrective spectacles 12 5 Education Electronic Appliances Maps, charts, and globes 12 ‘nil’ Air conditioners 28 18 Pencils, sharpeners, crayons, pastels 12 ‘nil’ Television (certain types) 28 18 Exercise books and notebooks 12 ‘nil’ Monitors and projectors 28 18 Eraser 5 ‘nil’ Dish washing machines 28 18 Source: Press Information Bureau. RBI Bulletin September 2025 41ARTICLE State of the Economy Table A3: Real Gross Value Added (GVA) Growth (Y-o-Y Growth, per cent) Sectors Share in Weighted 2023-24 2024-25 2025-26 2024-25 Contribution (Per cent) (percentage points) 2023-24 2024-25 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 I. Agriculture, Livestock, 14.4 0.4 0.7 5.7 3.7 1.5 0.9 1.5 4.1 6.6 5.4 3.7 Forestry & Fishing II. Industry 21.5 2.4 1.0 6.6 15.3 12.6 9.9 7.8 2.1 3.5 4.7 5.8 Mining and Quarrying 2.0 0.1 0.1 4.1 4.1 4.7 0.8 6.6 -0.4 1.3 2.5 -3.1 Manufacturing 17.2 2.1 0.8 7.3 17.0 14.0 11.3 7.6 2.2 3.6 4.8 7.7 Electricity, gas, water supply and other utility 2.4 0.2 0.1 4.1 11.7 10.1 8.8 10.2 3.0 5.1 5.4 0.5 services III. Services 64.1 5.8 4.8 12.1 8.3 8.5 8.0 7.2 7.4 7.5 7.9 9.0 Construction 9.1 0.9 0.8 9.2 14.6 10.0 8.7 10.1 8.4 7.9 10.8 7.6 Trade, hotels, transport, communication, and 18.5 1.4 1.1 11.0 5.4 8.0 6.2 5.4 6.1 6.7 6.0 8.6 services related to broadcasting Financial, real estate and 23.8 2.4 1.7 15.0 8.3 8.4 9.0 6.6 7.2 7.1 7.8 9.5 professional services Public administration, defense and other 12.7 1.1 1.1 9.3 8.9 8.4 8.7 9.0 8.9 8.9 8.7 9.8 services IV. GVA at basic prices 100.0 8.6 6.4 9.9 9.2 8.0 7.3 6.5 5.8 6.5 6.8 7.6 Sources: NSO; and RBI staff estimates. 42 RBI Bulletin September 2025Flow of Financial Resources to Commercial Sector in India during 2024-25 ARTICLE Flow of Financial Resources to credit from banks and non-bank sources as per cent to GDP increased to 81.9 per cent at end-March 2025 Commercial Sector in India from 80.2 per cent at end-March 2024. during 2024-25 Introduction by Amit Pawar^, Abhinandan Borad^^, Indian financial system has evolved into a diversified structure consequent to the initiation of Pawan Kumar*, John V. Guria^^, and a set of reforms in various segments of the economy Vishal Raina^ in the aftermath of the balance of payments crisis in the early 1990s. This evolution of the Indian financial Indian financial system has evolved into a diversified system has largely been driven by successive reforms structure since the early 1990s, reflecting the impact implemented across its various segments, in response of major reforms in the Indian economy including the to and supported by the evolving requirements of financial sector. Analysing the flow of financial resources a growing economy (RBI, 2024a). India’s financial to the commercial sector is vital for macro-financial system, which has traditionally been characterised surveillance and gauging the economy’s growth outlook. by bank-centric intermediation for the provision of This article highlights that while bank credit expansion credit, has undergone a structural transformation moderated during 2024-25, non-bank sources, both marked by the rising significance of non-bank domestic and foreign, played an important role in financial intermediaries (NBFIs) and the growing use bridging the funding gap for the commercial sector in of market-based instruments to meet the financing India. The increase in funding from non-bank sources requirements of the commercial sector. Accordingly, during 2024-25 was largely driven by equity issuances the recourse to market-based financing instruments amidst buoyancy in the domestic equity market, credit such as corporate bonds and equity issuances as well by Non-Banking Financial Companies (NBFCs), and as external sources such as foreign direct investment a rebound in short-term external credit. The recourse (FDI), external commercial borrowings (ECBs), and to non-bank sources by the commercial sector, amidst a short-term trade credit has increased appreciably in moderation in bank credit, reflects the adaptability of the the recent years, although the financing from banking financial system in meeting the evolving funding needs system continues to be the most important source. of the economy. The financing from banking system The sources of funding for the commercial sector continues to be the most important source, although the in India are varied and diverse (Chart 1). These are flow of financial resources from non-bank sources to the largely divided into bank credit and non-bank sources commercial sector increased in recent period. As at end- (including domestic and foreign). March 2025, the outstanding non-food bank credit as As the Indian financial system has been largely per cent to GDP increased to 55.1 per cent from 54.5 bank-dominated, bank credit growth is viewed per cent at end-March 2024. Overall, the outstanding as a key parameter to assess the flow of financial resources to commercial sector and growth outlook ^ Authors are from the Monetary Policy Department. ^^ Authors are from the Department of Economic and Policy Research. * Author of the economy. A softening of bank credit growth is is from the Department of Supervision. They are grateful to Dr. Rajiv often interpreted as a sign of weak aggregate demand Ranjan, Shri Indranil Bhattacharyya and Shri Mallavarapu Ramaiah for their guidance and suggestions. Views expressed in this article are those and, therefore, a potential risk to the near-term of the authors and do not represent the views of the Reserve Bank of India. growth outlook. However, given an increasing role RBI Bulletin September 2025 43ARTICLE Flow of Financial Resources to Commercial Sector in India during 2024-25 reflect the availability of actual funds and liquidity to Chart 1: Sources of Funding for Commercial Sector in India the commercial sector and its investment behaviour. Commercial At the aggregate level, it is observed that the flow Papers Non-Bank Sources of Credit: NBFCs, of non-food bank credit moderated by ₹3.4 lakh crore AIFIs and HFCs Corporate Domestic during 2024-25 (Table 1 and Chart 2). However, an Bonds Bank Credit Market-based Finance Equity Issuances: increase in flows of ₹4.5 lakh crore from non-bank IPOs/FPOs/Rights Issues/ Non-Bank External QIPs/Preferential Issues sources (both domestic and foreign) during 2024-25 Sources Commercial Borrowings Hybrid more than offset the moderation in the flow of bank Instruments: Short-term REITs/InvITs credit, resulting in an increase in total flow of financial Foreign Credit from Abroad resources to the commercial sector by ₹1.1 lakh crore. Foreign Direct The flows from non-bank domestic sources rose by Investment to India ₹3.7 lakh crore during 2024-25, mainly on account Note: NBFCs: Non-Banking Financial Companies; AIFIs: All India Financial Institutions; HFCs: Housing Finance Companies; IPOs: Initial Public Offerings; of a rise in equity issuances amidst buoyancy in the FPOs: Follow-on Public Offers; QIPs: Qualified Institutional Placements; REITs: Real Estate Investment Trusts; InvITs: Infrastructure Investment Trusts. domestic equity market and an increase in credit by Source: RBI staff’s visualisation. NBFCs. The flows from non-bank foreign sources of non-bank sources of funding, an assessment of a increased by ₹0.8 lakh crore during the same period, broader spectrum of flow of financial resources to primarily due to an increase in short-term credit from the commercial sector covering banks, non-banks and abroad, reflecting a rebound in India’s merchandise other sources, as alluded above, needs to be considered. imports. Against this backdrop, this article analyses the flow of financial resources and outstanding credit to the III. Flows from Banks commercial sector in India during 2024-25, based on In India, although market-based sources of multiple sources of funding. The remainder of the finance have grown considerably in recent years, article is organised as follows: Section II presents the bank credit remains the primary source of funding for consolidated statement on flow of financial resources to commercial sector in India. Section III discusses a wide range of sectors. The flow of non-food bank the trends and composition of credit from banks to credit moderated during 2024-25 following a robust the commercial sector, followed by an analysis of expansion in 2023-24 (Chart 3). It declined to ₹18.0 flows of financial resources from non-bank sources lakh crore in 2024-25 from ₹21.4 lakh crore in 2023-24. (including domestic and foreign) in Section IV. Section V discusses the outstanding credit from banks and The Reserve Bank increased the risk weights on non-bank sources to the commercial sector in India. unsecured personal loans and bank lending to NBFCs Section VI draws concluding observations. in November 2023 in a move aimed at strengthening II. Total Flow of Financial Resources to Commercial financial stability.1 This regulatory action raised the Sector capital requirements for banks on such exposures, The statement on flow of financial resources to 1 Risk weights on bank lending to NBFCs and retail loans excluding commercial sector from banks operating in India and housing, education, vehicle loans, and loans against gold and gold non-banks (including domestic and foreign sources) is jewellery were increased on November 16, 2023 (https://rbidocs.rbi.org. in/rdocs/notification/PDFs/REGULATORYMEASURES8785E7886A044B prepared to present total flow of financial resources to 678FB8AF2C6C051807.PDF). However, the risk weights on the exposures this sector. The financial flows in this statement are of SCBs to NBFCs are restored to their pre-November 2023 level w.e.f. from April 1, 2025 and the same are as per the external rating. Also, microfinance captured on a net basis (i.e., gross flows adjusted for loans in the nature of consumer credit are excluded from the applicability repayments/redemption/repatriation), as net flows of higher risk weights and are subject to a risk weight of 100 per cent. 44 RBI Bulletin September 2025Flow of Financial Resources to Commercial Sector in India during 2024-25 ARTICLE Table 1: Flow of Financial Resources to Commercial Sector in India (₹ crore) Source 2022-23 2023-24 2024-25 P A. Non-Food Bank Credit 18,19,026 21,40,243 17,98,321 B. Non-Bank Sources (B1+B2) 9,03,298 12,63,721 17,10,459 B1. Domestic Sources 5,27,181 10,20,302 13,85,609 1. Equity Issuances by Non-Financial Entities 1,16,111 1,35,008 3,81,161 2. Corporate Bond Issuances by Non-Financial Entities 1,12,822 1,67,374 1,97,795 3. Hybrid Instruments (REITs/ InvITs) by Non-Financial Entities 6,360 39,024 31,442 4. Commercial Paper Issuances by Non-Financial Entities -78,489 19,712 18,819 5. Credit by Housing Finance Companies (Net of Bank Borrowings) 72,111 1,41,816 1,34,852 6. Credit by RBI-regulated All India Financial Institutions 32,419 73,386 99,501 7. Credit by Non-Banking Financial Companies (Net of Bank Borrowings) 2,65,846 4,43,982 5,22,037 B2. Foreign Sources 3,76,118 2,43,419 3,24,850 1. External Commercial Borrowings by Non-Financial Entities -10,033 27,916 19,201 2. Short-term Credit from Abroad 51,136 -6,741 58,860 3. Foreign Direct Investment to India 3,35,015 2,22,244 2,46,788 C. Total Flow of Resources (A+B) 27,22,324 34,03,964 35,08,780 P: Provisional. Notes: i) Non-food bank credit pertains to scheduled commercial banks (SCBs) and excludes credit extended by co-operative banks. ii) Credit extended by banks, NBFCs and HFCs is inclusive of personal loans. iii) Data on all items are presented on a net basis, except equity and hybrid instruments which are on a gross basis. iv) All India Financial Institutions (AIFIs) include National Bank for Agriculture and Rural Development (NABARD), National Housing Bank (NHB), Small Industries Development Bank of India (SIDBI), Export-Import Bank of India (EXIM Bank), and National Bank for Financing Infrastructure and Development (NaBFID). Credit extended by AIFIs excludes refinancing to Scheduled Commercial Banks (SCBs), NBFCs, and HFCs, and direct loans to domestic and foreign governments/institutions. v) Data pertaining to HDFC Limited, which merged with HDFC Bank effective from July 1, 2023, is included under credit by Housing Finance Companies prior to its merger, while it is included under bank credit post-merger. vi) Data on credit by Housing Finance Companies (HFCs) and Non-Banking Financial Companies (NBFCs) has been adjusted for the conversion of some HFCs into NBFCs. Sources: RBI; SEBI; NABARD; EXIM Bank; SIDBI; NHB; NaBFID; and RBI staff estimates. effectively making it more expensive for them to concerns over the potentially unsustainable growth lend in these segments. The decision was driven by in unsecured credit, which could pose risks if left unchecked. Chart 2: Total Flow of Financial Resources to Commercial Sector The break-up of non-food bank credit flow into (₹ lakh crore) (a) ‘segments with increased risk weight (i.e., targeted 40 34.0 35.1 segments)’, and (b) ‘segments with unchanged risk 35 2.4 3.2 weight (i.e., non-targeted segments)’ shows that the 30 27.2 credit flow to the targeted segments had increased 10.2 25 3.8 13.9 significantly from ₹2.5 lakh crore in 2021-22 to 20 5.3 ₹6.6 lakh crore during 2022-23, while it increased 15 moderately in the case of non-targeted segments 10 21.4 (Chart 4a). The share of credit flow to the targeted 18.2 18.0 5 segments in total non-food credit flow increased from 21.0 per cent in 2021-22 to 38.1 per cent in 2022-23 0 2022-23 2023-24 2024-25 (Chart 4b). Non-Food Bank Credit Non-Bank Domestic Sources Non-Bank Foreign Sources In 2023-24, the credit flow to the targeted Sources: RBI; SEBI; NABARD; EXIM Bank; SIDBI; NHB; NaBFID; and RBI staff estimates. segments reduced to ₹4.7 lakh crore and its share RBI Bulletin September 2025 45ARTICLE Flow of Financial Resources to Commercial Sector in India during 2024-25 IV. Flows from Non-Banks Chart 3: Flow of Non-Food Bank Credit (₹ lakh crore) IV.1. Domestic Sources 25 IV.1.1. Market Sources 21.4 20 In recent years, the dynamism of Indian equity 18.2 18.0 markets and the expansion of a robust corporate 15 bond market, particularly for highly rated issuers, have significantly enhanced the access to market- 10 based financing avenues for corporates. Firms with strong credit profiles and robust balance sheets have 5 been well-positioned to leverage favourable market conditions to mobilise resources at competitive costs. 0 2022-23 2023-24 2024-25 During periods of robust performance and subdued Note: Chart is based on Section 42 return submitted by SCBs. volatility in the financial markets, the relative cost Sources: RBI; and RBI staff estimates. of market-based financing tends to decline vis-à- declined to 22.9 per cent in total non-food bank vis traditional bank credit, thereby incentivising credit, primarily due to the increased risk weights on corporates to raise funds through capital market instruments. During the last three years, commercial unsecured credit in November 2023. The credit flow sector regularly tapped financial markets, with to the targeted segments moderated further to ₹2.4 a rising trend in their contribution (Chart 5a). lakh crore in 2024-25 and its share in non-food bank Reflecting this trend, resource mobilisation through credit fell to 14.4 per cent, exerting a dampening equity issuances by non-financial entities increased effect on overall credit flow. sharply during 2024-25 (Chart 5b). This growth was Chart 4: Banks’ Non-food Credit Dynamics: Targeted Segments vs Non-targeted Segments a. Banks' Non-food Credit Flow b. Share in Non-food Credit Flow (₹ lakh crore) (Per cent) 22 100 20 18 80 16 61.9 14 60 79.0 77.1 12 10.8 15.8 85.6 10 14.5 40 8 9.6 6 4 20 38.1 6.6 2 4.7 21.0 22.9 2.5 2.4 14.4 0 0 2021-22 2022-23 2023-24 2024-25 2021-22 2022-23 2023-24 2024-25 Non-targeted Segments Targeted Segments Non-targeted Segments Targeted Segments Note: Data is based on Sector-wise and Industry-wise Bank Credit (SIBC) return. Sources: RBI; and RBI staff estimates. 46 RBI Bulletin September 2025Flow of Financial Resources to Commercial Sector in India during 2024-25 ARTICLE predominantly driven by heightened activity in mobilisation by non-financial corporate sector IPOs and FPOs, spurred by increased retail investor in the bond market (Chart 5d). This reflects an participation and favourable equity valuations in the increased reliance on market-based long-term post-pandemic market environment. The automobile, debt to fund infrastructure and capacity-building consumer services, and telecommunications sectors projects. accounted for the largest share of equity-based fund Furthermore, hybrid market instruments mobilisation, particularly through public issues and such as Real Estate Investment Trusts (REITs) and rights issues, in 2024-25 (Chart 5c). Infrastructure Investment Trusts (InvITs) have Corporate bond issuances from non-financial become another avenue of funding. Although smaller entities also increased in 2024-25 amidst a moderation in amount relative to equity and bonds, their growing in corporate bond yields. The monthly average yield contribution — evident in the increasing issuances on AAA-rated 3-year bonds of corporates fell by 33 over the past three years — indicates increasing bps in March 2025 vis-à-vis March 2024. Capital- investor appetite for asset-backed and income- intensive sectors such as housing, civil construction, generating vehicles. real estate, power generation and distribution, and telecommunications emerged as major issuers, For meeting short-term liquidity and working accounting for about half of the total resource capital needs, highly rated corporates have continued Chart 5: Flows from Market Sources a. Resource Mobilisation through Different Market Instruments b. Equity Issuances (₹ lakh crore) (₹ thousand crore, left scale; Index, right scale) 7 6.3 450 85000 6 400 77,415 80000 5 350 73,651 75000 300 4 3.6 250 70000 3 1.6 200 65000 2 150 58,992 60000 100 1 55000 50 0 0 50000 -1 2022-23 2023-24 2024-25 2022-23 2023-24 2024-25 -2 IPO/FPOs/Rights QIPs Equity Corporate Bonds REITs/InvITs Commercial Papers Preferential Issue Sensex c. Sector-wise Share in Public and Rights Issues of d. Sector-wise Share in Gross Corporate Bond Equity by Non-Financial Entities Issuances by Non-Financial Entities (Per cent) (Per cent) Automobile and Auto Components Housing/Civil Construction/Real Estate 2 20 27 6 13 Consumer Services 21 25 Power Generation and Distribution 11 18 39 4 Telecommunications 44 38 42 Telecommunications 18 15 Capital Goods 16 12 Diversified 8 78 Healthcare 8 10 17 12 Information Technology 108 3 1113 7 19 Roads and Highways 10 Miscellaneous/ Others 3 3 5 Others Inner Circle: 2022-23; Middle Circle: 2023-24; Inner Circle: 2022-23; Middle Circle: 2023-24; Outer Circle:2024-25 Outer Circle:2024-25 Note: In Chart 5a, equity includes resources raised by non-financial entities via IPOs, FPOs, Rights Issues, Qualified Institutional Placement and Preferential Allotment; and corporate bonds and commercial papers are net issuances by non-financial entities. Total of sector-wise share may not add up to 100 per cent due to rounding off. Sources: SEBI; PRIME Database; and RBI staff estimates. RBI Bulletin September 2025 47ARTICLE Flow of Financial Resources to Commercial Sector in India during 2024-25 to access the Commercial Paper (CP) segment of the Chart 6: Flows from Non-Bank Sources of Credit money market. CP issuances, while more volatile (₹ lakh crore) 8.0 7.6 than other instruments, are influenced by several 7.0 6.6 interrelated factors, including prevailing short-term interest rates, banking system liquidity, corporate 6.0 credit ratings, and seasonal working capital cycles. 5.0 Periods of monetary easing and surplus liquidity in 4.0 3.7 the system generally support greater CP issuance, as 3.0 corporates take advantage of lower borrowing costs. 2.0 Conversely, in a tightening interest rate environment, CP issuance is likely to moderate due to rising yields 1.0 and increased rollover risk. A stable monetary 0.0 2022-23 2023-24 2024-25 environment in 2023-24 and 2024-25 led to increased HFCs AIFIs NBFCs CP issuances following a decline in CP issuances in Sources: RBI; NABARD; EXIM Bank; SIDBI; NHB; NaBFID; and RBI staff estimates. 2022-23 amidst tight monetary conditions. IV.2. Foreign Sources IV.1.2. Non-Bank Sources of Credit External sources of funding, particularly Foreign Among the major non-bank sources of credit Direct Investment (FDI), continue to play a critical in India, NBFCs, AIFIs, and HFCs play a pivotal role role in supporting India’s external financing needs in complementing traditional banking channel. while fostering long-term economic development. These entities have become increasingly important FDI – a stable and non-debt creating source of in facilitating credit access to sectors and borrower capital – contributes not only to the augmentation of segments that are either underserved or inadequately domestic investment but also to broader structural served by the conventional banking system. gains. These include technology transfer, improved managerial practices, enhanced productivity, and During the last three years, total credit extended increased integration into global value chains. by NBFCs, AIFIs, and HFCs witnessed consistent Furthermore, FDI acts as a gauge of investor growth, reflecting their increasing role in India’s confidence and institutional credibility, thereby credit ecosystem. However, among these three improving market sentiment and liquidity. categories of entities, NBFCs have remained the According to India’s foreign investment policy, dominant source of non-bank credit (Chart 6). FDI entails investments through equity instruments Industry and retail sectors were extended a larger by non-resident investors in either (a) unlisted Indian share of NBFCs’ credit, with power sector accounting companies or (b) 10 per cent or more of the post- for bulk of the credit to industries at end-March issue paid-up equity capital (on a fully diluted basis) 2024 (RBI, 2024b).2 Further, in case of HFCs, housing of listed Indian companies. During 2024-25, net loans to individuals constitute a significant portion FDI flows to India increased despite an increase in of their credit. repatriation (Chart 7a). The services sector accounted 2 As per latest available data. for the largest share of gross FDI inflows in 2024- 48 RBI Bulletin September 2025Flow of Financial Resources to Commercial Sector in India during 2024-25 ARTICLE 25, followed by manufacturing, electricity and other sectors account for the largest share in ECBs (Chart energy sectors, retail and wholesale trade, and 7c). In terms of end-use of funds, more than half of transport (Chart 7b). total ECBs is used on capital or capacity expansion External Commercial Borrowings (ECBs) remain such as import or local sourcing of capital goods, new an important channel for corporates to access long- projects, modernisation and overseas acquisitions, term foreign capital.3 ECBs mostly require a minimum while about a third is used for the repayment of average maturity of three years and are subject to earlier ECBs and outstanding rupee loans.4 regulatory ceilings on cost and end-use restrictions. Short-term credit from abroad (i.e., short-term During the last three years, net ECB flows to non- trade credit) has witnessed a positive correlation with financial entities have exhibited significant variability, the performance of India’s merchandise trade during reflecting global interest rate cycles, shifting global the last three years. In 2024-25, short-term credit liquidity conditions and repayment obligations. Among non-financial sectors, manufacturing, and from abroad increased amidst a rebound in India’s electricity, gas, steam and air conditioning supply merchandise imports. Chart 7: Foreign Sources a. Flows from Non-Bank Foreign Sources b. Sector-wise Share in Gross FDI Inflows (₹ lakh crore) (Per cent) 5 Services 9 4 3.8 4 9 Manufacturing 3.2 4 5 9 3 2.4 9 43 35 39 Electricity and Other Energy 9 12 41 2 Retail and Wholesale Trade 9 7 1 11 Transport 12 25 0 21 Construction 2022-23 2023-24 2024-25 24 -1 Others Short-Term Credit from Abroad FDI to India Inner Circle: 2022-23; Middle Circle: 2023-24; ECB by Non-Financial Entities Outer Circle:2024-25 c. Sector-wise Share of ECBs by Non-Financial Entities (Per cent) 100 80 60 40 20 0 2022-23 2023-24 2024-25 Manufacturing Electricity, Gas, Steam and Air Conditioning Supply Transportation and Storage Mining and Quarrying Information and Communication Others Note: Total of sector-wise share may not add up to 100 per cent due to rounding off. Sources: RBI; and RBI staff estimates. 3 ECBs include loans including bank loans, floating/ fixed rate notes/ bonds/ debentures (other than fully and compulsorily convertible instruments), trade credits beyond three years, foreign currency convertible bonds (FCCBs), foreign currency exchangeable bonds (FCEBs), financial lease, and the plain vanilla Rupee denominated bonds issued overseas (which can be either placed privately or listed on exchanges as per host country regulations). 4 Based on average for last three years. RBI Bulletin September 2025 49ARTICLE Flow of Financial Resources to Commercial Sector in India during 2024-25 V. Outstanding Credit to Commercial Sector In 2024-25, although the flow of credit from banks to the commercial sector moderated, the The outstanding non-food bank credit increased flows from non-bank sources more than offset the to ₹182.1 lakh crore (55.1 per cent of GDP) at end- March 2025 from ₹164.1 lakh crore (54.5 per cent moderation in bank credit, resulting in a rise in flows of GDP) at end-March 2024 (Appendix Table A1). to this sector. The moderation in bank credit flow in Similarly, the outstanding credit from non-bank 2024-25 may be mainly attributable to a slowdown sources rose to ₹88.9 lakh crore (26.9 per cent of GDP) in credit to the targeted segments emanating from from ₹77.6 lakh crore (25.7 per cent of GDP) during an increase in risk weights on unsecured credit in this period.5 Consequently, total outstanding credit November 2023 aimed at strengthening financial from banks and non-bank sources to the commercial stability. sector increased to ₹270.9 lakh crore (81.9 per cent of The outstanding credit from banks and non- GDP) at end-March 2025 from ₹241.7 lakh crore (80.2 per cent of GDP) at end-March 2024. bank sources as per cent to GDP increased at end- March 2025 from their levels at end-March 2024. VI. Conclusion Consequently, overall outstanding credit to the A gradual liberalisation of the Indian financial commercial sector in India as per cent to GDP rose system has facilitated the creation of diverse sources during this period. of funding for the commercial sector. While banking system remains a major source of financing for References: the commercial sector in India, non-bank sources RBI (2024a). The changing nature of the financial (both domestic and foreign) have also emerged system: implications for resilience and long-term as important sources of finance in recent years. growth in emerging market economies. BIS Papers Therefore, it is imperative to analyse the total flow No. 148, 159-180. of financial resources to the commercial sector taking into account the flows from both banks and non-bank RBI (2024b). Report on Trend and Progress of Banking sources. in India 2023-24. 5 Data on non-bank sources excludes issuances of equities and hybrid instruments under domestic sources and foreign direct investment in equities under foreign sources. 50 RBI Bulletin September 2025Flow of Financial Resources to Commercial Sector in India during 2024-25 ARTICLE Appendix Table A1: Outstanding Credit to Commercial Sector in India (As at end-March) (Amount in ₹ crore; Figures in parentheses are per cent to GDP) Source 2023 2024 2025 P A. Non-Food Bank Credit 1,36,55,330 1,64,09,083 1,82,07,441 (50.8) (54.5) (55.1) B. Non-Bank Sources (B1 + B2) 74,43,091 77,56,314 88,85,434 (27.7) (25.7) (26.9) B1. Domestic Sources 53,95,038 56,59,037 66,37,411 (20.1) (18.8) (20.1) 1. Corporate Bond Issuances by Non-Financial Entities 16,58,140 18,25,514 20,23,310 2. Commercial Paper Issuances by Non-Financial Entities 89,816 1,09,528 1,28,347 3. Credit by Housing Finance Companies (Net of Bank Borrowings) 10,39,420 5,98,965 6,27,125 4. Credit by RBI-regulated All India Financial Institutions 3,51,224 4,24,610 5,24,111 5. Credit by Non-Banking Financial Companies (Net of Bank Borrowings) 22,56,439 27,00,421 33,34,518 B2. Foreign Sources 20,48,053 20,97,277 22,48,023 (7.6) (7.0) (6.8) 1. External Commercial Borrowings by Non-Financial Entities 10,29,403 10,71,240 11,33,592 2. Short-term Credit from Abroad 10,18,650 10,26,037 11,14,432 C. Total Credit (A+B) 2,10,98,421 2,41,65,397 2,70,92,875 (78.5) (80.2) (81.9) P: Provisional. Notes: i) Non-food bank credit pertains to scheduled commercial banks (SCBs) and excludes credit extended by co-operative banks. Including credit extended by co-operative banks (viz., urban co-operative banks, state co-operative banks, and district central co-operative banks), non- food bank credit at end-March 2023 and 2024 stood at 1,46,22,252 crore (54.4 per cent of GDP) and 1,74,63,724 crore (58.0 per cent of GDP), respectively. Accordingly, total outstanding credit at end-March 2023 and 2024 stood at 2,20,65,343 crore (82.1 per cent of GDP) and ₹ ₹ 2,52,20,038 crore (83.7 per cent of GDP), respectively. ₹ ii) Data on non-bank sources excludes issuances of equities and hybrid instruments under domestic sources and foreign direct investment in ₹ equities under foreign sources. iii) In case of corporate bonds, the outstanding data for end-March 2024 and 2025 are based on SEBI’s new series of data on bonds issued by financial and non-financial corporations. The outstanding data for end-March 2023 is worked out by adjusting the flow of 2023-24 from outstanding data for end-March 2024. iv) Flows based on outstanding data may not tally with the flows provided in Table 1 due to: (a) Merger of HDFC Limited with HDFC Bank on July 1, 2023; (b) Conversion of some Housing Finance Companies into Non-Banking Financial Companies; and (c) Valuation effect in case of foreign sources. v) Data is exclusive of current and non-current trade payables representing domestic liabilities in case of non-financial non-government public and private limited companies as data are not available. Sources: RBI; SEBI; NABARD; EXIM Bank; SIDBI; NHB; NaBFID; and RBI staff estimates. RBI Bulletin September 2025 51The Untold Story of FinTech Customers’ Experience ARTICLE The Untold Story of FinTech in 2023 (Tracxn, 2024; Statista, 2024). Finance apps alone account for 481 million downloads, led by Customers’ Experience digital wallet and payment apps (100 million), personal loan apps (93 million), and investment apps by Ashish Khobragade, Sakshi Awasthy, (64 million) in Q4 2023 (Statista, 2024). Over the past Mantisha and Rakhe Balachandran^ decade, FinTech funding has grown at a robust 21 per cent compound annual growth rate (CAGR), driven Understanding user experience is crucial for by global liquidity post-pandemic, with payments advancing FinTech innovations and shaping customer- and alternative lending segments dominating centric policies. This study analyses 5.69 million FinTech fundraising (Tracxn, 2024; Saroy et al., 2023). app reviews using machine learning techniques to Adoption is particularly high among younger users, uncover sentiments and key concerns in India’s FinTech with 52 per cent being under 25 years and 51 per cent ecosystem. Results reveal a generally positive user coming from semi-urban and rural areas (TransUnion experience, with emotions like trust and joy dominating CIBIL, 2023). To foster responsible innovation, across sectors. Topic modelling identifies customer the Reserve Bank has launched initiatives like the support, technical and app functionality, and loan- Regulatory Sandbox, the RBI Innovation Hub, and related concerns. Empirical findings from the fractional digital lending norms and SRO framework for FinTechs, probit model highlight the positive, albeit diminishing, balancing consumer protection and systemic stability. impact of market share on favourable review sentiment, Looking ahead, India’s FinTech market is projected to while data privacy and app updates emerge as significant grow from $110 billion to $420 billion over the next drivers. five years, with a CAGR of 31 per cent (Chaudhary, Introduction 2024). FinTechs have become pivotal in reshaping key The thriving FinTech ecosystem, while promising, financial segments, including payments, credit and often masks the underlying challenges faced by investment, thus, transforming the financial service customers as diverse users onboard and FinTech delivery across nations. The extant literature notes business models rapidly evolve to meet their varied that high transaction speed, personalisation, security needs. A customer-centric approach—grounded in and transparency are driving customer preferences understanding customer needs, safeguarding their for FinTech innovations (Feyen et al., 2023). India, interests and building trust—requires embedding the world’s third-largest and fastest-growing FinTech continuous feedback mechanisms into business ecosystem, is emerging as a global leader in digital strategies (Das, 2023). Traditional methods to address finance (RBI, 2024). With over 10,000 FinTech these issues, including usability testing (Nielsen, startups—the country ranks as the second-largest 1993), surveys, and focus groups (Morgan, 1993) app market globally, recording 26.4 billion downloads are limited by scalability, high cost, time lags and potential user or surveyor bias. A novel method, ^ Ashish Khobragade, Sakshi Awasthy and Rakhe Balachandran are from Department of Economic and Policy Research (DEPR), Reserve Bank of which is superior to formal surveys, is analysing India (RBI), and Mantisha was a research intern in DEPR, RBI. Authors are thankful to Shri Sarat Dhal for valuable comments and suggestions. online reviews for eliciting near real-time customer The views expressed in this article are those of the authors and do not represent the views of the Reserve Bank of India. feedback. With mobile applications (or apps) as RBI Bulletin September 2025 53ARTICLE The Untold Story of FinTech Customers’ Experience primary interfaces, users increasingly share feedback II. Literature review through ratings and reviews (Huebner et al., 2018), The proliferation of social networks, online influencing app adoption and purchase decisions consumers and user-friendly application interfaces (Burgers et al., 2016). However, the sheer volume, has significantly increased the volume of data, unstructured formats, spelling errors, emoticons, creating new opportunities for text-based research and multilingual content—often mixing English with (Zhao et al., 2020). App reviews have emerged as a regional languages—pose significant methodological valuable crowd-sourced indicator of user satisfaction challenges. (Vasa et al., 2012). Despite the diverse insights these reviews offer regarding user expectations and app Understanding user experience is crucial for usage, the systematic and timely analysis of the advancing FinTech innovations and shaping customer- growing volume of reviews across numerous apps centric policies. This study analyses 5.69 million remains a significant challenge (Huebner et al., 2018). FinTech app reviews using advanced machine learning Machine learning techniques have become techniques such as Distilled Bidirectional Encoder essential for extracting nuanced insights from large Representations from Transformers (DistilBERT) and sets of unstructured data (Pang and Lee, 2008). BERTopic to uncover sentiments and key concerns in Evidence suggests that applying sentiment analysis India’s FinTech ecosystem. Results reveal a generally to identify customer emotions such as trust, joy, fear, positive user experience, with emotions like trust and anger (Omotosho, 2021) enhances understanding and joy dominating across sectors. Topic modelling of user feedback regarding bank responsiveness, app of negative reviews identifies customer support, functionality and operational failures (Balcıoğlu, technical and app functionality, and loan-related 2024). Topic modelling techniques have also been concerns. Empirical findings from the fractional extensively used to extract valuable insights into app probit model (FPM) highlight the positive, albeit quality and sales (Khalid et al., 2014; Liang et al., 2015). diminishing, impact of market share on favourable Factors driving app satisfaction include the number of review sentiment, while data privacy and app updates downloads, app category (Pagano and Maalej, 2013), emerge as significant drivers. When interpreting the app functionalities (Luiz et al., 2018), and benefits study’s results, it is important to note that negative like delivery efficiency and customer support (Kumar app reviews may have prompted remedial actions by et al., 2023). Factors such as ease of use, perceived corresponding FinTechs, though customers may not usefulness, perceived value, performance expectancy, user experience, and perceived quality have also have updated their reviews. Nonetheless, recurring been identified for FinTech mobile apps using a set issues across apps highlight their prevalence of relevant words (Perea-Khalifi et al., 2024). Policy and warrant attention from FinTechs, SROs and makers worldwide are increasingly leveraging data policymakers. analytics tools to analyse user-generated content, The study is organised as follows: Section II such as social media posts, to inform decision- reviews the relevant literature. Section III details the making and policy development (Driss et al., 2019). data and methodology, while Section IV presents the Furthermore, app review data can serve as an early- major findings. Section V concludes with some policy warning system for predicting fraud and default rates perspectives. (Pranata et al., 2019). 54 RBI Bulletin September 2025The Untold Story of FinTech Customers’ Experience ARTICLE In the Indian context, while studies have applied primary business line as either payment or lending machine learning to extract insights from short- or banking; (c) FinTechs which have crossed the text social media data (Trivedi and Singh, 2021), threshold average deadpool age of three years (i.e., research on user experiences in FinTech apps using launched in or before 2022)3; and (d) apps having online reviews remains limited. A study on a peer- minimum of 50 reviews. to-peer (P2P) lending app revealed that users were III.2 Data Collection generally satisfied with loan processing times, with The open-access Python package google-play- less emphasis on ease of use, cost and risk (Gupta scraper (JoMingyu, 2019) was used to extract app and Mahajan, 2023). Another study demonstrated reviews. Over 5.69 million reviews, spanning April the positive impact of the Reserve Bank of India’s 2022 to August 2024, were collected, along with 2017 P2P lending guidelines on user sentiments, as assessed by the Valence Aware Dictionary and app-specific data like unique installations, review Sentiment Reasoner (VADER) model (RBI, 2024). counts and major app updates. Two factors guided the selection of the study period: first, exclusion of Against this backdrop, the paper seeks to potential distortions from the COVID-19 pandemic offer a thorough assessment of user adoption and on user sentiments and second, relevance from a satisfaction of Fintech applications. Unlike previous policy perspective. Analysing outdated reviews may studies, this research utilises a larger sample size not reveal current challenges in Indian FinTech and explores a wider range of factors, including app ecosystem, which require timely corrective measures. attributes, functionalities, company funding stages, FinTech categories, privacy issues, and regulatory Privacy and data safety policies for each app were affiliations. also compiled, covering aspects like permissions requested. Privacy data include an overview of III. Data and Methodology more than 13 types of permissions that applications III.1 Sample Selection may request from users, such as identity, contacts, This study analyses a sample of 107 business- location, SMS, phone details, photo/media/files, to-consumer (B2C) FinTechs in India, comprising 61 storage, camera, microphone, WiFi connection, device alternative lending apps, 25 payments apps and 21 and call information and others. FinTech-specific banking tech app.1 These FinTechs were identified data, including year of incorporation, funding raised, using Tracxn database2 and subsequently mapped to founder backgrounds, funding stage and annual the Google Play Store. The final selection of associated revenue, were sourced from the Tracxn database.4 apps was based on four criteria: (a) FinTech follows Major app updates were extracted from the respective a B2C business model; (b) FinTechs which have app pages on the Google Play Store. All the non- 1 Alternative lending, payments and banking tech apps are apps that categorical variables were aggregated (averaged) at the have lending, payments and banking as their primary business lines, respectively. It is possible that FinTech categories may overlap, thus, the app level for the subsequent cross-sectional analysis. primary business model is taken for classification into these categories. 2 FinTechs in the domains of payments, alternative lending and banking 3 The average deadpool age is computed from 429 deadpooled B2C technology, were shortlisted from Tracxn, a market intelligence platform, FinTechs in banking tech, payments, and lending identified from Tracxn, and verified through their respective official websites, yielding 376 valid where ‘deadpool’ denotes firms that cease to exist. To ensure meaningful firms. These account for about 60 per cent of total funding raised by sentiment analysis and relevance for policy making, the study focuses B2C FinTechs in India, with an even higher share for app-based firms. only on operational FinTechs older than three years, with defunct or very After excluding acquired entities without standalone financials, 107 new apps excluded. Any selection bias is minimal, and FinTech age is FinTechs with 5.69 million reviews were retained, forming a robust and controlled for in the empirical analysis. representative sample for analysing customer concerns. 4 Accessed as on September 26, 2024. RBI Bulletin September 2025 55ARTICLE The Untold Story of FinTech Customers’ Experience III.3 Sentiment Analysis Table 1: Accuracy Scores of Review Sentiment Sentiment analysis is performed to identify review Classifiers sentiments such as positive, negative, or neutral in (in per cent) line with extant literature (Omotosho, 2021; Mishev Model Training Set Test Set VADER 81.5 81.6 et al., 2020). In the FinTech sector, sentiment analysis DistilBERT 97.46 96.1 and deep learning models have been applied to assess Source: Authors’ calculations. user satisfaction and identify customer concerns in III.4 Emotion Classification FinTech applications (Masturoh and Pohan, 2021; Al Ryan et al., 2023; Huebner et al., 2018). Natural The National Research Council (NRC) - Canada Language Processing (NLP) tasks involving pre- emotion classifier, a rule-based approach, is used trained language models typically employ either to ascertain the eight types of emotions associated feature-based or fine-tuning approaches.5 In the with user adoption of FinTech apps, viz., ‘trust’, Indian context, where digital texts often mix regional ‘anticipation’, ‘joy’, ‘surprise’, ‘sadness’, ‘fear’, languages such as Hindi with English, fine-tuned ‘anger’, and ‘disgust’ (Mohammad and Turney, 2013). models like BERT have shown superior performance However, the emotion ‘surprise’ is excluded from (Wadhawan and Aggarwal, 2021). Accordingly, this this study due to ambiguity regarding its positive or study employs a fine-tuned DistilBERT, a faster and negative connotation. smaller transformer-based model (Sanh, 2019). III.5 Topic Modelling For training the data, a random sample of 4000 To uncover customer concerns in FinTech observations6 was drawn from the dataset. Each applications, topic modelling on labelled negative review in the sample was manually labelled into three reviews is employed. Traditional methods like Latent sentiments, viz., positive, negative, and neutral. This Dirichlet Allocation (LDA) and Non-negative Matrix pre-trained DistilBERT-based uncased model was fine- Factorisation (NMF) can yield homogenous or overly tuned on the labelled data to classify each review into broad topics, as their efficacy decreases with short, the aforementioned three sentiment categories. In unstructured, and complex text (Egger and Yu, 2022). terms of performance of the trained data, DistilBERT In contrast, BERTopic, a neural network model, model showed higher accuracy than VADER (Table provides more meaningful and consistent insights 1).7 Moreover, the training data was fairly balanced (Grootendorst, 2022; Krishnan, 2023). Thus, a semi- between positive and negative reviews in DistilBERT, supervised BERTopic with Term Frequency-Inverse making it the preferred model.8 Document Frequency (TF-IDF) is applied to negative 5 The feature-based approach relies on predefined word features to reviews across FinTech app segments. A topic model, assign sentiment scores, as seen in VADER. VADER, a lexicon and rule- based sentiment analysis model, is particularly effective among machine- one for each category of app, was trained on a sample learning-oriented techniques (Hutto and Gilbert, 2014). In contrast, the of reviews.9 The trained models were then used fine-tuning approach involves adjusting pre-trained models like BERT (Devlin et al., 2018) to classify text into sentiment categories. 6 The size of the labelled dataset is sufficient as the difference in accuracy 9 To construct a robust and well-generalised BERTopic models, diverse score in train and test data predictions is one per cent, indicative of no and representative samples of reviews were extracted for training from overfitting or underfitting. reviews spanning April 2022 to August 2024. The models were trained on 7 Accuracy is the proportion of all classifications that were correct, whether a smaller sample due to computing constraints, with each iteration using positive or negative. It is computed as: (True Positive + True Negative)/ a random subset of 30,000–40,000 reviews, resulting in category-wise (True Positive + True Negative + False Positive + False Negative). sample shares of 10, 20 and 30 per cent alternative lending, payments and 8 With neutral reviews at ~1 per cent, excluding them simplifies the banking tech apps, respectively. These trained models were then used to analysis to focus on the imbalance between positive and negative reviews, predict topics on a more recent set of reviews (April 2023 – August 2024), making it a binary classification problem. ensuring robust classification across evolving trends in customer reviews. 56 RBI Bulletin September 2025The Untold Story of FinTech Customers’ Experience ARTICLE to classify the remaining reviews into identified app installation is 0.54 per cent, marginally higher for topics.10 Only reviews exceeding 30 characters were payment and lending apps (0.6 per cent) compared analysed, resulting in 5,37,611 reviews; comprising to banking technology apps (Chart 1b). Albeit, the 2,77,003 reviews for Alternative Lending, 1,90,594 apps under study have received around six million for Payments, and 70,014 for Banking Tech. From reviews during the last two years, providing ample the topic model output, only coherent and specific opportunities to understand the major customer clusters were considered for analysis, thereby concerns. retaining 26.3 per cent of reviews in Alternative FinTech reviews are polarised in India, with Lending, 25.4 per cent in Payments, and 33.7 per cent around 20 per cent of total reviews belonging to in Banking Tech in the final analysis. For brevity and one star and 67 per cent of total reviews belonging better comprehension, topics were later manually to five stars (Chart 2). Thus, these extreme reviews clubbed in 11 broad themes. account for almost 87 per cent of total reviews in the FinTech ecosystem. In terms of sentiment analysis, IV. Empirical Results the numbers may vary. The divergence in sentiments IV.1 Stylised Findings between ratings and reviews can be attributed to the Among the three sectors under study, viz., underlying metrics: while ratings reflect the overall payment, lending and banking,11 apps with payment number of responses, including those without written or lending as their primary business are more feedback, sentiment analysis is confined to the sub- likely to get installed than banking (Chart 1a). The sample of worded reviews, suggesting that consumers likelihood of receiving a review per unique FinTech with extreme experiences are more likely to leave a Chart 1: Sector-wise FinTech App Landscape a. Average Installs per App b. Review per Install Payments 1.36 Payments 0.61 Alternative Lending 1.27 Banking Tech 0.42 Banking Tech 0.23 Alternative Lending 0.60 0 1 2 0.0 0.2 0.4 0.6 0.8 In crore In per cent Note: In Chart 1(a), Sector-wise average installs per app is computed: Total unique installations / Total number of apps in each sector. In Chart 1(b), Review per install is computed as: Total reviews of an app / Total unique installation of an app. Source: Authors’ calculations. 10 Since the BERTopic identifies reviews with dominant topic, reviews were classified into 295, 191, and 185 topics for alternative lending, payments and banking tech, respectively. Similar topics were clubbed into 11 broad themes (reported later in Table 3); while vague and incoherent topics were dropped. 11 Four outlier apps in terms of total installs (two payment apps and two banking apps) are excluded while calculating the average installs per app for each of these sectors. RBI Bulletin September 2025 57ARTICLE The Untold Story of FinTech Customers’ Experience Chart 2: Assessment of FinTech App Reviews 80 70 60 50 40 30 20 10 0 1 star 2 star 3 star 4 star 5 star Source: Authors’ calculations. worded review as compared to others (Schoenmueller The PRI also increases with age as the FinTechs cross et al., 2020; Hu et al., 2017). Further, there also exists the average age of being deadpooled (i.e., 3 years) a positive imbalance in FinTech reviews, which means [Chart 4c]. Similar relationship is observed with the that the share of positive reviews received by most of funding stages of FinTechs, with an initial increase the apps is higher than the share of negative reviews. from early stage to late stage followed by a mild decline for public FinTechs (Chart 4d). Notably, 61.7 per cent of all FinTech apps studied received a share of positive reviews in the 50 to 80 IV.2 Analysis of Emotions in Reviews per cent range. A smaller share of apps (7 per cent) Emotional expressions in the customer reviews received less than 20 per cent positive reviews. Thus, provide more insights regarding the satisfaction or the distribution of mean positive reviews is slightly dissatisfaction of customers. Following standard negatively skewed in the Indian FinTech ecosystem literature, this study examines three positive (Chart 3). Among the FinTech apps that were below emotions (trust, anticipation of better outcomes and the overall positive average, 58 per cent belong to joy) and four negative emotions (disgust, sadness, payments sector, 22 per cent belong to alternative fear and anger) [Omotosho, 2021]. lending and 20 per cent belong to the banking tech sector. The most positive emotion associated with the Indian FinTech ecosystem is trust across all sectors, Positive reviews per unique installation (PRI) of the app may be more relevant since majority of followed by anticipation of a good outcome and joy. customers do not leave a review. PRI is positively Across sectors, around 50 per cent of customers have skewed with majority of the apps having PRI below 1 expressed trust with the FinTech ecosystem. Among (Chart 4a). PRI varies according to characteristics of the negative emotions, the most expressed emotion FinTech apps. PRI increases with the segment-wise is anger, followed by sadness, fear and disgust. market share of the apps in terms of installs (Chart 4b). Anger is expressed by 14 per cent of customers. 58 RBI Bulletin September 2025 tnec rep nI Chart 3: Distribution of Positive Reviews Star Ratings Total Payments Lending Banking Source: Authors’ calculations. ycneuqerF 25 20 15 10 5 0 0 .2 .4 .6 .8 Share of Positive ReviewsThe Untold Story of FinTech Customers’ Experience ARTICLE Chart 4: Positive Review per Unique Install a. Distribution b. PRI versus Market Share 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0 5 10 15 c. PRI versus Age of Apps d. PRI versus Funding Stages Note: PRI is computed as: (Share of positive reviews * Number of reviews in 2 years) / (Unique installs since inception / age of the app * 2) Source: Authors' calculations. Overall, positive emotions dominate in all sectors, IV.3 Topic Modelling of Negative Reviews reflecting a generally favourable sentiment, but Negative reviews can function as an effective negative emotions warrant attention for targeted feedback mechanism from customers to FinTechs, improvements (Table 2). and can also provide macro-level insights to policy Table 2 : Sector-wise Emotion Classification makers. Employing topic modelling on negative (in per cent) reviews based on their embedded key messages Emotions Alternative Banking Payments Total Lending Tech represents an innovative approach to understanding Positive Emotions the overarching challenges faced by customers. Major Trust 47.63 53.31 48.62 48.98 issues highlighted by the sector-wise analysis of Anticipation 43.50 44.21 43.90 43.73 negative reviews using topic modelling are provided Joy 40.69 42.05 43.25 41.55 Negative Emotions in Table 3. Anger 13.68 15.35 14.99 14.31 A major concern identified across sectors is Sadness 13.10 12.86 13.10 13.05 Fear 11.11 10.86 11.27 11.10 customer support and service (CSS), which emerges as Disgust 10.26 8.28 8.50 9.46 the most significant concern for banking technology Note: Presence of emotions is scaled to total reviews in the sample period, providing an overview regarding the percentage of customers that customers, and the second-largest for payment expressed an emotion. Source: Authors’ calculations. tech and alternative lending tech users. Within this RBI Bulletin September 2025 59 IRP IRP IRP 1.5 1 0.5 Market Share (in per cent) 3.5 0.8 0.7 3.0 0.6 2.5 0.5 2.0 0.4 1.5 0.3 1.0 0.2 0.5 0.1 0 0.0 Early Middle Late Public 0 5 10 15 Age (in years) Stages of funding ytisneD 0 0 .5 1 1.5 2 2.5 PRIARTICLE The Untold Story of FinTech Customers’ Experience password (OTP) verification issues. Customers also Table 3: Major Concerns of FinTech Customers (in per cent) face slow app performance, server downtimes, and Broad Concerns Alternative Payments Banking update delays, alongside persistent bugs, glitches, Lending Tech and compatibility issues. Errors in essential features Credit/Loan related 52.02 15.64 4.66 such as payment processing, Know Your Customer Customer support and service 11.19 20.52 35.90 Technical issues and app 7.39 23.74 35.27 (KYC), and data synchronisation are prevalent, as are functionality issues following app updates, including functionality High interest rates and hidden 6.78 10.93 0.31 charges disruptions and forced reinstallation. Other issues Payment Processing and 7.18 7.96 9.93 include missing basic features such as scan-and-pay Settlement Related Issues Account related 6.54 6.02 3.99 and problems with available features like biometric Cashback/Rewards 3.21 7.83 0.99 authentication, unified payments interface setup, Harassment and unethical 1.38 0.21 0.93 password resets and repeated malware warnings. practices KYC and verification related 1.77 6.41 3.96 For alternative lending tech app users, loan and issues Promotional messages and 0.87 0.58 0.52 credit-related issues account for over 50 per cent of misleading advertisements complaints. Specific issues under this broad concern User data and privacy 1.68 0.16 3.54 can be categorised into three sub-topics, viz., loan Note: Figures indicate per cent of reviews in the total number of final reviews in each category. application and approval issues, credit limit related Source: Authors’ calculations. issues and data discrepancy issues. Loan application category, key issues include unresponsive customer related issues include delayed processing of loan support such as delayed or no responses to emails, applications or applications remaining under review calls, or chat queries; lack of effective escalation for extended periods, approved loans not being mechanisms and inadequate resolution for critical disbursed, and loan and offer rejections without or urgent issues or difficulties in reaching out to a clear reasons or transparency. Credit limit issues include issues with loan eligibility after repayment, human agent; rude or unprofessional behaviour from including repeated rejections despite good credit customer support staff; poor handling of technical scores or payment history, low initial credit limits issues, loan repayment problems, or account-related or credit limit reductions (after application) without queries; automated and generic responses without justification, and inability to increase credit limits actionable solutions to user complaints; frustration despite timely repayments. Customers have also with limited or unavailable support channels such as pointed out data discrepancy issues such as errors missing customer care numbers; and lack of effective in sanctioned versus disbursed loan amounts, resolution despite multiple communication and inaccurate or delayed updates to credit rating agencies grievance escalation. and negative impacts on credit scores due to errors in Another major concern is technical issues and reporting or hidden penalties. Additionally, the topic app functionality, which is the most prominent modelling analysis did not bring out prevalence of issue for payment tech apps, the second-largest for fraudulent apps in the ecosystem. This is because of banking tech, and the third-largest for alternative the removal of fraudulent lending apps from the Play lending tech. Specific issues include frequent app Store by the Reserve Bank of India and Self-Regulatory crashes, freezing and loading failures, inability to Organisations (SROs), in an effort to reduce digital login, and email, employment status and one-time lending frauds. 60 RBI Bulletin September 2025The Untold Story of FinTech Customers’ Experience ARTICLE IV.4 Determinants of FinTech Apps’ User Experience Table 4: Drivers of FinTech Apps’ User Experience – An Econometric Analysis Summarised Fractional Probit Regression Outputs Customers are expressing their sentiments in Dependent Variable: Share of (1) (2) (3) Positive Reviews reviews as presented in the previous sections. In Variables Baseline Baseline Trimmed Excluding Sample- this section, the determinants of positive review Outliers 1.78 SD sentiments are analysed using FPM, since the Age @ 0.114* 0.103 0.115** dependent variable - share of positive reviews of apps (0.068) (0.065) (0.058) Age2 -0.011** -0.011* -0.010** - lies between zero and one. (0.006) (0.005) (0.005) Log of total funding 0.020 0.022 0.003 Three variants of the FPM, viz., the full sample (0.023) (0.025) (0.023) (Model 1), the full sample excluding outliers (Model Medium data collection # 0.860*** 0.597** 0.118 (0.297) (0.244) (0.073) 2) and a trimmed sample by excluding observations High data collection # 0.662** 0.360 -0.030 beyond ±1.78 standard deviations from the mean (0.316) (0.265) (0.083) of the dependent variable (Model 3)12 are presented Segment market share $ 0.057*** 0.046** 0.045** (0.018) (0.021) (0.018) (Table 4; Average Marginal Effects are reported in Segment market share2 -0.0008*** -0.0006** -0.0006** Annex - Table 1). The preferred model is Model 2, (0.0003) (0.0003) (0.0003) App’s major update ! 0.292** 0.367** 0.190 which provides estimation on the full sample by (0.156) (0.163) (0.168) excluding outliers. As alluded to earlier, since not Update * Review per install 0.460** 0.545** 0.451* (0.207) (0.234) (0.238) all FinTech app users leave reviews and reviews are Review per install $ 0.265*** 0.256* 0.277** often polarised, the regression controls for review per (0.078) (0.139) (0.134) install (RPI) and review polarity13. Polarity -2.425** -3.95*** -2.732*** (1.171) (0.949) (0.899) The model indicates that the share of positive Constant 0.568 2.153** 1.881** (1.128) (0.924) (0.864) reviews flattens after reaching a certain age threshold. Observations 91 88 84 Further, apps with a larger market share exhibit a Log pseudolikelihood -58.80 -56.86 -55.00 significantly higher proportion of positive reviews. Prob > ch2 0.00 0.00 0.00 Pseudo R2 0.047 0.047 0.027 This relationship, however, diminishes over time. Age, Notes: reflecting the survival dynamics of apps, and market 1. Parentheses indicate robust standard errors. *, **, *** represent 10 per cent, 5 per cent and 1 per cent level of significance. share, representing business expansion strategies of 2. The sample size reduces from 107 to 91 owing to missing observations in log of total funding. apps, collectively highlight the alignment of positive 3. In Model 2, three outlier apps in variables like review per install (> 2 per reviews with the performance of individual apps cent); polarity (< 0.7) and app’s major updates (> 8) are excluded (one app each). within the FinTech ecosystem. 4. In Model 3, sample is trimmed by removing the apps that lie on extreme ends of the dependent variable (i.e., the share of positive reviews in total Two additional app-specific characteristics that reviews) by 1.78 standard deviation. 5. @ includes age of the app and not of the FinTech. significantly influence the share of positive app 6. # Relative to apps with low data collection (that seek least number of permission between 0-4). Data collection variable is constructed as a simple reviews are data privacy and major app updates. aggregation of the 13 types of permissions sought by FinTech apps. The value between 10 to 13 permissions is labelled 1 (high data collection); between 5 to 9 is labelled 2 (medium data collection); between 0 to 4 is labelled 3 (low 12 The upper bound, corresponding to the maximum value of the share data collection). High data collection results in lower privacy levels, while of positive reviews (dependent variable), is 0.921049. To exclude a low data collection ensures higher privacy proportionate number of apps with lower shares of positive reviews, 1.78 7. $ These variables are in percentage terms. standard deviation from the mean was selected. 8. ! Any major update to the app since the first review (post April 1, 2022). It is 13 Polarity is computed as the sum of number of 1 and 5 star rated reviews a dummy variable, if there is a major update, it is equal to 1 and 0, otherwise. as a share of total reviews. Source: Authors’ calculations. RBI Bulletin September 2025 61ARTICLE The Untold Story of FinTech Customers’ Experience Compared to apps collecting minimal information V. Policy Implications and Conclusion (zero to four permissions), apps requiring a moderate Consumer online reviews play a pivotal role level of user data (five to nine permissions) exhibit in technology adoption by offering near real-time a notably higher share of positive reviews. However, insights into user experiences. Using a large dataset the model indicates that further increases in of 5.69 million user-generated reviews, this study data collection (exceeding ten data points) do not applies advanced machine learning techniques to significantly affect the share of positive reviews, analyse sentiments and extract key user concerns, except in baseline model 1. This finding is consistent offering insights for policymakers and industry with insights from the topic modelling analysis, stakeholders to enhance user satisfaction in the which suggest that excessive data collection FinTech ecosystem. without commensurate service improvements can Indian FinTech apps, overall, deliver a positive lead to customer dissatisfaction, suggesting an user experience. Among the three sectors under inverse U-shaped relationship between permissions study—payment, alternative lending, and banking requested and user satisfaction. tech—apps in the payment and lending sectors are Updates are an important feature of apps, that more likely to be installed and receive a review per are generally aimed at improving app functionality. install. Sectoral analysis reveals that positive emotions, Consistent with this, the share of positive reviews including trust, anticipation of a better outcome is significantly higher for apps that received major and joy, are prevalent across all sectors, indicating a updates during the study period compared to generally favourable sentiment. However, prevalence those without such updates. This aligns with the of negative emotions such as anger, fear and sadness findings from the topic modelling exercise, where underscore areas for targeted improvements. Topic many customers highlighted concerns about app modelling results highlight customer support and functionality. Thus, major updates appear to address service as a major concern across sectors, with issues these issues, improving app performance and leading like unresponsive support, inadequate grievance to greater customer satisfaction (Perea-Khalifi et al., resolution and limited customer care contact channels. 2024). In the presence of major updates, the share of Technical issues and app functionality, including app positive reviews rises with increasing RPI, indicating crashes, login failures, and server downtimes, are that updates enhance functionality and encourage the most significant for payment tech and banking more users to share positive experiences. tech apps. For alternative lending apps, over half of the complaints relate to loan and credit issues, such The main findings remain consistent across the as processing delays, credit limit concerns and data full sample, including when outliers are retained discrepancies. (Model 1). For additional robustness checks, a third model is estimated using a trimmed sample Empirical analysis indicates that market share that includes only observations within ±1.78 has a positive, albeit diminishing, impact on user standard deviations of the dependent variable. The experience, highlighting the importance of a results remain broadly consistent, affirming their competitive FinTech ecosystem to sustain innovation. robustness. Key app characteristics, such as data privacy and 62 RBI Bulletin September 2025The Untold Story of FinTech Customers’ Experience ARTICLE major updates, are also significant drivers of positive Devlin, J., Chang, M., Lee, K., and Toutanova, K. (2019). reviews. 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RBI Bulletin September 2025 65ARTICLE The Untold Story of FinTech Customers’ Experience Annex Table 1: Drivers of FinTech Apps’ User Experience - Summarised Fractional Probit Average Marginal Effects (AMEs) Dependent Variable: Share of Positive Reviews (1) (2) (3) Variables Baseline Baseline Excluding Outliers Trimmed Sample-1.78 SD Age @ 0.002 -0.003 0.004 (0.006) (0.006) (0.006) Log of total funding 0.007 0.008 0.014 (0.008) (0.010) (0.009) Medium data collection # 0.322*** 0.225** 0.043 (0.102) (0.091) (0.028) High data collection # 0.247*** 0.137 -0.011 (0.109) (0.096) (0.031) Segment market share $ 0.02*** 0.016** 0.016*** (0.007) (0.007) (0.006) App’s major update ! 0.110* 0.136** 0.071 (0.057) (0.060) (0.006) Update * Review per install 0.170*** 0.202** 0.177** (0.076) (0.086) (0.088) Review per install $ 0.098*** 0.095** 0.103** (0.028) (0.051) (0.050) Polarity -0.897** -1.463*** -1.018** (0.431) (0.345) (0.333) Observations 91 87 83 Notes: 1. Parentheses indicate robust standard errors. *, **, *** represent 10 per cent, 5 per cent and 1 per cent level of significance. 2. AMEs measure the average change in the dependent variable resulting from a one-unit change in an independent variable, holding all other variables constant. 3. The sample size reduces from 107 to 91 owing to missing observations in log of total funding. 4. In Model 2, four outlier apps in variables like customer support concerns (>15 per cent of total negative concerns); review per install (> 2 per cent); polarity (< 0.7) and app’s major updates (> 8) are excluded (one app each). 5. In Model 3, sample is trimmed by removing the apps that lie on extreme ends of the dependent variable (i.e., the share of positive reviews in total reviews) by 1.78 standard deviation. 6. @ includes age of the app and not of the FinTech. 7. # Relative to apps with low data collection (that seek least number of permissions between 0-4). Data collection variable is constructed as a simple aggregation of the 13 types of permissions sought by FinTech apps. The value between 10 to 13 permissions is labelled 1 (high data collection); between 5 to 9 is labelled 2 (medium data collection); between 0 to 4 is labelled 3 (low data collection). High data collection results in lower privacy levels, while low data collection ensures higher privacy 8. $ These variables are in percentage terms. 9. ! Any major update to the app since the first review (post April 1, 2022). It is a dummy variable, if there is a major update, it is equal to 1 and 0, otherwise. Source: Authors’ calculations. 66 RBI Bulletin September 2025Review of Performance of the NBFC Sector ARTICLE Review of Performance of the and bonds, hire-purchase finance, and factoring. They play an important role in financing key economic NBFC Sector sectors such as infrastructure development, vehicle purchases (both commercial and personal), housing, by Abhyuday Harsh, Pallavi Pant, and consumer durables, thereby stimulating aggregate Nandini Jayakumar#, Rajnish Kumar Chandra demand, fostering employment opportunities, and Brijesh P^ and contributing to overall economic growth. The proliferation of NBFCs in India also points to Non-banking Financial Companies (NBFCs) play a their ability to adapt to market conditions through vital role in India’s economic growth. These institutions customised product offerings and quick service by providing finance for infrastructure, vehicles, housing, and consumer goods, improve aggregate demand, create delivery to diverse and niche segments. jobs, and contribute to economic expansion. The growing Over time NBFCs have grown in size and contribution of NBFCs to credit, particularly to the significance, implying that any significant disruption industrial and retail sectors, is evident in their rising in the sector could have repercussions on the financial credit-to-GDP ratio. Furthermore, the financial health of system and the real economy. The growing systemic the sector continued to be robust in terms of key indicators significance of NBFCs is underscored by the vigilant viz., return on assets, return on equity, net interest and nuanced regulatory oversight adopted by the margin, capital to risk-weighted assets ratio and non- Reserve Bank. A landmark development in this regard performing assets ratios. The increase in their share in was the implementation of the Scale-Based Regulation overall credit along with inter-connectedness with banks (SBR) framework since October 2022, which signified and financial markets, have implications for monetary a shift to a more nuanced, risk-calibrated system that policy transmission. acknowledges the heterogeneity within the NBFC Introduction sector. This reflects a delicate balancing act by the Non-Banking Financial Companies (NBFCs) Reserve Bank, between ensuring financial stability represent a critical and dynamic segment of India’s and facilitating innovation in the sector. financial system. Registered with the Reserve Bank The growing role of NBFCs in credit intermediation, under the RBI Act, 1934, NBFCs1 are engaged in a along with their interconnectedness with banks and variety of financial activities, including, inter alia, financial markets has highlighted their increasing provision of loans and advances, acquisition of shares significance in the transmission of monetary policy # The author is a Manager in Monetary Policy Department (MPD). impulses to the real economy, even as banks continue ^ Other authors are from the Department of Economic and Policy Research. The authors are grateful for the suggestions and encouragement to serve as the primary conduit. received from Shri M Ramaiah, Adviser. The authors are also thankful for the suggestions received from anonymous referees. The views expressed In this light, this article presents the recent in this article are those of the authors and do not represent views of the Reserve Bank of India. performance of India’s NBFC sector. The subsequent 1 Although merchant banking companies, stock exchanges, companies engaged in the business of stock-broking/sub-broking, nidhi companies, article is organised into the following sections. alternative investment fund companies, insurance companies and chit fund companies are NBFCs, they have been exempted from the Section II situates India’s non-banking sector in the requirement of registration with the Reserve Bank under Section 45-IA of the RBI Act, 1934. global context. Section III examines NBFCs’ balance RBI Bulletin September 2025 67ARTICLE Review of Performance of the NBFC Sector sheet, highlighting their growing significance within The narrow measure of Financial Stability India’s financial system. Building on this, section IV Board (FSB) includes NBFI entities involved in credit explores the issue of the effectiveness of monetary intermediation activities that could give rise to bank- policy transmission in this increasingly important like vulnerabilities. NBFI under the narrow measure segment. Section V details financial and prudential constituted around 30 per cent of total NBFI assets. indicators of the NBFC sector followed by the last Narrow measure for advanced economies grew at 10.8 section which concludes and discusses key emerging per cent whereas for emerging market economies, challenges. it expanded at 5.7 per cent. All economies except India and Saudi Arabia witnessed growth in narrow II. NBFIs: A Global Perspective measure3. For India, this can be attributed to economic At the global level, the total financial assets of function (EF2) which accounts for the largest share in non-banking financial institutions2 (NBFI) sector India and reported a contraction due to the merger of a demonstrated robust expansion, with a growth of large NBFI with a bank (Table 1). EF2 includes lending 8.5 per cent outpacing the banking sector’s growth institutions dependent on short-term funding and is of 3.3 per cent, at end-December 2023. The share of dominated by finance companies which specialise in NBFI in global financial assets stood at 49.1 per cent areas such as consumer finance, auto finance, retail at end-December 2023. Lending by NBFIs across globe mortgage provision, commercial property finance, and also increased by 4.1 per cent, compared to a 3.4 per equipment finance. cent rise in bank lending. Despite the prevailing high- interest rate environment, borrowings by NBFIs also The FSB conducted a survey on policy tools being remained strong. utilised in jurisdictions where EF2 is present, such as Table 1: Composition of Narrow Measure (At end-December 2023) Economic Entity Type Share Growth Function (Per cent) (EF) Global India Global India Collective investment vehicles with features that make them susceptible EF1 74.1 19.1 10.1 -10.5 to runs (e.g., money market funds, real estate funds) Lending dependent on short-term funding (e.g., consumer credit EF2 8.5 79.7 7.6 -2.3 companies, leasing companies) Market intermediation dependent on short-term funding (e.g., broker- EF3 7.0 0.7 16.2 49.8 dealers, custodial accounts) Facilitation of credit intermediation (e.g., credit insurers, monoline EF4 0.2 0.0 0.2 96.1 insurers) Securitisation-based credit intermediation (e.g., Securitisation vehicles, EF5 7.5 0.4 3.8 7.4 structured finance vehicles) Note: “Share” denotes the proportion of a specific EF relative to the total, i.e., the sum of EF1, EF2, EF3, EF4, and EF5, for either Global or India, as applicable. Similarly, the growth of a specific EF refers to its Y-o-Y growth, comparing December to December. Source: Global Monitoring Report on Non-Bank Financial Intermediation, 2024. 2 The Financial Stability Board (FSB) defines NBFI sector as a broad measure of all non-bank financial entities, comprising all financial institutions that are not central banks, banks, or public financial institutions. 3 The narrow measure comprises of five economic functions (EFs), namely EF1 to EF5. 68 RBI Bulletin September 2025Review of Performance of the NBFC Sector ARTICLE India. The survey responses indicated that the primary charges, ensuring fair practices in the charging of policies adopted included prudential requirements interest, and promoting responsible lending conduct. akin to those for banks, capital requirements, leverage III. Performance of the NBFC Sector4 limits, restrictions on significant risk exposures among others. Moreover, majority of responding III.1. Balance Sheet jurisdictions reported enforcing limits on liabilities The NBFC sector in terms of total assets/liabilities that NBFI entities can take on from banks and risky continued to register double-digit growth as of end- clients (Chart 1). Additional measures involved December 2024. The increase in risk-weights on select disclosure mandates, registration and authorization categories of retail loans5 by NBFCs in November procedures, as well as constraints on the range of 2023 contributed to the moderation in the growth activities, including the issuance of credit cards. of unsecured loans and advances across layers. In India, NBFCs are the largest component of Borrowings, which are the main source of funds and EF2. Given their systemic importance and diverse constitute about two-third of the total liabilities of business models, NBFCs are regulated through SBR, NBFCs, grew at a higher rate at end-December 2024 which envisages a layer-wise progressive increase in than a year ago (Table 2). regulatory intensity. Thus, NBFCs in the base layer Assets are subject to less stringent regulation than those in Loans and advances grew by 15.4 per cent at end middle and upper layers in view of their small size December 2024, at a slower rate than the preceding and limited interconnectedness. Apart from capital, year (Chart 2). As at end-December 2024, unsecured prudential, governance and disclosure guidelines, RBI loans constituted 24.0 per cent of gross loans and has concurrently emphasised on, enhanced customer advances compared with 26.8 per cent a year ago. protection, with recent measures mandating Key Fact Unsecured loans of upper layer NBFCs recorded Statements for loans, issuing guidelines on penal deceleration, broadly reflecting the impact of increased Chart 1: Policy Tools used by Jurisdictions for EF2 risk-weights. (Per cent of jurisdictions that responded to the survey) 100 The share of unsecured loans in the credit portfolio of middle layer NBFCs declined from around 80 32 per cent at end-December 2022 to 25 per cent at end- December 2024 (Chart 3a). In terms of growth, NBFC- 60 UL witnessed a sharp decline in growth of unsecured 40 credit, in relation to the middle layer , which recorded a marginal uptick (Chart 3b). 20 4 The analysis in this article is restricted only to NBFCs-UL and ML, 0 excluding CICs, PDs and HFCs. Bank prudential Capital Leverage Limits on large Liquidity Restrictions regulatory requirements limits exposures buffers on types of 5 Vide notification dated November 16, 2023, RBI announced regulatory regimes liabilities measures towards consumer credit and bank credit to NBFCs, which inter Available Similar tool is available alia included increase in risk weights on the consumer credit exposure of Available for some entities Not available NBFCs (outstanding as well as new) categorised as retail loans, excluding Source: Global Monitoring Report on Non-Bank Financial Intermediation, 2024. housing loans, educational loans, vehicle loans, loans against gold jewellery and microfinance/SHG loans. RBI Bulletin September 2025 69ARTICLE Review of Performance of the NBFC Sector Table 2: Consolidated Balance Sheet of NBFCs (At end-December) (₹ crore) 2023 2024 Items NBFC NBFC-UL NBFC-ML NBFC NBFC-UL NBFC-ML Share Capital 1,37,265 4,552 1,32,714 1,38,288 2,807 1,35,481 (18.2) ( -36.3) (21.8) (0.7) ( -38.3) (2.1) Reserves and Surplus 9,08,398 2,10,254 6,98,144 11,30,508 2,37,014 8,93,494 (15.6) (15.4) (15.6) (24.5) (12.7) (28.0) Public Deposits 1,06,435 85,779 20,656 1,23,348 1,02,439 20,909 ( -8.1) (28.4) ( -57.9) (15.9) (19.4) (1.2) Borrowings 31,78,623 7,97,075 23,81,549 36,96,651 9,20,520 27,76,131 (14.6) (13.9) (14.9) (16.3) (15.5) (16.6) Other Liabilities 3,37,404 54,013 2,83,391 3,77,917 54,930 3,22,987 (15.3) (1.8) (18.3) (12.0) (1.7) (14.0) Total Liabilities/Assets 46,68,126 11,51,673 35,16,453 54,66,712 13,17,710 41,49,002 (14.3) (14.2) (14.4) (17.1) (14.4) (18.0) Loans and Advances 37,15,229 10,21,556 26,93,673 42,87,573 11,55,044 31,32,529 (17.9) (18.4) (17.8) (15.4) (13.1) (16.3) Investments 5,15,402 61,122 4,54,280 7,27,957 75,128 6,52,830 (0.3) ( -10.7) (2.0) (41.2) (22.9) (43.7) Cash and Bank Balances 1,62,586 38,836 1,23,749 1,80,396 54,982 1,25,413 (0.8) ( -13.4) (6.2) (11.0) (41.6) (1.3) Other Assets 2,74,909 30,158 2,44,751 2,70,786 32,556 2,38,230 (6.6) ( -7.5) (8.7) ( -1.5) (8.0) ( -2.7) Notes: 1. Data are provisional. 2. Figures in parentheses are y-o-y growth in per cent. Sources: Supervisory returns; and authors’ calculations. Liabilities per cent of their total borrowings, respectively, at end-December 2024 (Chart 4). The sector witnessed NBFCs primarily raise funds from market and considerable deceleration in growth of share capital, banks, which accounted for 38.7 per cent and 37.4 partly due to uncertainty in market conditions. NBFCs Chart 2: Growth of Loans and Advances (Per cent) in the upper layer continued to experience decline in 30 share capital at end-December 2024. 25 Funds raised via issuance of debentures and 20 17.9 inter-corporate borrowings grew at a higher rate, 16.0 15.4 while growth in bank borrowing recorded moderation 15 12.0 at end-December 2024 (Table 3). 10 Apart from domestic market, NBFCs are 5 taking recourse to external commercial borrowings (ECBs). ECBs also contributes to diversification of 0 Dec-21 Dec-22 Dec-23 Dec-24 sources of funding. The share of NBFCs in total Total Secured Unsecured ECBs (registrations) has been rising over the years Note: Data are provisional. Sources: Supervisory returns; and authors’ calculations. (Chart 5). 70 RBI Bulletin September 2025Review of Performance of the NBFC Sector ARTICLE Chart 3: Trends in Unsecured Loans, by Layers a. Share in Loan portfolio b. Growth in Unsecured Lending (Per cent) (Per cent) 40 22.7 Dec-24 24.9 30 24.3 Dec-23 20 27.7 10 22.9 Dec-22 31.6 0 Dec-21 Dec-22 Dec-23 Dec-24 0 10 20 30 40 Upper layer Middle layer Middle layer Upper layer Note: Data are provisional. Sources: Supervisory returns; and authors’ calculations. III.2. Sectoral Credit 2024). Retail loans have been resilient growing in double digit, but weakness in industry and services Credit portfolio of NBFCs is dominated by outlook has contributed to moderation of their credit loans to industry and retail segment, constituting growth (RBIb, 2024). However, credit growth in around 72 per cent of total outstanding of the sector both segments continued to remain in double digits (Chart 6a). At end December 2024, credit to agriculture (Chart 6b). sector grew at a fast pace owing to robust kharif foodgrain production and good rabi prospects (RBIa, Table 3: Sources of Borrowings of NBFCs (₹ crore) Chart 4: Sources of Borrowings Items End- End- Percentage (At end-December, per cent) December December Variation 2023 2024 Dec-23 Dec-24 over over 20.3 Dec-22 Dec-23 1. Debentures 11,65,408 13,14,517 9.6 12.8 19.0 35.6 2. Borrowings from Banks 11,98,257 13,84,385 16.8 15.5 36.7 3.6 3.3 3.1 3.3 2023 2024 3. Borrowings from FIs 96,193 93,943 31.4 -2.3 4. Inter-corporate 1,03,699 1,33,754 2.0 29.0 borrowings 37.7 5. Commercial paper 1,05,903 1,14,820 37.1 8.4 6. Borrowings from 20,206 22,620 -6.0 11.9 37.4 Government 7. Subordinated debts 65,468 74,374 -7.2 13.6 Debentures Borrowings from banks Inter-corporate borrowings Other borrowings 8. Other borrowings 4,23,489 5,58,237 24.6 31.8 Commercial paper Total borrowings 31,78,623 36,96,651 14.6 16.3 Note: Data are provisional. Note: Data are provisional. Sources: Supervisory returns; and authors’ calculations. Sources: Supervisory Returns; and authors’ calculations. RBI Bulletin September 2025 71ARTICLE Review of Performance of the NBFC Sector Vehicle and loans against gold are the largest Chart 5: External Commercial Borrowings of NBFCs segments in retail portfolio of NBFCs, comprising (USD millions, left scale; per cent, right scale) 30,000 50 34.9 per cent and 12.6 per cent of total retail loans 43.0 respectively. Vehicle loans grew robustly in line with 25,000 40 rising demand, growing population, and rising annual 20,000 sales in passenger vehicle market in 2023-24 (SIAM, 28.3 27.2 30 2025). Gold loans which have a strong presence in 15,000 19.7 20.0 rural and semi urban markets also grew at double digit 20 10,000 catering to underserved sections of the society. Micro 10 finance loans saw a sharp deceleration in growth 5,000 (Table 4). Microfinance Industry Network (MFIN) has - 0 issued guardrails6, which capped loan outstanding per 2020-21 2021-22 2022-23 2023-24 2024-25 borrower. NBFCs Share in total ECBs (RHS) Notes: 1. ECB borrowings refer to registrations for ECB. IV. Monetary Policy Transmission 2. Data are provisional. Sources: DBIE, RBI; and authors’ calculations. Owing to their substantial credit intermediation to A layer wise analysis of the sector shows the crucial sectors of the economy and interlinkages with dominance of credit to industry by NBFCs in the banks and other financial entities, NBFCs have gained middle layer due to the presence of government salience in facilitating the transmission of monetary owned NBFCs, followed by retail and services loans. policy to the broader economy, even as banks continue Whereas upper layer is mainly concentrated in retail to be the primary channel of transmission. NBFCs’ loans segment with a share of more than 60 per cent, dependence on bank and market borrowings results followed by services (Chart 7). Chart 6: Sectoral Distribution of NBFC credit a. Share b. Growth (At end Dec-24, per cent) (Per cent) 1.8 19.4 Services 20.8 14.4 24.1 Retail loans 11.7 37.4 27.7 12.4 Industry 19.0 34.6 27.5 Agriculture 10.9 Retail loans Agriculture and allied activities 0 5 10 15 20 25 30 Others Industry Services Dec-24 Dec-23 Note: Data are provisional. Sources: Supervisory Returns; and authors’ calculations. 6 MFIN - a Self-Regulatory Organisation (SRO) - monitors emerging developments in the sector and based on it issues Directives and Advisories to members. During the year 2024, MFIN issued directive guardrails on 8 July, thereby limiting the number of microfinance lenders to a client to 4 and capping the total microfinance loans to a client at ₹ 2 lakhs. 72 RBI Bulletin September 2025Review of Performance of the NBFC Sector ARTICLE In this regard, an attempt has been made to Chart 7: Sectoral Credit Portfolio examine whether NBFCs’ borrowing and lending (At end-December 2024, share in per cent) 70 rates respond to changes in relevant rates by a 60 representative sample of top 100 NBFCs based on asset size. The methodology of loan pricing is not 50 uniform across NBFCs. While some NBFCs adopt their 40 own prime lending rates as interest rate benchmarks, 30 others rely on base rates/MCLRs of banks as external 20 benchmarks; a few do not have any interest rate benchmark for their loan pricing. The lack of 10 transparency has resulted in difficulty in assessing 0 Agriculture Industry Retail Other Services transmission of monetary policy in this segment of loans non food credit financial market (RBI, 2021; Patra, 2022). Middle layer Upper layer Note: Data are provisional. A dynamic panel model is estimated using Sources: Supervisory Returns; and authors’ calculations. generalised method of moments (GMM) methodology on an unbalanced panel8, covering the period from in a transmission mechanism that is more indirect7 March 2019 to December 2024 covering 86 per cent of as compared to banks. A change in policy rate impacts the assets of NBFC sector (at end-March 2024). NBFCs via their cost of funds, which moves when market and bank interest rates respond to monetary To understand transmission on the liabilities side policy. of NBFCs’ balance sheet, weighted average borrowing Table 4: Retail Loans of NBFCs (₹ crore) Items End-December 2023 End-December 2024 Percentage Variation Dec-23 over Dec-22 Dec-24 over Dec-23 1. Housing Loans 26,364 38,106 -4.4 44.5 2. Consumer Durables 42,343 50,297 43.6 18.8 3. Credit Card Receivables 53,479 60,603 28.7 13.3 4. Vehicle/Auto Loans 4,28,654 5,18,408 25.6 20.9 5. Education Loans 39,500 62,572 75.6 58.4 6. Advances against Fixed Deposits 124 174 -58.2 40.9 7. Advances to Individuals against Shares, Bonds 16,813 22,432 33.3 33.4 8. Advances to Individuals against Gold 1,43,745 1,87,350 20.0 30.3 9. Micro finance loan/SHG Loan 1,31,795 1,32,819 39.2 0.8 10. Others 3,13,740 4,11,962 27.0 31.3 Total Retail Loans 11,96,557 14,84,724 27.7 24.1 Note: Data are provisional. Sources: Supervisory returns; and authors’ calculations. 7 As NBFCs lack direct access to the liquidity adjustment facility (LAF) window, monetary policy transmission occurs indirectly through market-based channels, affecting borrowing cost and, consequently lending rates. 8 The analysis focusses on a sample of top 100 NBFCs owing to constraints on data availability and quality. RBI Bulletin September 2025 73ARTICLE Review of Performance of the NBFC Sector rate (WABR) of NBFCs is considered as the dependent as additional variables of interest. The vector X c i,t variable, which is calculated by using available represents various controls, which have been take–n1 instrument-wise borrowing rate of every NBFC, with a one-quarter lag to address potential endogeneity weighted by their respective outstanding amounts. concerns. θ are the controls for unobservable NBFC i Similarly, on the asset side, weighted average fixed effects. lending rate (WALR) is calculated by using available Results indicate that changes in the various sectoral lending rates weighted by their respective aforementioned relevant rates have a positive and outstanding amounts. Repo rate, weighted average significant effect on NBFCs’ WABR and WALR, call rate (WACR; operating target of monetary policy), implying transmission albeit incomplete. The and the 91day T-bill rate (benchmark rate representing summed coefficient of repo rate, WACR, 91-day T bill broader financial conditions) are used as independent rate, NBFC bond yield and WALR of banks to NBFCs is variables. Additionally, two other rates which are reported, which gives the cumulative impact of change relevant to NBFCs are also considered: average bond in the relevant interest rate on the WABR/ WALR of yield of AAA and AA-rated NBFCs and the WALR of NBFCs over three quarters. On the borrowing side, a bank lending to NBFCs. This is because NBFCs are one percentage point change in repo rate is associated largely dependent on banks and markets for their with a 0.24 percentage point change in WABR of NBFCs funding requirements9. A change in these rates affects over three quarters. Similar results with WACR (0.21) the cost of funds for NBFCs, in turn affecting their and 91-day T bill rate (0.19) are reported as well. In all lending rates. specifications, the coefficient of the lagged dependent NBFC-specific factors such as size (log of total variable is positive and highly significant, indicating assets), capital adequacy, (capital to risk-weighted persistence. Among controls, the coefficient for the assets) and profitability (return on assets, taken as a size and return on assets variables are negative and ratio of net profit to total assets) are used as controls. significant, indicating that larger and more profitable To control for the macroeconomic environment, real NBFCs can borrow at lower rates (Table 5). GDP growth rate and consumer price inflation are On the lending side, a one percentage point included. A dummy for the COVID-19 pandemic, change in repo rate is associated with a 0.33 percentage which takes the value one during June-September point change in WALR of NBFCs over three quarters 2020, and zero otherwise is also included. (0.36 percentage point change when WALR of Banks The following specification for regression is used: to NBFCs is considered). Similarly, WALR of NBFCs is n found to be positively associated with WACR (0.22), Y i,t αY i,t– βMP t–j δ c X c i,t covid t θ i ε i,t 91-day T-bill rate (0.24) and NBFC bond yield (0.17) as j 1 –1 = + + + + + well (Table 6). where Y is∑= W1 ABR or WALR of NBFC i in period t, i,t as the case may be. MP is main variable of interest, On the borrowing side, a key impediment to t which in different specifications stands for repo rate, transmission could be the higher cost of funds faced WACR, or 91-day T-bill rate. In the regressions with by NBFCs. NBFCs rely on bank and market borrowings WALR as the dependent variable, NBFCs bond yield for their funding requirements and unlike banks, do or WALR of bank lending to NBFCs are also included not have direct access to the liquidity adjustment 9 At end December 2024, bank borrowings (37.4) and debentures (35.6) facility (LAF) window. Consequently, reductions in together accounted for 73 per cent of NBFCs total borrowings. the repo rate may not immediately translate into 74 RBI Bulletin September 2025Review of Performance of the NBFC Sector ARTICLE Table 5: Transmission to Weighted-Average Table 6: Transmission to Weighted-Average Borrowing Rate of NBFCs Lending Rate of NBFCs Dependent Variable: WABR Dependent variable: WALR Repo WACR 91-day NBFC WALR of Repo rate WACR 91-day rate T-bill bond banks T-Bill rate rate yield to NBFCs WABR (-1) 0.443*** 0.442*** 0.448*** WALR (-1) 0.492*** 0.500*** 0.502*** 0.507*** 0.519*** (0.112) (0.119) (0.113) (0.0859) (0.0847) (0.0824) (0.0830) (0.0851) Size -0.407* -0.418* -0.400* Size -1.299*** -1.251*** -1.226*** -1.212*** -1.106*** (0.209) (0.214) (0.210) (0.405) (0.410) (0.395) (0.389) (0.371) CRAR -0.00413 -0.00425 -0.00442* CRAR -0.0146 -0.0137 -0.0157 -0.0163 -0.0165 (0.00269) (0.00265) (0.00252) (0.0128) (0.0138) (0.0130) (0.0134) (0.0123) ROA -0.0659** -0.0714** -0.0721** ROA -0.0518 -0.0416 -0.0405 -0.0116 -0.00700 (0.0281) (0.0313) (0.0334) (0.108) (0.116) (0.0986) (0.0907) (0.110) GDP -0.00440 0.000782 0.00216 GDP -0.0119* -0.0175** -0.0137 -0.0112 -0.0110 (0.00345) (0.00366) (0.00398) (0.00615) (0.00781) (0.0114) (0.00772) (0.00837) Inflation 0.0216 0.0230 0.0367 Inflation -0.0307 -0.0617 -0.0703 -0.0361 -0.0710 (0.0301) (0.0256) (0.0236) (0.113) (0.0997) (0.0910) (0.0857) (0.0836) Covid Dummy -0.0369 -0.0651 -0.0928 Covid Dummy 0.233 0.175 0.647 -0.00467 -0.129 (0.162) (0.194) (0.205) (0.474) (0.515) (0.728) (0.453) (0.503) j 0.244*** 0.206*** 0.190*** 0.329*** 0.221** 0.242*** 0.166** 0.357* Su2 =m 0med Coefficient (0.0613) (0.0496) (0.0437) Su2j =m 0med (0.117) (0.0844) (0.0818) (0.0709) (0.210) Constant 7.391*** 7.752*** 7.494*** Coefficient (2.229) (2.433) (2.350) Constant 18.64*** 18.92*** 18.63*** 18.40*** 14.91*** Observations 1684 1684 1684 (4.055) (4.164) (4.009) (3.891) (3.648) AR1 (p-value) 0.00 0.00 0.00 Observations 1624 1624 1624 1624 1624 AR1 (p-value) 0.00 0.00281 0.00 0.00 0.00 AR2 (p-value) 0.32 0.27 0.27 AR2 (p-value) 0.27 0.26 0.23 0.27 0.25 Hansen-J (p-value) 0.51 0.58 0.54 Hansen-J 0.48 0.41 0.40 0.47 0.36 Notes: 1. Standard errors in parentheses. (p-value) 2. * p<0.10, ** p<0.05, *** p<0.010. Notes: 1. Standard errors in parentheses. Source: Authors’ estimates. 2. * p<0.10, ** p<0.05, *** p<0.010. Source: Authors’ estimates. reduced cost of funding. Further, bank and market The Reserve Bank has been closely monitoring key funding to NBFCs may also be dependent on liquidity indicators, viz, Capital to Risk-weighted Assets Ratio conditions and perceived levels of riskiness, which (CRAR), Tier-1 capital ratio and net NPA ratio (NNPA) may further dampen transmission. On the lending under the prompt corrective action (PCA) framework, side, since NBFCs cater relatively to riskier borrower which has been effective for NBFCs10 since October segments, they charge higher interest rates to account 2022. So far, no NBFC has been placed under the PCA for potential defaults, which may further dampen framework by the Bank. adjustment of lending rates to changes in relevant Asset quality of the NBFC sector has continued to rates. improve in the recent years as reflected in consistent V. Financial and Prudential Indicators decline in NPA ratios. At end-December 2024, GNPA Profitability as indicated by return on assets and NNPA ratios stood at 3.4 and 1.2 per cent, respectively. While asset quality of middle layer is in (RoA), return on equity (RoE) and net interest margin (NIM) remained at healthy levels across the layers at 10 https://www.rbi.org.in/scripts/NotificationUser. end-December 2024 (Chart 8). aspx?Id=12208&Mode=0 RBI Bulletin September 2025 75ARTICLE Review of Performance of the NBFC Sector Chart 8: Profitability Indicators Chart 9: Asset Quality Ratios, by Layers (Per cent) (Per cent) 20 7 6 15 5 10 4 3.4 3.3 3.5 3 5 2 1.9 1.2 1.0 1 0 Dec-23 Dec-24 Dec-23 Dec-24 Dec-23 Dec-24 0 RoA RoE NIM GNPA NNPA GNPA NNPA GNPA NNPA Ratio Ratio Ratio Ratio Ratio Ratio Sector Upper layer Middle layer Sector Upper layer Middle layer Notes: 1. Return on Assets (RoA) = Net Profit/ Average Total Assets. Dec-21 Dec-22 Dec-23 Dec-24 1. Return on Equity (RoE) = Net Profit/ Average Total Equity. 2. Net Interest Margin (NIM) = Net Interest Income/Average Total Assets. Note: Data are provisional. Sources: Supervisory returns; and authors’ calculations. Sources: Supervisory Returns; and authors’ calculations. alignment with the overall sectoral trend, the upper layer kept CRAR of 20.6 per cent and 28.6 per cent, layer witnessed a mild uptick in NPA ratios at end- respectively (Chart 13). The disparity in the level of December 2024 (Chart 9). CRAR between the upper layer and the middle layer At end-December 2024, NBFCs’ credit portfolio is mainly due to difference in ownership structure. has stayed healthy, with moderation in GNPA ratios Large government-owned NBFCs - which are placed in across sectors, except agriculture and allied activities the middle layer by regulatory design have periodic (Chart 10). capital infusion, resulting in higher CRAR. In contrast the upper layer, which comprises, private NBFCs are After a significant improvement in the asset quality at end-December 2023, MSME credit portfolio Chart 10: GNPA Ratio, by Sector of NBFCs has continued to remain strong with stable (Per cent) 12 level of GNPA ratio at end-December 2024 (Chart 11). 10 Asset quality of retail loans continue to remain stable despite the strong growth in retail loans. 8 6.4 However, GNPA with regard to microfinance loans 6 increased at end-December 2024 (Chart 12). NBFCs 4.6 3.9 3.3 along with SROs in the microlending space should 4 remain vigilant to ensure responsible lending practices 2 and credit discipline among market participants. 0 NBFCs have consistently maintained capital Agriculture and Industry Services Retail Loans Allied Activities buffers on their balance sheet, even beyond the Dec-21 Dec-22 Dec-23 Dec-24 regulatory requirement. At end-December 2024, at Note: Data are provisional. Sources: Supervisory Returns; and authors’ calculations. an aggregate level, the upper layer and the middle 76 RBI Bulletin September 2025Review of Performance of the NBFC Sector ARTICLE Chart 11: GNPA Ratio of MSME Loans Chart 12: GNPA Ratio within Retail Loans (Per cent) (Per cent) 15 14 12 10 10 8 6.0 6 4.9 5 4.1 3.7 3.2 4 2.6 2 0 0 Vehicle/auto loans Advances to individuals Micro finance MSME Service MSME Industry Overall MSME against gold loan/SHG loan Dec-21 Dec-22 Dec-23 Dec-24 Dec-21 Dec-22 Dec-23 Dec-24 Note: Data are provisional. Note: Data are provisional. Sources: Supervisory Returns; and authors’ calculations. Sources: Supervisory Returns; and authors’ calculations. driven by profit and growth, often run with leverage As on December 2024, the sector has been and riskier loan books. maintaining LCR beyond the regulatory requirement (Chart 14). This cautious approach may be due In November 2019, the Reserve Bank introduced to the sector’s reliance on short-term funding for Liquidity Coverage Ratio (LCR) framework for NBFCs to strengthen their liquidity risk management. By long-term assets, which exposes them to liquidity mandating a buffer of High-Quality Liquid Assets11 mismatches and systemic risk. The LCR mitigates (HQLAs) sufficient to cover net cash outflows over a such vulnerabilities, promoting stability and market 30-day stress scenario, the LCR enhances the short- confidence. term resilience of NBFCs. To ensure a smooth transition, the LCR requirement was implemented Chart 13: Capital to Risk-weighted Assets Ratio in a phased manner from December 1, 2020. All non- (Per cent) deposit taking NBFCs with assets of ₹10,000 crore and 35 above, and all deposit-taking NBFCs, had to maintain a 30 minimum LCR starting at 50 per cent on December 1, 25 2020, and reach 100 per cent by December 1, 2024. All 20 non-deposit taking NBFCs with assets between ₹5,000 crore and ₹10,000 crore followed a similar trajectory 15 starting at 30 per cent. This calibrated approach 10 allowed NBFCs to gradually align with the new 5 norms while preserving sectoral liquidity and credit 0 flow. Dec-21 Dec-22 Dec-23 Dec-24 Sector Middle layer Upper layer Regulatory requirement 11 Due to data constraints, we utilise a conservative approach to Note: Data are provisional. estimate HQLA which might underestimate the quantum of actual HQLA Sources: Supervisory Returns; and authors’ calculations. maintained by NBFCs under extant regulations. RBI Bulletin September 2025 77ARTICLE Review of Performance of the NBFC Sector interlinkages with banks and other financial Chart 14: Liquidity Coverage Ratio institutions have implications for transmission of (Per cent) 450 120 monetary policy impulses to the financial sector and 400 100 350 real economy. Empirical analysis points to the fact 300 80 that there is monetary policy transmission to NBFCs’ 250 60 200 borrowing and lending rates, albeit, incomplete. 150 40 100 The prospects for segments like vehicle loans 20 50 and loans against gold appear robust, buoyed by 0 0 improvements in vehicle sales and rising gold prices. The introduction of LCR is set to further bolster Notes: 1. LCR = Stock of HQLA/total net cash outflows over the next 30 days. NBFCs’ short-term resilience. As the financial sector 2. HQLA= (Cash and Bank balances) *1.0 + (Government securities and government guaranteed bonds including treasury bills) *0.85 increasingly adopts artificial intelligence and machine 3. Net cash outflow = Stressed Outflows – min {Stressed Inflows, Stressed Outflows*0.75} learning, NBFCs must remain vigilant and proactively 4. Stressed Outflows = Total outflow*1.15 5. Stressed Inflows = Total inflow*0.75 address cyber challenges by leveraging these new Sources: Supervisory returns; and authors’ calculations. opportunities effectively. VI. Conclusion References FSB in its report noted that, globally NBFI sector’s growth outpaced growth of the traditional banking FSB. (2024). Global Monitoring Report on Non-Bank sector. In India, NBFCs continued to record double- Financial Intermediation. digit credit growth as of end-December 2024. This Patra, M. D. (2022). Lost in Transmission? Financial expansion is evident from a rising NBFC credit to Markets and Monetary Policy. Speech by Dr Michael GDP ratio sustained by lending to industry and retail Debabrata Patra, Deputy Governor, Reserve Bank of sector, which continue to dominate their portfolio. India. NBFC sector remains robust in terms of various RBI. (2021). Report on Currency and Finance. profitability and prudential indicators such as return RBIa. (2024). Minutes of the Monetary Policy on assets, return on equity, net interest margin, Committee Meeting, December 4-6. capital to risk weighted assets and non-performing RBIb. (2024). Report on Trend and Progress of Banking assets. The spike in growth rate of unsecured loans in India. was contained through increase in risk weights in November 2023. With regards to sources of finance, SIAM. (2025). Performance of Indian Auto Industry NBFCs continue to rely largely on bank borrowings and in 2023-24. Retrieved from https://www.siam.in/ debentures. NBFCs’ role in credit intermediation and statistics.aspx?mpgid=8&pgidtrail=9 78 RBI Bulletin September 2025 02-ceD 12-raM 12-nuJ 12-peS 12-ceD 22-raM 22-nuJ 22-peS 22-ceD 32-raM 32-nuJ 32-peS 32-ceD 42-raM 42-nuJ 42-peS 42-ceD LCR Regulatory requirement (RHS)Impact of UPI on Cash Demand – Evidence from National and ARTICLE Subnational Levels Impact of UPI on Cash Demand (Bachas et al., 2018; Aguilar et al., 2024; Aurazo and Franco, 2024; Cantú et al., 2024). At the same time, – Evidence from National and existing literature is also strewn with instances of Subnational Levels simultaneous rise in cash and digital payments (Bech et al., 2018; Chen et al., 2020; Caswell et al., 2020), by Sakshi Awasthy and Subrat Kumar Seet^ even as the transactional use of cash ebbs, or what is often described as the “paradox of banknotes” (Bailey, While the broader shift to digital payments is well- 2009). This trend has reinvigorated the debate on the established, regional adoption of the Unified Payments impact of digital payments on cash, with significant Interface (UPI) and its impact on cash demand remain implications for currency and liquidity management, underexplored. Using a dual empirical strategy - an underlying economic frictions, and broader autoregressive distributed lag model and panel quantile macroeconomic policy. regression - this study finds that higher UPI adoption is India’s fast payment system, Unified Payments associated with lower cash demand at both national and Interface (UPI), launched in 2016, offers a unique subnational levels, with state-level patterns suggesting empirical setting to study the evolving relationship non-linearity. Among other state-wise factors, income between cash and digital payments for three key and ATM density are positively associated with reasons. First, the scale of adoption has been cash demand, whereas workforce formalisation and unprecedented. UPI users have surged from around educational attainment are linked to lower cash reliance. 30 million in 2017 to over 420 million by 2024 (RBI, 2024; Reddy et al., 2024). Transaction volumes Introduction are nearing 200 billion a year, accounting for over Payments underpin all economic activity. In 80 per cent of total digital payments (RBI, 2025). a frictionless environment, the choice of payment Second, the launch of UPI closely followed a large- mode may have less bearing on real outcomes; scale financial inclusion drive i.e., Pradhan Mantri however, in practice, transaction costs and Jan Dhan Yojana, creating enabling conditions for information asymmetries render certain payment widespread digital uptake across socio-economic methods more efficient than others in shaping groups. Finally, notwithstanding the growth in economic growth (Dubey and Purnanandam, 2023). digital payments (especially UPI), currency in circulation has continued to rise, albeit at a slower The shift from cash to digital payments, particularly pace in recent years, reflecting a dynamic interplay fast payment systems, has been associated with between cash and digital modes. increased welfare, financial inclusion, credit access, economic formalisation and financial resilience While the broader shift to digital payments is well-established (Nachane et al., 2013; Chaudhari et ^ Authors are from the Department of Economic and Policy Research. al., 2019; Raj et al., 2020; Awasthy et al., 2022; RBI, Valuable insights provided by Dr. Rajiv Ranjan, former Executive Director, Shri M.M. Ramaiah, and Dr. Rakhe Balachandran are gratefully 2023), regional adoption of the UPI and its impact on acknowledged. Authors are grateful to the team from Department of Currency Management, including Shri Sanjeev Prakash, CGM-in-Charge; cash demand at the state-level remain underexplored. Pradip Bhuyan, and Baswaraj Patil for making available the currency Given India’s geographical and income diversity, chest data. Authors are thankful to Shri Gunveer Singh, CGM-in-Charge, Department of Payment and Settlement Systems for facilitating access national aggregates may obscure regional disparities, to region-wise UPI data. The views expressed in this paper are those of authors and do not represent the views of the Reserve Bank of India. as digital uptake may be concentrated in select RBI Bulletin September 2025 79ARTICLE Impact of UPI on Cash Demand – Evidence from National and Subnational Levels economic clusters, with cash being persistent in other Lippi, 2009). The demand for cash is traditionally regions. As per estimates, individuals in the top 20 attributed to three primary motives: the transaction per cent income group are twice as likely to use digital motive linked to economic activity (Fisher, 1911); payments as those in the bottom 40 per cent (NPCI, the precautionary motive, reflecting the need for 2020). More recent data show a steeper gradient, liquidity in uncertain situations; and the speculative with the top 10 per cent by consumption expenditure motive, driven by expectations about interest rate twice as likely to report the ability to use UPI as the movements (Keynes, 1954). Building on this, money bottom 25 per cent, though the overall ability stands demand is reconceptualised as a stable function of close to 50 per cent (NSO, 2025). As digital payments wealth, incorporating expected returns on alternative become central to economic activity, identifying assets such as bonds, equities, and durable goods regions that are excluded or lagging behind is crucial (Friedman, 1956). The seminal inventory (Baumol, - not only to promote inclusive access but also to 1952) and portfolio (Tobin, 1956) theoretical models address infrastructure gaps and risks to consumer extend the money demand function by incorporating protection. interest rates and transaction costs. More recent studies emphasise the negative impact of payment Against this backdrop, the paper examines innovations on physical currency (Columba, 2009; the impact of UPI on cash usage by modelling cash Oyelami and Yinusa, 2013; Huynh et al., 2014). demand at both national and subnational levels. Concurrently, a growing body of literature highlights Specifically, the study addresses four key research the coexistence of cash and digital payments, questions: (a) What is the impact of UPI on cash attributing sustained cash usage to precautionary demand at the all-India aggregate level? (b) What motives and economic uncertainties (Bech et al., regional patterns emerge in the adoption of UPI 2018; Caswell et al., 2020; Chen et al., 2020; Ardizzi and cash? (c) How does UPI influence cash demand et al., 2020). across states? and (d) Does this impact vary by state’s income levels? Given the limited empirical focus on In the Indian context, studies have found a regional trends, this study provides one of the first significant negative association between digital state-level assessments of cash to UPI substitution payments and currency demand, reflecting a in India. growing substitution effect (Nachane et al., 2013; Bhattacharya and Singh, 2016; Chaudhari et al., 2019; The remainder of the paper is structured as Raj et al., 2020; and Awasthy et al., 2022; Udupa et follows: Section II reviews the literature, followed al., 2025). At the regional level, however, empirical by descriptive analysis in Section III. Section IV research has largely focussed on digital payment outlines the data and methodology, while Section V adoption, instead of substitution dynamics. Using presents the empirical results. Section VI concludes. transaction level data from PhonePe, Dubey and Technical details and additional estimation outputs Purnanandam (2023) find that districts with higher are presented in Annexures I–III. post-UPI cashless payment intensity experienced II. Related Literature significantly greater household income growth. There exists a substantial body of theoretical Drawing on the same dataset, a report by ICRIER and empirical literature on the determinants of finds that COVID-19 accelerated digital adoption and money demand (Friedman, 1999; Alvarez and narrowed disparities in UPI’s user penetration across 80 RBI Bulletin September 2025Impact of UPI on Cash Demand – Evidence from National and ARTICLE Subnational Levels states and districts (Reddy et al. 2024). The report growth turned negative in 2023-24 and remained also identifies key drivers of digital adoption such modest in 2024-25, suggesting decline in inflation- as income levels, internet access, digital literacy, and adjusted cash demand. financial infrastructure. In contrast, digital payments (value) as a III. How does India Pay? share of GDP has risen sharply to over 800 per cent, with the pandemic acting as a catalyst for III.1. Aggregate-Level Insights into Payment Choice increased adoption in both volume and value terms India has a diverse payment ecosystem, (Chart 2a). Overall, total digital payments have encompassing both cash and a broad suite of exhibited robust growth over the last decade (2015- digital options. Currency in circulation (CIC)1 has 2025), recording a compound annual growth rate of normalised from a peak of 14.4 per cent of Gross 48 per cent by volume and 12.5 per cent by value. Domestic Product (GDP) in 2020–21 to 11.7 per cent Monthly trends show a broadly sustained digital in 2023–24 and further to 11.2 per cent in 2024–25. momentum amid tapering CIC growth (Chart 2b). CIC growth slowed to 4–6 per cent in recent years, driven by structural shift towards digital payments, The shift away from cash is also evident in the post-pandemic normalisation, phased withdrawal of decline in currency-to-demand deposits ratio to 1.31 2000 notes, and greater formalisation (Chart 1). A in 2024-25 from 1.68 in 2015-162 and a steady fall marginal rise (y-o-y) in 2024-25 reflects higher rural in ATM cash withdrawals (as a share of GDP) since ₹ demand and election-related spending. Real CIC 2018-19 (Charts 3 a and b). Chart 1: Trends in Currency in Circulation (per cent of GDP, left axis; Per cent growth (y-o-y), right axis) 16 40 14 30 12 20 10 10 8 0 6 -10 4 2 -20 0 -30 CIC/GDP CIC growth (y-o-y) (RHS) Sources: RBI; NSO. 1 Given anonymity associated with cash-based economic transactions, CIC is taken as a proxy for cash demand, in line with previous RBI studies (Nachane et al., 2013; Chaudhari et al., 2019; Raj et al., 2020) 2 Since digital payments are backed by bank deposits, mainly demand deposits, a decline in the CIC-to-demand deposits ratio—holding other factors constant—indicates a shift towards digital modes of transaction, whereas an increase in the ratio reflects a rising preference for cash. RBI Bulletin September 2025 81 50-4002 60-5002 70-6002 80-7002 90-8002 01-9002 11-0102 21-1102 31-2102 41-3102 51-4102 61-5102 71-6102 81-7102 91-8102 02-9102 12-0202 22-1202 32-2202 42-3202 52-4202ARTICLE Impact of UPI on Cash Demand – Evidence from National and Subnational Levels A possible driver behind the decline in cash per cent of total digital payment volumes and values, demand has been the rise of UPI. Transaction respectively, in 2024-25 (Table 1). volumes logged under the fast payment mode surged The strong UPI rally is underpinned by its to 18,586 crore in 2024-25 from 1,252 crore in 2019- open, technology-agnostic architecture that 20, with a marked acceleration post COVID-19. In less eases development of applications, user-friendly than a decade, UPI has become a leading payment design, and increasing digital awareness (Aurazo system, processing more than 17 billion transactions et al. 2024). Growing use of UPI for daily low-value a month and overall, accounting for 84 per cent and 9 transactions is evident from the rising share of peer- Chart 3: Trends in Demand for Cash a. CIC-Demand Deposits Ratio b. Cash Withdrawals/GDP (Ratio) (Per cent of GDP) 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Note: Figures for 2024-25 are provisional. In chart b, data include cash withdrawals from debit and credit cards. Dotted line presents the linear trend in both charts. Sources: RBI; NSO. 82 RBI Bulletin September 2025 21-1102 31-2102 41-3102 51-4102 61-5102 71-6102 81-7102 91-8102 02-9102 12-0202 22-1202 32-2202 42-3202 52-4202 20 18 16 14 12 10 8 6 4 2 0 21-1102 31-2102 41-3102 51-4102 61-5102 71-6102 81-7102 91-8102 02-9102 12-0202 22-1202 32-2202 42-3202 52-4202 Chart 2: Trends in Digital Payments a. Annual b. Monthly Per cent of GDP, left axis; Per cent growth (y-o-y), right axis Per cent growth (y-o-y) 1000 80 900 70 800 60 700 50 600 40 500 30 400 20 300 10 200 0 100 -10 0 -20 Note: Total Digital Payments include, inter alia, transactions under the Real Time Gross Settlement, National Electronic Funds Transfer, Immediate Payment Service, National Automated Clearing House, Unified Payments Interface, Aadhaar enabled Payment System, Bharat Bill Payment System, Cards and Prepaid Payment Instruments. Sources: RBI; NSO. 80-7002 01-9002 21-1102 41-3102 61-5102 81-7102 02-9102 22-1202 42-3202 120 100 80 60 40 20 0 -20 -40 -60 -80 Digital payments value/GDP Volume growth (RHS) Value growth (RHS) 9102-rpA 9102-peS 0202-beF 0202-luJ 0202-ceD 1202-yaM 1202-tcO 2202-raM 2202-guA 3202-naJ 3202-nuJ 3202-voN 4202-rpA 4202-peS 5202-beF Digital Payments Volume Digital Payments Value CICImpact of UPI on Cash Demand – Evidence from National and ARTICLE Subnational Levels Table 1: Growth in UPI Year Volume Value Average Ticket Size Share in Total Digital Share in Total Digital (crore) ( lakh crore) ( ) Payments Volume (per cent) Payments Value (per cent) 2016-17 2 0.1 3867 0.2 0.0 ₹ ₹ 2017-18 92 1.1 1200 6.3 0.1 2018-19 539 9 1627 23.2 0.5 2019-20 1,252 21 1703 36.8 1.3 2020-21 2,233 41 1838 51.1 2.9 2021-22 4,596 84 1831 63.8 4.8 2022-23 8,371 139 1662 73.5 6.7 2023-24 13,113 200 1525 79.7 8.2 2024-25 18,586 261 1404 84 9 Note: Average ticket size ( ) is computed as = ((Value/Volume)*1,00,000). Sources: RBI; NPCI. ₹ to-merchant (P2M) payments, narrowing ticket size commercial banks on behalf of the Reserve Bank of of UPI payments (Chart 4a), and the bulk of the P2M India. As all freshly issued notes pass through these volumes falling within the sub- 500 value band chests, their withdrawal patterns are assumed to reflect public cash demand. On average, the share of (Chart 4b). ₹ annual cash withdrawals from ATMs (through debit III.2. State-level Insights into Payment Choice and credit cards) to cash withdrawals at currency State-level analysis reveals regional variations chests stands at 80 per cent in 2024-25. shaped by income and structural factors. Due to In the absence of disaggregated UPI data, this unavailability of granular data on ATM withdrawals, study employs data from PhonePe (Pulse), a payment cash usage is proxied by withdrawals from currency service provider accounting for 58 per cent of total chests, which are regional repositories managed by UPI transaction volume and 53 per cent of value Chart 4: Composition of UPI Transactions a. P2P and P2M Transactions b. Ticket-wise UPI Bands (Billion, left axis; INR, right axis) (Per cent share in volume) 120 2000 100 1500 80 60 1000 40 500 20 0 0 Peer-to-peer Peer -to-merchant Total UPI ticket size (RHS) Source: NPCI. Note: Inner and outer circles pertain to P2M and P2P transactions, respectively. Source: NPCI. RBI Bulletin September 2025 83 12-0202 22-1202 32-2202 42-3202 52-4202 <₹500 ₹500 -2000 >₹2000ARTICLE Impact of UPI on Cash Demand – Evidence from National and Subnational Levels Chart 5: Share of PhonePe in UPI over time a. . Volume b. Value Crore, left axis; Per cent share, right axis INR lakh crore, left axis; Per cent share, right axis 20000 70 18000 60 16000 14000 50 12000 40 10000 30 8000 6000 20 4000 10 2000 0 0 Sources: PhonePe Pulse, NPCI, Authors' calculations. (Charts 5 a and b). This open-source dataset has indicate a broad-based and sustained decline in cash been widely used in studies examining UPI diffusion usage across most states over the past few years, across states and districts (Dubey and Purnanandam, suggesting a structural rather than transitory shift. 2023; Reddy et al., 2024). On the digital front, UPI intensity, proxied by Two factors support the generalisability of this PhonePe transactions, remains high in Telangana, Karnataka, Andhra Pradesh, Delhi and Maharashtra dataset as a proxy for overall UPI activity: First, in per capita volume terms, aligning closely with PhonePe’s growth trajectory has closely mirrored the presence of urban centres, economic hubs and overall UPI trends in recent years, with correlations between their growths being 0.99 for both volume and Chart 6: Cash Withdrawal Intensity in FY 2024-25 value. Second, PhonePe-based state-wise rankings (Per capita) exhibit a strong correlation with total state-wise UPI rankings in 2024, for which data was available Cash withdrawals per capita (r = 0.97). To ensure comparability, both cash and 102757 UPI indicators are normalised by state population, yielding measures of cash and UPI intensities. Cash intensity varies widely across states and Union Territories (UTs), with Goa, Delhi, Chandigarh, 11831 Arunachal Pradesh, Nagaland, Kerala, and Sikkim recording the highest per capita cash withdrawals (Chart 6), reflecting factors such as tourism and service-led cash usage, remittance inflows, rural areas’ cash dependence, limited digital infrastructure, older Note: Cash intensity = Cash withdrawals / population; where i = state. i i demography, and security constraints. Recent trends Source: RBI. 84 RBI Bulletin September 2025 8102 9102 0202 1202 2202 3202 4202 300 60 250 50 200 40 150 30 100 20 50 10 0 0 PhonePe UPI Total UPI Share (RHS) PhonePe UPI Total UPI Share (RHS) 8102 9102 0202 1202 2202 3202 4202Impact of UPI on Cash Demand – Evidence from National and ARTICLE Subnational Levels regions with high employment-driven migration UPI usage, however, continues to be concentrated, (Chart 7a). In contrast, UPI uptake remains modest with the top 10 states accounting for nearly 80 per in several cash-dependent regions such as the cent of total transaction volumes - a pattern that has North-Eastern states (Tripura, Manipur, Meghalaya, remained relatively stable over time. Nevertheless, Nagaland). Data from a nationwide survey suggest the trend decline in dispersion of UPI adoption relatively lower inter-state variation in the ability to across states is evident from the strengthening of use UPI for online banking transactions, with a sigma ( ) convergence since 2020, albeit at a gradual modest skew towards the southern and northern pace (Chart 8). This slower convergence may reflect σ states (NSO, 2025).3 Notably, Chandigarh, Himachal Pradesh, Kerala, Manipur, and Mizoram exhibit high Chart 8: Sigma Convergence in UPI Payments Across States reported ability to use UPI (Chart 7b). (σ (log of UPI per capita)) 1.2 In terms of growth, most states have witnessed 1.1 a surge in UPI adoption post pandemic (FY: 2022). 1.0 Although the overall trajectory of UPI payments 0.9 remains positive across states, the pace of growth 0.8 has moderated due to high base effect from the 0.7 pandemic year and a transition towards a more 0.6 stable, self-propelling adoption curve. 3 These estimates are based on unit level data from National Statistical Survey’s Comprehensive Modular Survey – Telcom, 80th Round released on May 29, 2025. The survey questionnaire includes a specific question posed to individual respondents: “Whether able to perform online banking Note: Sigma (σ) convergence refers to a reduction in the dispersion (standard transactions via devices like computers, or mobile?” The response options deviation) of a variable such as UPI volume or value per capita across units (e.g., states) over time. are: (i) yes, through UPI only; (ii) yes, through net banking or other means Source: Authors’ calculations. (except UPI) only; (iii) yes, both UPI and other means; and (iv) no. RBI Bulletin September 2025 85 2q9102 3q9102 4q9102 1q0202 2q0202 3q0202 4q0202 1q1202 2q1202 3q1202 4q1202 1q2202 2q2202 3q2202 4q2202 1q3202 2q3202 3q3202 4q3202 1q4202 2q4202 3q4202 4q4202 Chart 7: State-wise Variation in UPI Adoption in FY 2024-25 a. UPI Volume Intensity b. Ability to use UPI (Per capita) (Per cent) UPI Volume per capita UPI ability to use 205.6 78.3 5.1 22.5 Notes: (a) Chart a - UPI Volume intensity = UPI volume / population; where i = state; i i (b) Chart b - Ability of persons to perform online banking transactions using UPI as a share of total state population. Sources: PhonePe Pulse; CAMS Survey, NSS 80th Round, NSO (2025). Volume Linear (Volume) Value Linear (Value)ARTICLE Impact of UPI on Cash Demand – Evidence from National and Subnational Levels heterogeneity in digital infrastructure, extent of Building on the macro-level insights, cash formalisation, financial inclusion and literacy, and determinants at the state level are analysed using merchant acceptance across states. fixed-effects 8 panel quantile regression for 31 Indian states and UTs over the period Q2:2019 to Q1:2025, IV. Data and Methodology at the 25th, 50th, and 75th percentiles of the cash At the national level, an auto-regressive distribution. The model accounts for unobserved state- distributed lag (ARDL) model is estimated using specific heterogeneity and time effects. The sample quarterly data from Q2:2009 to Q4:2024 to assess period, beginning in 2019, captures the phase during UPI’s impact on cash demand in nominal and real which UPI gained traction. To examine heterogeneity terms.4 Key determinants include GDP, deposit rates across income groups, separate panel regressions are (proxied by major banks’ one year lower bounds), estimated for low, middle, and high-income states, the share of high-denomination notes in circulation5 stratified on the 25th, 50th, and 75th percentiles of net state domestic product (current prices). (store-of-value proxy), and UPI transaction volumes (substitutive effect)6, thereby accounting for As mentioned above, cash demand is measured transaction, precautionary, and speculative motives. by quarterly currency chest withdrawals and UPI Controlling for the high denomination notes’ share adoption by PhonePe transaction data. In the absence also helps isolate UPI’s impact on CIC, as high- of quarterly subnational GDP, economic activity value transactions may distort trends driven by is proxied using VIIRS VNP46A2 nighttime lights, predominantly small-value UPI payments. The sample which provides daily measurements of artificial period chosen reflects the structural shift following (human-generated) illumination at \~500-meter the Payment and Settlement Systems Act (2007) and spatial resolution. Quarterly state-level aggregates minimises the global financial crisis’s impact. Except are computed as the sum of the “Gap Filled DNB BRDF Corrected Nighttime Lights” band, using for interest rates, all variables are seasonally adjusted zonal statistics over state boundaries, thereby and log-transformed. Stationarity checks using the eliminating any high-frequency volatility. This data Augmented Dickey-Fuller (ADF) test confirm that all has been widely used to estimate output and growth, series are I(0) or I(1), validating the ARDL framework. especially in data-scarce granular geographical levels, Key shocks, including withdrawal of specified bank and to better capture informal sector activity (Lahiri, notes in 2016 and COVID-19 lockdowns are captured 2020; Beyer et al., 2022; Mathen et al., 2024). Other through quarterly dummies.7 control variables include ATM density (financial 4 The following long-run equation is estimated: CiC t infrastructure), employee provident fund organisation GDP t INT t HDN t UPI t μ t; where k are 0 long 1-run coefficients. ln( ) = ψ + ψ ln (EPFO) net payroll additions (formalisation), Periodic 2 3 4 ( ) + ψ + ψ + ψ ln(1 + ) + ψ 5 High denomination notes include 500, 1000 (before their withdrawal) Labour Force Survey (PLFS)’s educational attainment and 2000 notes. 6 Since UPI data is unavailable fo₹ r the ₹ period before 2016, log (1 + below higher-secondary level (literacy), and Telecom ₹ actual UPI transactions) is used as the variable to ensure continuity. This Regulatory Authority of India’s internet subscriptions variable remains constant for pre-2016 quarters, thereby not affecting the estimation. (digital infrastructure). All variables, except internet 7 A dummy for the 2000 note withdrawal in May 2023 was initially subscribers and education attainment levels, are included but found insignificant and thus, excluded from the final model. The effect may have b₹een subsumed by the share of high-denomination notes variable, which likely accounts for its explanatory power in the main 8 Hausman Test validates the use of fixed effects model over random regression. effects. 86 RBI Bulletin September 2025Impact of UPI on Cash Demand – Evidence from National and ARTICLE Subnational Levels normalised by state population and log-transformed. Table 2: Impact of Unified Payments Interface on Year fixed effects control for broad macroeconomic Currency in Circulation trends, while intra-year shocks like festivals, state Dependent Variable: Log of Currency in Circulation elections, and COVID-19 are captured through Nominal Real Variables (1) (2) (1) (2) quarterly dummies. While these regression estimates Model Type ARDL ARDL ARDL ARDL do not necessarily imply causality, they provide (3,2,0) (3,2,0,0,0) (3,2,0) (3,3,0,0,0) insights on the magnitude of these factors. Cross- Income 0.86*** 0.83*** 0.84*** 0.79*** (0.03) (0.04) (0.06) (0.10) state summary statistics and correlation heatmap are Interest Rate -0.05*** -0.05*** -0.04*** -0.03** provided in Annex I. (0.01) (0.01) (0.01) (0.01) UPI Volume -0.016*** -0.013*** V. Impact of UPI on Cash Demand: Empirical (0.01) (0.01) Evidence HDN Share 0.005*** 0.005** (0.01) (0.01) V.1. National Level Insights Intercept 1.80*** 1.96*** 1.95* 2.36 (0.60) (0.60) (1.03) (1.50) The UPI volumes are negatively associated with Cointegration Tests cash demand across models both in nominal and Bounds Test: F statistic # 89.6 134.7 283.37 318.3 real terms, underscoring its role as a substitute for Error Correction -0.26*** -0.24*** -0.30*** -0.28*** Coefficient (0.01) (0.01) (0.01) (0.01) cash (Table 2). Income (GDP) emerges as the primary Model Tests determinant of cash demand with elasticities ranging Adjusted R squared 0.99 0.99 0.99 0.99 from 0.79 to 0.86, indicating a positive association SIC and AIC -4.73 and -4.74 and -4.52 and -4.48 and -5.13 -5.21 -4.91 -4.98 between economic activity and cash usage. Deposit Post-estimation Tests interest rates exhibit a negative and statistically LM Test of 0.63 0.08 0.49 0.05 significant effect, reflecting the opportunity cost of Autocorrelation: Probability holding cash. Conversely, the higher denomination BPG Heteroscedasticity 0.91 0.85 0.73 0.79 banknotes share shows a small but positive effect, Test: Probability CUSUM and CUSUM Stable Stable Stable Stable consistent with its store-of-value role (Model 2). squared stability test The post-estimation diagnostics confirm the absence Notes: (a) The standard errors are in parentheses. ***, ** and * refer to significance levels at 1 per cent, 5 per cent and 10 per cent, of serial autocorrelation and heteroscedasticity at 5 respectively. per cent level. The error correction coefficient, which (b) CIC, income and UPI are natural logarithm transformed. Real CIC refers to CIC deflated by the Consumer Price Index (CPI) captures the speed at which short-run deviations to adjust for price levels and reflect the purchasing power of money. adjust to the long-run equilibrium, shows that 24-30 (c) Model 1 is the baseline model without UPI and HDN share. per cent of deviations are corrected within a single Model 2 incorporate UPI volume and HDN share. (d) All the models have relevant dummy variables for withdrawal quarter. Moreover, the Bounds test F-statistic exceeds of specified bank notes, COVID-19 first wave and second wave. (e) As robustness check, the share of UPI in total digital the upper bound of the critical values, confirming transactions was also considered, which takes the value of zero for the pre-2016 period. The results confirm the negative the existence of a long-run relationship between association between UPI share and cash demand. Further, the these variables. inclusion of the COVID-19 Stringency Index revealed a positive and statistically significant impact. (f) # Critical values for F statistic at 5 per cent level are around 3.0 Owing to the specified bank note withdrawal, and 6.0 for I(0) and I(1) assumptions, respectively. the dummy coefficient for Q4:2016 and Q1:2017 (g) In post-estimation checks, null hypothesis is no serial correlation for LM test, and homoscedasticity for BPG test. is negative and statistically significant (Annex II). Source: Authors’ calculations. RBI Bulletin September 2025 87ARTICLE Impact of UPI on Cash Demand – Evidence from National and Subnational Levels Further, dummy variables for both the first and second Table 3: State-wise Impact of UPI Volume on Cash waves of the pandemic are positive and statistically Demand – By Cash Quantiles significant, suggesting that the increase in currency Dependent Variable: Log of Currency Chest demand during the lockdown was driven by Withdrawals per Capita precautionary and store-of-value motives, consistent (1) (2) (3) (4) Variables Full 25th 50th 75th with previous findings (Caswell et al., 2020; Chen et sample Quantile Quantile Quantile al., 2020; Awasthy et al., 2022; RBI, 2023). (Low (Mid (High cash) cash) cash) V.2. State Level Insights Economic activity# 0.25*** 0.29*** 0.26*** 0.21*** (0.05) (0.06) (0.05) (0.06) V.2.1. By Cash Quantiles UPI Volume# -0.13** -0.12*** -0.13*** -0.15*** (0.05) (0.03) (0.03) (0.04) Consistent with the aggregate regression, UPI Volume squared# 0.03*** 0.03*** 0.03*** 0.03*** (0.01) (0.00) (0.00) (0.01) economic activity as proxied by nighttime lights ATM density# 0.77** 0.52*** 0.76*** 1.04*** exhibits a strong and statistically significant (0.29) (0.19) (0.15) (0.21) Degree of formalisation# -0.11*** -0.12*** -0.11*** -0.10*** association with cash usage across all states (Table 3, (0.03) (0.03) (0.03) (0.04) Model 1). While its influence remains consistently Degree of formalisation 0.01*** 0.01*** 0.01*** 0.01*** squared# (0.00) (0.00) (0.00) (0.00) positive across the conditional distribution of cash Education attainment -0.01 -0.00 -0.01** -0.01** demand, it marginally attenuates from lower to level (0.00) (0.00) (0.00) (0.00) Internet Subscriber Base@ 0.05 0.05 0.05 0.06 upper quantiles of cash usage (Models 2 – 4). (0.04) (0.04) (0.03) (0.05) Covid Dummy 0.04*** 0.05* 0.04** 0.04 UPI volumes per capita display a negative and (0.01) (0.03) (0.02) (0.03) non-linear association, given the negative linear term State Election Dummy 0.05*** 0.05** 0.05*** 0.04* (0.01) (0.02) (0.02) (0.02) coupled with a positive squared term. This indicates Festival Dummy 0.05** 0.05*** 0.05*** 0.05** that increases in UPI usage substitute for cash, Constant 17.27*** however, beyond an estimated threshold (log UPI (2.40) Year Fixed Effects Yes Yes Yes Yes per capita = 2.18) and as digital adoption matures, Observations 688 688 688 688 the substitution effect moderates, possibly reflecting R-squared 0.43 saturation or behavioural inertia. Plotting the UPI F statistic 66.07 coefficient across different cash quantiles indicates Prob > F 0.00 a stronger substitution effect in upper quantiles, Number of States 31 implying that in cash-intensive states, digital adoption Notes: a) The standard errors in parentheses are clustered by state. ***, ** and * refer to significance levels at 1 per cent, 5 per cent and exerts a stronger dampening impact on cash usage 10 per cent, respectively. b) # Variables are in per capita terms and log transformed. (Chart 9). This pattern may reflect a combination of c) @ Variable is in quarter-on-quarter growth terms. higher initial cash dependence, policy and market d) Due to data unavailability for Ladakh, Lakshadweep, Dadra and Nagar Haveli, Sikkim and Puducherry, the sample size of the efforts, and steeper early-stage learning curves in number of states and UTs is reduced to 31. e) These results control for year fixed effects. digital adoption. Similar non-linear dynamics are f) State-wise degree of formalisation is computed as the log of net payroll additions under EPFO adjusted for population. observed for UPI value per capita (Table 1:Annex III). g) While state-wise CPI was included as control, it was found to be statistically insignificant, possibly due to its effect being Internet subscriber base, as a proxy for digital absorbed by economic activity and overall limited cross-state variation. Additionally, rural and urban population proxies infrastructure, exerts only a weak influence, with were considered; however, as these are based on Census borderline significance at the median quantile. 2011 data, they were excluded from the fixed-effects panel regression. The degree of formalisation displays a concave Source: Authors’ calculations. 88 RBI Bulletin September 2025Impact of UPI on Cash Demand – Evidence from National and ARTICLE Subnational Levels states display the strongest substitution elasticity, Chart 9: UPI Volume Impact on Cash Demand by Quantiles indicating that they are at a critical inflection point (Coef(cid:26)icient Estimate, Quantile of Cash Demand Distribution) in the ongoing digital transition (Table 4). Economic UPI Coefficients Across Quantiles activity is positively associated with cash demand .05 0 Table 4: State-wise Impact of UPI Volume on Cash Demand – By Income Groups -.05 Dependent Variable: Log of Currency Chest -.1 Withdrawals per Capita -.15 (1) (2) (3) -.2 Variables Low Mid High Income Income Income 0 .2 .4 .6 .8 1 States States States Quadratic Term (log_upivol_pc_sq) Economic activity# 0.26*** 0.22** 0.41*** Linear Term (log_upivol_pc) (0.07) (0.09) (0.10) Note: The chart plots the coefficients of log(UPI volume per capita) [linear UPI Volume# -0.15* -0.22*** -0.08* impact] and log (UPI volume per capita squared) [quadratic impact] in various (0.08) (0.06) (0.05) quantile regressions. Source: Authors’ calculations. UPI Volume squared# 0.05** 0.04*** 0.01* (0.02) (0.01) (0.01) ATM density# 1.22** 0.45 0.58 relationship with cash demand. Initial formalisation (0.54) (0.31) (0.43) is associated with lower cash reliance, possibly due Degree of formalisation# -0.06 -0.09*** -0.16 (0.08) (0.02) (0.13) to improved access to banking and digital wage Degree of formalisation squared# 0.01 0.01*** 0.01 payments, which wears off later (post log of degree (0.01) (0.00) (0.01) of formalisation = 5.8). This pattern suggests that Education attainment level -0.01* 0.01 -0.01** (0.01) (0.01) (0.00) informal sector remains more cash-intensive, with Internet Subscriber Base@ 0.07 0.01 0.05 lower willingness to adopt digital payments (Ligon (0.06) (0.02) (0.21) et al., 2019), possibly owing to limited integration Covid dummy 0.04* 0.03 0.02 (0.02) (0.03) (0.02) with formal financial networks (Lahiri, 2020). Further, State Election Dummy 0.03 0.06** 0.01 states with higher proportions of population with at (0.03) (0.03) (0.02) least higher secondary education show lower cash Festival Dummy 0.03 0.08 0.08** (0.02) (0.05) (0.03) demand at median and upper quantiles, reflecting Constant 21.50*** 13.47*** 16.52*** the positive relationship between education and (4.88) (2.10) (3.46) Year Fixed Effects Yes Yes Yes digital alternatives. Structural shocks, along with Observations 244 235 209 policy and seasonal dummies such as COVID-19, R-squared 0.50 0.54 0.50 state elections, festivals and the marriage season are Number of States 14 15 11 Notes: (a) Low, mid and high-income states pertain to the 25th, 50th and all positively and significantly associated with spikes 75th percentile, respectively, of the net state domestic product (current prices). in cash demand across the distribution, reaffirming (b) The standard errors in parentheses are clustered by state. ***, its episodic and precautionary nature in line with Raj ** and * refer to significance levels at 1 per cent, 5 per cent and 10 per cent, respectively. et al., (2020). (c) # Variables are in per capita terms and log transformed. (d) @ Variable is in quarter-on-quarter growth terms. V.2.2. By Income Groups (e) Due to data unavailability for Ladakh, Lakshadweep, Dadra and Nagar Haveli, Sikkim and Puducherry, the sample size of states Although UPI adoption exhibits a non-linear and UTs is reduced to 31. (f) These results control for year fixed effects. relationship across income groups, mid-income Source: Authors’ calculations. RBI Bulletin September 2025 89ARTICLE Impact of UPI on Cash Demand – Evidence from National and Subnational Levels in all income groups, but its magnitude is higher lights, and ATM density are positively associated in high-income states. ATM density is associated with cash demand, whereas workforce formalisation with higher cash usage only in low-income states and higher educational attainment are linked to than in more affluent ones, underscoring their lower cash reliance. Income-group-wise segregation continued dependence on traditional access points. shows that mid-income states exhibit the strongest Formalisation of the workforce is negatively associated substitution elasticity, while lower-income states with cash usage, though only in mid-income states may unlock untapped substitution potential and that too up to a threshold. Additionally, higher through improved literacy and greater workforce education levels are linked with lower cash demand formalisation. in low and high income states. Similar results prevail These findings suggest that a one-size-fits-all for UPI values per capita (Annex III, Table 2). approach may not be sufficient for adoption and VI. Conclusion sustained usage of UPI. Region-specific targeted interventions aligned with each state’s demographic, The study examines the impact of UPI on cash infrastructural, and behavioural context are likely demand in India. Using a dual empirical strategy to be effective. Expanding digital infrastructure and of autoregressive distributed lag model and panel financial literacy interventions, incentivising digital quantile regression, the article finds that higher UPI wage transfers, and building trust in digital modes adoption is associated with lower cash demand at may accelerate cash-to-UPI transition across the both national and subnational levels. At the aggregate spectrum. level, descriptive trends indicate a structural shift in References: India’s payment landscape, evident from currency growth moderating from pandemic levels and Aguilar, A., Frost, J., Guerra, R., Kamin, S., and sustained UPI expansion with narrowing ticket sizes. Tombini, A. (2024). Digital Payments, Informality Empirically, income, proxied by GDP, is positively and Economic Growth. BIS Working Papers No. 1196. associated with cash demand, while UPI and interest Alvarez, F., and Lippi, F. (2009). Financial rates exhibit a negative effect. 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Review of Economics and Statistics. 38 (3), 241–247. Raj, J., Bhattacharyya, I., Behera S.R., John, J., and Talwar, B.A. (2020). Modelling and Forecasting Udupa, G., Bhuyan P., Verma D.K., and Kulkarni Currency Demand in India: A Heterodox Approach. N. (2025). Economic Activity and Banknotes: New Reserve Bank of India Occasional Papers 41 (1), 1–45. Approaches. RBI May 2025 Bulletin. 92 RBI Bulletin September 2025Impact of UPI on Cash Demand – Evidence from National and ARTICLE Subnational Levels Annex I Table 1: State-wise Summary Statistics of Select Variables Variable Type Mean Std. dev. Min Max Observations Cash Withdrawals overall 9.06 0.66 5.34 10.56 792 between 0.54 7.85 10.19 33 within 0.38 6.56 10.31 24 Night Lights (Economic activity) overall -4.38 0.49 -5.68 -2.40 792 between 0.44 -5.25 -2.98 33 within 0.23 -5.13 -3.80 24 UPI overall 1.04 1.48 -3.30 4.45 792 between 0.92 -0.75 3.00 33 within 1.17 -2.39 3.48 24 ATM overall -8.49 0.52 -9.64 -7.30 792 between 0.52 -9.55 -7.33 33 within 0.06 -8.72 -8.30 24 Formalisation overall 9.85 2.52 1.39 13.78 718 between 2.48 4.53 13.35 31 within 0.62 3.66 12.41 24 Education Levels overall 66.88 8.37 49.70 87.10 792 between 8.24 53.70 85.34 33 within 2.05 57.47 73.26 24 Internet Subscriber Base Growth overall 0.02 0.12 -0.68 2.14 759 between 0.01 -0.01 0.07 33 within 0.12 -0.73 2.09 23 Note: All variables, except education and internet subscribers, are in per capita terms and log transformed. Source: Authors’ calculations. Chart 1: Correlation Heat Map of Select Variables Cash Withdrawals 1.000 Night Lights 0.370 1.000 UPI Volume 0.170 0.334 1.000 ATM Density 0.632 0.309 0.340 1.000 Formalisation -0.243 -0.182 0.507 0.228 1.000 Education Level 0.187 0.036 -0.418 -0.172 -0.664 1.000 Cash Withdrawals Night Lights UPI Volume ATM Density Formalisatio Edn ucation Level Note: All these variables, except education, are normalised by population and log transformed. Source: Authors’ calculations. RBI Bulletin September 2025 93ARTICLE Impact of UPI on Cash Demand – Evidence from National and Subnational Levels Annex II Table 1: Short-run Drivers of Currency Demand in India Dependent Variable: LCIC (Log of Currency in Circulation) Nominal Real (1) (2) (1) (2) Model Type ARDL (3,2,0) ARDL (3,2,0,0,0) ARDL (3,2,0) ARDL (3,2,0,0,0) (a) (b) (a) (b) D(LCiC) (-1) -0.23*** -0.26*** -0.20*** -0.25*** (0.02) (0.02) (0.03) (0.02) D(LCiC) (-2) -0.15*** -0.14*** -0.13*** -0.12*** (0.02) (0.02) (0.03) (0.03) D(Income) 0.08* 0.07* 0.14*** 0.18*** (0.04) (0.04) (0.04) (0.03) D(Income) (-1) 0.11*** 0.12*** 0.21*** 0.26*** (0.03) (0.03) (0.04) (0.04) Dummy: SBN withdrawal -0.31*** -0.33*** 0.09*** (0.01) (0.01) (0.03) Dummy: COVID first wave 0.10*** 0.10*** -0.30*** -0.32*** (0.01) (0.01) (0.01) (0.01) Dummy: COVID second wave 0.03*** 0.03*** 0.12*** 0.13*** (0.01) (0.01) (0.01) (0.01) Notes: (a) The standard errors are in parentheses. ***, ** and * refer to significance levels at 1 per cent, 5 per cent and 10 per cent, respectively. (b) CIC, income and UPI are natural logarithm transformed. (c) Model 1 is the baseline model without UPI and HDN share. Model 2 incorporates UPI volume and HDN share. Source: Authors’ calculations. 94 RBI Bulletin September 2025Impact of UPI on Cash Demand – Evidence from National and ARTICLE Subnational Levels Annex III Table 1: State-wise Impact of UPI Value on Cash Demand – By Cash Quantiles Dependent Variable: Log of Currency Chest Withdrawals per Capita (1) (2) (3) (4) Variables Full sample 25th Quantile 50th Quantile 75th Quantile (Low cash) (Mid cash) (High cash) Economic activity # 0.27*** 0.31*** 0.28*** 0.23*** (0.05) (0.06) (0.05) (0.07) UPI Value # -0.65*** -0.62*** -0.65*** -0.69*** (0.21) (0.11) (0.09) (0.13) UPI Value squared # 0.04*** 0.03*** 0.04*** 0.04*** (0.01) (0.01) (0.00) (0.01) ATM density # 0.68** 0.45** 0.66*** 0.93*** (0.29) (0.19) (0.15) (0.22) Degree of formalisation # -0.12*** -0.13*** -0.12*** -0.12*** (0.03) (0.04) (0.03) (0.04) Degree of formalisation squared # 0.01*** 0.01*** 0.01*** 0.01*** (0.00) (0.00) (0.00) (0.00) Education attainment level -0.01 -0.01 -0.01*** -0.01*** (0.01) (0.00) (0.00) (0.00) Internet Subscriber Growth @ 0.06 0.04 0.06 0.07 (0.04) (0.05) (0.04) (0.05) Covid dummy 0.06*** 0.06** 0.06** 0.05 (0.01) (0.03) (0.02) (0.03) State Election Dummy 0.04*** 0.04** 0.04** 0.04 (0.01) (0.02) (0.02) (0.02) Festival Dummy 0.04* 0.04** 0.04*** 0.03 (0.02) (0.02) (0.02) (0.02) Constant 19.59*** (3.12) Year Fixed Effects Yes Yes Yes Yes Observations 688 688 688 688 R-squared 0.39 F statistic 74.11 Prob > F 0.00 Number of States 31 Notes: a) The standard errors in parentheses are clustered by state. ***, ** and * refer to significance levels at 1 per cent, 5 per cent and 10 per cent, respectively. b) # Variables are in per capita terms and log transformed. c) @ Variable is in quarter-on-quarter growth terms. d) Due to data unavailability for Ladakh, Lakshadweep, Dadra and Nagar Haveli, Sikkim and Puducherry, the sample size of the number of states and UTs is reduced to 31. e) These results control for year fixed effects. Source: Authors’ calculations. RBI Bulletin September 2025 95ARTICLE Impact of UPI on Cash Demand – Evidence from National and Subnational Levels Table 2: State-wise Impact of UPI Value on Cash Demand – By Income Groups Dependent Variable: Log of Currency Chest Withdrawals per Capita (1) (2) (3) Variables Low Income States Mid Income States High Income States Economic activity # 0.33*** 0.21** 0.41*** (0.07) (0.09) (0.10) UPI Value # -1.13** -0.90*** -0.35* (0.52) (0.08) (0.17) UPI Value squared # 0.07** 0.05*** 0.02 (0.03) (0.01) (0.01) ATM density # 1.13* 0.33 0.55 (0.62) (0.32) (0.45) Degree of formalisation # -0.08 -0.10*** -0.17 (0.08) (0.02) (0.13) Degree of formalisation squared # 0.01 0.01*** 0.01* (0.01) (0.00) (0.01) Education attainment level -0.01* 0.00 -0.01** (0.01) (0.02) (0.00) Internet Subscriber Growth @ 0.07 0.01 0.05 (0.06) (0.02) (0.21) Covid dummy 0.07*** 0.05 0.03 (0.02) (0.03) (0.02) State Election Dummy 0.03 0.06** 0.01 (0.03) (0.02) (0.02) Festival Dummy 0.00 0.07 0.07** (0.02) (0.06) (0.03) Constant 25.70*** 16.84*** 18.04*** (7.66) (2.34) (4.04) Year Fixed Effects Yes Yes Yes Observations 244 235 209 R-squared 0.44 0.50 0.48 Number of States 14 15 11 Notes: (a) Low, mid and high-income states pertain to the 25th, 50th and 75th percentile, respectively, of the NSDP (current prices). (b) The standard errors in parentheses are clustered by state. ***, ** and * refer to significance levels at 1 per cent, 5 per cent and 10 per cent, respectively. (c) # Variables are in per capita terms and log transformed. (d) @ Variable is in quarter-on-quarter growth terms (e) Due to data unavailability for Ladakh, Lakshadweep, Dadra and Nagar Haveli, Sikkim and Puducherry, the sample size of the number of states and UTs is reduced to 31. (f) These results control for year fixed effects. Source: Authors’ calculations. 96 RBI Bulletin September 2025Is Consumption Inequality Declining? – What the 2022-23 NSSO Survey Tells Us ARTICLE Is Consumption Inequality leading to a corresponding rise in consumption inequality? Several important factors over the last Declining? – What the 2022-23 decade like the implementation of the goods and NSSO Survey Tells Us services tax (GST), and occurrence of unexpected shocks, like the COVID-19 pandemic, are postulated Kaustubh, Satadru Das, to have substantial effect on inequality. While some analysts have pointed to anecdotal evidence and Pawan Gopalakrishnan, and data from other sources to argue that these events Debojyoti Mazumder^ contributed to rising inequality (Himanshu, 2017; Jha and Lahoti, 2022; Ghosh, 2024), other studies suggest This article assesses consumption inequality in India that the impacts of these events were either temporary by analysing convergence or divergence in household or led to a reduction in inequality (Chanda and Cook, consumption expenditure among various groups. 2022; Gupta et al., 2021). Enhanced labour mobility Further, the article estimates the incidence of poverty across states and well-targeted policy measures could across states by updating the Rangarajan Committee help mitigate the impact of rising income inequality poverty line. The study finds that there is a broad trend on consumption. towards convergence in consumption expenditure among households but there are a few notable exceptions. A Considering the contrasting findings and significant decline in poverty incidence across states in hypothesis, the Household Consumption India is also found. Expenditure Survey of 2022-23 by National Sample Survey Organization (NSSO) provides an unparalleled Introduction opportunity to examine these issues in detail. The In recent years, India has experienced a survey was released by the Ministry of Statistics deceleration in consumption growth, a trend with and Programme Implementation (MoSPI) in July significant implications for economic development, 2024, after a gap of eleven years. Covering 2.6 lakh poverty reduction, and social equity. Consumption, households, this dataset offers granular insights into a critical driver of economic growth, reflects not only consumption behaviours across income groups, states, aggregate demand but also the living standards and and rural-urban divides, enabling a comprehensive well-being of households. Analysing consumption analysis of the drivers of inequality and the extent inequality and regional convergence is vital, as of regional convergence. Understanding these trends disparities in consumption can perpetuate economic is particularly relevant for India, where addressing and social inequities, while convergence can indicate consumption disparities is essential to achieving progress towards more inclusive growth. While sustainable development goals and fostering equitable trends in real per capita gross state domestic product economic progress. By investigating these dynamics, (GSDP) show growing disparity among states over the this study aims to inform policy interventions that past decade (Kaustubh and Ghosh, 2023; 2025), it can promote balanced growth and ensure that the becomes essential to examine how is it materialising benefits of development are more evenly distributed in the context of consumption. This prompts a crucial across the population. question: Is the growing spatial income inequality Most of the discussions in media and academic ^ The authors are from the Department of Economic and Policy Research. research regarding the HCES 2022-23 have focussed The views expressed in this article are those of the authors and do not represent the views of the Reserve Bank of India. on the aggregate numbers from the survey. These RBI Bulletin September 2025 97ARTICLE Is Consumption Inequality Declining? – What the 2022-23 NSSO Survey Tells Us articles have analysed the major trends emerging in dimensions, including between urban and rural the consumption pattern of the Indian economy by households, between high consumption and low comparing the surveys of 2011-12 and 2022-23. The consumption households, and across states. This findings of these articles can be broadly summarised decrease in consumption inequality could also have as follows: there is an increase in the average monthly implications for poverty levels. To explore this, the per capita expenditure (MPCE) between 2011-12 and poverty incidence is estimated by appropriately 2022-23 in both rural and urban areas; a decrease in adjusting the consumption expenditure based poverty the rural and urban gap in consumption; a decline in line recommended by the Rangarajan Committee in the share of food in total consumption expenditure; 2014 to current prices. Results from these estimations and finally, a decrease in consumption inequality reveal a significant decline in poverty incidence across income deciles (Rampal, 2024; Iyer, 2024; across states. Nageswaran et al., 2024). The rest of the article is organised as follows: Previous studies have found a greater spatial Section II analyses variation in consumption inequality in consumption of non-food items expenditure across expenditure classes, Section compared to food items and spatial inequality in III presents variation between urban and rural rural MPCE is more than urban MPCE between states households, while Section IV examines variation in terms of MPCE based on HCES 2022-23 (Mitra across states. Section V updates the Rangarajan and Shrivastav, 2024). In similar line, (Bonu, 2024) Committee poverty line for the large states and analysed the consumption inequality among social calculates the incidence of poverty. Finally, Section VI groups. It is found that Scheduled Tribes in Odisha, concludes by offering some policy insights. Jharkhand and Chhattisgarh are at the bottom of II. Convergence across Expenditure Classes the consumption pyramid. Broadly, they provided To analyse the disparity across expenditure evidence that the scheduled tribes are at bottom of classes, the ratio between the mean expenditure of the consumer pyramid followed by scheduled castes each expenditure class and the median expenditure in almost all states. These studies focused only of the entire sample is calculated. The exercise is on HCES 2022-23 to analyse various dimensions of done separately for urban and rural households1. consumption inequalities. However, the importance For convergence, the ratios should increase for the of comparative analysis between different HCES fractiles below median and decrease for fractiles rounds were broadly overlooked. above median between 2011-12 and 2022-23. The present article contributes to this discussion It is observed that in urban areas, the ratio by analysing the trends in spatial and temporal increased for most of the fractiles below median and variation in consumption expenditure by analysing decreased for most of the fractiles above median micro-data from the HCES 2022-23 and comparing them with previous rounds of NSSO consumption 1 Inequality is defined as the difference between the average MPCE of the surveys. top fractiles and the bottom fractiles. In the charts, the ratio between the average MPCE of each quantiles and the median MPCE are plotted as bars. This ratio will be lesser than 1 for quantiles below 50 and greater than 1 Through comparative analysis of NSSO survey for quantiles above 50. An increase in the ratio for the fractiles below 50 rounds of 2011-12 and 2022-23, the study finds a will imply a movement towards the median and a fall in the ratio for the fractiles above 50 will also imply a movement towards median, therefore, reduction in expenditure inequality across several implying a decrease in inequality. 98 RBI Bulletin September 2025Is Consumption Inequality Declining? – What the 2022-23 NSSO Survey Tells Us ARTICLE (Chart 1). In rural areas, the ratio has remained classes. Specifically, the urban premium has increased unchanged for most of the fractiles below median but among the bottom 30 per cent and has decreased for has decreased for the top four fractiles (Chart 2). It top 70 per cent of consumption fractiles (Chart 4). can be concluded that inequality across expenditure This implies that the urban-rural gap has fallen for classes has decreased for both urban and rural better-off households but has increased for poorer households compared to 2011-12, although the households. decline has been more in urban areas. Next, the estimated urban premium is compared III. Convergence between urban and rural across states to assess if it is lower in richer states households Chart 3: Urban Expenditure Premium across Time The urban-rural gap is measured using the urban premium which is defined as the ratio of real urban MPCE and real rural MPCE. The real values are calculated by discounting the nominal MPCE for urban and rural households with their respective consumer price indices (CPIs) so that all values can be expressed in terms of 2011-12 prices. This ratio has seen a decline in trend since reaching its peak in 2009-10, indicating that the urban-rural gap in terms of consumption has continued to decline since 2009- 10 (Chart 3). However, further analysis shows that the fall in Sources: NSSO various rounds; and authors’ estimates. urban premium is not uniform across expenditure RBI Bulletin September 2025 99 )laeR( ECPM laruR/nabrU Chart 2: Rural Inequality Measure: 2011-12 vs. 2022-23 Note: The horizontal axis contains expenditure classes in terms of quantiles. Sources: NSSO 2011-12 and 2022-23; and authors’ estimates. 1.95 1.91 1.90 1.84 1.85 1.80 1.78 1.75 1.75 1.70 1.66 1.65 1.60 1.58 1.55 1.50 1987-88 1993-94 2004-05 2009-10 2011-12 2022-23 erutidnepxE naideM htiw oitaR 4 3 2 1 0 Quantiles 2011-12 2022-23 5-0 01-5 02-01 03-02 04-03 05-04 06-05 07-06 08-07 09-08 59-09 001-59 Chart 1: Urban Inequality Measure: 2011-12 vs. 2022-23 6 5 4 3 2 1 0 2011-12 2022-23 Note: The horizontal axis contains expenditure classes in terms of quantiles. Sources: NSSO 2011-12 and 2022-23; and authors’ estimates. erutidnepxE naideM htiw oitaR 5-0 01-5 02-01 03-02 04-03 05-04 06-05 07-06 08-07 09-08 59-09 001-59 QuantilesARTICLE Is Consumption Inequality Declining? – What the 2022-23 NSSO Survey Tells Us by NSSO for the survey). The relationship between Chart 4: Urban Expenditure Premium: 2011-12 vs. 2022-23 the state-wise urban premium and MPCE is examined separately for 2011-12 and 2022-23. The urban premium has a negative relationship with states’ average MPCE. This implies that states with higher MPCE have lower urban-rural gap (Chart 5). The relationship, however, has changed between 2011-12 and 2022-23. Comparing the slopes of the fitted lines in the scatter plots, it is observed that this relationship has become weaker. The fall in slope is primarily due to the urban premium falling in states which have lower MPCE than the national average. This indicates the urban-rural gap Sources: NSSO 2011-12 and 2022-23; and Authors’ estimates. has substantially decreased in the lower MPCE states between 2011-12 and 2022-23. (states with higher average MPCE) as compared to IV. Convergence across States poorer states and if that relationship has remained same over time. State-wise average MPCE are The literature on inter-state disparity in economic calculated by weighting the average urban and rural outcomes and its dynamics in the Indian context MPCE in a state by the respective urban and rural has mostly focussed on the growing divergence in population from the 2010-11 Census (the same used per capita incomes2 among Indian states, leading Chart 5: Relation between Urban Expenditure Premium and MPCE, State-wise: 2011-12 vs. 2022-23 Sources: NSSO 2011-12 and 2022-23; and authors’ estimates. 2 State level per capita incomes are measured using state level gross state domestic product (GSDP). 100 RBI Bulletin September 2025 )laeR( ECPM laruR/nabrU 2.80 2.30 1.80 1.30 0.80 0.30 Quantiles 2011-12 2022-23 5-0 01-5 02-01 03-02 04-03 05-04 06-05 07-06 08-07 09-08 59-09 001-59Is Consumption Inequality Declining? – What the 2022-23 NSSO Survey Tells Us ARTICLE to increasing spatial economic inequality. Broadly, (Log(MPCE) ) for 32 states and UTs as shown in i these studies have shown that there exists a beta- equation 1. T,2h01e1- 1s2tandard errors (e) are made robust to i divergence in per capita GSDP across Indian states heteroskedasticity. A statistically significant negative during recent decades (Ghosh and Kaustubh, 2023; coefficient of log of MPCE in 2011-12 (β) will denote 2025). As regarding to the dynamics of MPCE unconditional beta-convergence and the higher between Indian states, (Acharya et al., 2021) had magnitude of the coefficient will denote higher speed done a detailed study based on earlier rounds of of convergence (Barro and Sala-i-Martin,1992; 1995). NSSO consumption expenditure surveys and found Real MPCE α β Log(MPCE) e (1) evidence of overall divergence in MPCE between i i i ,2011-12 the states over the period between 1993-94 to 2011- ∆ Testing th=e a b+o ve* regression obtain +s a significant 12, and some evidence of conditional convergence negative relationship between growth in MPCE and when states were grouped based on their standards the log of MPCE in 2011-12 for both urban and rural of living. Some opinion pieces in news media like areas, indicating beta-convergence in per capita (Mahambare and Jyoti, 2024) in the context of the consumption across states (Table 1). This implies that household consumption survey round of 2022-23 has states with lower MPCE in 2011-12 experienced faster pointed to anecdotal evidence of dichotomy between consumption growth, resulting in a reduction of divergent per-capita GSDP/NSDP and convergent inter-state inequality in consumption between 2011- MPCE in some states. For instance, while Gujarat 12 and 2022-23. However, the speed of convergence and Maharashtra have higher per capita NSDP than in rural areas is higher than urban areas as reflected Rajasthan, the MPCE in rural Rajasthan is higher by the higher magnitude of β for rural areas compared than rural Gujarat and Maharashtra. They speculate to urban areas. that such a dichotomy may be attributed to several To better visualise the beta convergence in factors such as high labour mobility which generates MPCE across states, a scatter diagram between state- higher incomes in labour-exporting states compared wise nominal MPCE of 2011-12 (in log) and the to their GSDP levels, and government policies and corresponding growth in real MPCE from 2011-12 to schemes designed to mitigate the impact of growing 2022-23 is plotted for both rural and urban sectors spatial economic inequality on the living standards (Chart 6). For beta convergence, the fitted line of of people. the scatter plot must have a negative slope, as is Given this context, the present study investigates observed. The vertical dashed line in black is the the inter-state dynamics of MPCE between the period national average MPCE in 2011-12. States on the left 2011-12 to 2022-23. A state-level beta-convergence of the vertical line had lower MPCE than the national analysis is performed for the period 2011-12 to 2022- average in 2011-12. 23, separately for urban and rural areas. Specifically, beta-convergence occurs when regions with lower Table 1: Beta-Convergence Tests initial values experience faster growth than their Variable Rural Urban wealthier counterparts. This relationship is examined β -83.8*** -72.3* by regressing growth in urban and rural real MPCE α 333.6*** 302.1** between 2011-12 and 2022-23 ( Real MPCE) of the Note: *** denotes 1 per cent significance level, ** denotes 5 per i cent significance level and * denotes 10 per cent significance level. ith state on the logarithm of its MPCE in 2011-12 Source: Author’s calculations based on HCES 2011-12 and 2022-23. ∆ RBI Bulletin September 2025 101ARTICLE Is Consumption Inequality Declining? – What the 2022-23 NSSO Survey Tells Us Chart 6: Convergence in Real MPCE across States Sources: NSSO 2011-12 and 2022-23; and authors’ estimates. States like Odisha, Madhya Pradesh, Uttar be noted that while the per capita GSDP of Indian Pradesh, Chhattisgarh, Jharkhand, and West Bengal states has been diverging (Ghosh and Kaustubh, had lower MPCE than the national average and lie 2023; 2025), the MPCE in both rural and urban areas below the fitted curve (red line), implying that these is showing signs of convergence. This suggests that states will take comparatively longer time to reach living standards across these regions are becoming the all-India average MPCE as their real MPCE growth more similar, even as their per capita economic was below the level suggested by the convergence production continues to diverge. estimation in relation to their MPCE in 2011-12. On V. Estimating the Poverty Line the other hand, most north-eastern states as well as Tamil Nadu and Andhra Pradesh (including Telangana) This article follows the methodology proposed had higher MPCE than the national average in 2011- by the Rangarajan Expert Committee on poverty, 12 and lie above the fitted line, indicating that their constituted by the Planning Commission in 2011 real MPCE growth was above the level suggested by to estimate incidence of poverty. Consumption- the convergence estimation. based poverty lines determine the amount of money required to purchase a basket of essential items Thus, the data shows evidence of unconditional for a household. Micro-level data on household beta-convergence in per-capita consumption consumption expenditure can reveal the percentage expenditure across states for both rural and urban areas for the period of 2011-12 to 2023-24. This is in of households spending more than the amount contrast to the results found by (Acharya et al. 2021) specified by the poverty line. A decline in inequality for the period between 1993-94 to 2011-12-. However, in a growing economy will imply expenditure of similar to the findings of the earlier study, there are poorer households is increasing faster than richer heterogeneities in the speed of convergence between households. To the extent that there exists spatial rural and urban areas, and across states. It should variation in convergence of consumption expenditure, 102 RBI Bulletin September 2025Is Consumption Inequality Declining? – What the 2022-23 NSSO Survey Tells Us ARTICLE there may be difference in poverty reduction across below the updated poverty line in urban and rural states. areas of each state using the HCES 2022-23 micro-data are obtained. A few recent papers have calculated the state-wise incidence of poverty in India based on consumption The method suggested by the Rangarajan data of HCES 2022-23 (Bhasin and Bhalla, 2024; Sethu Committee, which submitted its report to the Planning et al., 2024). They have either used the World Bank Commission in 2014, had some distinct advantages purchasing power parity (PPP) poverty lines of $1.9 over the previous official poverty line constructed and $3.2 or domestic poverty lines (suggested by by the Tendulkar committee. First, the Rangarajan Tendulkar Committee or the Rangarajan Committee) Committee’s methodology defines different PLBs for after inflating them with CPI headline inflation urban and rural populations based on the difference to account for change in prices. However, there in the consumption pattern between the two groups. are issues with these approaches. There is a lack Second, the methodology re-links poverty line of consensus regarding the PPP conversion factor with minimum calorie needs after the Tendulkar required to convert World Bank’s poverty lines, which Committee’s methodology delinked them. Third, the are defined in dollars, into rupees. For inflating the Rangarajan Committee report gives the weightage of Rangarajan or Tendulkar poverty line with headline each item in the PLB for urban and rural areas, which CPI inflation, the weights of consumption items in makes it possible to calculate the nominal value of the CPI basket can be very different from the weights these baskets in 2022-23 prices for each state.3 of those items in the poverty line basket (PLB). For Table 2 contains the state-wise poverty line in example, the weight of food in the Rangarajan PLB 2011-12 as reported in the Rangarajan Committee for rural areas is 57 per cent compared to 54 per Report and the poverty line for 2022-23, which is cent weightage in the rural CPI consumption basket. estimated using the methodology described above for Similarly, the weight for food in Rangarajan PLB for large Indian states. urban areas is 47 per cent compared to 36 per cent Using micro-data from HCES 2022-23, the weightage in urban CPI consumption basket. Thus, percentage of population below the poverty line in the headline CPI inflation may not be the appropriate metric to gauge the inflation in the PLB. each of the states in 2022-23 were calculated. These are reported in Table 3 along with the poverty percentages To tackle this, a price index based on the weight of the states in 2009-10 and 2011-12, respectively, from of each item in the Rangarajan PLB, rather than using the Rangarajan report of 2014. A substantial decrease the item weights from the CPI basket, is developed. in the incidence of poverty is observed in both rural The price changes for each item between 2011- and urban areas across each state in HCES 2022-23 12 and 2022-23 are calculated, adjusting for these compared to HCES 2011-12. Other studies which have changes by the respective item’s weight in the PLB used the HCES 2022-23 have also found substantial to determine inflation for the PLB during this period. decline in poverty during the period although the This analysis was carried out separately for urban poverty rates calculated by them is different from and rural households in each state. Following this, the Rangarajan Committee poverty lines in nominal 3 It may be noted that the poverty line estimated in the article is based on the methodology proposed in the 2014 Rangarajan Committee report. terms were updated by adjusting the 2012 poverty line Ten years have passed since the release of the report, and consumption patterns may have shifted with new items becoming more important in with the inflation rates calculated as discussed in the the consumption basket. This may necessitate creation of a new poverty line which may capture the current reality better (Bhasin and Bhalla, 2024; previous step. Finally, the percentage of households Dev et al., 2024). RBI Bulletin September 2025 103ARTICLE Is Consumption Inequality Declining? – What the 2022-23 NSSO Survey Tells Us Table 2: State-wise Poverty Line 2011-12 and Table 3: Per centage of population below poverty 2022-23 using Rangarajan Methodology in 2009-10, 2010-11, and 2022-23 for select (In Rupees) Indian States State Rural Urban Rural Urban State Rural Urban Rural Urban Rural Urban 2011-12 2011-12 2022-23 2022-23 2009-102009-102011-122011-122022-232022-23 Andhra Pradesh 1032 1371 1903 2588 Andhra Pradesh 27.0 30.5 12.7 15.6 1.2 2.2 Assam 1067 1420 1968 2586 Assam 42.9 40.2 42.0 34.2 8.7 5.5 Bihar 971 1229 1724 2277 Bihar 65.1 55.0 40.1 50.8 5.9 9.1 Chattisgarh 912 1230 1586 2149 Chattisgarh 58.9 36.5 49.2 43.7 25.1 13.3 Delhi 1492 1538 2577 2592 Delhi 4.9 24.7 11.9 15.7 0.9 2.6 Gujarat 1103 1507 2014 2664 Gujarat 37.0 35.6 31.4 22.2 5.9 4.1 Haryana 1128 1528 2083 2696 Haryana 19.2 24.8 11.0 15.3 4.1 4.3 Himachal Pradesh 1067 1412 1895 2547 Himachal Pradesh 11.2 22.5 11.1 8.8 0.4 2.0 Jammu & Kashmir 1044 1403 1980 2653 Jammu & Kashmir 14.4 32.4 12.6 21.6 4.2 4.1 Jharkhand 904 1272 1621 2356 Jharkhand 55.3 42.1 45.9 31.3 16.6 12.6 Karnataka 975 1373 1784 2599 Karnataka 24.3 26.7 19.8 25.1 0.9 3.3 Kerala 1054 1354 1982 2563 Kerala 9.7 23.7 7.3 15.3 1.4 4.3 Madhya Pradesh 942 1340 1707 2521 Madhya Pradesh 51.3 45.0 45.2 42.1 9.6 11.6 Maharashtra 1078 1560 2006 2791 Maharashtra 27.6 30.3 22.5 17.0 11.3 8.6 Odisha 876 1205 1608 2182 Odisha 50.0 41.2 47.8 36.3 8.6 10.2 Punjab 1127 1479 2048 2622 Punjab 14.8 28.6 7.4 17.6 0.6 2.6 Rajasthan 1036 1406 1887 2561 Rajasthan 31.9 38.5 21.4 22.5 6.8 6.7 Tamil Nadu 1082 1380 2041 2608 Tamil Nadu 25.9 29.7 24.3 20.3 2.1 1.9 Uttar Prdesh 890 1330 1622 2429 Uttar Prdesh 46.3 49.6 38.1 45.7 5.7 9.9 West Bengal 934 1373 1755 2572 West Bengal 37.8 36.6 30.1 29.0 7.5 12.4 Note: Telangana is included in Andhra Pradesh; Ladakh is included in Note: Andhra Pradesh includes Telangana; Jammu and Kashmir includes Jammu and Kashmir. Ladakh. Sources: HCES 2022-23; Consumer Price Index, MoSPI; and authors’ Sources: HCES 2022-23, and author’s calculations. calculations. the estimates in this study, this is due to differences present study does not construct any poverty line but in methodology. The paper by (Bhasin and Bhalla, updates the Rangarajan poverty line using item-wise 2024) estimated total poverty headcount ratio of 2.3 price indices. per cent overall in 2022-23 which was 12.2 in 2011- Conclusion 12. The poverty line they estimated is based on the This study compares the consumption World Bank’s measure of consumption expenditure of 1.99 USD per day which they convert into Rupee expenditure numbers from the Household terms using purchasing power parity measure. (Sethu Consumption Expenditure Survey of 2022-23 by the at al., 2024) also estimate poverty incidence using the NSSO with the survey done in 2011-12 focussing HCES 2022-23 and found poverty rates of 23 per cent on convergence across various dimensions such as and 27.4 per cent for rural and urban respectively, expenditure classes, urban-rural areas, and states. for all India. Their poverty rate is based on a poverty The results show that there is a broad trend of line constructed by them which has updated calorific convergence in MPCE. A closer analysis reveals that needs based on the occupation and age of the family there are some exceptions to the broad trend. The members of the household. It is to be noted that the decrease in inequality in consumption expenditure 104 RBI Bulletin September 2025Is Consumption Inequality Declining? – What the 2022-23 NSSO Survey Tells Us ARTICLE across expenditure deciles and regions may be Ghosh, T. and Kaustubh. (2023). Growth explained by more effective delivery of various decomposition of the Indian states using panel data government schemes. Also, the study re-estimated techniques. Applied Economics, 56(39), 4664–4684. the poverty line for each state with this data keeping Ghosh, T., and Kaustubh. (2025). Growth Divergence the Rangarajan Committee definition. The estimated between Indian States. Economic and Political incidence of poverty shows an overall reduction of Weekly, 60(6) poverty across states in India. Gupta, A., Malani, A., and Woda, B. (2021). Inequality References in India declined during covid. NBER Working Paper. Acharya, D., Rath, B.N., Parida, T.K. (2021). Convergence Himanshu. (2017, November 6). Demonetisation, in Monthly Per Capita Expenditure and the Rural– inequality and informal sector. Mint. Urban Dichotomy: Evidence from Major Indian States. In: Batabyal, A.A., Higano, Y., Nijkamp, P. (eds) Iyer, Vaidyanathan P. (2024, February 26). 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Bonu, S. (2024, August 22). Consumption Expenditure by Social Groups: Findings from India’s Household Nageswaran, V. A., Guru, Anuradha and Bisht, D. S. Consumption Expenditure Survey, 2022-23. Isaac (2024, March 6). India’s consumption numbers fit Centre for Public Policy, Ashoka University. into our big picture of progress. Mint. Chanda, A., and Cook, J. C. (2022). Was India’s Rampal, Nikhil (2024, February 27). Here are 5 demonetization redistributive? Insights from key takeaways from Household Consumption satellites and surveys. Journal of Macroeconomics. Expenditure Survey 2022-2023. The Print. Dev, M. and Rangarajan, C. (2024, March 12). With Sethu, C. A., Abhinav Surya, L. T. and Ruthu, C. A. new consumption survey, the need for new indices, (2024), Poverty in India: The Rangarajan Method Indian Express. and the 2022–23 Household Consumption and Ghosh, S. (2024, January 15). India’s K-shaped Expenditure Survey. Review of Agrarian Studies, recovery debate has economists divided. Mint. 14(2). RBI Bulletin September 2025 105Infrastructure - An Engine of India’s Growth Express ARTICLE Infrastructure - An Engine of between infrastructure and growth as well as welfare and cites global experiences. Section IV encapsulates India’s Growth Express the development of infrastructure in India, especially the recent phase of its rapid growth with a reoriented by Ashutosh Raravikar and Abhishek Ranjan^ approach. The results of our empirical work on infrastructure and growth in India are explained. During the last decade India has placed a policy Section V deals with the approaches on financing of thrust on development of infrastructure with an infrastructure. Section VI concludes. integrated and inclusive approach. In this context, this study analyses the relationship between infrastructure II. Review of Literature and economic growth by creating indices of infrastructure The need for development of infrastructure using different methods. The results indicate that for long term growth of economy has been pointed infrastructure has positively and significantly impacted out in several research studies across the world. the GDP growth. Hence the policy focus on expansion The research has brought to the fore the significant in broad-based infrastructure would take the growth role of public investment and its positive impact on story ahead. Besides, given the large requirement productivity and growth of national income. Munnel of investment in infrastructure, exploring suitable (1992) showed that recession in America during 1970s financing options and strategies would enable filling the was associated with reduction in government capital infrastructure gap faster. expenditure. Agenor (2010) propounded that public Introduction investment in physical and social infrastructure enabled a country to attain steady economic growth India aims and aspires to become a developed over long run. The research works by Bougheas nation by 2047. It has emerged as a fastest growing and others have also argued the positive relation economy in the world. Of late, there has been a policy between infrastructure and growth. David Aschauer thrust on expansion in infrastructure. Infrastructure (1993) advocated the need for using fiscal policy as is a significant factor for broad-based and rapid an instrument for creation of infrastructure through growth as it generates the facilitative framework public expenditure. Paul and Schwartz (1996) argued from both supply and demand side. Infrastructure that investment in infrastructure positively affected is an engine that could drive the Indian growth the rise in productivity. However, some of the works express further ahead. This article delves into found no or inconclusive or weak relation between various dimensions of infrastructure and attempts the two. For instance, Elburz et al., (2017) found no to explore and establish the empirical relationship relation between investment on infrastructure and between infrastructure and economic growth in output. India. It also analyses various ways for financing the infrastructure. Section II takes an overview of the Research studies pertaining to Indian economy existing literature on the subject in both global and indicate positive relationship between the two. Indian context. Section III explains the mechanism Dash and Sahoo (2010) argued that creation of infrastructure positively and significantly impacted ^ Authors are from Department of Economic and Policy Research. Inputs from Rajib Das are thankfully acknowledged. The views expressed in this the output growth in Indian economy. Kumari article are the personal views of authors and do not represent the views of the Reserve Bank of India. and Sharma (2017) propounded that in case of RBI Bulletin September 2025 107ARTICLE Infrastructure - An Engine of India’s Growth Express India, the infrastructure development favourably benefit schemes and delivery of goods and services impacted economic growth with a lag of 1-2 years. to the targeted beneficiaries. Digital infrastructure Rath et al., (2022) showed an important and positive consists of physical and software-based set up or relation between states’ capital outlay and gross arrangements that are essential for delivery of goods state domestic product (GSDP) in India and that and services, remote works and other needs. These the present year’s decision was impacted by past include data centres, information technology (IT) values of capital outlay. Mishra et al., (2017) found staff, fibre, hardware, software, operating systems significant association between economic growth and etc. Digital infrastructure is a core utility that capital outlay. Thus, the significance of expenditure, contributes to economic development and growth. especially government expenditure, on investment The quality of human life hinges on meeting in infrastructure was advocated for GDP growth. the basic needs and living in a friendly and pleasant environment. This is enabled by social infrastructure. III. Infrastructure, Growth and Welfare Social infrastructure involves building and Infrastructure is a set of physical structures maintaining the set up and facilities which support that facilitate economic activity and support the social services for attaining the human development capacity of economic operations. It consists of and well-being. These comprise of education, health “physical facilities, institutions and organisational and social protection schemes tailored for various structures or the social and economic foundations sections such as old people, unorganised workers for the operation of the society” (UNCTAD, 2008). and aspirational regions. Social infrastructure raises Infrastructure is also the social overhead capital that human efficiency and skills, causing rise in output. comprises basic services for functioning of productive Expenditure on infrastructure benefits the activities. It has two components - economic or economic growth through its multiplier effects physical and social. The former mainly consists of (World Bank, 2023). Expansion in infrastructure the means of transport and communication that increases the output through raising productivity of boost economic activities, and the latter comprise factors of production as well as aggregate demand. of educational institutions, healthcare units etc., This occurs through rise in connectivity, expediting that impact the welfare of people. Lack of sufficient the movement of goods and people, reduction in transport network causes inadequate availability of cost, facilitating the ease of doing business and raw materials and hinders movement of finished increasing the access of people to various facilities products to markets. Farmers get lower price causing and financial services. The public investment in reduction in rural incomes and hence percolation infrastructure yields larger multipliers than private of growth. Physical infrastructure reduces cost spending because the former has a potential to of production and increases factor productivity. increase the productive capacity of the economy, Development of infrastructure involves building up directly as well as indirectly, through crowding in of assets needed by the country including housing, private investment, apart from its positive impact on transportation, telecommunication, sanitation and aggregate demand. Moreover, it increases output in commercial establishments. both short and long term and decreases government Technology has been a revolutionary enabler debt to GDP ratio (IMF, 2014). The cost of financing providing the last mile connectivity of government is also lower in case of public investment. 108 RBI Bulletin September 2025Infrastructure - An Engine of India’s Growth Express ARTICLE Global experience showed a strong correlation on further infrastructure development. In 2006 and association between infrastructure and level the Viability Gap Funding Scheme (VGFS) was of economic development and growth. Various launched to provide finances to projects that were countries across the world were benefitted by their economically or socially beneficial but financially not infrastructure led strategies. The experiences of the viable. Thereunder 40 per cent of capital expenditure US, Germany, Italy, Ethiopia, Brazil, Mexico, South was provided as a grant. The last decade witnessed Korea, Thailand, Japan, China and sub-Saharan a significant upward shift in infrastructure growth. Africa indicate that the large public investment in Digital Public Infrastructure (DPI) emerged in India infrastructure helped positive and faster growth in 2009 with launch of Aadhar as a channel for through various channels such as increase in factor doorstep service delivery. The significance of digital productivity and refinement in the quality of infrastructure was realised further during the post- institutions. Moreover, there was also a rise in per Covid scenario when physical interactions became capita income, and the growth was equitable. Thus, non-feasible. international experience across various countries IV.2 Flight to Higher Orbits corroborates the significant and positive impact India aspires for development of an ecosystem of government led infrastructure development on to attain economic growth which would promote growth and welfare. all-round development leading to generation of IV. Infrastructure Development in India employment, enabled by skills that are in sync with IV.1 Historical Evolution output having global quality standards. All these would lead to prosperity for the welfare of all people. Efforts After independence, there began an era of were initiated in this direction during the last decade. planned development comprising of the building of United Nation (UN)’s Sustainable Development infrastructure in India. During the first two decades, Goal (SDG) no. 9 aims to “develop quality, reliable, institutionalisation of savings and credit, and sustainable and resilient infrastructure including establishment of development finance institutions regional and trans-border infrastructure to support (DFIs) facilitated the financing to infrastructure. The economic development and human well-being with next era of social control over banking witnessed a focus on affordable and equitable access for all”.1 transformation in financial infrastructure across the The focus on infrastructure is an integral part of the country that aided the construction of infrastructure. “Bhartiya Development Model” with an objective The 1990s began with the paradigm shift in economic of “sustained, fast, inclusive growth” (Niti Aayog, management of the nation with liberalisation, 2023). With “social overhead capital” there have privatisation and globalisation followed by been attempts to penetrate economic development institutional, structural and financial sector to the remotest parts of the nation. Realising its reforms, which gave an impetus to infrastructure development through market-oriented solutions. significance and to get the full multiplier effect of Foreign direct investment in infrastructure was infrastructure, government adopted the “forward liberalised. The recommendations of Expert Group looking programmatic approach” in 2019. These on Commercialisation of Infrastructure Projects mainly include National Monetisation Pipeline headed by Rakesh Mohan prepared the blueprint 1 Economic Survey 2018-19 (Vol. 2), Government of India. RBI Bulletin September 2025 109ARTICLE Infrastructure - An Engine of India’s Growth Express (NMP), National Infrastructure Pipeline (NIP), Public- Chart 1: Capital Expenditure of Central Private Partnership (PPP) for expanding physical Government as per cent of GDP infrastructure, fiscal reforms, financing programs, (Per cent) 3.5 measures for greening of economy and promotion of 3.0 digital and social infrastructure. The comprehensive program adopted recently by the government has 2.5 been elaborated in Annex A. 2.0 During the last decade capital expenditure 1.5 of central government has witnessed a steady rise 1.0 from ₹1.9 lakh crore in 2013-14 to ₹10.2 lakh crore in 2024-25 with a remarkable upward shift since 0.5 2018-19 (Chart 1). A high growth in infrastructure 0.0 occurred during the period (Chart 2). The growth in capital expenditure and growth in infrastructure Source: Handbook of Statistics on the Indian Economy 2024-25, RBI. were associated with the rise in GDP (Charts 3 and 4). various regions across the world including India IV.3 The Empirical Research in 2019 was significant at 0.75. The correlation IV.3.1 Various Estimations coefficient between the overall infrastructure quality Various research studies have underscored the and per capita GDP for 154 countries including India need for a big push to infrastructure. The correlation coefficient between per capita infrastructure in 2018 was 0.76.2 The correlations of investment in investment and per capita GDP for 54 countries in infrastructure pertaining to rail, road, airport and 110 RBI Bulletin September 2025 41-3102 51-4102 61-5102 71-6102 81-7102 91-8102 02-9102 12-0202 22-1202 32-2202 42-3202 52-4202 Chart 2: Index Number of Infrastructure Industries 300.0 250.0 200.0 150.0 100.0 50.0 0.0 Overall index Electricity Coal Steel Cement Crude oil Petroleum refinery products Natural Gas Fertilisers Note: 1. Eight core industries have been referred to as Infrastructure Industries. 2. Base Year: 2011-12 = 100 Source: Handbook of Statistics on the Indian Economy 2024-25, RBI. 41-3102 51-4102 61-5102 71-6102 81-7102 91-8102 02-9102 12-0202 22-1202 32-2202 42-3202 52-4202 2 Economic Survey 2022-23, Government of India.Infrastructure - An Engine of India’s Growth Express ARTICLE Chart 3: Growth of Infrastructure and GDP (Per cent) 12 10 8 6 4 2 0 -2 -4 -6 -8 Growth rate of GDP Growth rate of index of infrastructure industries Source: Handbook of Statistics on the Indian Economy 2024-25, RBI. inland modes with GDP for India were quite high (Datt and Ravallion, 1998) and boosted agricultural with values greater than 0.90 (GoI, 2019). Various and rural development (Bingswanger et al., 1993 and empirical works have estimated the output elasticity Fan et al., 2000) and overall economic growth in of infrastructure (during late 1980s & early 1990s) India (GoI, 2023). ranged between 0.38 – 0.56 (Khan, 2015). For India, The fiscal multiplier effect for infrastructure Sahoo (2011) has found it to be upto 0.5. The output investment was estimated by various entities. It was elasticity of public infrastructure was 8 per cent (Bom in the range of 0.8 up to 2 years, 1.5 for 2-5 years and Lighthart, 2013). The gains of infrastructure and 1.6 in recession3. The value of infrastructure investment were substantial and the value of output investment multiplier was larger than the multiplier and welfare (in terms of private consumption) for overall public expenditure. In India, the estimated multipliers of public investment on infrastructure values of multiplier worked out between 2.5-3.5 were computed at 1-1.4 and 0.8 respectively (IMF, (KPMG, 2024). It means that for each Rupee spent for 2016), when infrastructure was adequately effective. creation of infrastructure, the growth in GDP would The infrastructure positively affected quality of life and national security (Baldwin, Dixon, 2008), be ₹2.50 to ₹3.50. The multiplier value was higher during the contractionary phase of the economy environment and welfare (Bristow and Nelthorp, 2000), output and employment (Gu, Macdonald, as compared to the expansionary period, thereby 2009), regional development (Nijkamp, 1986). benefitting more when needed. Moreover, its impact Infrastructure favourably impacted the productivity lasted for a longer period than that of revenue and efficiency of manufacturing firms (Mitra et al., expenditure. Accordingly, the growth of expenditure 2002). It played crucial role in reduction of poverty for creation of infrastructure or capital expenditure as a proportion of gross domestic product (GDP) 3 Estimate by S & P Global as quoted by Economic Advisory Council to Prime Minister. proved vital for growth. RBI Bulletin September 2025 111 41-3102 51-4102 61-5102 71-6102 81-7102 91-8102 02-9102 12-0202 22-1202 32-2202 42-3202 52-4202 Chart 4: Growth of Capital Expenditure and GDP (Per cent) 12 10 8 6 4 2 0 -2 -4 -6 -8 Growth rate of GDP Central government capital expenditure (as per cent of GDP) Source: Handbook of Statistics on the Indian Economy 2024-25, RBI. 41-3102 51-4102 61-5102 71-6102 81-7102 91-8102 02-9102 12-0202 22-1202 32-2202 42-3202 52-4202ARTICLE Infrastructure - An Engine of India’s Growth Express IV.3.2 Empirical Exercise Table 2: Regression Estimates We look at the relationship between infrastructure Mode 1 Model 2 Model 3 (Average) (PCA) (DFM) and GDP. For studying this, we create an infrastructure Intercept 6.25*** 6.76*** 6.77*** index and see the relationship with GDP. The data 0.97 0.91 0.91 utilized in our study covers the period from 2006 to Infrastructure Index 0.22** 0.17* 0.16* 2021 and is on an annual frequency. The data on real 0.19 0.74 0.74 Adjusted R-squared 0.18 0.11 0.11 GDP, per capita GDP and road network are sourced Significant codes: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.1 from the CEIC4. All remaining infrastructure indicators have been taken from the World Development Data source: The data utilized in our study covers Indicators5 of the World Bank. The year-on-year the period 2006 - 2021 with an annual frequency. growth rate has been computed for each indicator. We The data on real GDP and road network are from create three different indices based on the growth rate the CEIC. All remaining infrastructure indicators are of the infrastructure variable. In the first method, we from World Development Indicators of the World take simple average of the growth rates. In the second Bank. method, we take principal component of the growth The results clearly indicate that the infrastructure rates, and in the third, we create an index based on index explains a part of the GDP growth. The dynamic factor model (DFM). The first two are linear infrastructure index is significant, implying that methods and their weights are given in Table 1. infrastructure has played an important role in the In simple average, we assume that each of the economic growth of the country. variables works in a similar way. We find that the access to electricity, clean fuel and technologies for In addition to above, the study is extended to cooking, length of roads and telephone subscription the states level to create a panel data with Gross have significant weight in the principal component State Domestic Product (GSDP) as the dependent analysis (PCA) index, and any increase in these variable and the independent variables : 1) Foreign variables will significantly change the index. Equation Direct Investment (FDI), 2) Proposed Investment, 1 represents the regression model. The results of 3) Agricultural Yield, 4) Registered Vehicles, 5) Road regression are given in Table 2. Length, 6) Telecom Subscribers and 7) Electronic GDP a b Infrastructure Index ϵ (1) Exports for each state. It is an unbalanced panel Table= 1: I+nfrastructure Variables+ and Weights containing data from 2012-2025 with total 431 observations. Hausman test is used and results are Infrastructure Variable PCA Weight Equal Weight Access to clean fuels and technologies found in favour of fixed effect rather than random for cooking (per cent of population) 0.58 0.2 effect (with p-value < 0.001). The regression is Access to electricity (per cent of population) 0.62 0.2 represented by Equation 2 and estimates are given Fixed telephone subscriptions in Table 3. (per hundred people) 0.32 0.2 Air transport, freight (million ton-km) 0.18 0.2 Length of Roads: Highways: Public Works Department: National 0.38 0.2 Log(GSDP) ~ Log(FDI) + Log(Proposed_ 4 https://www.ceicdata.com/en Investment) + Agricultural_Yield + Registered_ 5 https://databank.worldbank.org/source/world-development-indicators (2) Vehicles + Log(Roads_Length) + Log(Telecom_ Subscribers) + Log(Electronics_Exports) 112 RBI Bulletin September 2025Infrastructure - An Engine of India’s Growth Express ARTICLE increase in investment is associated with 0.118 per Table 3: Fixed Effect Panel Regression Estimates cent increase in GSDP. An increase of 1 per cent in Coefficients Estimate Standard T-Value Pr(>|t|) Error telecom subscribers results into an increase of 0.15 log_FDI 0.013 0.0028 4.425 1.25e-05 *** per cent in GSDP, and an increase of 1 per cent in log_Proposed 0.118 0.0140 8.457 < 2e-16 *** electronic export growth results into GSDP growth Investment Agricultural_Yield 0.000 0.0001 3.906 1.12e-04 *** of 0.008 per cent. Rest of the variables have small Registered_Vehicles 0.000 0.0001 -3.508 5.04e-04 *** impact on GSDP. The significance of the results log_Roads_Length 0.000 0.0000 2.857 4.49e-03 *** further corroborates our hypothesis. log_Telecom_Subscribers 0.145 0.0033 4.436 1.19e-05 *** log_Electronic Exports 0.008 0.0027 3.063 2.35e-03 ** V. Financing of Infrastructure Adjusted R-Squared: 0.15238, Significant codes: ‘***’ 0.001 ‘**’ 0.01 In order to enable India becoming a developed country by 2047, the annual real GDP growth would The above results strengthen our hypothesis be required at 7.6 per cent (Behera et al., 2023). that infrastructure plays important role in GDP To attain the goal of US$ 5 trillion economy, the growth. We further compute cluster-robust standard investment in infrastructure needs to be made at errors for a fixed effects regression model, addressing an annual rate of 8-10 per cent for next five years the issues like heteroskedasticity and within-group (NaBFID, 2022). For this, the required annual correlation (Hoechle, D., 2007). It computes robust expenditure on infrastructure would be 7-8 per cent standard errors clustered by “state”, relaxing the of GDP i.e., about US$ 200 billion6. In this context, identical and independent distribution assumptions, an assumption used in ordinary least squares (OLS). the financing of infrastructure investment assumes The small-sample correction method adjusts for finite significance. Funding of infrastructure involves clusters. The coefficients remain significant with various challenges. These include asset-liability the recomputed coefficient significance test statistic mismatches arising from high sunk costs and long (t-statistics, p-values) (Table 4). gestation periods, spike in costs due to delays in approvals, violation of agreements, problems in land The results show that 1 per cent increase in acquisition, complicated legal mechanisms regarding foreign direct investment (FDI) is associated with distribution of returns, sharing of risks among all 0.013 per cent increase in GSDP and 1 per cent stakeholders and difficulties in price determination. Table 4: Robust Fixed Effect Panel Regression The problems get accentuated due to interdependence Estimates of projects and non-availability of complimentary Coefficients Estimate Standard T-Value Pr (>|t|) units causing time overruns with cascading effects in Error absence of holistic planning. Absence of developed log_FDI 0.013 0.0030 4.178 3.59e-05 *** debt raising market and financial system coupled log_Proposed Investment 0.118 0.0161 7.375 8.77e-13 *** Agricultural_Yield 0.000 0.0001 3.458 6.02e-04 *** with default by a non-banking financial company Registered_Vehicles 0.000 0.0001 -3.027 2.61e-03 ** (NBFC) and high non-performing assets (NPAs) of log_Roads_Length 0.000 0.0000 2.101 3.62e-02 ** banks in previous decade adversely impacted the log_Telecom_Subscribers 0.145 0.0036 4.038 6.26e-05 *** funding of infrastructure sector. log_Electronic Exports 0.008 0.0030 2.744 6.31e-03 ** Significant codes: ‘***’ 0.001 ‘**’ 0.01 6 Economic Survey 2018-19, Government of India. RBI Bulletin September 2025 113ARTICLE Infrastructure - An Engine of India’s Growth Express Over the years, the Reserve Bank and government Foreign Commonwealth and Development Office. have taken various measures to facilitate the funding It would facilitate funding the urban bodies and of infrastructure. These are outlined in Annex B. developing financial instruments. Its MoU with New Due to long gestation period and avoidance of risk development Bank (NDB) would augment exchange taking by the private institutions, the development of technical expertise and research and capacity finance institutions (DFIs) need to take up the work building (NaBFID, 2025). to address market failure and attain the broader Going forward, taking further initiatives by objectives of the nation. In India, National Bank for NaBFID could contribute more to its mission. With Financing Infrastructure Development (NaBFID) was the expansion in scale of large long term institutional set up in 2022 by an Act of Parliament (NaBFID Act, investors like provident funds and insurance 2021) as a main development finance institution companies, it may tap funding from them. It may for funding the infrastructure7. It has commenced also secure a high credit rating to tap global and work on providing stable finance for infrastructure, domestic sources of finance. It may develop its own developing the derivatives and bond markets with sustainable financing model to reduce dependence liquidity and depth and exploring the novel funding on resources from government. It may develop a instruments for financing the infrastructure. NaBFID strong governance and assurance mechanism coupled grants loans for infrastructure, obtains investments with knowledge base and expertise. It may refine from private sector and institutions including abroad, its skills in evaluation and dynamic monitoring of refinances the present loans, facilitates resolution its financed projects with surveys etc. Systems may of disputes, provides expertise, technology and be put in place for resolution of stressed assets. consultancy services in funding the infrastructure. It may provide funding for climate resistant low It provides a bouquet of services including term carbon infrastructure and technology to promote lending, debentures, bonds, guarantees and letters sustainable growth. It can provide technical advisory of comfort. These would also facilitate effective services for infrastructure projects and development management of infrastructure projects that are facing of bond market. Provision of products like credit issues of unviable costs and delays. Its activities default swaps (CDS) and facilitating loan syndication involve coordination with various stakeholders to would go a long way in this regard (Rao, 2024). facilitate institution building for developing long term It would be desirable that the funding institution infrastructure. NaBFID has sanctioned over ₹1 lakh exert control over the performance of the funded crore by 2023-24 and has also raised the disbursement entities in terms of achievement of objectives of (Rao, 2024). As a part of multilateral institutional the infrastructure projects. The arrangements of partnership, it has signed a memorandum of such ‘disciplining’ would need to be in place. The understanding (MoU) with International Finance principle of “allocation discipline” (Kumar, 2022) Corporation (IFC) for developing public private based on “reciprocal control mechanisms” (Amsden, partnership (PPP) projects through mobilisation of 2001) may be adhered to. A performance tracking private finance, especially for climate risk mitigation and interventions would enable the financing entity such as renewable energy. It has signed a Letter of to exert a control, and the beneficiary entities would Intent (LoI) with Asian Development Bank (ADB) and reciprocate by attaining the goals of the projects. 7 https://www.nabfid.org The financer would need to deploy the rigorous 114 RBI Bulletin September 2025Infrastructure - An Engine of India’s Growth Express ARTICLE procedures for scrutinising the proposals for finance commenced in India since mid-1990s for funding to assess the feasibility and fulfilment of objectives the local urban infrastructure. Its growth was of projects. The phased provision of funding as per facilitated through funding by a US agency, SEBI’s the progress of work would be desirable. The strong regulatory framework on municipal bonds, grants and effective monitoring would be helpful. In case under AMRUT Scheme, and guidance under SEBI’s of a failure or default, a system of restructuring Information Database and Repository on Municipal or liquidation would need to be in place. The Bonds for issuance and listing of municipal debt securitisation would play a crucial role therein. securities. Of late there has been a rise in municipal Securitisation is a significant instrument in funding bond financing. Municipal bond index was launched infrastructure. It makes credit market diversified. recently by National Stock Exchange (NSE) which Banks get benefitted in terms of releasing their tracks performance of bonds issued by corporations capital and asset liability management. The small having investment grade rating. This would enhance and medium sized banks face constraints in their transparency in municipal bond market and expand capacity for credit appraisals. Securitisation enables the investor base. Municipal bond issuance and their them financing large infrastructure projects after the market are presently at nascent stage. Outstanding project has started working. In this context, there municipal bond issuance stood at ₹4,204 crore as on is a need to develop the market for securitisation. March 31, 2024 which worked out to 0.09 per cent For this purpose, it would be imperative to resolve of outstanding corporate bonds and 0.01 per cent of various constraints and take initiatives such as GDP. Investor base is limited to private placement increasing the transparency through sufficient of major part of the bond issuance. For enabling the disclosures, raising demand for receivables for longer expansion in urban infrastructure, the municipal tenures, participation of long-term institutional bond market needs to develop rapidly. For this, the investors, expansion in investor base, development improvement in financial performance and credit of secondary market, and resolution of tax-related ratings of municipal corporations would be essential. and legal issues including foreclosure laws. That would boost the investor confidence and India is witnessing fast urbanisation. For broaden the market participation. Some of the local improving the standards of living in urban areas and bodies have recently started issuance of green bonds attracting the persons and investments to towns, for projects having a positive ecological impact. This development of urban infrastructure would be would promote sustainable urban development. The the key. Rapid progress towards creation of smart issuance involves extra cost. However, as the market cities and proactive initiatives by urban local bodies develops, the cost would decline. The Fourteenth would contribute towards attaining these objectives. Finance Commission recommended creation of an Municipal corporations may augment their capital intermediary to help the local bodies accessing bond expenditure through finding suitable and novel markets. Moreover, local tax reforms, innovative funding mechanisms based on bonds and lands to strategies for augmenting tax and non-tax revenues, increase their incomes. Issuance of municipal bonds revisions in user charges for municipal services and including green bonds would enable mobilising taxes, and performance-linked municipal grants finances by municipal corporations on a sustainable would go a long way in enhancing the magnitude of basis (RBI, 2024). The issuance of municipal bonds infrastructure financing. RBI Bulletin September 2025 115ARTICLE Infrastructure - An Engine of India’s Growth Express External commercial borrowing (ECB) is another investor base, harnessing the potential of capital effective avenue for funding infrastructure. It markets and infrastructure funds, development of enables the entities to borrow overseas funds at a diversified financial instruments and expanding the lower cost and/or easier accessibility depending on securitisation of loans. the factors such as interest rates and exchange rates. The issue of asset liability mismatches faced This reduces the cost of infrastructure projects. On by banks in case of infrastructure funding could be the other hand, excessive overseas borrowing and resolved if they lend a larger portion at floating rates. adverse exchange rate movements coupled with Besides, there is a need for developing alternative hedging costs increase their indebtedness with sources for funding infrastructure to avoid financial the risk of debt trap and pose challenges for stability risks arising from any stress in assets of the external sector sustainability and financial the banking system. Investment bankers can play a stability of the country in general. Progressive significant role in financing infrastructure through liberalisation of ECB policy, rule-based dynamic limit provision of innovative ways of funding and acting as introduced by Reserve Bank, small proportion of a link between government and private sector. There India’s external debt and the strength of external is a need to develop a bond market for infrastructure sector would enable bridging the gap in infrastructure financing. It would elongate the maturity profile financing through ECB. Backing the ECBs with of debt, reduce maturity mismatches in the books government guarantees for viable infrastructure of lenders, broaden the financing base, expand the projects may be helpful. instruments in risk management, enable stronger Sustainable infrastructure funding by banks corporate governance and minimise the impact of borrowers on financers (Khan, 2015). Developing would hinge upon the health of such assets. To the market for sovereign green bonds may be prevent the building of high level of stressed assets in accorded a priority, as they have a long tenure with infrastructure with banking system, certain measures lower refinancing risk, contain greenium (premium would be useful. First, the realistic assessment of over plain bonds), impart stable funding source to time schedules of project completion and appropriate government for climate related infrastructure and structuring of their financing with equity and loans finance the green transition. would keep the costs at lowest level and increase the feasibility in funding. Second, the schedule of Finally, as the domestic savings fall short of the repayments may be fixed according to cash inflows of needed resources, exploring the ways of infrastructure projects to avoid stressing. Third, project appraisals financing through stable external sources such should be done by financiers realistically with as concessional multilateral institutional finance respect to its designs, projections, identification of and external commercial borrowing with sovereign risks and matching those with risk appetites of the guarantees would be crucial. It can fund the resource financiers. Fourth, project financing may be done gap and impart viability and competitiveness to through a diversified mix of sources and instruments projects through technological knowledge. This including corporate bonds. Fifth, the loan pricing by should be done while ensuring systemic stability lenders should reflect risks and be dynamic to adjust and covering the risks from unhedged portion of with changing risk profiles. Sixth, the institutional funds. 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ADB Economics Working Paper Series, Asian Development Bank (No. 231). 120 RBI Bulletin September 2025Infrastructure - An Engine of India’s Growth Express ARTICLE Annex A: India’s Infrastructure Programme Broad-based Institutional Set-Up Parvatmala, Bharatmala, Sagarmala, e-Sanchit, reforms in various processes and GST reforms. Government To enhance the participation of private sector announced several measures for states such as capital in infrastructure projects, the government initiated expenditure-linked additional borrowing facilities National Monetisation Pipeline (NMP), National and long-term interest-free loans to increase their Infrastructure Pipeline (NIP) and Public-Private capital expenditure. A system for credit rating for Partnership (PPP). NIP takes a complete view of infrastructure projects for risk assessment has been development of infrastructure in the country. It launched. In addition, some complementing reforms enables the investors to plan their investments in have been made including establishment of a devoted infrastructure. NIP is envisaged with an investment institution for infrastructure funding viz., National of ₹111 lakh crore during 2020-2025 to be financed Bank for Financing Infrastructure and Development by central government, state governments and private (NaBFID), public private partnership through sector entities. Presently it has 8,964 projects having Model Concession Agreements, funding options of investment of ₹108 lakh crore. NIP works on the Infrastructure Investment Trusts (InvITs) and Real platform of Invest India Grid (IIG) which is a central Estate Investment Trusts (REITs), recapitalisation portal that coordinates and tracks the progress of of development financial institutions (DFIs) and all infrastructure projects. In addition to creation of development of social infrastructure. For financing infrastructure, there is also a focus on modernisation of existing set-up including airports and ports. With the developmental expenditure of PPP projects, the proposed integration of project monitoring India Infrastructure Project Development Fund group (PMG) and NIP portals with NIP as a point of (IIPDF) Scheme commenced in 2022. The central entering the data and PMG utilising the data, there government’s capital expenditure as a proportion of will be a substantial saving of time. The financing for GDP received a big push as it rose from an average 1.7 infrastructure is received from private, government per cent (2009-2020) to 3.1 per cent in 2024-25. and multilateral entities. Funding is also generated To reduce the cost structures and increase the from monetisation of assets through NMP which efficiency, the Gati Shakti and National Logistic Policy was initiated in 2021 for creation of assets through (NLP) were initiated. PM Gati Shakti framework monetisation by tapping investment from private envisages creation of an umbrella platform for all sector. Therein the assets are leased or licensed to infrastructure projects with a complete database. The entities in private sector for maintaining and operating “seven engines” in NIP viz., mass transport, ports, for certain period on basis of consideration. The logistic infrastructure, railways, roads, airports and consideration amounts are invested by government waterways would be integrated with PM Gati Shakti- for infrastructure creation. National Master Plan. National Logistics Policy (NLP) Various other new initiatives taken by was launched in 2022 to coordinate and converge the government for infrastructural growth include infrastructural activities of all government entities Indian Customs Electronic Data Interchange Gateway and have an integrated logistics framework. Its vision (ICEGATE), Window Interface for Trade (SWIFT), involves development of integrated, cost-effective and National Rail Plan, Ude Desh Ka Aam Nagrik (UDAN), resilient logistics ecosystem backed by technology. It RBI Bulletin September 2025 121ARTICLE Infrastructure - An Engine of India’s Growth Express is being executed through a Comprehensive Logistics proceeds would be utilised for reducing the carbon Action Plan (CLAP). It would boost the manpower intensity in the economy. skills, digital infrastructure and enhance the services. Digital Infrastructure The goals thereunder involve the reduction in In 2015, the government launched Digital India logistics cost to match international benchmarks by Programme (DIP) that provides high speed internet 2030 and improvement in India’s ranking in logistics for service delivery, private space on public cloud for performance index to achieve place in top 25 nations digital storage and sharing of documents, and unique by 2030. So far, it has reduced the cost by about 50 per digital identity to citizens. Telecommunication services cent (Virmani, 2023). The introduction of multi-modal connectivity for linking different modes of transport are provided in remote areas for connectivity and would facilitate the movements of people and goods provision of affordable services with Comprehensive among various types of transports. It would expand Telecom Development Plan (CTDP), National the reach of connectivity, reduce the cost and travel Frequency Allocation Plan (NFAP) and universal time. access to broadband services. With the e-Marketplace ‘MyScheme’, people can get information on suitable Greening of Economy government schemes. National Artificial Intelligence As per Article 48-A in Constitution on Portal (NAIP) has been developed to provide artificial commitment to environment preservation, and in intelligence (AI) ecosystem. With the dynamic digital view of the threats posed by global climate change, landscape, the standards for internet use like Open the government has taken several measures. In 2008, Credit Enablement Network (OCEN) for borrowing and National Action Plan on Climate Change (NAPCC) was lending modalities have been developed. The digital initiated for development of sustainable habitats, adoptions have been made all pervasive in various increasing energy efficiency, decreasing the intensity areas such as healthcare, education, financial services of emissions and expanding the area under forests. and agriculture. A wide gamut of services like direct National Adaptation Fund on Climate Change (NAFCC) benefit transfer (DBT) enabled by Jandhan Aadhar was set up in 2015 for sections and sectors vulnerable Mobile (JAM) trinity, national database for unorganised to climate change. Coalition for Disaster Resilient workers ‘e-Shram’ portal, Unified Payment Interface Infrastructure (CDRI) was established as an initiative (UPI), DigiLocker, MyGov, Co-Win, financial data towards disaster management system. Green Growth sharing with Account Aggregator Framework (AAF), Equity Fund (GGEF) was launched in 2018 for green digitisation of tax administration, Mission Drone infrastructure expenditures. For mobilising funds for Shakti, e-Rupee and Trade Receivables Discounting green infrastructure, in January 2023 the non-resident System (TReDS) indicate the major milestones in the investors were provided unrestricted access to certain journey of India’s digital infrastructure development. categories of central government securities including sovereign green bonds under fully accessible route With the thrust placed on development of (FAR). In 2022, government issued the Framework digital and communications infrastructure, an for Sovereign Green Bonds and accordingly the expansion was witnessed in terms of access, usage Reserve Bank notified in 2023 the issuance calendar and interoperability. The multiplier effect of digital for marketable Sovereign Green Bonds (SGBs) for infrastructure on growth has been very large due to mobilising finances for green infrastructure. The multiple tasks done through mobile phone, Aadhar- 122 RBI Bulletin September 2025Infrastructure - An Engine of India’s Growth Express ARTICLE based identification for grant of benefits and extension conducive conditions for sustainable and inclusive of financial, educational services and health facilities growth. Recent initiatives on these lines include to the unreached sections. There has been a progress National Education Policy (NEP), improvement in towards provision of services based on e-governance. school facilities, increasing the number of teachers, The new initiatives such as the innovations pertaining rise in proportion of public expenditure on health, to digital trade through Open Network for Digital increasing the number of primary and community Commerce (ONDC) and control access to user data health centres, doctors and health personnel, Ayushman Bharat scheme, expansion in coverage with AAF are expected to further strengthen the of health insurance, e-Sanjeevani for telemedicine, digital infrastructure landscape in near future. provision of adequate potable water, electricity, Social Infrastructure empowerment of women, and rural economic The agenda under United Nation’s Sustainable empowerment through measures like SVAMITVA for Development Goals adopted by India focuses on digital land records. elimination of poverty, inequality and creation of RBI Bulletin September 2025 123ARTICLE Infrastructure - An Engine of India’s Growth Express Annex B: Policy Measures on Infrastructure Financing 1. Considering the delays in execution of measures for deepening off-shore rupee bond infrastructure projects due to various reasons, market, establishment of reporting platforms the time and cost overruns have been allowed for transparency, operationalisation of delivery subject to stipulations. versus payment (DvP) settlement of over the counter (OTC) transactions to curb the risk, 2. Banks have been allowed to issue guarantees permitting repo in corporate bonds, permitting to other lenders for infrastructure projects if credit enhancement of corporate bonds for the former bear at least 5 per cent of the cost of infrastructure by banks and allowing credit projects and perform appraisal and monitoring default swaps (CDS) in corporate bonds for risk of credit. management. The corporate bond issuances have 3. Banks have been permitted to fund the equity of increased because of these measures. promoters in case of acquiring shares in company 7. As the finance providers are required to be executing infrastructure project in India. compensated for risk of construction at prior 4. Flexible structuring of lending has been stage, the measures such as permission to permitted to banks for alignment of cash flows set up infrastructure development funds with repayments. (IDFs) as mutual funds (MFs) and non-bank 5. Infrastructure bonds issuance by banks has been financial companies (NBFCs) for acquiring post permitted and the assets have been exempted construction assets from banks and ‘take out from propriety sector lending norms in addition financing’ have been taken. The increased role to exemption granted for funds from reserve of IDFs and InvITs would boost funding of requirements. Rupee denominated bonds in infrastructure. overseas capital markets (Masala Bonds) have 8. Consideration of debt due to the lenders of PPP also been allowed to be raised by banks. projects as secured, has been allowed to certain 6. As the fund raising for infrastructure would extent. require a developed bond market and as the latter 9. Since there are safeguards for infrastructure would reduce risk for banks, the Reserve Bank financing like escrow accounts, the provisioning has taken various measures for development of on sub-standard unsecured loan accounts in corporate bond market. These include permitting infrastructure has been reduced to 20 per cent, the issuance of long term bonds by banks provided banks have arrangements to escrow the coupled with cross holdings of a proportion of cash flows and have first claim thereon. primary issuance by them, allowing them to provide credit enhancement up to 50 per cent of 10. As the infrastructure sector has historically and the issuance of bond, simplification of issuance potentially large proportion of accumulating procedures for corporate bonds, standardisation stressed assets, banks have been provided of market conventions, allowing reissuances with several instruments to safeguard of corporate bonds for raising market liquidity, themselves against stressed assets such as increasing the limit for foreign portfolio flexible restructuring scheme with inclusion of investors (FPIs), reduction in withholding tax, infrastructure sectors like construction, extra 124 RBI Bulletin September 2025Infrastructure - An Engine of India’s Growth Express ARTICLE time for project completion and corrective action 13. Foreign portfolio investors (FPIs) were allowed to plan. invest in debt securities of Real Estate Investment Trusts (REITs) and InvITs. 11. Banks have been permitted to set longer repayment period for infrastructure loans but 14. Takeout financing has a significant role in provide funding for initial years or a shorter funding infrastructure as it enables banks to period and periodic refinancing through other fund long tenure projects with medium tenure banks or instruments, termed as 5/25 scheme. finances through reduction in their exposure and Such flexibilities however need to be judiciously better management of their assets and liabilities. utilised by the lenders without concealing the Accordingly, banks have been permitted to stress in assets. refinance project lending through partial takeout funding without prior agreement with other 12. Guidelines were issued in 2014 allowing banks financial institutions and set a longer period for to mobilise long term finances for infrastructure redemption. lending with minimum regulatory pre- emption like cash reserve ratio (CRR). In 2023 15. An Urban Infrastructure Development Fund the regulatory framework for IDF-NBFCs was (UIDF) has been set up by using the shortfall in reviewed by the Reserve Bank to facilitate their priority sector lending with an initial corpus of greater role and harmonising the regulations. ₹10,000 crore. RBI Bulletin September 2025 125CURRENT STATISTICS Select Economic Indicators Reserve Bank of India Money and Banking Prices and Production Government Accounts and Treasury Bills Financial Markets External Sector Payment and Settlement Systems Occasional SeriesCURRENT STATISTICS Contents No. Title Page 1 Select Economic Indicators 129 Reserve Bank of India 2 RBI – Liabilities and Assets 130 3 Liquidity Operations by RBI 131 4 Sale/ Purchase of U.S. Dollar by the RBI 132 4A Maturity Breakdown (by Residual Maturity) of Outstanding Forwards of RBI (US$ Million) 133 5 RBI's Standing Facilities 133 Money and Banking 6 Money Stock Measures 134 7 Sources of Money Stock (M) 135 3 8 Monetary Survey 136 9 Liquidity Aggregates 137 10 Reserve Bank of India Survey 138 11 Reserve Money – Components and Sources 138 12 Commercial Bank Survey 139 13 Scheduled Commercial Banks' Investments 139 14 Business in India – All Scheduled Banks and All Scheduled Commercial Banks 140 15 Deployment of Gross Bank Credit by Major Sectors 141 16 Industry-wise Deployment of Gross Bank Credit 142 17 State Co-operative Banks Maintaining Accounts with the Reserve Bank of India 143 Prices and Production 18 Consumer Price Index (Base: 2012=100) 144 19 Other Consumer Price Indices 144 20 Monthly Average Price of Gold and Silver in Mumbai 144 21 Wholesale Price Index 145 22 Index of Industrial Production (Base: 2011-12=100) 149 Government Accounts and Treasury Bills 23 Union Government Accounts at a Glance 149 24 Treasury Bills – Ownership Pattern 150 25 Auctions of Treasury Bills 150 Financial Markets 26 Daily Call Money Rates 151 27 Certificates of Deposit 152 28 Commercial Paper 152 29 Average Daily Turnover in Select Financial Markets 152 30 New Capital Issues by Non-Government Public Limited Companies 153 RBI Bulletin September 2025 127CURRENT STATISTICS No. Title Page External Sector 31 Foreign Trade 154 32 Foreign Exchange Reserves 154 33 Non-Resident Deposits 154 34 Foreign Investment Inflows 155 35 Outward Remittances under the Liberalised Remittance Scheme (LRS) for Resident Individuals 155 36 Indices of Nominal Effective Exchange Rate (NEER) and Real Effective Exchange Rate (REER) of the Indian Rupee 156 37 External Commercial Borrowings (ECBs) – Registrations 157 38 India’s Overall Balance of Payments (US $ Million) 158 39 India's Overall Balance of Payments (` Crore) 159 40 Standard Presentation of BoP in India as per BPM6 (US $ Million) 160 41 Standard Presentation of BoP in India as per BPM6 (` Crore) 161 42 India’s International Investment Position 162 Payment and Settlement Systems 43 Payment System Indicators 163 Occasional Series 44 Small Savings 165 45 Ownership Pattern of Central and State Governments Securities 166 46 Combined Receipts and Disbursements of the Central and State Governments 167 47 Financial Accommodation Availed by State Governments under various Facilities 168 48 Investments by State Governments 169 49 Market Borrowings of State Governments 170 50 (a) Flow of Financial Assets and Liabilities of Households - Instrument-wise 171 50 (b) Stocks of Financial Assets and Liabilities of Households- Select Indicators 174 Notes: .. = Not available. – = Nil/Negligible. P = Preliminary/Provisional. PR = Partially Revised. 128 RBI Bulletin September 2025CURRENT STATISTICS No. 1: Select Economic Indicators 2023-24 2024-25 2025-26 Item 2024-25 Q4 Q1 Q4 Q1 1 2 3 4 5 1 Real Sector (% Change) 1.1 GVA at Basic Prices 6.4 7.3 6.5 6.8 7.6 1.1.1 Agriculture 4.6 0.9 1.5 5.4 3.7 1.1.2 Industry 4.5 9.9 7.8 4.7 5.8 1.1.3 Services 7.5 8.0 7.2 7.9 9.0 1.1a Final Consumption Expenditure 6.5 6.3 7.0 4.7 7.1 1.1b Gross Fixed Capital Formation 7.1 6.0 6.7 9.4 7.8 2024 2025 2024-25 Jun. Jul. Jun. Jul. 1 2 3 4 5 1.2 Index of Industrial Production 4. 0 4. 9 5 . 0 1 . 5 3 . 5 2 Money and Banking (% Change) 2.1 Scheduled Commercial Banks 2.1.1 Deposits 10.3 11.1 10.6 10.1 10.2 2.1.2 Credit # 11.0 17.4 13.7 9.5 10.0 2.1.2.1 Non-food Credit # 11.0 17.4 13.7 9.3 9.9 2.1.3 Investment in Govt. Securities 9. 7 8. 6 8 . 1 8 . 8 6 . 6 2.2 Money Stock Measures 2.2.1 Reserve Money (M0) 4.3 7.4 7.2 4.9 4.7 2.2.2 Broad Money (M3) 9.4 10.1 9.7 9.5 9.6 3 Ratios (%) 3.1 Cash Reserve Ratio 4.00 4.50 4.50 4.00 4.00 3.2 Statutory Liquidity Ratio 18.00 18.00 18.00 18.00 18.00 3.3 Cash-Deposit Ratio 4.3 5.1 5.1 4.4 4.3 3.4 Credit-Deposit Ratio 80.8 79.3 79.3 78.9 79.2 3.5 Incremental Credit-Deposit Ratio # 86.1 56.0 53.1 28.7 33.5 3.6 Investment-Deposit Ratio 29.7 28.9 29.8 28.6 28.8 3.7 Incremental Investment-Deposit Ratio 28.1 6.4 28.8 -0.1 3.8 4 Interest Rates (%) 4.1 Policy Repo Rate 6.25 6.50 6.50 5.50 5.50 4.2 Fixed Reverse Repo Rate 3.35 3.35 3.35 3.35 3.35 4.3 Standing Deposit Facility (SDF) Rate * 6.00 6.25 6.25 5.25 5.25 4.4 Marginal Standing Facility (MSF) Rate 6.50 6.75 6.75 5.75 5.75 4.5 Bank Rate 6.50 6.75 6.75 5.75 5.75 4.6 Base Rate 9.10/10.40 9.10/10.40 9.10/10.40 9.10/10.30 8.50/10.30 4.7 MCLR (Overnight) 8.15/8.45 8.10/8.60 8.10/8.60 7.95/8.25 7.95/8.20 4.8 Term Deposit Rate >1 Year 6.00/7.25 6.00/7.30 6.00/7.30 5.85/6.70 5.85/6.70 4.9 Savings Deposit Rate 2.70/3.00 2.70/3.00 2.70/3.00 2.50/2.75 2.50/2.50 4.10 Call Money Rate (Weighted Average) 6.35 6.67 6.59 5.29 5.55 4.11 91-Day Treasury Bill (Primary) Yield 6.52 6.80 6.67 5.41 5.40 4.12 182-Day Treasury Bill (Primary) Yield 6.52 6.92 6.79 5.54 5.52 4.13 364-Day Treasury Bill (Primary) Yield 6.47 6.96 6.80 5.57 5.57 4.14 10-Year G-Sec Par Yield (FBIL) 6.62 7.04 6.97 6.34 6.41 5 Reference Rate and Forward Premia 5.1 INR-US$ Spot Rate (Rs. Per Foreign Currency) 85.5 8 83.4 5 83.7 3 85.5 6 86.5 2 5.2 INR-Euro Spot Rate (Rs. Per Foreign Currency) 92.32 89.25 90.86 100.20 101.73 5.3 Forward Premia of US$ 1-month (%) 3.12 1.10 1.11 1.65 1.81 3-month (%) 2.56 1.14 1.20 1.66 1.76 6-month (%) 2.28 1.26 1.43 1.79 1.85 6 Inflation (%) 6.1 All India Consumer Price Index 4.6 5.1 3.6 2.1 1.6 6.2 Consumer Price Index for Industrial Workers 3.39 3.7 2.1 2.5 2.7 6.3 Wholesale Price Index 2.3 3.4 2.1 -0.2 -0.6 6.3.1 Primary Articles 5.2 9.2 3.2 -3.2 -5.0 6.3.2 Fuel and Power -1.3 0.5 1.9 -3.1 -2.4 6.3.3 Manufactured Products 1.7 1.5 1.6 1.9 2.0 7 Foreign Trade (% Change) 7.1 Imports 6.2 4.6 11.2 -3.7 8.6 7.2 Exports 0. 1 2. 4 0 . 6 -0 . 1 7 . 3 Note : Financial Benchmark India Pvt. Ltd. (FBIL) has commenced publication of the G-Sec benchmarks with effect from March 31, 2018 as per RBI circularFMRD.DIRD. 7/14.03.025/2017-18 dated March 31, 2018. FBIL has started dissemination of reference rates w.e.f. July 10, 2018. #: Bank credit growth and related ratios for all fortnights from December 3, 2021 to November 18, 2022 are adjusted for past reporting errors by select scheduled commercial banks (SCBs). Data include the impact of merger of a non-bank with a bank w.e.f. July 1, 2023. *: As per Press Release No. 2022-2023/41 dated April 08, 2022. RBI Bulletin September 2025 129CURRENT STATISTICS Reserve Bank of India No. 2: RBI - Liabilities and Assets * (₹ Crore) Item As on the Last Friday/ Friday 2024-25 2024 2025 Aug. Aug. 01 Aug. 08 Aug. 15 Aug. 22 Aug. 29 1 2 3 4 5 6 7 1 Issue Department 1.1 Liabilities 1.1.1 Notes in Circulation 3683836 3458493 3753870 3779110 3788176 3773434 3763879 1.1.2 Notes held in Banking Department 11 14 17 17 16 14 13 1.1/1.2 Total Liabilities (Total Notes Issued) or Assets 3683847 3458507 3753886 3779127 3788192 3773448 3763892 1.2 Assets 1.2.1 Gold 235379 188699 260533 267638 265785 263628 271256 1.2.2 Foreign Securities 3448129 3269461 3492976 3511227 3522214 3509319 3492206 1.2.3 Rupee Coin 340 347 378 261 194 501 429 1.2.4 Government of India Rupee Securities - - - - - - - 2 Banking Department 2.1 Liabilities 2.1.1 Deposits 1709285 1702744 1797387 1748116 1732715 1739682 1737992 2.1.1.1 Central Government 100 100 100 101 100 100 101 2.1.1.2 Market Stabilisation Scheme - - - - - 2.1.1.3 State Governments 42 42 42 42 42 43 42 2.1.1.4 Scheduled Commercial Banks 943060 1019456 1001761 926802 954551 932900 959655 2.1.1.5 Scheduled State Co-operative Banks 7776 8019 8011 8472 8122 8011 8031 2.1.1.6 Non-Scheduled State Co-operative Banks 5963 5186 5256 5333 5188 5022 5120 2.1.1.7 Other Banks 46963 49701 47560 47715 47538 47994 48027 2.1.1.8 Others 593085 479121 635149 649765 601228 630623 583536 2.1.1.9 Financial Institution Outside India 112296 141119 99508 109887 115944 114989 133480 2.1.2 Other Liabilities 2150508 1917383 2257657 2347314 2349220 2303451 2399650 2.1/2.2 Total Liabilities or Assets 3859793 3620127 4055044 4095429 4081935 4043133 4137642 2.2 Assets 2.2.1 Notes and Coins 11 14 17 17 16 14 13 2.2.2 Balances Held Abroad 1413591 1790736 1631246 1639427 1639085 1618220 1691216 2.2.3 Loans and Advances 2.2.3.1 Central Government - - - - - - - 2.2.3.2 State Governments 26284 13381 32232 42792 34130 27463 19623 2.2.3.3 Scheduled Commercial Banks 251984 6968 1100 4352 1187 1818 1950 2.2.3.4 Scheduled State Co-op.Banks - - - - - - - 2.2.3.5 Industrial Dev. Bank of India - - - - - - - 2.2.3.6 NABARD - - - - - - - 2.2.3.7 EXIM Bank - - - - - - - 2.2.3.8 Others 36426 8547 10320 10361 11106 11006 10975 2.2.3.9 Financial Institution Outside India 111768 141402 99465 109553 115461 114364 132802 2.2.4 Bills Purchased and Discounted 2.2.4.1 Internal - - - - - - - 2.2.4.2 Government Treasury Bills - - - - - - - 2.2.5 Investments 1560630 1317280 1786267 1781635 1776673 1769940 1767243 2.2.6 Other Assets 459101 341800 494397 507292 504276 500308 513821 2.2.6.1 Gold 429510 330135 474804 487753 484376 480446 494347 * Data are provisional. 130 RBI Bulletin September 2025CURRENT STATISTICS No. 3: Liquidity Operations by RBI (₹ Crore) Date Standing OMO (Outright) Net Injection (+)/ Liquidity Absorption (-) Liquidity Adjustment Facility Facilities (1+3+5+7+9-2-4-6 -8) Sale Purchase Variable Variable Reverse Rate Repo Rate MSF SDF Repo Reverse Repo Repo 1 2 3 4 5 6 7 8 9 10 Jul. 1, 2025 - - - - 1233 255381 - - - -254148 Jul. 2, 2025 - - - - 3410 299291 -1030 - - -296911 Jul. 3, 2025 - - - - 1111 326770 - - - -325659 Jul. 4, 2025 - - - 100010 1282 332158 - - - -430886 Jul. 5, 2025 - - - - 216 285155 - - - -284939 Jul. 6, 2025 - - - - 339 250650 - - - -250311 Jul. 7, 2025 - - - - 1051 250865 -230 - - -250044 Jul. 8, 2025 - - - - 1072 214021 -240 - - -213189 Jul. 9, 2025 - - - 97315 1081 136036 -186 - - -232456 Jul. 10, 2025 - - - - 1078 124621 -45 - - -123588 Jul. 11, 2025 - - - 151633 1223 187091 365 - - -337136 Jul. 12, 2025 - - - - 85 127893 - - - -127808 Jul. 13, 2025 - - - - 81 132452 - - - -132371 Jul. 14, 2025 - - - - 838 116037 - - - -115199 Jul. 15, 2025 - - - 57450 869 97432 - - - -154013 Jul. 16, 2025 - - - - 879 109064 -18 - - -108203 Jul. 17, 2025 - - - - 808 106279 -5 - - -105476 Jul. 18, 2025 - - - 200027 951 116590 293 - - -315373 Jul. 19, 2025 - - - - 819 111162 - - - -110343 Jul. 20, 2025 - - - - 1179 103669 - - - -102490 Jul. 21, 2025 - - - - 10750 56852 361 - - -45741 Jul. 22, 2025 - - - - 13273 63745 2063 - - -48409 Jul. 23, 2025 - - 50001 - 820 78428 1829 - - -25778 Jul. 24, 2025 - - 1421 - 362 117991 - - - -116208 Jul. 25, 2025 - - - 125008 1906 174961 -104 - - -298167 Jul. 26, 2025 - - - - 484 119530 - - - -119046 Jul. 27, 2025 - - - - 484 121441 - - - -120957 Jul. 28, 2025 - - - - 938 109847 - - - -108909 Jul. 29, 2025 - - - 46058 1584 108957 - - - -153431 Jul. 30, 2025 - - - - 1408 94716 - - - -93308 Jul. 31, 2025 - - - 13075 1649 114195 - - - -125621 RBI Bulletin September 2025 131CURRENT STATISTICS No. 4: Sale/ Purchase of U.S. Dollar by the RBI i) Operations in onshore / offshore OTC segment Item 2024 2025 2024-25 Jul. Jun. Jul. 1 2 3 4 1 Net Purchase/ Sale of Foreign Currency (US $ Million) (1.1-1.2) -34511 6934 -3661 -2540 1.1 Purchase (+) 364200 23569 1164 0 1.2 Sale (–) 398711 16635 4825 2540 2 ₹ equivalent at contract rate (₹ Crores) -291233 57887 -31808 -22267 3 Cumulative (over end-March) (US $ Million) -34511 5402 -3557 -6097 (₹ Crore) -291233 44872 -31881 -54148 4 Outstanding Net Forward Sales (-)/ Purchase (+) at the end of month (US -84345 -9100 -60390 -57850 $ Million) ii) Operations in currency futures segment Item 2024 2025 2024-25 Jul. Jun. Jul. 1 2 3 4 1 Net Purchase/ Sale of Foreign Currency (US $ Million) (1.1-1.2) 0 0 0 0 1.1 Purchase (+) 31415 2144 0 0 1.2 Sale (–) 31415 2144 0 0 2 Outstanding Net Currency Futures Sales (-)/ Purchase (+) at the end of 0 -340 0 0 month (US $ Million) 132 RBI Bulletin September 2025CURRENT STATISTICS No. 4 A : Maturity Breakdown (by Residual Maturity) of Outstanding Forwards of RBI (US $ Million) Item As on July 31 , 2025 Long (+) Short (-) Net (1-2) 1 2 3 1. Upto 1 month 0 7695 -7695 2. More than 1 month and upto 3 months 0 10835 -10835 3. More than 3 months and upto 1 year 0 19220 -19220 4. More than 1 year 0 20100 -20100 Total (1+2+3+4) 0 57850 -57850 No. 5: RBI’s Standing Facilities (₹ Crore) Item As on the Last Reporting Friday 2024-25 2024 2025 Aug. 23 Mar. 21 Apr. 18 May. 30 Jun. 27 Jul. 25 Aug. 22 1 2 3 4 5 6 7 8 1 MSF 9961 1818 9961 2003 1540 1065 1906 1818 2 Export Credit Refinance for Scheduled Banks 2.1 Limit - - - - - - - - 2.2 Outstanding - - - - - - - - 3 Liquidity Facility for PDs 3.1 Limit 9900 9900 9900 14900 14900 14900 14900 14900 3.2 Outstanding 9517 8541 9517 7999 8595 7010 10299 10985 4 Others 4.1 Limit 76000 76000 76000 76000 76000 76000 76000 76000 4.2 Outstanding - - - - - - - - 5 Total Outstanding (1+2.2+3.2+4.2) 19478 10359 19478 10002 10135 8075 12205 12803 RBI Bulletin September 2025 133CURRENT STATISTICS Money and Banking No. 6: Money Stock Measures (₹ Crore) Item Outstanding as on March 31/last reporting Fridays of the month/ reporting Fridays 2024-25 2024 2025 Jul. 26 Jun. 27 Jul. 11 Jul. 25 1 2 3 4 5 1 Currency with the Public (1.1 + 1.2 + 1.3 – 1.4) 3630751 3426089 3722652 3729016 3707458 1.1 Notes in Circulation include CBDC-Retail (R) and CBDC-Wholesale (W). 3687816 3502874 3783575 3787633 3763742 1.2 Circulation of Rupee Coin 35889 33563 36909 36909 37314 1.3 Circulation of Small Coins 743 743 743 743 743 1.4 Cash on Hand with Banks includes CBDC-W. 93696 111091 98575 96269 94341 2 Deposit Money of the Public 2953329 2678638 3324072 3054960 3126308 2.1 Demand Deposits with Banks 2840023 2587413 3215375 2944828 3018666 2.2 'Other' Deposits with Reserve Bank 113307 91225 108697 110133 107642 3 M1 (1 + 2) 6584081 6104726 7046725 6783976 6833766 4 Post Office Saving Bank Deposits 212331 199017 212331 212331 212331 5 M2 (3 + 4) 6796412 6303743 7259056 6996307 7046097 6 Time Deposits with Banks 20702508 19568786 21183731 21357280 21306108 7 M3 (3 + 6) 27286589 25673513 28230456 28141256 28139873 8 Total Post Office Deposits 1443555 1361211 1443555 1443555 1443555 9 M4 (7 + 8) 28730144 27034724 29674011 29584811 29583428 134 RBI Bulletin September 2025CURRENT STATISTICS No. 7 : Sources of Money Stock (M) 3 (₹ Crore) Sources Outstanding as on March 31/last reporting Fridays of the month/reporting Fridays 2024-25 2024 2025 Jul. 26 Jun. 27 Jul. 11 Jul. 25 1 2 3 4 5 1 Net Bank Credit to Government 8510825 7678213 8520033 8614205 8541317 1.1 RBI’s net credit to Government (1.1.1–1.1.2) 1508105 1060910 1513361 1586327 1502150 1.1.1 Claims on Government 1591591 1341836 1806229 1808369 1810116 1.1.1.1 Central Government 1558903 1320596 1786164 1787938 1786091 1.1.1.2 State Governments 32688 21239 20066 20431 24026 1.1.2 Government deposits with RBI 83485 280926 292869 222042 307966 1.1.2.1 Central Government 83443 280883 292826 221999 307924 1.1.2.2 State Governments 42 42 42 42 43 1.2 Other Banks’ Credit to Government 7002720 6617302 7006672 7027878 7039167 2 Bank Credit to Commercial Sector 19068129 17577818 19283371 19266495 19300981 2.1 RBI’s credit to commercial sector 38246 10935 9029 7965 12383 2.2 Other banks’ credit to commercial sector 19029883 17566883 19274342 19258530 19288597 2.2.1 Bank credit by commercial banks 18243972 16813935 18486200 18469893 18501377 2.2.2 Bank credit by co-operative banks 766659 733892 769075 767978 767165 2.2.3 Investments by commercial and co-operative banks in other securities 19252 19056 19067 20660 20056 3 Net Foreign Exchange Assets of Banking Sector (3.1 + 3.2) 6148527 5800754 6437976 6406286 6467260 3.1 RBIs net foreign exchange assets (3.1.1 - 3.1.2) 5550947 5434047 5840396 5808706 5869680 3.1.1 Gross foreign assets 5550956 5434044 5840398 5808708 5869677 3.1.2 Foreign liabilities 9 -2 2 2 -3 3.2 Other banks’ net foreign exchange assets 597580 366707 597580 597580 597580 4 Government’s Currency Liabilities to the Public 36632 34306 37652 37652 38057 5 Banking Sector’s Net Non-monetary Liabilities 6477524 5417577 6048576 6183381 6207741 5.1 Net non-monetary liabilities of RBI 2147427 1718640 2168644 2176784 2235868 5.2 Net non-monetary liabilities of other banks (residual) 4330098 3698937 3879932 4006598 3971873 M₃(1+2+3+4–5) 27286589 25673513 28230456 28141256 28139873 RBI Bulletin September 2025 135CURRENT STATISTICS No. 8: Monetary Survey (₹ Crore) Item Outstanding as on March 31/last reporting Fridays of the month/reporting Fridays 2024-25 2024 2025 Jul. 26 Jun. 27 Jul. 11 Jul. 25 1 2 3 4 5 Monetary Aggregates NM₁ (1.1+1.2.1+1.3) 6584081 6104726 7046725 6783976 6833766 NM₂ (NM₁ + 1.2.2.1) 15768688 14799222 16446990 16262123 16288486 NM₃ (NM₂ +1.2.2.2 + 1.4 = 2.1 + 2.2 + 2.3 – 2.4 – 2.5) 27909568 26361016 28786422 28701812 28681808 1 Components 1.1 Currency with the Public 3630751 3426089 3722652 3729016 3707458 1.2 Aggregate Deposits of Residents 23250261 21908513 24104854 24007378 24029154 1.2.1 Demand Deposits 2840023 2587413 3215375 2944828 3018666 1.2.2 Time Deposits of Residents 20410239 19321100 20889478 21062550 21010489 1.2.2.1 Short-term Time Deposits 9184607 8694495 9400265 9478148 9454720 1.2.2.1.1 Certificates of Deposits (CDs) 527375 420069 516691 528946 507798 1.2.2.2 Long-term Time Deposits 11225631 10626605 11489213 11584403 11555769 1.3 'Other' Deposits with RBI 113307 91225 108697 110133 107642 1.4 Call/Term Funding from Financial Institutions 915248 935190 850218 855286 837554 2 Sources 2.1 Domestic Credit 28802443 26384962 29048840 29141826 29125727 2.1.1 Net Bank Credit to the Government 8510825 7678213 8520033 8614205 8541317 2.1.1.1 Net RBI credit to the Government 1508105 1060910 1513361 1586327 1502150 2.1.1.2 Credit to the Government by the Banking System 7002720 6617302 7006672 7027878 7039167 2.1.2 Bank Credit to the Commercial Sector 20291618 18706750 20528808 20527622 20584410 2.1.2.1 RBI Credit to the Commercial Sector 38246 10935 9029 7965 12383 2.1.2.2 Credit to the Commercial Sector by the Banking System 20253372 18695815 20519778 20519657 20572027 2.1.2.2.1 Other Investments ( Non-SLR Securities) 1208294 1112229 1208081 1241827 1243910 2.2 Government's Currency Liabilities to the Public 36632 34306 37652 37652 38057 2.3 Net Foreign Exchange Assets of the Banking Sector 5605462 5304425 5954722 5880727 5943463 2.3.1 Net Foreign Exchange Assets of the RBI 5550947 5434047 5840396 5808706 5869680 2.3.2 Net Foreign Currency Assets of the Banking System 54514 -129622 114326 72021 73784 2.4 Capital Account 4481192 4317778 5040017 5048368 5127183 2.5 Other items (net) 2053777 1044899 1214776 1310026 1298257 136 RBI Bulletin September 2025CURRENT STATISTICS No. 9: Liquidity Aggregates (₹ Crore) 2024-25 2024 2025 Aggregates Jul. May Jun. Jul. 1 2 3 4 5 1 NM₃ 27896780 26361016 28594932 28786422 28681808 2 Postal Deposits 756786 720419 756786 756786 756786 3 L₁ ( 1 + 2) 28653566 27081435 29351718 29543208 29438594 4 Liabilities of Financial Institutions 95148 68324 116492 113786 113786 4.1 Term Money Borrowings 10 748 4 5 5 4.2 Certificates of Deposit 80810 54670 101755 98755 98755 4.3 Term Deposits 14328 12905 14733 15026 15027 5 L₂ (3 + 4) 28748714 27149759 29468210 29656993 29552381 6 Public Deposits with Non-Banking Financial Companies 121178 .. .. 129567 .. 7 L₃ (5 + 6) 28869892 .. .. 29786560 .. Note : F igures in the columns might not add up to the total due to rounding off of numbers. RBI Bulletin September 2025 137CURRENT STATISTICS No. 10: Reserve Bank of India Survey (₹ Crore) Item Outstanding as on March 31/last reporting Fridays of the month/reporting Fridays 2024-25 2024 2025 Jul. 26 Jun. 27 Jul. 11 Jul. 25 1 2 3 4 5 1 Components 1.1 Currency in Circulation 3724448 3537180 3821227 3825285 3801799 1.2 Bankers’ Deposits with the RBI 991488 1039059 994142 990947 978898 1.2.1 Scheduled Commercial Banks 926001 976073 933483 930199 918229 1.3 ‘Other’ Deposits with the RBI 113307 91225 108697 110133 107642 Reserve Money (1.1 + 1.2 + 1.3 = 2.1 + 2.2 + 2.3 – 2.4 – 2.5) 4829243 4667464 4924066 4926365 4888339 2 Sources 2.1 RBI’s Domestic Credit 1389090 917751 1214662 1256791 1216470 2.1.1 Net RBI credit to the Government 1508105 1060910 1513361 1586327 1502150 2.1.1.1 Net RBI credit to the Central Government (2.1.1.1.1 + 2.1.1.1.2 + 2.1.1.1.3 + 2.1.1.1.4 – 2.1.1.1.5) 1475460 1039713 1493338 1565939 1478167 2.1.1.1.1 Loans and Advances to the Central Government - - - - - 2.1.1.1.2 Investments in Treasury Bills - - - - - 2.1.1.1.3 Investments in dated Government Securities 1558574 1320373 1785715 1787655 1785639 2.1.1.1.3.1 Central Government Securities 1558574 1320373 1785715 1787655 1785639 2.1.1.1.4 Rupee Coins 329 223 449 282 451 2.1.1.1.5 Deposits of the Central Government 83443 280883 292826 221999 307924 2.1.1.2 Net RBI credit to State Governments 32646 21197 20023 20388 23983 2.1.2 RBI’s Claims on Banks -157261 -154094 -307728 -337501 -298063 2.1.2.1 Loans and Advances to Scheduled Commercial Banks -157261 -154094 -307728 -337501 -298063 2.1.3 RBI’s Credit to Commercial Sector 38246 10935 9029 7965 12383 2.1.3.1 Loans and Advances to Primary Dealers 9182 9062 7010 5881 10299 2.1.3.2 Loans and Advances to NABARD - - - - - 2.2 Government’s Currency Liabilities to the Public 36632 34306 37652 37652 38057 2.3 Net Foreign Exchange Assets of the RBI 5550947 5434047 5840396 5808706 5869680 2.3.1 Gold 668162 483062 722374 723791 741528 2.3.2 Foreign Currency Assets 4882794 4950982 5118023 5084916 5128149 2.4 Capital Account 1875114 1774642 2127550 2127699 2179408 2.5 Other Items (net) 272313 -56002 41094 49084 56460 No. 11: Reserve Money - Components and Sources (₹ Crore) Item Outstanding as on March 31/last Fridays of the month/Fridays 2024-25 2024 2025 Jul. 26 Jun. 27 Jul. 4 Jul. 11 Jul. 18 Jul. 25 1 2 3 4 5 6 7 Reserve Money (1.1 + 1.2 + 1.3 = 2.1 + 2.2 + 2.3 + 2.4 + 2.5 – 2.6) 4829243 4667464 4924066 4923222 4926365 4964256 4888339 1 Components 1.1 Currency in Circulation 3724448 3537180 3821227 3821421 3825285 3811252 3801799 1.2 Bankers' Deposits with RBI 991488 1039059 994142 992989 990947 1045162 978898 1.3 ‘Other’ Deposits with RBI 113307 91225 108697 108812 110133 107843 107642 2 Sources 2.1 Net Reserve Bank Credit to Government 1508105 1060910 1513361 1673148 1586327 1603965 1502150 2.2 Reserve Bank Credit to Banks -157261 -154094 -307728 -430882 -337501 -315666 -298063 2.3 Reserve Bank Credit to Commercial Sector 38246 10935 9029 8297 7965 8235 12383 2.4 Net Foreign Exchange Assets of RBI 5550947 5434047 5840396 5806062 5808706 5823240 5869680 2.5 Government's Currency Liabilities to the Public 36632 34306 37652 37652 37652 37652 38057 2.6 Net Non- Monetary Liabilities of RBI 2147427 1718640 2168644 2171055 2176784 2193169 2235868 138 RBI Bulletin September 2025CURRENT STATISTICS No. 12: Commercial Bank Survey (₹ Crore) Item Outstanding as on last reporting Fridays of the month/ reporting Fridays of the month 2024-25 2024 2025 Jul. 26 Jun. 27 Jul. 11 Jul. 25 1 2 3 4 5 1 Components 1.1 Aggregate Deposits of Residents 22288331 20945882 23131393 23030976 23054208 1.1.1 Demand Deposits 2698049 2444220 3072874 2802034 2876368 1.1.2 Time Deposits of Residents 19590283 18501662 20058519 20228941 20177840 1.1.2.1 Short-term Time Deposits 8788876 8288307 9002651 9079791 9057108 1.1.2.1.1 Certificates of Deposits (CDs) 527375 420069 516691 528946 507798 1.1.2.2 Long-term Time Deposits 10741960 10130153 11003241 11097523 11069799 1.2 Call/Term Funding from Financial Institutions 915248 935190 850218 855286 837554 2 Sources 2.1 Domestic Credit 26156690 24247935 26419739 26438568 26502935 2.1.1 Credit to the Government 6697298 6312657 6696348 6715694 6726284 2.1.2 Credit to the Commercial Sector 19459392 17935278 19723391 19722874 19776651 2.1.2.1 Bank Credit 18243972 16813935 18486200 18469893 18501377 2.1.2.1.1 Non-food Credit 18207441 16785745 18421811 18410900 18444703 2.1.2.2 Net Credit to Primary Dealers 15458 16966 37618 19563 39782 2.1.2.3 Investments in Other Approved Securities 630 1111 454 554 544 2.1.2.4 Other Investments (in non-SLR Securities) 1199332 1103267 1199119 1232864 1234948 2.2 Net Foreign Currency Assets of Commercial Banks (2.2.1-2.2.2-2.2.3) 54514 -129622 114326 72021 73784 2.2.1 Foreign Currency Assets 529621 284197 577466 524521 526520 2.2.2 Non-resident Foreign Currency Repatriable Fixed Deposits 292270 247686 294252 294730 295619 2.2.3 Overseas Foreign Currency Borrowings 182837 166133 168887 157771 157117 2.3 Net Bank Reserves (2.3.1+2.3.2-2.3.3) 791777 1229518 1327882 1352241 1298767 2.3.1 Balances with the RBI 882415 976073 933483 930199 918229 2.3.2 Cash in Hand 81874 99350 86671 84541 82475 2.3.3 Loans and Advances from the RBI 172512 -154094 -307728 -337501 -298063 2.4 Capital Account 2581908 2518965 2888296 2896498 2923604 2.5 Other items (net) (2.1+2.2+2.3-2.4-1.1-1.2) 807812 472008 603594 698238 685360 2.5.1 Other Demand and Time Liabilities (net of 2.2.3) 878795 732752 877929 814152 832554 2.5.2 Net Inter-Bank Liabilities (other than to PDs) 118268 149498 135597 105815 140516 Figures in parentheses include the impact of merger of a non-bank with a bank. No. 13: Scheduled Commercial Banks’ Investments (₹ Crore) Item As on 2024 2025 March 21, 2025 Jul. 26 Jun. 27 Jul. 11 Jul. 25 1 2 3 4 5 1 SLR Securities 6697928 6313767 6696802 6716248 6726828 2 Other Government Securities (Non-SLR) 165500 157537 160012 160009 160190 3 Commercial Paper 63163 52091 63802 61337 63539 4 Shares issued by 4.1 PSUs 13874 12950 13835 15651 14823 4.2 Private Corporate Sector 95984 93831 98318 96026 97826 4.3 Others 7664 7367 7764 8017 7715 5 Bonds/Debentures issued by 5.1 PSUs 130308 121092 145962 139677 132293 5.2 Private Corporate Sector 248138 242161 260094 241944 245757 5.3 Others 150000 138806 152384 159471 157888 6 Instruments issued by 6.1 Mutual funds 119867 96391 91749 131978 138862 6.2 Financial institutions 204865 181041 205220 218755 216055 Note: 1. Data against column Nos. (1), (2) & (3) are Final and for column Nos. (4) & (5) data are Provisional. 2. Data include the impact of merger of a non bank with a bank w.e.f. July 1, 2023. RBI Bulletin September 2025 139CURRENT STATISTICS No. 14: Business in India - All Scheduled Banks and All Scheduled Commercial Banks (₹ Crore) Item As on the Last Reporting Friday (in case of March)/ Last Friday All Scheduled Banks All Scheduled Commercial Banks 2024 2025 2024 2025 2024-25 2024-25 Jul. Jun. Jul. Jul. Jun. Jul. 1 2 3 4 5 6 7 8 Number of Reporting Banks 208 208 194 195 135 135 120 121 1 Liabilities to the Banking System 458011 500022 502739 476008 451305 495646 496493 469955 1.1 Demand and Time Deposits from Banks 315675 285475 376910 347964 309414 281386 371106 342422 1.2 Borrowings from Banks 112027 138779 100647 105216 111976 138752 100641 105206 1.3 Other Demand and Time Liabilities 30310 75768 25182 22828 29916 75507 24746 22327 2 Liabilities to Others 25053097 23505340 25831875 25688230 24557481 23027726 25322680 25177053 2.1 Aggregate Deposits 23055487 21654100 23916476 23843715 22580601 21193568 23425645 23349827 2.1.1 Demand 2748263 2493596 3121710 2925734 2698049 2444220 3072874 2876368 2.1.2 Time 20307224 19160504 20794766 20917980 19882552 18749348 20352771 20473459 2.2 Borrowings 920568 939516 854691 841980 915248 935190 850218 837554 2.3 Other Demand and Time Liabilities 1077042 911725 1060708 1002536 1061632 898968 1046816 989672 3 Borrowings from Reserve Bank 311466 7161 1065 1906 311466 7161 1065 1906 3.1 Against Usance Bills /Promissory Notes - - - - - - - - 3.2 Others 311466 7161 1065 1906 311466 7161 1065 1906 4 Cash in Hand and Balances with Reserve Bank 985044 1097637 1041975 1022191 964289 1075424 1020154 1000704 4.1 Cash in Hand 84399 101799 89278 85044 81874 99350 86671 82475 4.2 Balances with Reserve Bank 900645 995838 952697 937147 882415 976073 933483 918229 5 Assets with the Banking System 432645 432441 490913 458151 348496 363113 398514 369221 5.1 Balances with Other Banks 273720 242359 340579 327705 215801 193127 273831 264197 5.1.1 In Current Account 13239 12352 25103 15890 10619 9487 22785 13780 5.1.2 In Other Accounts 260481 230007 315476 311815 205182 183640 251046 250417 5.2 Money at Call and Short Notice 44772 29827 42285 36664 25838 13930 24041 17911 5.3 Advances to Banks 43856 42711 33677 28849 39504 41613 31558 27214 5.4 Other Assets 70296 117544 74373 64933 67353 114443 69084 59900 6 Investment 6850574 6466548 6854341 6887824 6697928 6313767 6696802 6726828 6.1 Government Securities 6842024 6458194 6845977 6878471 6697298 6312657 6696348 6726284 6.2 Other Approved Securities 8550 8354 8365 9353 630 1111 454 544 7 Bank Credit 18708286 17254613 18956024 18968340 18243972 16813935 18486200 18501377 7a Food Credit 87145 78811 116363 108648 36531 28190 64389 56674 7.1 Loans, Cash-credits and Overdrafts 18370704 16938015 18613283 18627704 17909851 16500433 18146380 18163165 7.2 Inland Bills-Purchased 76523 68987 80511 77657 74963 67638 79560 77094 7.3 Inland Bills-Discounted 222320 208216 225234 226698 221059 207073 223845 225406 7.4 Foreign Bills-Purchased 15357 16061 14007 13538 15122 15852 13787 13316 7.5 Foreign Bills-Discounted 23382 23334 22988 22743 22977 22940 22627 22395 Note: Data in column Nos. (4) & (8) are Provisional Data include the impact of merger of a non-bank with a bank w.e.f. July 1, 2023. 140 RBI Bulletin September 2025CURRENT STATISTICS No. 15: Deployment of Gross Bank Credit by Major Sectors (₹ Crore) Outstanding as on Growth(%) Mar. 21, Financial Sector 2025 2024 2025 year so far Y-o-Y Jul. 26 Jun. 27 Jul. 25 2025-26 2025 1 2 3 4 % % I. Bank Credit (II + III) 18243936 16814792 18483098 18501872 1.4 10.0 II. Food Credit 36531 28190 64389 56674 55.1 101.0 III. Non-food Credit 18207404 16786602 18418709 18445197 1.3 9.9 1. Agriculture & Allied Activities 2287071 2156320 2305993 2313845 1.2 7.3 2. Industry (Micro and Small, Medium and Large) 3937149 3724547 3947139 3947778 0.3 6.0 2.1 Micro and Small 791721 729948 872577 883101 11.5 21.0 2.2 Medium 360475 317323 357618 363994 1.0 14.7 2.3 Large 2784953 2677277 2716943 2700683 -3.0 0.9 3. Services 5161462 4623910 5140226 5113966 -0.9 10.6 3.1 Transport Operators 258409 244262 266029 265924 2.9 8.9 3.2 Computer Software 32915 27356 36232 36579 11.1 33.7 3.3 Tourism, Hotels & Restaurants 83091 79202 84781 85458 2.8 7.9 3.4 Shipping 7305 7076 8042 8727 19.5 23.3 3.5 Aviation 46026 44637 50750 45213 -1.8 1.3 3.6 Professional Services 195956 171393 196406 194935 -0.5 13.7 3.7 Trade 1186787 1043753 1174129 1179121 -0.6 13.0 3.7.1. Wholesale Trade¹ 648619 545170 629888 637065 -1.8 16.9 3.7.2 Retail Trade 538168 498582 544241 542056 0.7 8.7 3.8 Commercial Real Estate 532757 484531 555519 560514 5.2 15.7 3.9 Non-Banking Financial Companies (NBFCs)² of which, 1636098 1528856 1596490 1568925 -4.1 2.6 3.9.1 Housing Finance Companies (HFCs) 323146 322052 331346 315767 -2.3 -2.0 3.9.2 Public Financial Institutions (PFIs) 228678 202991 211702 198406 -13.2 -2.3 3.10 Other Services³ 1182118 992845 1171848 1168570 -1.1 17.7 4. Personal Loans 5952299 5507740 6126535 6161047 3.5 11.9 4.1 Consumer Durables 23402 24606 23376 23114 -1.2 -6.1 4.2 Housing 3010477 2810108 3067120 3081152 2.3 9.6 4.3 Advances against Fixed Deposits 141101 120373 145540 140433 -0.5 16.7 4.4 Advances to Individuals against share & bonds 10080 9422 9886 9730 -3.5 3.3 4.5 Credit Card Outstanding 284366 275601 292602 291088 2.4 5.6 4.6 Education 137456 123066 139587 141537 3.0 15.0 4.7 Vehicle Loans 622794 590981 642866 643654 3.3 8.9 4.8 Loan against gold jewellery⁴ 208735 132535 277025 294166 40.9 122.0 4.9 Other Personal Loans 1513889 1421047 1528534 1536172 1.5 8.1 5. Priority Sector (Memo) (i) Agriculture & Allied Activities⁵ 2287804 2196939 2274259 2288548 0.0 4.2 (ii) Micro & Small Enterprises⁶ 2240503 1998597 2460012 2489085 11.1 24.5 (iii) Medium Enterprises⁷ 601451 511874 591992 596343 -0.8 16.5 (iv) Housing 746651 748840 856626 940427 26.0 25.6 (v) Education Loans 62825 61523 64355 68839 9.6 11.9 (vi) Renewable Energy 10325 7075 12563 12160 17.8 71.9 (vii) Social Infrastructure 1316 2937 828 943 -28.4 -67.9 (viii) Export Credit 12479 12298 13047 12875 3.2 4.7 (ix) Others 47900 58548 46467 44331 -7.5 -24.3 (x) Weaker Sections including net PSLC- SF/MF 1820904 1743686 1825579 1842667 1.2 5.7 Notes: (1) Data are provisional. Bank credit, Food credit and Non-food credit data are based on Section-42 return, which covers all scheduled commercial banks (SCBs), while sectoral non-food credit data are based on sector-wise and industry-wise bank credit (SIBC) return, which covers select banks accounting for about 95 per cent of total non-food credit extended by all SCBs, pertaining to the last reporting Friday of the month. (2) Data since July 28, 2023 include the impact of the merger of a non-bank with a bank. 1 Wholesale trade includes food procurement credit outside the food credit consortium. 2 NBFCs include HFCs, PFIs, Microfinance Institutions (MFIs), NBFCs engaged in gold loan and others. 3 “Other Services” include Mutual Fund (MFs), Banking and Finance other than NBFCs and MFs, and other services which are not indicated elsewhere under services. 4 Since May 2024, a bank has changed the classification of a category of agricultural loan into “Loans against gold jewellery” under retail segment. 5 “Agriculture and Allied Activities” under the priority sector also include priority sector lending certificates (PSLCs). 6 “Micro and Small Enterprises” under the priority sector include credit to micro and small enterprises in industry and services sectors and also include PSLCs. 7 “Medium Enterprises” under the priority sector include credit to medium enterprises in industry and services sectors. RBI Bulletin September 2025 141CURRENT STATISTICS No. 16: Industry-wise Deployment of Gross Bank Credit (₹ Crore) Outstanding as on Growth(%) Financial 2024 2025 Y-o-Y Mar. 21, year so far Industry 2025 Jul. 26 Jun. 27 Jul. 25 2025-26 2025 1 2 3 4 % % 2 Industries (2.1 to 2.19) 3937149 3724547 3947139 3947778 0.3 6.0 2.1 Mining & Quarrying (incl. Coal) 56756 54719 56945 54751 -3.5 0.1 2.2 Food Processing 219527 205744 223136 216389 -1.4 5.2 2.2.1 Sugar 28522 22622 22862 20158 -29.3 -10.9 2.2.2 Edible Oils & Vanaspati 20927 18179 21342 21048 0.6 15.8 2.2.3 Tea 5084 6058 4950 4925 -3.1 -18.7 2.2.4 Others 164994 158886 173983 170258 3.2 7.2 2.3 Beverage & Tobacco 35513 30470 34636 34731 -2.2 14.0 2.4 Textiles 277267 255111 277281 270465 -2.5 6.0 2.4.1 Cotton Textiles 107227 94890 105615 98267 -8.4 3.6 2.4.2 Jute Textiles 4288 4125 4411 4329 1.0 4.9 2.4.3 Man-Made Textiles 49091 45754 49102 48545 -1.1 6.1 2.4.4 Other Textiles 116661 110341 118153 119324 2.3 8.1 2.5 Leather & Leather Products 12980 12548 13207 13385 3.1 6.7 2.6 Wood & Wood Products 27826 24458 28303 28053 0.8 14.7 2.7 Paper & Paper Products 52848 47826 52731 52961 0.2 10.7 2.8 Petroleum, Coal Products & Nuclear Fuels 154178 136958 154602 157907 2.4 15.3 2.9 Chemicals & Chemical Products 267814 254881 271055 268991 0.4 5.5 2.9.1 Fertiliser 32011 34891 31958 29718 -7.2 -14.8 2.9.2 Drugs & Pharmaceuticals 88738 82308 86613 86835 -2.1 5.5 2.9.3 Petro Chemicals 26892 27879 31955 30494 13.4 9.4 2.9.4 Others 120172 109803 120529 121944 1.5 11.1 2.10 Rubber, Plastic & their Products 103464 89581 102610 102513 -0.9 14.4 2.11 Glass & Glassware 13443 12431 13243 12920 -3.9 3.9 2.12 Cement & Cement Products 59752 60728 59183 59668 -0.1 -1.7 2.13 Basic Metal & Metal Product 433502 402716 442055 440996 1.7 9.5 2.13.1 Iron & Steel 300156 285055 301600 298486 -0.6 4.7 2.13.2 Other Metal & Metal Product 133345 117661 140455 142509 6.9 21.1 2.14 All Engineering 240135 204685 248780 252030 5.0 23.1 2.14.1 Electronics 52862 45156 55370 57446 8.7 27.2 2.14.2 Others 187272 159529 193411 194585 3.9 22.0 2.15 Vehicles, Vehicle Parts & Transport Equipment 119057 109189 121183 121116 1.7 10.9 2.16 Gems & Jewellery 85734 82940 88818 91482 6.7 10.3 2.17 Construction 150701 141194 150855 146725 -2.6 3.9 2.18 Infrastructure 1322831 1301135 1316796 1325756 0.2 1.9 2.18.1 Power 682953 636926 695401 704850 3.2 10.7 2.18.2 Telecommunications 118940 129614 104278 106891 -10.1 -17.5 2.18.3 Roads 311219 327581 316840 316260 1.6 -3.5 2.18.4 Airports 9156 8004 9355 8296 -9.4 3.7 2.18.5 Ports 5916 6331 5530 5505 -6.9 -13.0 2.18.6 Railways 13595 11835 11510 11457 -15.7 -3.2 2.18.7 Other Infrastructure 181052 180844 173882 172498 -4.7 -4.6 2.19 Other Industries 303822 297232 291720 296940 -2.3 -0.1 Note: (1) Data since July 28, 2023 include the impact of the merger of a non-bank with a bank. 142 RBI Bulletin September 2025CURRENT STATISTICS No. 17: State Co-operative Banks Maintaining Accounts with the Reserve Bank of India (₹ Crore) Last Reporting Friday (in case of March)/Last Friday/ Item Reporting Friday 2024 2025 2024-25 Jun. 28 Apr. 18 Apr. 25 May 02 May 16 May 30 Jun. 13 Jun. 27 1 2 3 4 5 6 7 8 9 Number of Reporting Banks 34 33 34 34 34 34 34 34 34 1 Aggregate Deposits (2.1.1.2+2.2.1.2) 146871.0 133938.0 145054.5 147251.7 147608.7 147866.9 145985.2 147828.9 147839.5 2 Demand and Time Liabilities 2.1 Demand Liabilities 2921 5.6 27801.7 27277.2 26936.5 28452.9 27298.2 26 758.2 2652 9.6 26248 .7 2.1.1 Deposits 2.1.1.1 Inter-Bank 9022.9 7904.7 8714.1 8298.2 8119.3 8033.8 7428.2 7289.6 6767.4 2.1.1.2 Others 14063.9 14567 .8 13668.7 14069.6 14316.8 13861.7 11836.7 13791.0 13170.9 2.1.2 Borrowings from Banks 700.0 350.0 1289.0 824.2 2912.2 721.2 1543.3 2.1.3 Other Demand Liabilities 5428.9 5329.2 4544.4 4568.8 4727.9 4578.4 4581.2 4727.8 4767.0 2.2 Time Liabilities 201100.7 185708.9 199471.9 199412.2 199704.6 200375.1 199917.5 199176.8 199275.4 2.2.1 Deposits 2.2.1.1 Inter-Bank 66874.3 64501.4 66627.7 64779.7 64977.2 64945.2 64334.4 63644.4 63111.1 2.2.1.2 Others 132807.1 119370.2 131385.8 133182.1 133291.9 134005.2 134148.5 134037.9 134668.6 2.2.2 Borrowings from Banks 643.9 653.2 615.5 615.5 615.5 615.5 615.5 615.5 615.5 2.2.3 Other Time Liabilities 775.4 1184.1 842.9 834.9 820.0 809.2 819.0 878.9 880.3 3 Borrowing from Reserve Bank 699.5 499.9 499.8 499.8 499.8 499.8 499.8 499.8 4 Borrowings from a notified bank / Government 126928.5 85281.4 120340.2 117224.0 113687.2 112391.9 113039.0 113368.7 113728.9 4.1 Demand 53459.8 23887.4 50684.0 50291.4 48334.5 47731.0 47805.0 48429.0 48853.6 4.2 Time 73468.7 61394.0 69656.2 66932.6 65352.6 64660.9 65234.0 64939.6 64875.3 5 Cash in Hand and Balances with Reserve Bank 13390.9 13323.7 15967.2 19115.8 12935.0 15919.7 16813.3 14110.1 23560.3 5.1 Cash in Hand 1052.1 759.4 813.7 741.3 970.1 756.2 772.5 824.3 774.2 5.2 Balance with Reserve Bank 12338.8 12564.3 15153.5 18374.6 11964.9 15163.5 16040.7 13285.8 22786.0 6 Balances with Other Banks in Current Account 1656.3 1631.9 1856.2 1487.3 1306.3 1197.9 1102.6 1230.4 1132.7 7 Investments in Government Securities 77220.1 75500.4 79265.3 78742.6 78309.8 79425.0 79798.1 80061.3 80872.4 8 Money at Call and Short Notice 26531.1 20740.5 22162.6 20185.1 22926.3 53472.9 21442.9 18248.3 19854.6 9 Bank Credit (10.1+11) 174828.8 134324.1 174573.0 185733.8 173379.6 173468.6 173065.3 173173.8 171391.3 10 Advances 10.1 Loans, Cash-Credits and Overdrafts 174590.4 134111.9 174312.5 185468.1 173105.4 173203.9 172775.8 172882.6 171119.8 10.2 Due from Banks 12460 7.6 135046.8 119426.7 118050.3 116990.1 1 16484.5 116 407.6 11647 6.4 117780. 4 11 Bills Purchased and Discounted 238.4 212.2 260.5 265.6 274.2 264.7 289.5 291.2 271.5 RBI Bulletin September 2025 143CURRENT STATISTICS Prices and Production No. 18: Consumer Price Index (Base: 2012=100) Group/Sub group 2024-25 Rural Urban Combined Rural Urban Combined Aug.24 Jul.25 Aug.25 (P) Aug.24 Jul.25 Aug.25 (P) Aug.24 Jul.25 Aug.25 (P) 1 2 3 4 5 6 7 8 9 10 11 12 1 Food and beverages 198.6 205.3 201.1 200.2 198.4 199.9 207.1 207.0 207.8 202.7 201.6 202.8 1.1 Cereals and products 195.0 193.7 194.6 192.6 197.0 197.5 191.9 197.3 197.8 192.4 197.1 197.6 1.2 Meat and fish 222.3 231.9 225.7 220.1 225.5 222.8 229.2 236.3 233.7 223.3 229.3 226.6 1.3 Egg 192.8 197.5 194.6 188.0 196.3 193.3 190.7 202.3 197.4 189.0 198.6 194.9 1.4 Milk and products 186.3 187.0 186.6 186.2 190.4 190.8 187.1 192.1 192.4 186.5 191.0 191.4 1.5 Oils and fats 175.4 165.5 171.8 164.1 197.9 202.3 157.1 182.1 184.5 161.5 192.1 195.8 1.6 Fruits 188.3 194.2 191.0 186.1 210.2 211.7 197.5 217.2 215.9 191.4 213.5 213.7 1.7 Vegetables 222.1 269.6 238.2 245.0 196.4 203.9 291.2 242.6 249.1 260.7 212.1 219.2 1.8 Pulses and products 208.0 213.5 209.8 212.5 182.8 182.0 219.0 187.2 186.4 214.7 184.3 183.5 1.9 Sugar and confectionery 130.4 132.6 131.2 130.7 134.7 135.7 132.8 136.4 137.4 131.4 135.3 136.3 1.10 Spices 228.5 223.9 227.0 229.6 221.6 221.6 225.0 219.2 218.9 228.1 220.8 220.7 1.11 Non-alcoholic beverages 185.2 173.9 180.5 183.8 190.9 191.4 172.8 180.5 180.6 179.2 186.6 186.9 1.12 Prepared meals, snacks, sweets 199.4 209.7 204.2 198.5 205.3 206.1 208.3 217.5 217.8 203.0 211.0 211.5 2 Pan, tobacco and intoxicants 207.3 212.6 208.7 206.8 211.6 212.1 213.1 217.9 218.2 208.5 213.3 213.7 3 Clothing and footwear 197.9 186.7 193.5 197.2 201.3 201.4 186.0 190.7 191.0 192.8 197.1 197.3 3.1 Clothing 198.8 188.8 194.9 198.0 202.2 202.4 188.1 193.0 193.3 194.1 198.6 198.8 3.2 Footwear 192.7 174.7 185.2 192.3 195.5 195.7 174.2 178.2 178.4 184.8 188.3 188.5 4 Housing -- 181.5 181.5 -- -- -- 181.1 185.7 186.7 181.1 185.7 186.7 5 Fuel and light 181.2 169.7 176.9 180.9 184.0 184.5 169.8 175.3 175.2 176.7 180.7 181.0 6 Miscellaneous 189.3 180.7 185.1 188.3 197.4 198.2 180.1 188.1 188.8 184.3 192.9 193.6 6.1 Household goods and services 185.7 177.1 181.6 184.9 188.4 188.8 176.4 181.0 181.9 180.9 184.9 185.5 6.2 Health 198.4 193.2 196.4 197.3 205.4 206.0 192.2 200.3 200.7 195.4 203.5 204.0 6.3 Transport and communication 175.5 164.8 169.9 176.1 179.4 179.8 165.3 168.1 168.2 170.4 173.5 173.7 6.4 Recreation and amusement 180.1 175.5 177.5 179.6 182.8 183.1 174.9 178.7 179.2 177.0 180.5 180.9 6.5 Education 190.8 186.2 188.1 191.7 196.8 197.3 186.5 193.7 194.2 188.7 195.0 195.5 6.6 Personal care and effects 204.3 206.2 205.1 199.1 229.8 232.3 201.0 232.0 234.3 199.9 230.7 233.1 General Index (All Groups) 194.9 190.0 192.6 195.4 197.6 198.7 190.3 194.3 195.0 193.0 196.1 197.0 Source: National Statistical Office, Ministry of Statistics and Programme Implementation, Government of India. P: Provisional No. 19: Other Consumer Price Indices Item Base Year Linking 2024-25 2024 2025 Factor Jul. Jun. Jul. 1 2 3 4 5 6 1 Consumer Price Index for Industrial Workers 2016 2.88 142.6 142.7 145.0 146.5 2 Consumer Price Index for Agricultural Labourers 2019 9.69 - 134.3 134.1 135.3 3 Consumer Price Index for Rural Labourers 2019 9.78 - 134.3 134.4 135.7 Source: Labour Bureau, Ministry of Labour and Employment, Government of India. No. 20: Monthly Average Price of Gold and Silver in Mumbai Item 2024-25 2024 2025 Jul. Jun. Jul. 1 2 3 4 1 Standard Gold (₹ per 10 grams) 75842 71189 97176 97581 2 Silver (₹ per kilogram) 89131 88058 105444 110958 Source: India Bullion & Jewellers Association Ltd., Mumbai for Gold and Silver prices in Mumbai. 144 RBI Bulletin September 2025CURRENT STATISTICS No. 21: Wholesale Price Index (Base: 2011-12 = 100) Commodities Weight 2024-25 2024 2025 Aug. Jun. Jul.(P) Aug.(P) 1 2 3 4 5 6 1 ALL COMMODITIES 100.000 154.9 154.4 153.7 154.4 155.2 1.1 PRIMARY ARTICLES 22.618 192.5 195.1 186.1 188.0 191.0 1.1.1 FOOD ARTICLES 15.256 205.3 209.0 198.0 199.7 202.6 1.1.1.1 Food Grains (Cereals+Pulses) 3.462 210.1 210.0 203.0 204.1 205.3 1.1.1.2 Fruits & Vegetables 3.475 241.4 259.2 212.4 220.9 231.4 1.1.1.3 Milk 4.440 185.8 185.9 190.2 190.1 190.7 1.1.1.4 Eggs, Meat & Fish 2.402 173.4 173.1 174.0 171.8 173.2 1.1.1.5 Condiments & Spices 0.529 232.7 236.8 199.5 200.0 199.6 1.1.1.6 Other Food Articles 0.948 213.6 205.7 222.8 221.0 218.6 1.1.2 NON-FOOD ARTICLES 4.119 161.7 160.2 160.7 164.3 169.1 1.1.2.1 Fibres 0.839 161.4 161.2 162.7 165.0 168.0 1.1.2.2 Oil Seeds 1.115 181.5 178.6 190.6 197.8 203.5 1.1.2.3 Other non-food Articles 1.960 138.7 140.0 137.8 137.7 139.5 1.1.2.4 Floriculture 0.204 277.4 248.6 210.5 234.5 269.4 1.1.3 MINERALS 0.833 229.0 227.6 228.8 229.0 235.1 1.1.3.1 Metallic Minerals 0.648 219.2 217.4 219.4 219.6 227.3 1.1.3.2 Other Minerals 0.185 263.4 263.6 261.9 261.7 262.4 1.1.4 CRUDE PETROLEUM & NATURAL GAS 2.410 151.3 155.0 139.2 140.3 139.7 1.2 FUEL & POWER 13.152 150.0 148.3 142.3 144.6 143.6 1.2.1 COAL 2.138 135.6 135.6 136.7 136.3 136.3 1.2.1.1 Coking Coal 0.647 143.4 143.4 146.4 146.4 146.4 1.2.1.2 Non-Coking Coal 1.401 125.8 125.8 126.6 126.6 126.6 1.2.1.3 Lignite 0.090 232.4 232.0 224.5 215.0 215.0 1.2.2 MINERAL OILS 7.950 156.2 156.9 146.7 149.6 149.5 1.2.3 ELECTRICITY 3.064 144.1 134.8 134.5 137.3 133.3 1.3 MANUFACTURED PRODUCTS 64.231 142.6 141.3 144.7 144.6 144.9 1.3.1 MANUFACTURE OF FOOD PRODUCTS 9.122 172.0 166.5 177.3 177.3 178.4 1.3.1.1 Processing and Preserving of meat 0.134 155.7 153.0 158.2 158.4 158.3 1.3.1.2 Processing and Preserving of fish, Crustaceans, Molluscs and products thereof 0.204 144.9 143.8 147.4 146.6 149.3 1.3.1.3 Processing and Preserving of fruit and Vegetables 0.138 132.6 131.6 135.7 135.9 135.8 1.3.1.4 Vegetable and Animal oils and Fats 2.643 168.5 150.5 181.5 182.2 185.0 1.3.1.5 Dairy products 1.165 180.8 178.5 183.9 183.8 184.0 1.3.1.6 Grain mill products 2.010 186.9 184.0 185.2 185.7 186.6 1.3.1.7 Starches and Starch products 0.110 167.0 173.3 154.7 152.8 150.7 1.3.1.8 Bakery products 0.215 170.5 168.4 176.8 176.6 176.5 1.3.1.9 Sugar, Molasses & honey 1.163 139.1 139.5 143.2 143.0 144.1 1.3.1.10 Cocoa, Chocolate and Sugar confectionery 0.175 160.6 157.1 178.7 177.3 177.0 1.3.1.11 Macaroni, Noodles, Couscous and Similar farinaceous products 0.026 156.7 150.8 159.7 160.6 160.1 1.3.1.12 Tea & Coffee products 0.371 190.7 202.8 203.8 198.9 194.5 1.3.1.13 Processed condiments & salt 0.163 192.6 191.9 189.7 190.6 190.1 1.3.1.14 Processed ready to eat food 0.024 152.7 151.9 156.4 156.6 157.3 1.3.1.15 Health supplements 0.225 185.1 185.9 188.8 187.8 187.8 1.3.1.16 Prepared animal feeds 0.356 204.1 208.6 199.9 200.8 203.8 1.3.2 MANUFACTURE OF BEVERAGES 0.909 134.1 134.0 135.4 135.2 135.6 1.3.2.1 Wines & spirits 0.408 136.0 135.6 138.8 138.3 138.8 1.3.2.2 Malt liquors and Malt 0.225 138.7 138.3 139.9 140.1 140.7 1.3.2.3 Soft drinks; Production of mineral waters and Other bottled waters 0.275 127.5 128.1 126.7 126.4 126.7 1.3.3 MANUFACTURE OF TOBACCO PRODUCTS 0.514 177.8 176.0 179.8 179.9 179.9 1.3.3.1 Tobacco products 0.514 177.8 176.0 179.8 179.9 179.9 RBI Bulletin September 2025 145CURRENT STATISTICS No. 21: Wholesale Price Index (Contd.) (Base: 2011-12 = 100) Commodities Weight 2024-25 2024 2025 Aug. Jun. Jul.(P) Aug.(P) 1 2 3 4 5 6 1.3.4 MANUFACTURE OF TEXTILES 4.881 136.3 135.9 136.4 136.6 137.8 1.3.4.1 Preparation and Spinning of textile fibres 2.582 121.4 122.3 120.2 119.9 120.7 1.3.4.2 Weaving & Finishing of textiles 1.509 158.3 155.7 160.1 161.0 163.0 1.3.4.3 Knitted and Crocheted fabrics 0.193 124.0 123.7 125.1 125.3 127.3 1.3.4.4 Made-up textile articles, Except apparel 0.299 160.4 160.5 161.0 161.1 160.8 1.3.4.5 Cordage, Rope, Twine and Netting 0.098 142.7 141.1 151.8 155.3 158.9 1.3.4.6 Other textiles 0.201 134.9 135.4 132.7 133.1 133.0 1.3.5 MANUFACTURE OF WEARING APPAREL 0.814 153.4 152.9 155.5 156.0 155.9 1.3.5.1 Manufacture of Wearing Apparel (woven), Except fur Apparel 0.593 150.9 150.3 153.8 154.1 154.1 1.3.5.2 Knitted and Crocheted apparel 0.221 160.1 159.8 160.1 161.0 160.7 1.3.6 MANUFACTURE OF LEATHER AND RELATED PRODUCTS 0.535 125.3 124.9 127.5 127.6 127.9 1.3.6.1 Tanning and Dressing of leather; Dressing and Dyeing of fur 0.142 106.1 104.1 112.2 112.1 110.7 1.3.6.2 Luggage, HandbAgs, Saddlery and Harness 0.075 142.5 143.1 141.1 141.0 141.9 1.3.6.3 Footwear 0.318 129.7 129.8 131.1 131.3 132.3 1.3.7 MANUFACTURE OF WOOD AND PRODUCTS OF WOOD AND CORK 0.772 149.2 149.5 150.4 150.1 150.0 1.3.7.1 Saw milling and Planing of wood 0.124 141.1 140.3 142.2 141.6 142.7 1.3.7.2 Veneer sheets; Manufacture of plywood, Laminboard, Particle board and Other panels and Boards 0.493 148.6 149.1 149.4 149.3 148.7 1.3.7.3 Builder's carpentry and Joinery 0.036 215.3 216.4 216.1 215.3 215.3 1.3.7.4 Wooden containers 0.119 140.6 141.1 143.3 142.7 143.3 1.3.8 MANUFACTURE OF PAPER AND PAPER PRODUCTS 1.113 139.2 139.8 140.0 139.8 139.8 1.3.8.1 Pulp, Paper and Paperboard 0.493 144.6 145.5 143.9 143.8 143.9 1.3.8.2 Corrugated paper and Paperboard and Containers of paper and Paperboard 0.314 147.3 146.1 150.9 151.1 150.9 1.3.8.3 Other articles of paper and Paperboard 0.306 122.4 124.0 122.6 121.9 121.9 1.3.9 PRINTING AND REPRODUCTION OF RECORDED MEDIA 0.676 187.3 187.0 189.6 190.3 191.4 1.3.9.1 Printing 0.676 187.3 187.0 189.6 190.3 191.4 1.3.10 MANUFACTURE OF CHEMICALS AND CHEMICAL PRODUCTS 6.465 136.5 136.7 137.1 137.0 137.1 1.3.10.1 Basic chemicals 1.433 138.6 137.9 141.7 141.2 140.9 1.3.10.2 Fertilizers and Nitrogen compounds 1.485 143.1 143.2 143.0 142.9 142.9 1.3.10.3 Plastic and Synthetic rubber in primary form 1.001 133.6 134.1 134.0 134.6 135.1 1.3.10.4 Pesticides and Other agrochemical products 0.454 128.8 129.8 131.6 131.2 131.6 1.3.10.5 Paints, Varnishes and Similar coatings, Printing ink and Mastics 0.491 139.5 140.7 136.8 136.3 137.1 1.3.10.6 Soap and Detergents, Cleaning and Polishing preparations, Perfumes and Toilet preparations 0.612 139.7 139.0 142.4 142.6 142.7 1.3.10.7 Other chemical products 0.692 135.4 136.2 133.5 133.1 132.8 1.3.10.8 Man-made fibres 0.296 104.9 106.9 103.1 103.5 102.9 1.3.11 MANUFACTURE OF PHARMACEUTICALS, MEDICINAL CHEMICAL AND BOTANICAL PRODUCTS 1.993 144.3 144.8 145.7 146.0 146.0 1.3.11.1 Pharmaceuticals, Medicinal chemical and Botanical products 1.993 144.3 144.8 145.7 146.0 146.0 1.3.12 MANUFACTURE OF RUBBER AND PLASTICS PRODUCTS 2.299 129.0 129.1 129.5 129.1 129.5 1.3.12.1 Rubber Tyres and Tubes; Retreading and Rebuilding of Rubber Tyres 0.609 115.6 114.8 115.7 115.2 114.9 1.3.12.2 Other Rubber Products 0.272 112.1 113.6 113.3 114.3 113.8 1.3.12.3 Plastics products 1.418 138.1 138.2 138.5 137.9 138.7 1.3.13 MANUFACTURE OF OTHER NON-METALLIC MINERAL PRODUCTS 3.202 131.5 129.8 133.4 133.5 133.7 1.3.13.1 Glass and Glass products 0.295 163.2 163.5 163.5 163.5 162.9 1.3.13.2 Refractory products 0.223 121.6 119.6 123.2 123.7 124.0 1.3.13.3 Clay Building Materials 0.121 124.4 121.6 131.0 129.3 133.4 1.3.13.4 Other Porcelain and Ceramic Products 0.222 124.6 124.6 125.8 125.6 125.6 1.3.13.5 Cement, Lime and Plaster 1.645 130.4 127.7 132.8 133.1 133.1 146 RBI Bulletin September 2025CURRENT STATISTICS No. 21: Wholesale Price Index (Contd.) (Base: 2011-12 = 100) Commodities Weight 2024-25 2024 2025 Aug. Jun. Jul.(P) Aug.(P) 1 2 3 4 5 6 1.3.13.6 Articles of Concrete, Cement and Plaster 0.292 139.2 138.4 140.1 139.5 140.2 1.3.13.7 Cutting, Shaping and Finishing of Stone 0.234 134.4 133.7 138.3 138.0 138.2 1.3.13.8 Other Non-Metallic Mineral Products 0.169 95.2 96.8 93.6 94.9 94.0 1.3.14 MANUFACTURE OF BASIC METALS 9.646 139.7 138.3 138.6 137.5 137.4 1.3.14.1 Inputs into steel making 1.411 133.6 131.2 131.4 131.2 129.8 1.3.14.2 Metallic Iron 0.653 141.8 145.4 129.5 128.6 128.3 1.3.14.3 Mild Steel - Semi Finished Steel 1.274 117.9 114.4 117.1 116.2 115.9 1.3.14.4 Mild Steel -Long Products 1.081 140.4 138.6 137.2 135.4 135.8 1.3.14.5 Mild Steel - Flat products 1.144 134.2 136.4 134.4 133.0 132.2 1.3.14.6 Alloy steel other than Stainless Steel- Shapes 0.067 135.4 134.6 133.2 130.8 128.8 1.3.14.7 Stainless Steel - Semi Finished 0.924 131.1 128.6 128.7 122.6 123.0 1.3.14.8 Pipes & tubes 0.205 164.7 165.2 166.7 163.2 161.3 1.3.14.9 Non-ferrous metals incl. precious metals 1.693 157.4 154.6 161.4 162.7 164.1 1.3.14.10 Castings 0.925 144.9 144.0 143.4 143.4 143.6 1.3.14.11 Forgings of steel 0.271 172.2 170.7 177.9 173.9 174.3 1.3.15 MANUFACTURE OF FABRICATED METAL PRODUCTS, EXCEPT MACHINERY AND EQUIPMENT 3.155 136.0 136.6 137.0 136.6 136.9 1.3.15.1 Structural Metal Products 1.031 130.8 131.5 131.4 131.5 132.1 1.3.15.2 Tanks, Reservoirs and Containers of Metal 0.660 149.5 151.2 151.6 149.7 149.2 1.3.15.3 Steam generators, Except Central Heating Hot Water Boilers 0.145 109.8 111.5 112.1 112.5 113.2 1.3.15.4 Forging, Pressing, Stamping and Roll-Forming of Metal; Powder Metallurgy 0.383 138.0 138.4 135.0 133.8 135.7 1.3.15.5 Cutlery, Hand Tools and General Hardware 0.208 102.0 101.9 104.7 104.8 105.5 1.3.15.6 Other Fabricated Metal Products 0.728 144.9 144.7 146.8 147.2 146.8 1.3.16 MANUFACTURE OF COMPUTER, ELECTRONIC AND OPTICAL PRODUCTS 2.009 121.5 121.6 122.1 122.5 122.1 1.3.16.1 Electronic Components 0.402 117.9 117.4 120.3 120.8 120.1 1.3.16.2 Computers and Peripheral Equipment 0.336 134.2 136.2 131.4 131.4 130.4 1.3.16.3 Communication Equipment 0.310 146.0 145.4 146.9 147.0 147.2 1.3.16.4 Consumer Electronics 0.641 101.1 101.1 100.2 100.6 100.0 1.3.16.5 Measuring, Testing, Navigating and Control equipment 0.181 119.9 118.1 126.6 126.6 126.6 1.3.16.6 Watches and Clocks 0.076 167.9 166.3 173.7 173.3 175.0 1.3.16.7 Irradiation, Electromedical and Electrotherapeutic equipment 0.055 114.4 119.7 109.9 115.4 115.4 1.3.16.8 Optical instruments and Photographic equipment 0.008 107.4 106.9 118.1 118.1 117.9 1.3.17 MANUFACTURE OF ELECTRICAL EQUIPMENT 2.930 133.7 133.5 134.8 134.6 135.0 1.3.17.1 Electric motors, Generators, Transformers and Electricity distribution and Control apparatus 1.298 132.3 131.7 133.3 132.7 133.2 1.3.17.2 Batteries and Accumulators 0.236 141.3 141.8 144.1 144.8 144.9 1.3.17.3 Fibre optic cables for data transmission or live transmission of images 0.133 118.6 120.7 115.9 115.7 115.9 1.3.17.4 Other electronic and Electric wires and Cables 0.428 154.4 153.3 158.3 158.6 159.2 1.3.17.5 Wiring devices, Electric lighting & display equipment 0.263 118.4 118.9 118.5 118.2 118.2 1.3.17.6 Domestic appliances 0.366 131.8 132.4 130.3 130.4 130.8 1.3.17.7 Other electrical equipment 0.206 123.4 122.6 125.6 125.5 125.5 1.3.18 MANUFACTURE OF MACHINERY AND EQUIPMENT 4.789 130.8 130.6 132.5 132.3 132.5 1.3.18.1 Engines and Turbines, Except aircraft, Vehicle and Two wheeler engines 0.638 132.8 132.4 136.6 136.6 137.1 1.3.18.2 Fluid power equipment 0.162 134.5 134.0 134.9 134.8 135.0 1.3.18.3 Other pumps, Compressors, Taps and Valves 0.552 118.5 118.5 119.9 120.2 120.0 1.3.18.4 Bearings, Gears, Gearing and Driving elements 0.340 128.5 126.4 131.9 130.0 129.9 1.3.18.5 Ovens, Furnaces and Furnace burners 0.008 86.6 86.7 87.9 88.2 86.6 1.3.18.6 Lifting and Handling equipment 0.285 130.0 129.5 130.8 131.0 131.2 RBI Bulletin September 2025 147CURRENT STATISTICS No. 21: Wholesale Price Index (Concld.) (Base: 2011-12 = 100) Commodities Weight 2024-25 2024 2025 Aug. Jun. Jul.(P) Aug.(P) 1 2 3 4 5 6 1.3.18.7 Office machinery and Equipment 0.006 130.2 130.2 130.2 130.2 130.2 1.3.18.8 Other general-purpose machinery 0.437 145.3 149.2 143.8 141.8 142.4 1.3.18.9 Agricultural and Forestry machinery 0.833 145.5 144.3 146.8 146.4 146.2 1.3.18.10 Metal-forming machinery and Machine tools 0.224 123.2 122.8 127.3 127.4 127.4 1.3.18.11 Machinery for mining, Quarrying and Construction 0.371 89.8 88.8 92.7 93.0 92.9 1.3.18.12 Machinery for food, Beverage and Tobacco processing 0.228 126.1 126.2 127.8 128.0 127.0 1.3.18.13 Machinery for textile, Apparel and Leather production 0.192 141.4 141.9 139.9 139.6 144.9 1.3.18.14 Other special-purpose machinery 0.468 144.9 144.8 147.1 147.5 147.9 1.3.18.15 Renewable electricity generating equipment 0.046 69.2 69.1 69.4 69.4 69.4 1.3.19 MANUFACTURE OF MOTOR VEHICLES, TRAILERS AND SEMI-TRAILERS 4.969 129.9 129.8 130.6 130.7 130.7 1.3.19.1 Motor vehicles 2.600 130.6 130.3 131.1 131.2 131.2 1.3.19.2 Parts and Accessories for motor vehicles 2.368 129.1 129.3 130.1 130.1 130.1 1.3.20 MANUFACTURE OF OTHER TRANSPORT EQUIPMENT 1.648 145.2 144.6 150.4 151.1 151.8 1.3.20.1 Building of ships and Floating structures 0.117 180.5 177.9 190.7 190.7 190.7 1.3.20.2 Railway locomotives and Rolling stock 0.110 108.9 110.0 109.8 110.0 111.0 1.3.20.3 Motor cycles 1.302 146.0 145.4 151.4 152.1 152.9 1.3.20.4 Bicycles and Invalid carriages 0.117 134.9 134.6 137.1 138.2 138.2 1.3.20.5 Other transport equipment 0.002 163.2 162.4 165.8 165.9 165.6 1.3.21 MANUFACTURE OF FURNITURE 0.727 160.3 159.0 164.6 164.8 164.7 1.3.21.1 Furniture 0.727 160.3 159.0 164.6 164.8 164.7 1.3.22 OTHER MANUFACTURING 1.064 183.8 174.1 224.5 226.8 227.7 1.3.22.1 Jewellery and Related articles 0.996 185.4 175.0 228.6 231.1 232.0 1.3.22.2 Musical instruments 0.001 201.9 201.4 204.3 204.3 203.5 1.3.22.3 Sports goods 0.012 164.9 163.6 171.4 171.9 172.4 1.3.22.4 Games and Toys 0.005 163.1 163.6 164.7 160.2 162.6 1.3.22.5 Medical and Dental instruments and Supplies 0.049 158.6 159.7 162.1 162.1 160.9 2 FOOD INDEX 24.378 192.9 193.1 190.2 191.3 193.5 Source: Office of the Economic Adviser, Ministry of Commerce and Industry, Government of India. 148 RBI Bulletin September 2025CURRENT STATISTICS No. 22: Index of Industrial Production (Base:2011-12=100) Industry Weight 2023-24 2024-25 April-July July 2024-25 2025-26 2024 2025 1 2 3 4 5 6 7 General Index 100.00 146.7 152.6 150.9 154.4 149.8 155.0 1 Sectoral Classification 1.1 Mining 14.37 128.9 132.8 129.6 124.5 116.1 107.7 1.2 Manufacturing 77.63 144.7 150.6 147.6 153.3 148.8 156.9 1.3 Electricity 7.99 198.3 208.6 221.1 219.0 220.2 221.5 2 Use-Based Classification 2.1 Primary Goods 34.05 147.7 153.5 154.8 152.6 150.1 147.6 2.2 Capital Goods 8.22 106.6 112.6 106.4 115.5 114.0 119.7 2.3 Intermediate Goods 17.22 157.3 164.0 161.0 169.4 164.6 174.1 2.4 Infrastructure/ Construction Goods 12.34 176.3 188.2 183.8 197.5 179.7 201.0 2.5 Consumer Durables 12.84 118.6 128.0 125.9 130.7 126.6 136.3 2.6 Consumer Non-Durables 15.33 153.7 151.4 149.3 147.8 147.1 147.8 Source : Central Statistics Office, Ministry of Statistics and Programme Implementation, Government of India. Government Accounts and Treasury Bills No. 23: Union Government Accounts at a Glance (₹ Crore) Financial Year April – July 2025-26 Percentage to Budget Item (Budget 2025-26 2024-25 Estimates (Actuals) (Actuals) Estimates) 2025-26 2024-25 1 2 3 4 5 1 Revenue Receipts 3420409 1065420 1017020 31.1 32.5 1.1 Tax Revenue (Net) 2837409 661812 715224 23.3 27.7 1.2 Non-Tax Revenue 583000 403608 301796 69.2 55.3 2 Non Debt Capital Receipt 76000 29789 6386 39.2 8.2 2.1 Recovery of Loans 29000 7138 6381 24.6 22.8 2.2 Other Receipts 47000 22651 5 48.2 0.0 3 Total Receipts (excluding borrowings) (1+2) 3496409 1095209 1023406 31.3 31.9 4 Revenue Expenditure 3944255 1216699 1039091 30.8 28.0 of which : 4.1 Interest Payments 1276338 446690 327887 35.0 28.2 5 Capital Expenditure 1121090 346926 261260 30.9 23.5 6 Total Expenditure (4+5) 5065345 1563625 1300351 30.9 27.0 7 Revenue Deficit (4-1) 523846 151279 22071 28.9 3.8 8 Fiscal Deficit (6-3) 1568936 468416 276945 29.9 17.2 9 Gross Primary Deficit (8-4.1) 292598 21726 -50942 7.4 -11.3 Source: Controller General of Accounts (CGA), Ministry of Finance, Government of India and Union Budget 2025-26. RBI Bulletin September 2025 149CURRENT STATISTICS No. 24: Treasury Bills – Ownership Pattern (₹ Crore) 2024-25 2024 2025 Item Jul. 26 Jun. 20 Jun. 27 Jul. 4 Jul. 11 Jul. 18 Jul. 25 1 2 3 4 5 6 7 8 1 91-day 1.1 Banks 26554 2466 18639 18898 17315 16732 16088 15002 1.2 Primary Dealers 25258 9749 21671 18582 17761 16823 16781 17505 1.3 State Governments 40315 46140 71291 80904 81477 87277 92777 71677 1.4 Others 115688 95885 96090 95120 97524 97045 97731 98093 2 182-day 2.1 Banks 44887 51248 51017 49943 48673 50941 51612 54296 2.2 Primary Dealers 62218 57730 55998 57055 54535 52998 51354 56692 2.3 State Governments 11078 14922 18888 16888 16888 17888 17419 17460 2.4 Others 104994 119522 93886 92901 94692 91961 90934 80911 3 364-day 3.1 Banks 72304 90700 71580 71571 73723 74980 75410 74597 3.2 Primary Dealers 86939 137182 74466 76439 78576 76859 74963 74963 3.3 State Governments 37389 38525 44898 45967 46328 46459 46563 47732 3.4 Others 162757 164118 160754 159789 154500 155860 157527 157340 4 14-day Intermediate 4.1 Banks 4.2 Primary Dealers 4.3 State Governments 188072 175531 156490 155018 85981 142202 138198 184745 4.4 Others 572 1008 1297 428 229 2607 1638 1026 Total Treasury Bills (Excluding 14 day 790381 828188 779176 784059 781993 785823 789158 766268 Intermediate T Bills) # # 14D intermediate T-Bills are non-marketable unlike 91D, 182D and 364D T-Bills. These bills are ‘intermediate’ by nature as these are liquidated to replenish shortfall in the daily minimum cash balances of State Governments. Note: Primary Dealers (PDs) include banks undertaking PD business. No. 25: Auctions of Treasury Bills (Amount in ₹ Crore) Date of Notified Bids Received Bids Accepted Total Cut- Implicit Yield Auction Amount Total Face Value Total Face Value Issue off at Cut-off Price Number Number (6+7) Price (per cent) Competitive Non- Competitive Non- ( ₹ ) Competitive Competitive 1 2 3 4 5 6 7 8 9 10 91-day Treasury Bills 2025-26 Jul. 2 9000 129 38698 1202 34 8975 1202 10177 98.68 5.3699 Jul. 9 9000 151 40780 5814 41 8986 5814 14800 98.67 5.3872 Jul. 16 9000 140 36181 7429 40 8971 7429 16400 98.68 5.3851 Jul. 23 9000 161 37548 5127 35 8973 5127 14100 98.67 5.3872 Jul. 30 10000 102 25879 16274 60 9976 16274 26250 98.67 5.3970 182-day Treasury Bills 2025-26 Jul. 2 6000 100 28293 23 25 5977 23 6000 97.33 5.5001 Jul. 9 6000 103 22512 2311 50 5989 2311 8300 97.31 5.5371 Jul. 16 6000 171 36447 300 28 5980 300 6280 97.31 5.5399 Jul. 23 6000 145 33760 1009 28 5991 1009 7000 97.32 5.5291 Jul. 30 6000 122 30192 1007 29 5993 1007 7000 97.32 5.5206 364-day Treasury Bills 2025-26 Jul. 2 5000 97 18105 471 58 4989 471 5460 94.76 5.5494 Jul. 9 5000 81 21498 2492 36 4950 2492 7442 94.73 5.5787 Jul. 16 5000 113 24851 1332 47 4986 1332 6318 94.72 5.5924 Jul. 23 5000 122 29165 1254 26 4987 1254 6240 94.74 5.5701 Jul. 30 5000 103 26370 164 35 4989 164 5153 94.74 5.5673 150 RBI Bulletin September 2025CURRENT STATISTICS Financial Markets No. 26: Daily Call Money Rates (Per cent per annum) Range of Rates Weighted Average Rates As on Borrowings/ Lendings Borrowings/ Lendings 1 2 July 01 ,2025 4.75-5.40 5.31 July 02 ,2025 4.70-5.35 5.27 July 03 ,2025 4.75-5.35 5.26 July 04 ,2025 4.75-5.35 5.26 July 05 ,2025 4.70-5.30 4.91 July 07 ,2025 4.75-5.35 5.26 July 08 ,2025 4.50-5.35 5.26 July 09 ,2025 4.80-5.45 5.32 July 10 ,2025 4.75-5.45 5.35 July 11 ,2025 4.75-5.55 5.45 July 14 ,2025 4.75-5.40 5.31 July 15 ,2025 4.75-5.50 5.38 July 16 ,2025 4.75-5.45 5.36 July 17 ,2025 4.75-5.50 5.35 July 18 ,2025 4.75-5.45 5.33 July 19 ,2025 4.75-5.40 5.00 July 21 ,2025 4.75-5.70 5.48 July 22 ,2025 4.75-5.80 5.62 July 23 ,2025 4.75-5.85 5.73 July 24 ,2025 4.75-5.65 5.54 July 25 ,2025 4.75-5.50 5.39 July 28 ,2025 4.75-5.40 5.34 July 29 ,2025 4.75-5.45 5.38 July 30 ,2025 4.75-5.42 5.37 July 31 ,2025 4.75-5.55 5.48 August 01 ,2025 4.75-5.55 5.44 August 02 ,2025 4.75-5.24 4.95 August 04 ,2025 4.75-5.45 5.37 August 05 ,2025 4.75-5.45 5.36 August 06 ,2025 4.75-5.50 5.33 August 07 ,2025 4.75-6.00 5.44 August 08 ,2025 4.75-5.70 5.55 August 11 ,2025 4.75-5.45 5.36 August 12 ,2025 4.85-5.55 5.45 August 13 ,2025 4.75-5.55 5.46 August 14 ,2025 4.85-5.68 5.46 Note: Includes Notice Money. RBI Bulletin September 2025 151CURRENT STATISTICS No. 27: Certificates of Deposit 2024 2025 Item Aug. 23 Jul. 11 Jul. 25 Aug. 8 Aug. 22 1 2 3 4 5 1 Amount Outstanding (₹ Crore) 446580.44 525253.75 508451.73 511313.00 494942.79 1.1 Issued during the fortnight (₹ Crore) 46185.89 18575.50 18550.54 27981.81 36879.16 2 Rate of Interest (per cent) 7.03-7.68 5.67-6.49 5.59-6.63 5.64-6.24 5.66-6.27 No. 28: Commercial Paper Item 2024 2025 Jul. 31 Jun. 15 Jun. 30 Jul. 15 Jul. 31 Aug. 15 Aug. 31 1 2 3 4 5 6 7 1 Amount Outstanding (₹ Crore) 458911.05 549258.30 500000.60 534009.15 547229.30 554479.70 543870.10 1.1 Reported during the fortnight (₹ Crore) 67966.95 102447.00 58021.75 79530.05 73858.50 56758.35 60807.60 2 Rate of Interest (per cent) 6.89-12.07 5.67-11.63 5.71-13.84 5.51-12.67 5.57-13.84 5.68-12.67 5.72-13.83 No. 29: Average Daily Turnover in Select Financial Markets (₹ Crore) Item 2024-25 2024 2025 Jul. 26 Jun. 20 Jun. 27 Jul. 4 Jul. 11 Jul. 18 Jul. 25 1 2 3 4 5 6 7 8 1 Call Money 18990 17812 24157 28538 25874 29963 28444 29057 2 Notice Money 2506 228 6749 307 6276 535 6323 380 3 Term Money 941 618 903 990 1098 1679 1361 1240 4 Triparty Repo 692068 693314 802836 702250 858204 663802 788486 682533 5 Market Repo 578912 566606 763218 632175 789565 633563 750243 610007 6 Repo in Corporate Bond 5212 3283 9639 8409 8251 9481 9722 9721 7 Forex (US $ million) 131877 103577 118707 165416 137181 122259 127800 123941 8 Govt. of India Dated Securities 56065 139737 132864 121551 114628 82402 89891 82508 9 State Govt. Securities 3971 6747 5182 4910 6521 4755 8566 7952 10 Treasury Bills 10.1 91-Day 2514 3301 6647 3997 4351 4625 3984 4981 10.2 182-Day 2218 4933 4672 3436 3339 2508 4595 3470 10.3 364-Day 1854 4467 3093 1967 3392 2562 3923 2395 10.4 Cash Management Bills 0 0 0 0 0 0 0 11 Total Govt. Securities (8+9+10) 66622 159184 152459 135862 132231 96851 110959 101306 11.1 RBI 1715 2077 816 596 33 176 811 627 152 RBI Bulletin September 2025CURRENT STATISTICS No. 30: New Capital Issues by Non-Government Public Limited Companies (Amount in ₹ Crore) 2024-25 2024-25 (Apr.-Jul.) 2025-26 (Apr.-Jul.) * Jul. 2024 Jul. 2025 * Security & Type of Issue No. of Amount No. of Amount No. of Amount No. of Amount No. of Amount Issues Issues Issues Issues Issues 1 2 3 4 5 6 7 8 9 10 1 Equity Shares 464 210190 154 50281 132 51219 42 9001 59 28160 1.1 Public 322 190478 106 42592 90 41596 31 6210 45 26132 1.2 Rights 142 19712 48 7690 42 9623 11 2791 14 2029 2 Public Issue of 43 8149 12 2716 17 4086 2 262 6 2000 Bonds/ Debentures 3 Total (1+2) 507 218339 166 52997 149 55305 44 9263 65 30161 3.1 Public 365 198627 118 45307 107 45682 33 6472 51 28132 3.2 Rights 142 19712 48 7690 42 9623 11 2791 14 2029 Notes : 1. Since April 2020, monthly data on equity issues is compiled on the basis of their listing date. 2. Figures in the columns might not add up to the total due to rounding off numbers. 3. The table covers only public and rights issuances of equity and debt. It does not include data on private placement of debt, qualified institutional placements and preferential allotments. Source : Securities and Exchange Board of India. * : Data is Provisional RBI Bulletin September 2025 153CURRENT STATISTICS External Sector No. 31: Foreign Trade 2024 2025 2024-25 Item Unit Jul. Mar. Apr. May Jun. Jul. 1 2 3 4 5 6 7 1 Exports ₹ Crore 3701070 290134 363598 328021 327855 301901 320672 US $ Million 437416 34707 41968 38338 38485 35144 37238 1.1 Oil ₹ Crore 534917 48417 42467 61423 47820 39650 37385 US $ Million 63341 5792 4902 7179 5613 4616 4341 1.2 Non-oil ₹ Crore 3166153 241716 321131 266598 280035 262251 283287 US $ Million 374075 28915 37066 31159 32872 30529 32897 2 Imports ₹ Crore 6089909 497180 550211 555329 516260 463159 556189 US $ Million 720241 59475 63507 64904 60601 53916 64587 2.1 Oil ₹ Crore 1570226 121162 164684 177212 125635 118534 134126 US $ Million 185779 14494 19008 20712 14748 13799 15575 2.2 Non-oil ₹ Crore 4519683 376018 385527 378117 390624 344624 422063 US $ Million 534462 44981 44499 44193 45853 40118 49012 3 Trade Balance ₹ Crore -2388839 -207046 -186613 -227308 -188405 -161257 -235517 US $ Million -282825 -24768 -21539 -26567 -22116 -18772 -27349 3.1 Oil ₹ Crore -1035309 -72744 -122217 -115789 -77815 -78884 -96741 US $ Million -122438 -8702 -14107 -13533 -9134 -9183 -11234 3.2 Non-oil ₹ Crore -1353530 -134302 -64395 -111519 -110590 -82373 -138776 US $ Million -160387 -16066 -7433 -13034 -12982 -9589 -16115 Note: Data in the table are provisional. Source: Directorate General of Commercial Intelligence and Statistics. No. 32: Foreign Exchange Reserves 2024 2025 Item Unit Sep. 06 Jul. 25 Aug. 01 Aug. 08 Aug. 15 Aug. 22 Aug. 29 1 2 3 4 5 6 7 1 Total Reserves ₹ Crore 5785515 6040880 6030417 6081131 6086782 6046195 6125356 US $ Million 689235 698192 688871 693618 695106 690720 694230 1.1 Foreign Currency Assets ₹ Crore 5071259 5095487 5091494 5119915 5130564 5096748 5152367 US $ Million 604144 588926 581607 583979 585903 582251 583937 1.2 Gold ₹ Crore 520331 741528 735336 755391 750161 744074 765603 US $ Million 61988 85704 83998 86160 85667 85003 86769 Volume (Metric Tonnes) 853.64 879.98 879.98 879.98 879.98 879.98 879.98 1.3 SDRs SDRs Million 13702 13707 13707 13707 13707 13709 13709 ₹ Crore 155056 162741 162582 164304 164467 164003 165665 US $ Million 18472 18809 18572 18741 18782 18736 18775 1.4 Reserve Tranche Position in IMF ₹ Crore 38869 41125 41005 41521 41591 41371 41721 US $ Million 4631 4753 4694 4739 4754 4731 4749 * Difference, if any, is due to rounding off. Note: Exclude investment in foreign currency denominated bonds issued by IIFC (UK), SDRs transferred by Government of India to RBI, foreign currency received under SAARC and ACU currency swap arrangements and RBI’s contribution to funding of Nexus Global Payments. Foreign currency assets in US dollar take into account appreciation/depreciation of non-US currencies (such as Euro, Sterling, Yen and Australian Dollar) held in reserves. Foreign exchange holdings are converted into rupees at rupee-US dollar RBI holding rates. No. 33: Non-Resident Deposits (US $ Million) Scheme Outstanding Flows 2024 2025 2024-25 2025-26 2024-25 Jul. Jun. Jul. (P) Apr.-Jul. Apr.-Jul.(P) 1 2 3 4 5 6 1 NRI Deposits 164677 157157 168327 167862 5820 4657 1.1 FCNR(B) 32809 28572 33583 33581 2839 772 1.2 NR(E)RA 100733 99981 102750 102029 1780 2418 1.3 NRO 31135 28603 31993 32251 1201 1468 P: Provisional. 154 RBI Bulletin September 2025CURRENT STATISTICS No. 34: Foreign Investment Inflows (US $ Million) 2024-25 2025-26 (P) 2024 (P) 2025 (P) Item 2024-25 Apr.-Jul. Apr.-Jul. Jul. Jun. Jul. 1 2 3 4 5 6 1.1 Net Foreign Direct Investment (1.1.1-1.1.2) 959 3536 10751 -2688 2507 5046 1.1.1 Direct Investment to India (1.1.1.1-1.1.1.2) 29130 10937 21435 331 5413 7304 1.1.1.1 Gross Inflows/Gross Investments 80615 28316 37712 5539 9571 11105 1.1.1.1.1 Equity 50993 19693 28178 3291 7175 8760 1.1.1.1.1.1 Government (SIA/FIPB) 2208 321 1371 112 1004 11 1.1.1.1.1.2 RBI 34686 13863 19894 2099 5337 6366 1.1.1.1.1.3 Acquisition of shares 13124 5207 6046 1002 571 2306 1.1.1.1.1.4 Equity capital of unincorporated bodies 975 302 867 78 263 78 1.1.1.1.2 Reinvested earnings 22759 7037 7688 1812 1959 1812 1.1.1.1.3 Other capital 6863 1587 1846 436 438 533 1.1.1.2 Repatriation/Disinvestment 51486 17379 16277 5207 4159 3801 1.1.1.2.1 Equity 49525 16679 15743 5005 4026 3666 1.1.1.2.2 Other capital 1960 700 534 202 133 135 1.1.2 Foreign Direct Investment by India 28171 7402 10684 3019 2905 2258 (1.1.2.1+1.1.2.2+1.1.2.3-1.1.2.4) 1.1.2.1 Equity capital 16945 5100 6201 2372 2038 1449 1.1.2.2 Reinvested Earnings 6846 2282 2454 571 628 571 1.1.2.3 Other Capital 7955 1350 2966 260 445 561 1.1.2.4 Repatriation/Disinvestment 3575 1330 938 183 205 323 1.2 Net Portfolio Investment (1.2.1+1.2.2+1.2.3-1.2.4) 3564 6785 -1116 5840 2435 -2724 1.2.1 GDRs/ADRs - - - - - - 1.2.2 FIIs 3283 6725 -2 5828 2726 -2483 1.2.3 Offshore funds and others - - - - - - 1.2.4 Portfolio investment by India -281 -59 1113 -12 291 241 1 Foreign Investment Inflows 4523 10320 9635 3152 4943 2322 P: Provisional No. 35: Outward Remittances under the Liberalised Remittance Scheme (LRS) for Resident Individuals (US $ Million) 2024 2025 Item 2024-25 Jul. May Jun. Jul. 1 2 3 4 5 1 Outward Remittances under the LRS 29563.12 2754.05 2313.16 2127.39 2452.93 1.1 Deposit 705.26 41.68 54.65 42.12 46.24 1.2 Purchase of immovable property 322.82 24.54 41.69 37.75 39.48 1.3 Investment in equity/debt 1698.94 120.86 104.94 206.12 156.19 1.4 Gift 2938.69 275.26 233.30 190.51 223.53 1.5 Donations 11.81 0.68 1.98 1.26 0.73 1.6 Travel 16964.57 1662.13 1389.23 1235.17 1445.34 1.7 Maintenance of close relatives 3722.03 337.40 322.54 262.97 298.11 1.8 Medical Treatment 81.19 8.62 6.72 5.59 6.26 1.9 Studies Abroad 2918.91 272.16 149.78 138.76 229.25 1.10 Others 198.90 10.72 8.32 7.15 7.80 RBI Bulletin September 2025 155CURRENT STATISTICS No. 36: Indices of Nominal Effective Exchange Rate (NEER) and Real Effective Exchange Rate (REER) of the Indian Rupee 2024 2025 2023-24 2024-25 Aug. Jul. Aug. Item 1 2 3 4 5 40-Currency Basket (Base: 2015-16=100) 1 Trade-Weighted 1.1 NEER 90.75 91.03 90.80 86.57 85.40 1.2 REER 103.71 105.26 105.50 100.19 98.79 2 Export-Weighted 2.1 NEER 93.13 93.52 93.46 88.62 87.35 2.2 REER 101.22 102.34 102.85 97.45 95.97 6-Currency Basket (Trade-weighted) 1 Base : 2015-16 =100 1.1 NEER 83.62 82.38 82.02 78.58 77.47 1.2 REER 101.66 102.72 102.50 98.15 97.14 2 Base : 2022-23 =100 2.1 NEER 97.31 95.87 95.46 91.44 90.16 2.2 REER 99.86 100.90 100.69 96.41 95.42 Note: Data for 2024-25 and 2025-26 so far is provisional. 156 RBI Bulletin September 2025CURRENT STATISTICS No. 37: External Commercial Borrowings (ECBs) – Registrations (Amount in US $ Million) Item 2024-25 2024 2025 Jul. Jun. Jul. 1 2 3 4 1 Automatic Route 1.1 Number 1328 119 112 126 1.2 Amount 47800 3581 2733 3220 2 Approval Route 2.1 Number 51 0 1 1 2.2 Amount 13384 0 750 101 3 Total (1+2) 3.1 Number 1379 119 113 127 3.2 Amount 61184 3581 3483 3321 4 Weighted Average Maturity (in years) 5.05 5.20 4.90 5.30 5 Interest Rate (per cent) 5.1 Weighted Average Margin over alternative reference rate (ARR) for Floating Rate Loans@ 1.48 1.45 1.70 1.61 5.2 Interest rate range for Fixed Rate Loans 0.00-11.67 0.00-10.00 0.00-10.50 0.00-10.80 Borrower Category I. Corporate Manufacturing 13900 626 954 299 II. Corporate-Infrastructure 15462 1393 1819 1260 a.) Transport 614 0 5 0 b.) Energy 6900 559 155 724 c.) Water and Sanitation 28 0 0 0 d.) Communication 13 0 0 0 e.) Social and Commercial Infrastructure 184 0 7 0 f.) Exploration,Mining and Refinery 5356 800 0 160 g.) Other Sub-Sectors 2367 34 1652 376 III. Corporate Service-Sector 3226 349 19 379 IV. Other Entities 1026 4 0 0 a.) units in SEZ 26 4 0 0 b.) SIDBI 0 0 0 0 c.) Exim Bank 1000 0 0 0 V. Banks 0 0 0 0 VI. Financial Institution (Other than NBFC ) 0 0 0 0 VII. NBFCs 26318 1195 682 1372 a). NBFC- IFC/AFC 12389 0 97 121 b). NBFC-MFI 459 16 18 15 c). NBFC-Others 13470 1179 567 1236 VIII. Non-Government Organization (NGO) 0 0 0 0 IX. Micro Finance Institution (MFI) 0 0 0 0 X. Others 1252 14 9 11 Note: Based on applications for ECB/Foreign Currency Convertible Bonds (FCCBs) which have been allotted loan registration number during the period. @ With effect from July 01, 2023, the benchmark rate is changed to Alternative Reference Rate (ARR). RBI Bulletin September 2025 157CURRENT STATISTICS No. 38: India’s Overall Balance of Payments (US$ Million) Apr-Jun 2024 Apr-Jun 2025 (P) Credit Debit Net Credit Debit Net Item 1 2 3 4 5 6 Overall Balance Of Payments (1+2+3) 507129 501903 5226 545826 541318 4508 1 Current Account (1.1+ 1.2) 241832 250508 -8676 256736 259106 -2370 1.1 Merchandise 111158 174963 -63805 113087 181551 -68464 1.2 Invisibles (1.2.1+1.2.2+1.2.3) 130674 75545 55129 143649 77555 66094 1.2.1 Services 88465 48784 39681 97428 49507 47920 1.2.1.1 Travel 7352 9171 -1819 5855 9085 -3230 1.2.1.2 Transportation 8506 8609 -103 7708 8395 -687 1.2.1.3 Insurance 903 593 310 926 613 313 1.2.1.4 G.n.i.e. 161 309 -147 134 323 -188 1.2.1.5 Miscellaneous 71542 30102 41440 82805 31092 51712 1.2.1.5.1 Software Services 41926 4479 37447 47324 5853 41471 1.2.1.5.2 Business Services 23000 16625 6375 29511 15868 13643 1.2.1.5.3 Financial Services 2215 1267 948 1945 703 1242 1.2.1.5.4 Communication Services 519 444 75 503 380 123 1.2.2 Transfers 29520 3215 26304 34048 3029 31019 1.2.2.1 Official 18 312 -293 20 217 -197 1.2.2.2 Private 29502 2904 26598 34028 2812 31216 1.2.3 Income 12689 23546 -10857 12174 25019 -12845 1.2.3.1 Investment Income 10552 22568 -12016 10044 23992 -13948 1.2.3.2 Compensation of Employees 2137 978 1159 2130 1027 1103 2 Capital Account (2.1+2.2+2.3+2.4+2.5) 264502 251395 13107 289090 281389 7700 2.1 Foreign Investment (2.1.1+2.1.2) 183768 176600 7168 173561 166248 7313 2.1.1 Foreign Direct Investment 23925 17701 6224 27222 21517 5705 2.1.1.1 In India 22777 12171 10606 26607 12476 14131 2.1.1.1.1 Equity 16402 11673 4728 19418 12078 7340 2.1.1.1.2 Reinvested Earnings 5225 5225 5876 5876 2.1.1.1.3 Other Capital 1151 498 653 1313 398 914 2.1.1.2 Abroad 1147 5529 -4382 615 9041 -8426 2.1.1.2.1 Equity 1147 2728 -1580 615 4752 -4137 2.1.1.2.2 Reinvested Earnings 0 1712 -1712 0 1884 -1884 2.1.1.2.3 Other Capital 0 1090 -1090 0 2405 -2405 2.1.2 Portfolio Investment 159844 158899 945 146339 144731 1608 2.1.2.1 In India 159240 158343 897 145201 142720 2481 2.1.2.1.1 FIIs 159240 158343 897 145201 142720 2481 2.1.2.1.1.1 Equity 139824 140833 -1009 123636 118245 5391 2.1.2.1.1.2 Debt 19416 17510 1906 21565 24475 -2910 2.1.2.1.2 ADR/GDRs 0 0 0 0 0 0 2.1.2.2 Abroad 604 556 48 1138 2011 -872 2.2 Loans (2.2.1+2.2.2+2.2.3) 31815 26686 5129 65326 59337 5989 2.2.1 External Assistance 3640 2267 1373 3120 2398 722 2.2.1.1 By India 6 26 -20 6 11 -5 2.2.1.2 To India 3634 2241 1393 3114 2387 727 2.2.2 Commercial Borrowings 12627 11098 1529 46754 42206 4548 2.2.2.1 By India 4138 4255 -117 36024 35153 871 2.2.2.2 To India 8489 6843 1646 10730 7053 3677 2.2.3 Short Term to India 15548 13321 2228 15453 14734 719 2.2.3.1 Buyers' credit & Suppliers' Credit >180 days 13729 13321 408 15453 13689 1764 2.2.3.2 Suppliers' Credit up to 180 days 1820 0 1820 0 1045 -1045 2.3 Banking Capital (2.3.1+2.3.2) 36380 33511 2870 33634 35189 -1555 2.3.1 Commercial Banks 36259 33511 2749 33625 35189 -1564 2.3.1.1 Assets 10705 13570 -2865 8579 13083 -4504 2.3.1.2 Liabilities 25554 19941 5614 25046 22106 2939 2.3.1.2.1 Non-Resident Deposits 23426 19401 4025 23778 20164 3614 2.3.2 Others 121 0 121 10 0 10 2.4 Rupee Debt Service 0 61 -61 0 61 -61 2.5 Other Capital 12538 14537 -1999 16568 20554 -3986 3 Errors & Omissions 795 0 795 0 823 -823 4 Monetary Movements (4.1+ 4.2) 0 5226 -5226 0 4508 -4508 4.1 I.M.F. 0 0 0 0 0 0 4.2 Foreign Exchange Reserves (Increase - / Decrease +) 5226 -5226 4508 -4508 Note: P: Preliminary. 158 RBI Bulletin September 2025CURRENT STATISTICS No. 39: India’s Overall Balance of Payments (₹ Crore) Apr-Jun 2024 Apr-Jun 2025 (P) Credit Debit Net Credit Debit Net Item 1 2 3 4 5 6 Overall Balance Of Payments (1+2+3) 4230634 4187037 43597 4669620 4631057 38563 1 Current Account (1.1+ 1.2) 2017438 2089818 -72379 2196416 2216691 -20275 1.1 Merchandise 927317 1459598 -532281 967474 1553197 -585723 1.2 Invisibles (1.2.1+1.2.2+1.2.3) 1090122 630220 459902 1228942 663494 565447 1.2.1 Services 738001 406971 331030 833507 423543 409964 1.2.1.1 Travel 61335 76511 -15177 50087 77720 -27633 1.2.1.2 Transportation 70959 71816 -856 65943 71818 -5876 1.2.1.3 Insurance 7534 4950 2584 7924 5245 2679 1.2.1.4 G.n.i.e. 1346 2575 -1229 1148 2761 -1612 1.2.1.5 Miscellaneous 596827 251118 345709 708405 265999 442406 1.2.1.5.1 Software Services 349760 37363 312397 404862 50070 354792 1.2.1.5.2 Business Services 191873 138694 53178 252469 135754 116715 1.2.1.5.3 Financial Services 18478 10572 7906 16636 6012 10624 1.2.1.5.4 Communication Services 4331 3702 629 4305 3251 1053 1.2.2 Transfers 246264 26824 219440 291287 25911 265375 1.2.2.1 Official 153 2599 -2446 172 1858 -1686 1.2.2.2 Private 246112 24225 221887 291114 24053 267061 1.2.3 Income 105857 196425 -90569 104148 214040 -109892 1.2.3.1 Investment Income 88028 188267 -100239 85926 205255 -119329 1.2.3.2 Compensation of Employees 17829 8158 9670 18222 8785 9437 2 Capital Account (2.1+2.2+2.3+2.4+2.5) 2206565 2097219 109346 2473204 2407326 65878 2.1 Foreign Investment (2.1.1+2.1.2) 1533057 1473256 59801 1484840 1422274 62567 2.1.1 Foreign Direct Investment 199586 147666 51920 232886 184078 48808 2.1.1.1 In India 190016 101538 88478 227624 106733 120892 2.1.1.1.1 Equity 136827 97382 39445 166121 103325 62795 2.1.1.1.2 Reinvested Earnings 43588 0 43588 50274 0 50274 2.1.1.1.3 Other Capital 9600 4156 5444 11230 3407 7822 2.1.1.2 Abroad 9570 46128 -36558 5262 77345 -72084 2.1.1.2.1 Equity 9570 22755 -13184 5262 40654 -35392 2.1.1.2.2 Reinvested Earnings 0 14278 -14278 0 16116 -16116 2.1.1.2.3 Other Capital 0 9095 -9095 0 20575 -20575 2.1.2 Portfolio Investment 1333471 1325590 7881 1251954 1238196 13759 2.1.2.1 In India 1328434 1320949 7485 1242216 1220994 21223 2.1.2.1.1 FIIs 1328434 1320949 7485 1242216 1220994 21223 2.1.2.1.1.1 Equity 1166461 1174878 -8416 1057724 1011603 46121 2.1.2.1.1.2 Debt 161973 146071 15901 184493 209391 -24898 2.1.2.1.2 ADR/GDRs 0 0 0 0 0 0 2.1.2.2 Abroad 5037 4641 396 9738 17202 -7464 2.2 Loans (2.2.1+2.2.2+2.2.3) 265411 222623 42788 558876 507639 51237 2.2.1 External Assistance 30365 18913 11451 26690 20513 6177 2.2.1.1 By India 52 217 -166 52 94 -42 2.2.1.2 To India 30313 18696 11617 26638 20419 6219 2.2.2 Commercial Borrowings 105337 92583 12753 399987 361074 38913 2.2.2.1 By India 34517 35497 -980 308187 300735 7452 2.2.2.2 To India 70820 57087 13733 91800 60339 31461 2.2.3 Short Term to India 129710 111126 18583 132199 126052 6147 2.2.3.1 Buyers' credit & Suppliers' Credit >180 days 114529 111126 3402 132199 117110 15089 2.2.3.2 Suppliers' Credit up to 180 days 15181 0 15181 0 8942 -8942 2.3 Banking Capital (2.3.1+2.3.2) 303498 279556 23942 287747 301047 -13300 2.3.1 Commercial Banks 302487 279556 22931 287664 301047 -13384 2.3.1.1 Assets 89303 113205 -23902 73395 111925 -38530 2.3.1.2 Liabilities 213184 166351 46833 214268 189122 25147 2.3.1.2.1 Non-Resident Deposits 195426 161851 33575 203422 172503 30920 2.3.2 Others 1011 0 1011 84 0 84 2.4 Rupee Debt Service 0 508 -508 0 524 -524 2.5 Other Capital 104599 121277 -16677 141740 175841 -34101 3 Errors & Omissions 6630 0 6630 0 7040 -7040 4 Monetary Movements (4.1+ 4.2) 0 43597 -43597 0 38563 -38563 4.1 I.M.F. 0 0 0 0 0 0 4.2 Foreign Exchange Reserves (Increase - / Decrease +) 0 43597 -43597 0 38563 -38563 Note: P: Preliminary. RBI Bulletin September 2025 159CURRENT STATISTICS No. 40: Standard Presentation of BoP in India as per BPM6 (US$ Million) Item Apr-Jun 2024 Apr-Jun 2025 (P) Credit Debit Net Credit Debit Net 1 2 3 4 5 6 1 Current Account (1.A+1.B+1.C) 241831 250477 -8646 256736 259087 -2351 1.A Goods and Services (1.A.a+1.A.b) 199623 223747 -24124 210514 231059 -20544 1.A.a Goods (1.A.a.1 to 1.A.a.3) 111158 174963 -63805 113087 181551 -68464 1.A.a.1 General merchandise on a BOP basis 111119 166616 -55497 112707 174065 -61359 1.A.a.2 Net exports of goods under merchanting 39 0 39 380 0 380 1.A.a.3 Nonmonetary gold 8347 -8347 7486 -7486 1.A.b Services (1.A.b.1 to 1.A.b.13) 88465 48784 39681 97428 49507 47920 1.A.b.1 Manufacturing services on physical inputs owned by others 268 22 246 253 40 213 1.A.b.2 Maintenance and repair services n.i.e. 81 238 -157 76 267 -191 1.A.b.3 Transport 8506 8609 -103 7708 8395 -687 1.A.b.4 Travel 7352 9171 -1819 5855 9085 -3230 1.A.b.5 Construction 1478 563 915 1098 891 207 1.A.b.6 Insurance and pension services 903 593 310 926 613 313 1.A.b.7 Financial services 2215 1267 948 1945 703 1242 1.A.b.8 Charges for the use of intellectual property n.i.e. 341 4448 -4107 446 5352 -4906 1.A.b.9 Telecommunications, computer, and information services 42541 5215 37326 47932 6470 41462 1.A.b.10 Other business services 23000 16625 6375 29511 15868 13643 1.A.b.11 Personal, cultural, and recreational services 1175 1249 -74 1210 1157 54 1.A.b.12 Government goods and services n.i.e. 161 309 -147 134 323 -188 1.A.b.13 Others n.i.e. 444 475 -31 334 345 -11 1.B Primary Income (1.B.1 to 1.B.3) 12689 23546 -10857 12174 25019 -12845 1.B.1 Compensation of employees 2137 978 1159 2130 1027 1103 1.B.2 Investment income 8660 21944 -13284 8498 23345 -14846 1.B.2.1 Direct investment 3384 12672 -9288 3156 14533 -11377 1.B.2.2 Portfolio investment 70 2411 -2341 113 1815 -1702 1.B.2.3 Other investment 1110 6641 -5531 1001 6826 -5825 1.B.2.4 Reserve assets 4095 220 3876 4228 170 4058 1.B.3 Other primary income 1892 624 1268 1545 647 898 1.C Secondary Income (1.C.1+1.C.2) 29520 3185 26335 34048 3010 31038 1.C.1 Financial corporations, nonfinancial corporations, households, and NPISHs 29502 2904 26598 34028 2812 31216 1.C.1.1 Personal transfers (Current transfers between resident and/non-resident households) 28644 1989 26655 33162 2061 31101 1.C.1.2 Other current transfers 857 914 -57 866 750 115 1.C.2 General government 18 281 -263 20 198 -178 2 Capital Account (2.1+2.2) 185 150 35 177 577 -400 2.1 Gross acquisitions (DR.)/disposals (CR.) of non-produced nonfinancial assets 4 45 -41 23 398 -374 2.2 Capital transfers 182 105 76 154 179 -26 3 Financial Account (3.1 to 3.5) 264317 256501 7816 288913 285339 3574 3.1 Direct Investment (3.1A+3.1B) 23925 17701 6224 27222 21517 5705 3.1.A Direct Investment in India 22777 12171 10606 26607 12476 14131 3.1.A.1 Equity and investment fund shares 21627 11673 9953 25294 12078 13217 3.1.A.1.1 Equity other than reinvestment of earnings 16402 11673 4728 19418 12078 7340 3.1.A.1.2 Reinvestment of earnings 5225 5225 5876 5876 3.1.A.2 Debt instruments 1151 498 653 1313 398 914 3.1.A.2.1 Direct investor in direct investment enterprises 1151 498 653 1313 398 914 3.1.B Direct Investment by India 1147 5529 -4382 615 9041 -8426 3.1.B.1 Equity and investment fund shares 1147 4439 -3292 615 6636 -6021 3.1.B.1.1 Equity other than reinvestment of earnings 1147 2728 -1580 615 4752 -4137 3.1.B.1.2 Reinvestment of earnings 1712 -1712 1884 -1884 3.1.B.2 Debt instruments 0 1090 -1090 0 2405 -2405 3.1.B.2.1 Direct investor in direct investment enterprises 1090 -1090 2405 -2405 3.2 Portfolio Investment 159844 158899 945 146339 144731 1608 3.2.A Portfolio Investment in India 159240 158343 897 145201 142720 2481 3.2.1 Equity and investment fund shares 139824 140833 -1009 123636 118245 5391 3.2.2 Debt securities 19416 17510 1906 21565 24475 -2910 3.2.B Portfolio Investment by India 604 556 48 1138 2011 -872 3.3 Financial derivatives (other than reserves) and employee stock options 6053 9666 -3613 5501 10588 -5087 3.4 Other investment 74496 65009 9487 109851 103996 5855 3.4.1 Other equity (ADRs/GDRs) 0 0 0 0 0 0 3.4.2 Currency and deposits 23547 19401 4146 23788 20164 3624 3.4.2.1 Central bank (Rupee Debt Movements; NRG) 121 0 121 10 0 10 3.4.2.2 Deposit-taking corporations, except the central bank (NRI Deposits) 23426 19401 4025 23778 20164 3614 3.4.2.3 General government 0 0 3.4.2.4 Other sectors 0 0 3.4.3 Loans (External Assistance, ECBs and Banking Capital) 29100 27475 1626 59721 59629 92 3.4.3.A Loans to India 24956 23193 1763 23691 24465 -774 3.4.3.B Loans by India 4144 4281 -137 36030 35163 866 3.4.4 Insurance, pension, and standardized guarantee schemes 47 133 -86 43 92 -49 3.4.5 Trade credit and advances 15548 13321 2228 15453 14734 719 3.4.6 Other accounts receivable/payable - other 6253 4680 1574 10848 9378 1470 3.4.7 Special drawing rights 0 0 3.5 Reserve assets 0 5226 -5226 0 4508 -4508 3.5.1 Monetary gold 0 0 3.5.2 Special drawing rights n.a. 0 0 3.5.3 Reserve position in the IMF n.a. 0 0 3.5.4 Other reserve assets (Foreign Currency Assets) 0 5226 -5226 0 4508 -4508 4 Total assets/liabilities 264317 256501 7816 288913 285339 3574 4.1 Equity and investment fund shares 169302 167301 2001 156227 149648 6579 4.2 Debt instruments 88762 79295 9467 121838 121805 33 4.3 Other financial assets and liabilities 6253 9906 -3652 10848 13886 -3038 5 Net errors and omissions 795 0 795 0 823 -823 Note: P: Preliminary. 160 RBI Bulletin September 2025CURRENT STATISTICS No. 41: Standard Presentation of BoP in India as per BPM6 (₹ Crore) Apr-Jun 2024 Apr-Jun 2025 (P) Item Credit Debit Net Credit Debit Net 1 2 3 4 5 6 1 Current Account (1.A+1.B+1.C) 2017436 2089564 -72128 2196412 2216528 -20117 1.A Goods and Services (1.A.a+1.A.b) 1665317 1866568 -201251 1800981 1976740 -175759 1.A.a Goods (1.A.a.1 to 1.A.a.3) 927317 1459598 -532281 967474 1553197 -585723 1.A.a.1 General merchandise on a BOP basis 926993 1389964 -462971 964221 1489154 -524933 1.A.a.2 Net exports of goods under merchanting 324 0 324 3253 0 3253 1.A.a.3 Nonmonetary gold 0 69634 -69634 0 64043 -64043 1.A.b Services (1.A.b.1 to 1.A.b.13) 738001 406970 331030 833507 423543 409964 1.A.b.1 Manufacturing services on physical inputs owned by others 2234 183 2051 2162 341 1821 1.A.b.2 Maintenance and repair services n.i.e. 676 1983 -1307 654 2286 -1632 1.A.b.3 Transport 70959 71816 -856 65943 71818 -5876 1.A.b.4 Travel 61335 76511 -15177 50087 77720 -27633 1.A.b.5 Construction 12327 4693 7635 9390 7619 1771 1.A.b.6 Insurance and pension services 7534 4950 2584 7924 5245 2679 1.A.b.7 Financial services 18478 10572 7906 16636 6012 10624 1.A.b.8 Charges for the use of intellectual property n.i.e. 2843 37103 -34261 3818 45787 -41969 1.A.b.9 Telecommunications, computer, and information services 354891 43507 311384 410065 55353 354712 1.A.b.10 Other business services 191873 138694 53178 252469 135754 116715 1.A.b.11 Personal, cultural, and recreational services 9803 10418 -615 10353 9894 459 1.A.b.12 Government goods and services n.i.e. 1346 2575 -1229 1148 2761 -1612 1.A.b.13 Others n.i.e. 3703 3965 -262 2858 2953 -95 1.B Primary Income (1.B.1 to 1.B.3) 105857 196425 -90569 104148 214040 -109892 1.B.1 Compensation of employees 17829 8158 9670 18222 8785 9437 1.B.2 Investment income 72241 183061 -110820 72705 199717 -127012 1.B.2.1 Direct investment 28232 105711 -77480 27000 124332 -97331 1.B.2.2 Portfolio investment 582 20112 -19530 969 15529 -14560 1.B.2.3 Other investment 9262 55405 -46143 8563 58401 -49838 1.B.2.4 Reserve assets 34166 1833 32333 36172 1455 34717 1.B.3 Other primary income 15787 5206 10581 13221 5537 7683 1.C Secondary Income (1.C.1+1.C.2) 246262 26570 219692 291283 25749 265534 1.C.1 Financial corporations, nonfinancial corporations, households, and NPISHs 246112 24225 221887 291114 24053 267061 1.C.1.1 Personal transfers (Current transfers between resident and/non-resident households) 238960 16597 222364 283709 17634 266075 1.C.1.2 Other current transfers 7151 7628 -477 7405 6419 986 1.C.2 General government 150 2345 -2195 168 1695 -1527 2 Capital Account (2.1+2.2) 1547 1253 295 1515 4936 -3421 2.1 Gross acquisitions (DR.)/disposals (CR.) of non-produced nonfinancial assets 32 375 -343 199 3401 -3202 2.2 Capital transfers 1515 878 637 1316 1535 -219 3 Financial Account (3.1 to 3.5) 2205020 2139817 65203 2471694 2441116 30578 3.1 Direct Investment (3.1A+3.1B) 199586 147666 51920 232886 184078 48808 3.1.A Direct Investment in India 190016 101538 88478 227624 106733 120892 3.1.A.1 Equity and investment fund shares 180416 97382 83034 216395 103325 113069 3.1.A.1.1 Equity other than reinvestment of earnings 136827 97382 39445 166121 103325 62795 3.1.A.1.2 Reinvestment of earnings 43588 0 43588 50274 0 50274 3.1.A.2 Debt instruments 9600 4156 5444 11230 3407 7822 3.1.A.2.1 Direct investor in direct investment enterprises 9600 4156 5444 11230 3407 7822 3.1.B Direct Investment by India 9570 46128 -36558 5262 77345 -72084 3.1.B.1 Equity and investment fund shares 9570 37033 -27463 5262 56770 -51509 3.1.B.1.1 Equity other than reinvestment of earnings 9570 22755 -13184 5262 40654 -35392 3.1.B.1.2 Reinvestment of earnings 0 14278 -14278 0 16116 -16116 3.1.B.2 Debt instruments 0 9095 -9095 0 20575 -20575 3.1.B.2.1 Direct investor in direct investment enterprises 0 9095 -9095 0 20575 -20575 3.2 Portfolio Investment 1333471 1325590 7881 1251954 1238196 13759 3.2.A Portfolio Investment in India 1328434 1320949 7485 1242216 1220994 21223 3.2.1 Equity and investment fund shares 1166461 1174878 -8416 1057724 1011603 46121 3.2.2 Debt securities 161973 146071 15901 184493 209391 -24898 3.2.B Portfolio Investment by India 5037 4641 396 9738 17202 -7464 3.3 Financial derivatives (other than reserves) and employee stock options 50493 80637 -30144 47060 90579 -43519 3.4 Other investment 621470 542327 79143 939793 889701 50093 3.4.1 Other equity (ADRs/GDRs) 0 0 0 0 0 0 3.4.2 Currency and deposits 196437 161851 34586 203506 172503 31003 3.4.2.1 Central bank (Rupee Debt Movements; NRG) 1011 0 1011 84 0 84 3.4.2.2 Deposit-taking corporations, except the central bank (NRI Deposits) 195426 161851 33575 203422 172503 30920 3.4.2.3 General government 0 0 0 0 0 0 3.4.2.4 Other sectors 0 0 0 0 0 0 3.4.3 Loans (External Assistance, ECBs and Banking Capital) 242762 229201 13561 510918 510131 787 3.4.3.A Loans to India 208194 193487 14707 202679 209303 -6624 3.4.3.B Loans by India 34569 35714 -1146 308239 300829 7410 3.4.4 Insurance, pension, and standardized guarantee schemes 396 1109 -714 366 783 -417 3.4.5 Trade credit and advances 129710 111126 18583 132199 126052 6147 3.4.6 Other accounts receivable/payable - other 52166 39039 13127 92803 80231 12572 3.4.7 Special drawing rights 0 0 0 0 0 0 3.5 Reserve assets 0 43597 -43597 0 38563 -38563 3.5.1 Monetary gold 0 0 0 0 0 0 3.5.2 Special drawing rights n.a. 0 0 0 0 0 0 3.5.3 Reserve position in the IMF n.a. 0 0 0 0 0 0 3.5.4 Other reserve assets (Foreign Currency Assets) 0 43597 -43597 0 38563 -38563 4 Total assets/liabilities 2205020 2139817 65203 2471694 2441116 30578 4.1 Equity and investment fund shares 1412373 1395681 16693 1336545 1280262 56282 4.2 Debt instruments 740481 661501 78980 1042346 1042060 286 4.3 Other financial assets and liabilities 52166 82636 -30470 92803 118794 -25990 5 Net errors and omissions 6630 0 6630 0 7040 -7040 Note: P: Preliminary. RBI Bulletin September 2025 161CURRENT STATISTICS No. 42: India’s International Investment Position (US$ Million) Item As on Financial Year/Quarter End 2024-25 2024 2025 Mar. Dec. Mar. Assets Liabilities Assets Liabilities Assets Liabilities Assets Liabilities 1 2 3 4 5 6 7 8 1. Direct investment Abroad/in India 270441 556812 242271 542952 260755 547104 270441 556812 1.1 Equity Capital* 173559 521931 153343 511142 166493 512997 173559 521931 1.2 Other Capital 96882 34881 88927 31810 94262 34107 96882 34881 2. Portfolio investment 13763 272061 12469 277239 12173 276521 13763 272061 2.1 Equity 8727 141938 10942 162061 9356 155573 8727 141938 2.2 Debt 5036 130123 1527 115178 2817 120948 5036 130123 3. Other investment 186700 640384 132617 574786 170526 619693 186700 640384 3.1 Trade credit 33422 131203 33413 123722 33213 135606 33422 131203 3.2 Loan 25891 250551 17547 221396 22523 240588 25891 250551 3.3 Currency and Deposits 79332 167598 53519 154787 68630 165713 79332 167598 3.4 Other Assets/Liabilities 48055 91032 28138 74880 46160 77785 48055 91032 4. Reserves 668326 646419 635701 668326 5. Total Assets/ Liabilities 1139230 1469257 1033776 1394977 1079156 1443318 1139230 1469257 6. Net IIP (Assets - Liabilities) -330027 -361201 -364162 -330027 Note: * Equity capital includes share of investment funds and reinvested earnings. 162 RBI Bulletin September 2025CURRENT STATISTICS Payment and Settlement Systems No. 43: Payment System Indicators PART I - Payment System Indicators - Payment & Settlement System Statistics System Volume (Lakh) Value (₹ Crore) FY 2024-25 2024 2025 FY 2024-25 2024 2025 Jul. Jun. Jul. Jul. Jun. Jul. 1 2 3 4 5 6 7 8 A. Settlement Systems Financial Market Infrastructures (FMIs) 1 CCIL Operated Systems (1.1 to 1.3) 47.40 4.54 5.30 5.21 296218030 25280807 30959727 32781616 1.1 Govt. Securities Clearing (1.1.1 to 1.1.3) 17.87 1.71 1.77 1.63 185733719 17139102 17946468 19293299 1.1.1 Outright 10.56 1.03 1.05 0.84 16056018 1524120 1611829 1274228 1.1.2 Repo 4.72 0.44 0.51 0.55 77286611 7150905 7894091 8664018 1.1.3 Tri-party Repo 2.58 0.23 0.21 0.24 92391091 8464077 8440549 9355053 1.2 Forex Clearing 28.06 2.71 3.39 3.48 100639565 7417106 11968143 12739681 1.3 Rupee Derivatives @ 1.46 0.12 0.14 0.10 9844746 724598 1045117 748636 B. Payment Systems I Financial Market Infrastructures (FMIs) - - - - - - - - 1 Credit Transfers - RTGS (1.1 to 1.2) 3024.55 246.68 254.88 277.67 201387682 15970680 19012360 18863902 1.1 Customer Transactions 3010.32 245.48 253.73 276.46 181153129 14531533 16983278 16624624 1.2 Interbank Transactions 14.23 1.19 1.15 1.21 20234553 1439147 2029082 2239279 II Retail 2 Credit Transfers - Retail (2.1 to 2.6) 2061014.91 161225.51 200519.84 212335.07 79781976 6480748 6957478 7319321 2.1 AePS (Fund Transfers) @ 3.64 0.31 0.30 0.31 190 13 16 16 2.2 APBS $ 32964.43 2481.39 2841.94 2705.04 554034 28605 48247 42355 2.3 IMPS 56249.68 4902.84 4481.05 4821.90 7139110 593177 606356 631411 2.4 NACH Cr $ 16938.86 1479.29 1325.97 1628.20 1670223 132397 134188 143081 2.5 NEFT 96198.05 8006.14 7920.52 8500.14 44361464 3662264 3764742 3993960 2.6 UPI @ 1858660.25 144355.54 183950.06 194679.48 26056955 2064292 2403931 2508498 2.6.1 of which USSD @ 17.24 1.40 1.18 1.59 185 15 21 31 3 Debit Transfers and Direct Debits (3.1 to 3.3) 21659.95 1734.76 1891.36 1912.72 2208583 175789 210754 217899 3.1 BHIM Aadhaar Pay @ 230.08 19.25 18.27 19.95 6907 575 615 619 3.2 NACH Dr $ 19762.28 1588.11 1728.39 1753.66 2199327 175014 209956 217102 3.3 NETC (linked to bank account) @ 1667.59 127.40 144.70 139.11 2349 200 183 179 4 Card Payments (4.1 to 4.2) 63861.15 5294.35 5672.82 5982.42 2605110 217435 218552 231987 4.1 Credit Cards (4.1.1 to 4.1.2) 47740.76 3837.80 4587.10 4861.81 2109197 172670 183088 193849 4.1.1 PoS based $ 24571.10 1970.94 2338.70 2432.75 795022 62284 67468 70380 4.1.2 Others $ 23169.66 1866.86 2248.41 2429.06 1314175 110386 115620 123469 4.2 Debit Cards (4.2.1 to 4.2.1 ) 16120.39 1456.56 1085.72 1120.61 495914 44765 35463 38138 4.2.1 PoS based $ 11980.33 1068.58 812.90 832.19 332556 28600 23079 23921 4.2.2 Others $ 4140.06 387.98 272.82 288.42 163358 16165 12384 14216 5 Prepaid Payment Instruments (5.1 to 5.2) 70254.08 5356.71 6868.39 7124.06 216751 16327 20740 20758 5.1 Wallets 52898.40 4009.69 5338.82 5431.45 154066 11386 17308 16915 5.2 Cards (5.2.1 to 5.2.2) 17355.68 1347.01 1529.57 1692.61 62686 4941 3432 3842 5.2.1 PoS based $ 8240.14 713.97 595.05 660.66 11512 940 888 908 5.2.2 Others $ 9115.54 633.05 934.51 1031.95 51174 4001 2544 2935 6 Paper-based Instruments (6.1 to 6.2) 6095.38 531.00 447.43 494.59 7113350 610685 550176 607504 6.1 CTS (NPCI Managed) 6095.38 531.00 447.43 494.59 7113350 610685 550176 607504 6.2 Others 0.00 – – – – – – – Total - Retail Payments (2+3+4+5+6) 2222885.46 174142.33 215399.84 227848.86 91925771 7500984 7957700 8397469 Total Payments (1+2+3+4+5+6) 2225910.01 174389.01 215654.72 228126.54 293313453 23471665 26970060 27261371 Total Digital Payments (1+2+3+4+5) 2219814.63 173858.01 215207.29 227631.95 286200103 22860979 26419884 26653867 RBI Bulletin September 2025 163CURRENT STATISTICS PART II - Payment Modes and Channels System Volume (Lakh) Value (₹ Crore) FY 2024-25 2024 2025 FY 2024-25 2024 2025 Jul. Jun. Jul. Jul. Jun. Jul. 1 2 3 4 5 6 7 8 A. Other Payment Channels 1 Mobile Payments (mobile app based) (1.1 to 1.2) 1756976.91 138523.69 170640.11 180368.04 39206221 3141241 3467488 3658470 1.1 Intra-bank $ 110801.96 8897.45 9560.57 10205.70 7207439 584669 611428 634819 1.2 Inter-bank $ 1646174.95 129626.24 161079.54 170162.34 31998782 2556572 2856060 3023651 2 Internet Payments (Netbanking / Internet Browser Based) @ (2.1 to 2.2) 47478.09 4305.75 3635.82 3987.57 131858133 10958678 12824960 12803198 2.1 Intra-bank @ 13056.37 1227.41 839.90 935.66 69086996 5820942 6918867 6734105 2.2 Inter-bank @ 34421.72 3078.34 2795.92 3051.90 62771136 5137737 5906093 6069093 B. ATMs 3 Cash Withdrawal at ATMs $ (3.1 to 3.3) 60308.11 5069.38 4382.08 4472.41 3063077 250318 229907 232955 3.1 Using Credit Cards $ 97.25 8.54 6.28 6.63 5084 433 345 362 3.2 Using Debit Cards $ 59965.70 5040.14 4358.58 4447.66 3046987 248968 228735 231722 3.3 Using Pre-paid Cards $ 245.16 20.70 17.22 18.13 11005 917 827 871 4 Cash Withdrawal at PoS $ (4.1 to 4.2) 3.58 0.29 0.14 0.13 37 3 1 1 4.1 Using Debit Cards $ 3.33 0.27 0.11 0.11 35 3 1 1 4.2 Using Pre-paid Cards $ 0.25 0.02 0.02 0.02 3 0 0 0 5 Cash Withrawal at Micro ATMs @ 11640.55 944.29 944.62 996.95 296622 23498 25646 25574 5.1 AePS @ 11640.55 944.29 944.62 996.95 296622 23498 25646 25574 PART III - Payment Infrastructures (Lakh) System As on March 2024 2025 2025 Jul. Jun. Jul. 1 2 3 4 Payment System Infrastructures 1 Number of Cards (1.1 to 1.2) 11006.97 10677.71 11163.78 11243.50 1.1 Credit Cards 1098.85 1045.68 1111.97 1116.23 1.2 Debit Cards 9908.12 9632.02 10051.80 10127.27 2 Number of PPIs @ (2.1 to 2.2) 13401.35 15211.55 13520.65 13746.53 2.1 Wallets @ 8678.44 11419.62 8681.92 8870.16 2.2 Cards @ 4722.91 3791.93 4838.73 4876.37 3 Number of ATMs (3.1 to 3.2) 2.56 2.55 2.51 2.49 3.1 Bank owned ATMs $ 2.20 2.21 2.15 2.13 3.2 White Label ATMs $ 0.36 0.34 0.36 0.36 4 Number of Micro ATMs @ 14.82 14.78 14.59 14.67 5 Number of PoS Terminals 110.98 89.72 117.91 119.21 6 Bharat QR @ 67.18 61.87 67.21 66.65 7 UPI QR * 6579.30 5851.14 6782.51 6881.22 @: New inclusion w.e.f. November 2019 #: Data reported by Co-operative Banks, LABs and RRBs included with effect from December 2021. $ : Inclusion separately initiated from November 2019 - would have been part of other items hitherto. *: New inclusion w.e.f. September 2020; Includes only static UPI QR Code Notes : 1. Data is provisional. 2. ECS (Debit and Credit) has been merged with NACH with effect from January 31, 2020. 3. The data from November 2019 onwards for card payments (Debit/Credit cards) and Prepaid Payment Instruments (PPIs) may not be comparable with earlier months/ periods, as more granular data is being published along with revision in data definitions. 4. Only domestic financial transactions are considered. The new format captures e-commerce transactions; transactions using FASTags, digital bill payments and card-to-card transfer through ATMs, etc.. Also, failed transactions, chargebacks, reversals, expired cards/ wallets, are excluded. Part I-A. Settlement systems 1.1.3: Tri- party Repo under the securities segment has been operationalised from November 05, 2018. Part I-B. Payments systems 4.1.2: ‘Others’ includes e-commerce transactions and digital bill payments through ATMs, etc. 4.2.2: ‘Others’ includes e-commerce transactions, card to card transfers and digital bill payments through ATMs, etc. 5: Available from December 2010. 5.1: includes purchase of goods and services and fund transfer through wallets. 5.2.2: includes usage of PPI Cards for online transactions and other transactions. 6.1: Pertain to three grids – Mumbai, New Delhi and Chennai. 6.2: ‘Others’ comprises of Non-MICR transactions which pertains to clearing houses managed by 21 banks. Part II-A. Other payment channels 1: Mobile Payments – o Include transactions done through mobile apps of banks and UPI apps. o The data from July 2017 includes only individual payments and corporate payments initiated, processed, and authorised using mobile device. Other corporate payments which are not initiated, processed, and authorised using mobile device are excluded. 2: Internet Payments – includes only e-commerce transactions through ‘netbanking’ and any financial transaction using internet banking website of the bank. Part II-B. ATMs 3.3 and 4.2: only relates to transactions using bank issued PPIs. Part III. Payment systems infrastructure 3: Includes ATMs deployed by Scheduled Commercial Banks (SCBs) and White Label ATM Operators (WLAOs). WLAs are included from April 2014 onwards. 164 RBI Bulletin September 2025CURRENT STATISTICS Occasional Series No. 44: Small Savings (₹ Crore) Scheme 2023-24 2024 2025 Feb. Dec. Jan. Feb. 1 2 3 4 5 1 Small Savings Receipts 232460 14570 11133 12581 11379 Outstanding 1865029 1819758 1982465 1994553 2005585 1.1 Total Deposits Receipts 161344 10025 8734 9178 8077 Outstanding 1298795 1268920 1395484 1404661 1412738 1.1.1 Post Office Saving Bank Deposits Receipts 17229 1520 1090 2702 814 Outstanding 191692 218498 201999 204701 205515 1.1.2 Sukanya Samriddhi Yojna Receipts 35174 2233 2244 2347 2282 Outstanding 157611 109222 177007 179354 181636 1.1.3 National Saving Scheme, 1987 Receipts 0 0 0 0 0 Outstanding 0 0 0 0 0 1.1.4 National Saving Scheme, 1992 Receipts 0 0 0 0 0 Outstanding 0 0 0 0 0 1.1.5 Monthly Income Scheme Receipts 26696 1927 827 1279 1045 Outstanding 269007 267205 282142 283421 284466 1.1.6 Senior Citizen Scheme 2004 Receipts 38167 2153 1531 1922 1952 Outstanding 175472 173476 194605 196527 198479 1.1.7 Post Office Time Deposits Receipts 25341 2632 2125 2853 2108 Outstanding 305776 303000 330912 333764 335872 1.1.7.1 1 year Time Deposits Outstanding 140423 138552 159174 161578 163358 1.1.7.2 2 year Time Deposits Outstanding 11967 11730 14299 14476 14637 1.1.7.3 3 year Time Deposits Outstanding 8932 8782 10308 10487 10645 1.1.7.4 5 year Time Deposits Outstanding 144454 143936 147131 147223 147232 1.1.8 Post Office Recurring Deposits Receipts 18713 -420 1025 -1831 -25 Outstanding 197134 195727 207269 205438 205413 1.1.9 Post Office Cumulative Time Deposits Receipts 0 0 0 0 0 Outstanding 0 0 0 0 0 1.1.10 Other Deposits Receipts 8 -20 -108 -95 -100 Outstanding 1754 1444 1195 1100 1000 1.1.11 PM Care for children Receipts 16 0 0 1 1 Outstanding 349 348 355 356 357 1.2 Saving Certificates Receipts 56069 3940 2226 3019 2858 Outstanding 418021 414597 438074 440601 443112 1.2.1 National Savings Certificate VIII issue Receipts 16853 1446 430 796 762 Outstanding 183905 180181 192621 193417 194179 1.2.2 Indira Vikas Patras Receipts 0 0 0 0 0 Outstanding 0 0 0 0 0 1.2.3 Kisan Vikas Patras Receipts 0 0 0 0 0 Outstanding 0 0 0 0 0 1.2.4 Kisan Vikas Patras - 2014 Receipts 20939 1428 1113 1376 1247 Outstanding 220560 219498 228707 230083 231330 1.2.5 National Saving Certificate VI issue Receipts 0 0 0 0 0 Outstanding 0 0 0 0 0 1.2.6 National Saving Certificate VII issue Receipts 0 0 0 0 0 Outstanding 0 0 0 0 0 1.2.7 M.S. Certificates Receipts 18277 1066 683 847 849 Outstanding 18277 17235 25303 26150 26999 1.2.8 Other Certificates Outstanding -4721 -2317 -8557 -9049 -9396 1.3 Public Provident Fund Receipts 15047 605 173 384 444 Outstanding 148213 136241 148907 149291 149735 Note : Data on receipts from April 2017 are net receipts, i.e., gross receipt minus gross payment. Source: Accountant General, Post and Telegraphs. RBI Bulletin September 2025 165CURRENT STATISTICS No. 45 : Ownership Pattern of Central and State Governments Securities (Per cent) Central Government Dated Securities 2024 2025 Category Jun. Sep. Dec. Mar. Jun. 1 2 3 4 5 (A) Total (in ₹. Crore) 10946860 11271589 11422728 11642652 11854200 1 Commercial Banks 37.52 37.55 37.98 36.18 35.28 2 Co-operative Banks 1.42 1.35 1.36 1.29 1.29 3 Non-Bank PDs 0.70 0.77 0.65 0.76 0.59 4 Insurance Companies 26.11 25.95 26.14 25.81 25.95 5 Mutual Funds 2.87 3.14 3.11 2.68 2.46 6 Provident Funds 4.41 4.25 4.25 4.24 4.35 7 Pension Funds 4.74 4.86 5.05 4.91 4.96 8 Financial Institutions 0.57 0.63 0.64 0.71 0.74 9 Corporates 1.44 1.60 1.45 1.49 1.26 10 Foreign Portfolio Investors 2.34 2.80 2.81 3.12 2.80 11 RBI 11.92 11.16 10.55 12.78 14.21 12 Others 5.97 5.92 6.01 6.01 6.13 12.1 State Governments 2.13 2.19 2.21 2.25 2.29 State Governments Securities 2024 2025 Category Jun. Sep. Dec. Mar. Jun. 1 2 3 4 5 (B) Total (in ₹. Crore) 5727482 5909490 6055711 6399564 6524417 1 Commercial Banks 33.85 34.39 35.11 35.40 35.54 2 Co-operative Banks 3.38 3.29 3.22 3.08 3.02 3 Non-Bank PDs 0.59 0.60 0.53 0.61 0.60 4 Insurance Companies 25.85 25.56 25.16 24.07 24.12 5 Mutual Funds 2.08 1.93 1.89 1.93 1.84 6 Provident Funds 22.94 23.02 22.90 23.60 23.72 7 Pension Funds 4.87 4.87 4.82 5.07 4.96 8 Financial Institutions 1.58 1.57 1.58 1.48 1.59 9 Corporates 2.03 1.95 1.97 2.05 1.93 10 Foreign Portfolio Investors 0.05 0.04 0.03 0.05 0.02 11 RBI 0.62 0.60 0.58 0.55 0.54 12 Others 2.17 2.18 2.19 2.10 2.12 12.1 State Governments 0.26 0.26 0.26 0.25 0.25 Treasury Bills 2024 2025 Category Jun. Sep. Dec. Mar. Jun. 1 2 3 4 5 (C) Total (in ₹. Crore) 858193 747242 760045 790381 784059 1 Commercial Banks 47.79 44.74 40.45 46.58 42.87 2 Co-operative Banks 1.49 1.58 1.22 2.17 1.80 3 Non-Bank PDs 2.69 2.28 1.41 2.09 1.10 4 Insurance Companies 5.78 5.26 4.73 4.23 4.07 5 Mutual Funds 14.50 15.06 15.41 16.15 15.72 6 Provident Funds 0.60 0.26 0.04 0.20 0.09 7 Pension Funds 0.00 0.00 0.00 0.02 0.00 8 Financial Institutions 6.56 6.36 6.77 7.73 6.31 9 Corporates 4.79 4.66 4.56 4.50 3.77 10 Foreign Portfolio Investors 0.20 0.15 0.12 0.09 0.02 11 RBI 0.00 0.00 0.00 0.00 0.00 12 Others 15.59 19.65 25.29 16.23 24.26 12.1 State Governments 11.55 14.95 20.11 11.23 18.34 Notes: (1) The table format is revised since monthly Bulletin for the month of June 2023. (2) Central Government Dated Securities include special securities and Sovereign Gold Bonds. (3) State Government Securities include special bonds issued under Ujwal DISCOM Assurance Yojana (UDAY). (4) Bank PDs are clubbed under Commercial Banks. (5) The category ‘Others’ comprises State Governments, DICGC, PSUs, Trusts, Foreign Central Banks, HUF/ Individuals etc. (6) Data since September 2023 includes the impact of the merger of a non-bank with a bank. 166 RBI Bulletin September 2025CURRENT STATISTICS No. 46: Combined Receipts and Disbursements of the Central and State Governments (₹ Crore) Item 2019-20 2020-21 2021-22 2022-23 2023-24 RE 2024-25 BE 1 2 3 4 5 6 1 Total Disbursements 5410887 6353359 7098451 7880522 9110725 9800798 1.1 Developmental 3074492 3823423 4189146 4701611 5514584 5862996 1.1.1 Revenue 2446605 3150221 3255207 3574503 3965270 4195108 1.1.2 Capital 588233 550358 861777 1042159 1453849 1526993 1.1.3 Loans 39654 122844 72163 84949 95464 140895 1.2 Non-Developmental 2253027 2442941 2810388 3069896 3467270 3800321 1.2.1 Revenue 2109629 2271637 2602750 2895864 3266628 3537378 1.2.1.1 Interest Payments 955801 1060602 1226672 1377807 1562660 1711972 1.2.2 Capital 141457 169155 175519 171131 196073 259346 1.2.3 Loans 1941 2148 32119 2902 4569 3597 1.3 Others 83368 86995 98916 109015 128871 137481 2 Total Receipts 5734166 6397162 7156342 7855370 9054999 9650488 2.1 Revenue Receipts 3851563 3688030 4823821 5447913 6379349 7209647 2.1.1 Tax Receipts 3231582 3193390 4160414 4809044 5456913 6142276 2.1.1.1 Taxes on commodities and services 2012578 2076013 2626553 2865550 3248450 3631569 2.1.1.2 Taxes on Income and Property 1216203 1114805 1530636 1939550 2204462 2506181 2.1.1.3 Taxes of Union Territories (Without Legislature) 2800 2572 3225 3943 4001 4526 2.1.2 Non-Tax Receipts 619981 494640 663407 638870 922436 1067371 2.1.2.1 Interest Receipts 31137 33448 35250 42975 49552 57273 2.2 Non-debt Capital Receipts 110094 64994 44077 62716 86733 118239 2.2.1 Recovery of Loans & Advances 59515 16951 27665 15970 55895 45125 2.2.2 Disinvestment proceeds 50578 48044 16412 46746 30839 73114 3 Gross Fiscal Deficit [ 1 - ( 2.1 + 2.2 ) ] 1449230 2600335 2230553 2369892 2644642 2472912 3A Sources of Financing: Institution-wise 3A.1 Domestic Financing 1440548 2530155 2194406 2332768 2619811 2456959 3A.1.1 Net Bank Credit to Government 571872 890012 627255 687904 346483 ... 3A.1.1.1 Net RBI Credit to Government 190241 107493 350911 529 -257913 ... 3A.1.2 Non-Bank Credit to Government 868676 1640143 1567151 1644864 2273328 ... 3A.2 External Financing 8682 70180 36147 37124 24832 15952 3B Sources of Financing: Instrument-wise 3B.1 Domestic Financing 1440548 2530155 2194406 2332768 2619811 2456959 3B.1.1 Market Borrowings (net) 971378 1696012 1213169 1651076 1962969 1983757 3B.1.2 Small Savings (net) 209232 458801 526693 358764 434151 447511 3B.1.3 State Provident Funds (net) 38280 41273 28100 13880 21386 19857 3B.1.4 Reserve Funds 10411 4545 42153 68803 52385 -33653 3B.1.5 Deposits and Advances -14227 25682 42203 51989 35819 -10138 3B.1.6 Cash Balances -323279 -43802 -57891 25152 55726 150310 3B.1.7 Others 548753 347643 399980 163104 57374 -100684 3B.2 External Financing 8682 70180 36147 37124 24832 15952 4 Total Disbursements as per cent of GDP 26.9 32.0 30.1 29.2 30.8 30.0 5 Total Receipts as per cent of GDP 28.5 32.2 30.3 29.1 30.7 29.6 6 Revenue Receipts as per cent of GDP 19.2 18.6 20.4 20.2 21.6 22.1 7 Tax Receipts as per cent of GDP 16.1 16.1 17.6 17.8 18.5 18.8 8 Gross Fiscal Deficit as per cent of GDP 7.2 13.1 9.5 8.8 9.0 7.6 … : Not available; RE: Revised Estimates; BE: Budget Estimates Source : Budget Documents of Central and State Governments. Notes: GDP data is based on 2011-12 base. GDP for 2024-25 is from Union Budget 2024-25. Data pertains to all States and Union Territories. 1 & 2: Data are net of repayments of the Central Government (including repayments to the NSSF) and State Governments. 1.3: Represents compensation and assignments by States to local bodies and Panchayati Raj institutions. 2: Data are net of variation in cash balances of the Central and State Governments and includes borrowing receipts of the Central and State Governments. 3A.1.1: Data as per RBI records. 3B.1.1: Borrowings through dated securities. 3B.1.2: Represent net investment in Central and State Governments’ special securities by the National Small Savings Fund (NSSF). This data may vary from previous publications due to adjustments across components with availability of new data. 3B.1.6: Include Ways and Means Advances by the Centre to the State Governments. 3B.1.7: Include Treasury Bills, loans from financial institutions, insurance and pension funds, remittances, cash balance investment account. RBI Bulletin September 2025 167CURRENT STATISTICS No. 47: Financial Accommodation Availed by State Governments under various Facilities (₹ Crore) During July-2025 Sr. State/Union Territory Special Drawing Ways and Means Overdraft (OD) No Facility (SDF) Advances (WMA) Average Number Average Number Average Number amount of days amount of days amount of days availed availed availed availed availed availed 1 2 3 4 5 6 7 1 Andhra Pradesh 6012.90 31 765.83 13 701.57 1 2 Arunachal Pradesh - - - - - - 3 Assam 474.28 7 - - - - 4 Bihar - - - - - - 5 Chhattisgarh - - - - - - 6 Goa - - - - - - 7 Gujarat - - - - - - 8 Haryana 313.77 1 - - - - 9 Himachal Pradesh - - 411.89 21 140.27 5 10 Jammu & Kashmir UT 37.45 7 74.81 7 - - 11 Jharkhand 677.06 9 - - - - 12 Karnataka - - - - - - 13 Kerala 1262.45 31 407.81 12 - - 14 Madhya Pradesh - - - - - - 15 Maharashtra - - - - - - 16 Manipur 96.68 17 88.49 8 - - 17 Meghalaya 279.30 26 - - - - 18 Mizoram - - - - - - 19 Nagaland 263.53 16 - - - - 20 Odisha - - - - - - 21 Puducherry - - - - - - 22 Punjab 4774.33 31 1140.12 28 833.11 2 23 Rajasthan 2877.15 26 1313.80 3 - - 24 Tamil Nadu - - - - - - 25 Telangana 5046.19 31 1750.66 28 1933.57 11 26 Tripura - - - - - - 27 Uttar Pradesh - - - - - - 28 Uttarakhand 1022.48 23 - - - - 29 West Bengal - - - - - - Notes: 1. SDF is availed by State Governments against the collateral of Consolidated Sinking Fund (CSF), Guarantee Redemption Fund (GRF) & Auction Treasury Bills (ATBs) balances and other investments in government securities. 2. WMA is advance by Reserve Bank of India to State Governments for meeting temporary cash mismatches. 3. OD is advanced to State Governments beyond their WMA limits. 4. Average amount availed is the total accommodation (SDF/WMA/OD) availed divided by number of days for which accommodation was extended during the month. 5. - : Nil. Source: Reserve Bank of India. 168 RBI Bulletin September 2025CURRENT STATISTICS No. 48: Investments by State Governments (₹ Crore) As on end of July 2025 Consolidated Guarantee Sr. State/Union Government Auction Treasury Sinking Fund Redemption Fund No Territory Securities Bills (ATBs) (CSF) (GRF) 1 2 3 4 5 1 Andhra Pradesh 11966 1181 0 0 2 Arunachal Pradesh 3040 8 0 5350 3 Assam 7730 94 0 0 4 Bihar 13890 - 0 19000 5 Chhattisgarh 8508 987 0 8268 6 Goa 1164 473 0 0 7 Gujarat 15863 688 0 2000 8 Haryana 2706 1756 0 0 9 Himachal Pradesh - - 0 0 10 Jammu & Kashmir UT 37 36 0 0 11 Jharkhand 3094 - 0 780 12 Karnataka 20953 776 0 65925 13 Kerala 3342 - 0 0 14 Madhya Pradesh - 1320 0 1500 15 Maharashtra 73889 2221 0 0 16 Manipur 72 145 0 0 17 Meghalaya 1320 112 0 0 18 Mizoram 522 83 0 0 19 Nagaland 1959 48 0 0 20 Odisha 18946 2120 0 15636 21 Puducherry 599 - 0 2050 22 Punjab 10400 948 0 0 23 Rajasthan 2884 375 0 5750 24 Tamil Nadu 3567 - 0 968 25 Telangana 8167 1790 0 0 26 Tripura 1361 31 0 0 27 Uttarakhand 5865 315 0 0 28 Uttar Pradesh 16554 2914 0 15000 29 West Bengal 14311 1067 0 10000 Total 252706 19487 0 152227 Notes: 1. CSF and GRF are reserve funds maintained by some State Governments with the Reserve Bank of India. 2. ATBs include investment by State Governments in Treasury bills of 91 days, 182 days and 364 days in the primary market. 3. - : Not Applicable (not a member of the scheme). RBI Bulletin September 2025 169CURRENT STATISTICS No. 49: Market Borrowings of State Governments (₹ Crore) 2025-26 Total amount 2023-24 2024-25 raised, so far in May June July 2025-26 Sr. State No. Gross Net Gross Net Gross Net Gross Net Gross Net Amount Amount Amount Amount Amount Amount Amount Amount Amount Amount Gross Net Raised Raised Raised Raised Raised Raised Raised Raised Raised Raised 1 2 3 4 5 6 7 8 9 10 11 12 13 1 Andhra Pradesh 68400 55330 78205 57123 6822 4322 14000 13000 5600 3300 32172 25372 2 Arunachal Pradesh 902 672 1010 704 - - - - - - - -130 3 Assam 18500 16000 19000 13850 2600 2600 - - 1400 1400 4900 3950 4 Bihar 47612 29910 47546 30890 - - - - 6000 6000 6000 6000 5 Chhattisgarh 32000 26213 24500 16913 1000 1000 1000 1000 - -700 3970 3270 6 Goa 2550 1560 1050 250 100 -50 100 100 100 - 300 -100 7 Gujarat 30500 11947 38200 16280 8500 4500 1500 300 3000 3000 13000 5240 8 Haryana 47500 28364 49500 31710 5000 3100 3000 425 3000 945 13000 6470 9 Himachal Pradesh 8072 5856 7359 4725 - - 800 800 1919 1919 4919 4269 10 Jammu & Kashmir UT 16337 13904 13170 11416 800 300 705 705 1100 600 3605 2605 11 Jharkhand 1000 -2505 3500 -2005 - - - - - -1000 - -1000 12 Karnataka 81000 63003 92025 71525 - - - -1000 - - - -1000 13 Kerala 42438 26638 53666 37966 5000 3500 5000 4000 5000 2500 17000 10000 14 Madhya Pradesh 38500 26264 63400 47206 5000 5000 3277 2277 6800 5300 15077 12577 15 Maharashtra 110000 79738 123000 90917 - -3500 8000 6500 24000 21000 45500 37500 16 Manipur 1426 1076 1500 1037 750 750 - - 250 100 1000 650 17 Meghalaya 1364 912 1882 997 - - 500 430 - -50 850 630 18 Mizoram 901 641 1169 939 - - 125 50 100 100 225 150 19 Nagaland 2551 2016 1550 950 - -100 - -100 - - - -200 20 Odisha 0 -4658 20780 17780 - - - - 3000 3000 3000 3000 21 Puducherry 1100 475 1600 880 - - 200 200 - -200 200 - 22 Punjab 42386 29517 40828 32466 5500 4600 4500 2858 5000 4400 20800 16058 23 Rajasthan 73624 49718 75185 49479 8600 6600 9500 4938 5500 4000 29100 19038 24 Sikkim 1916 1701 1951 1621 - - - - - - - - 25 Tamil Nadu 113001 75970 123625 89894 7300 1300 13000 9750 7000 5500 31300 17550 26 Telangana 49618 39385 56209 42199 4500 1152 8500 7200 8500 6000 25900 17752 27 Tripura 0 -550 0 -150 300 300 - - - -200 800 600 28 Uttar Pradesh 97650 85335 45000 23185 3000 1000 - -3233 3000 1000 9000 -2233 29 Uttarakhand 6300 3800 10400 8000 - - 1000 250 1000 1000 3000 2250 30 West Bengal 69910 48910 76500 54600 - -1500 7500 6000 5500 4000 13000 7500 Grand Total 1007058 717140 1073310 753345 64772 34874 82207 56449 96769 72914 297618 197767 - : Nil. Note: The State of J&K has ceased to exist constitutionally from October 31, 2019 and the liabilities of the State continue to remain as liabilities of the new UT of Jammu and Kashmir. Source: Reserve Bank of India. 170 RBI Bulletin September 2025CURRENT STATISTICS No. 50 (a): Flow of Financial Assets and Liabilities of Households - Instrument-wise (Amount in ` Crore) 2022-23 Item Q1 Q2 Q3 Q4 Annual Net Financial Assets (I-II) 287802.7 297217.6 293954.9 451660.3 1330635.4 Per cent of GDP 4.4 4.6 4.3 6.4 4.9 I. Financial Assets 577822.4 632335.6 748109.7 968986.1 2927253.7 Per cent of GDP 8.9 9.8 11.0 13.6 10.9 of which: 1.Total Deposits (a+b) 185429.1 317361.2 280233.1 325852.7 1108876.2 (a) Bank Deposits 163172.4 299532.7 256399.7 307866.8 1026971.5 i. Commercial Banks 158613.3 300565.0 248459.8 284968.0 992606.2 ii. Co-operative Banks 4559.0 -1032.4 7939.8 22898.9 34365.3 (b) Non-Bank Deposits 22256.8 17828.6 23833.5 17985.9 81904.7 of which: Other Financial Institutions (i+ii) 6504.8 2076.7 8081.6 2234.0 18897.1 i. Non-Banking Financial Companies 4230.6 3267.2 3246.9 3945.8 14690.4 ii. Housing Finance Companies 2274.2 -1190.5 4834.7 -1711.8 4206.6 2. Life Insurance Funds 73357.5 151737.1 167581.7 156268.5 548944.9 3. Provident and Pension Funds (including PPF) 146719.1 118171.9 136388.4 216513.6 617793.1 4. Currency 66438.9 -54579.3 76760.1 148990.1 237609.7 5. Investments 51502.6 48530.1 49778.6 64150.6 213961.9 of which: (a) Mutual Funds 35443.5 44484.0 40205.9 58954.5 179087.8 (b) Equity 13560.9 1378.2 6434.1 1664.9 23038.1 6. Small Savings (excluding PPF) 54375.1 51114.5 37367.7 57210.6 200068.0 II. Financial Liabilities 290019.7 335118.0 454154.8 517325.8 1596618.3 Per cent of GDP 4.5 5.2 6.7 7.3 5.9 Loans/Borrowings 1. Financial Corporations (a+b) 289781.5 334879.7 453916.6 517087.5 1595665.3 (a) Banking Sector 234235.0 263450.2 370782.9 383843.2 1252311.4 of which: i. Commercial Banks 230283.8 261265.3 368304.6 331291.0 1191144.8 (b) Other Financial Institutions 55546.4 71429.5 83133.7 133244.3 343353.9 i. Non-Banking Financial Companies 30531.7 36650.3 55791.7 94565.3 217539.1 ii. Housing Finance Companies 22336.7 33031.2 24903.3 36745.8 117017.0 iii. Insurance Corporations 2678.0 1747.9 2438.7 1933.2 8797.8 2. Non-Financial Corporations (Private Corporate Business) 33.7 33.7 33.7 33.7 135.0 3. General Government 204.5 204.5 204.5 204.5 818.0 RBI Bulletin September 2025 171CURRENT STATISTICS No. 50 (a): Flow of Financial Assets and Liabilities of Households - Instrument-wise (Contd.) (Amount in ` Crore) 2023-24 Item Q1 Q2 Q3 Q4 Annual Net Financial Assets (I-II) 349607.1 283994.4 294431.6 666547.4 1594580.4 Per cent of GDP 4.8 3.9 3.8 8.4 5.3 I. Financial Assets 671244.1 810128.8 805066.2 1187279.1 3473718.2 Per cent of GDP 9.3 11.2 10.4 14.9 11.5 of which: 1.Total Deposits (a+b) 266680.3 407948.0 296931.3 406706.9 1378266.4 (a) Bank Deposits 253004.1 501768.5 277432.0 390720.4 1422924.9 i. Commercial Banks 243833.9 502260.7 280096.7 383460.6 1409651.9 ii. Co-operative Banks 9170.2 -492.2 -2664.7 7259.8 13273.0 (b) Non-Bank Deposits 13676.2 -93820.5 19499.4 15986.5 -44658.5 of which: Other Financial Institutions (i+ii) -485.4 -107982.1 5337.7 1824.9 -101304.9 i. Non-Banking Financial Companies 6119.3 4782.3 4895.8 1942.9 17740.3 ii. Housing Finance Companies -6604.7 -112764.4 441.9 -118.0 -119045.2 2. Life Insurance Funds 157301.9 140356.8 160135.2 189267.6 647061.4 3. Provident and Pension Funds (including PPF) 163686.0 148356.1 153435.1 253882.9 719360.2 4. Currency -48636.2 -36700.8 56719.0 146643.8 118025.7 5. Investments 41014.3 72664.6 79238.2 108336.6 301253.8 of which: (a) Mutual Funds 32085.6 55768.8 60134.6 90973.0 238962.1 (b) Equity 3756.7 7146.3 9941.1 8236.1 29080.1 6. Small Savings (excluding PPF) 91197.8 77504.1 58607.4 82441.4 309750.7 II. Financial Liabilities 321637.1 526134.4 510634.6 520731.7 1879137.8 Per cent of GDP 4.5 7.3 6.6 6.5 6.2 Loans/Borrowings 1. Financial Corporations (a+b) 321519.8 526016.2 510516.4 520613.5 1878665.8 (a) Banking Sector 213606.3 868873.9 402647.1 392330.5 1877457.7 of which: i. Commercial Banks 208026.5 875654.0 389898.0 382557.9 1856136.4 (b) Other Financial Institutions 107913.6 -342857.7 107869.2 128283.0 1208.0 i. Non-Banking Financial Companies 81448.8 59683.7 85031.8 100836.5 327000.7 ii. Housing Finance Companies 23784.0 -404294.0 21233.4 25852.9 -333423.7 iii. Insurance Corporations 2680.7 1752.6 1604.0 1593.6 7631.0 2. Non-Financial Corporations (Private Corporate Business) 33.7 34.7 34.7 34.7 138.0 3. General Government 83.5 83.5 83.5 83.5 334.0 172 RBI Bulletin September 2025CURRENT STATISTICS No. 50 (a): Flow of Financial Assets and Liabilities of Households - Instrument-wise (Concld.) (Amount in ` Crore) 2024-25 Item Q1 Q2 Q3 Q4 Annual Net Financial Assets (I-II) 551994.2 496676.1 271043.1 674489.0 1994202.4 Per cent of GDP 7.0 6.3 3.2 7.6 6.0 I. Financial Assets 840665.3 901135.4 689663.5 1129381.1 3560845.4 Per cent of GDP 10.6 11.5 8.1 12.8 10.8 of which: 1.Total Deposits (a+b) 274567.9 403591.4 158320.8 418183.6 1254663.6 (a) Bank Deposits 254885.4 388328.6 141290.0 401577.5 1186081.4 i. Commercial Banks 251171.1 389734.0 147864.7 395337.4 1184107.2 ii. Co-operative Banks 3714.3 -1405.4 -6574.7 6240.0 1974.2 (b) Non-Bank Deposits 19682.4 15262.8 17030.8 16606.1 68582.2 of which: Other Financial Institutions (i+ii) 7461.4 3041.8 4809.8 4385.1 19698.2 i. Non-Banking Financial Companies 6289.7 3230.0 4444.5 4220.0 18184.2 ii. Housing Finance Companies 1171.7 -188.2 365.4 165.1 1514.0 2. Life Insurance Funds 175427.0 178835.2 90159.4 90393.0 534814.6 3. Provident and Pension Funds (including PPF) 170218.2 170219.6 170758.3 281332.6 792528.6 4. Currency 34212.5 -57615.2 70840.8 162236.1 209674.1 5. Investments 120638.2 152637.1 159255.2 103720.8 536251.4 of which: (a) Mutual Funds 106987.0 137618.0 124132.0 97193.0 465930.0 (b) Equity 14448.0 15645.0 36063.1 7410.3 73566.5 6. Small Savings (excluding PPF) 65601.6 53467.4 40329.0 73515.0 232913.0 II. Financial Liabilities 288671.1 404459.3 418620.4 454892.1 1566642.9 Per cent of GDP 3.7 5.2 4.9 5.2 4.7 Loans/Borrowings 1. Financial Corporations (a+b) 288492.4 404280.6 418441.7 454713.3 1565928.0 (a) Banking Sector 205040.4 322147.7 319626.6 387045.6 1233860.3 of which: i. Commercial Banks 208525.3 321241.4 302569.3 379856.5 1212192.4 (b) Other Financial Institutions 83452.0 82132.9 98815.0 67667.7 332067.7 i. Non-Banking Financial Companies 65813.7 65488.7 75764.5 39833.9 246900.8 ii. Housing Finance Companies 15125.2 14233.6 20561.4 25756.8 75677.0 iii. Insurance Corporations 2513.1 2410.7 2489.1 2077.1 9489.9 2. Non-Financial Corporations (Private Corporate Business) 34.7 34.7 34.7 34.7 139.0 3. General Government 144.0 144.0 144.0 144.0 576.0 Notes : 1. Net Financial Savings of households refer to the net financial assets, which are measured as difference of financial asset and liabilities flows. 2. Preliminary estimates for 2024-25 and revised estimates for 2022-23 and 2023-24. 3. The preliminary estimates for 2024-25 will undergo revision with the release of first revised estimates of national income, consumption expenditure, savings, and capital formation, 2024-25 by the NSO. 4. Non-bank deposits apart from other financial institutions, comprises state power utilities, co-operative non credit societies etc. 5. Figures in the columns may not add up to the total due to rounding off. RBI Bulletin September 2025 173CURRENT STATISTICS No. 50 (b): Stocks of Financial Assets and Liabilities of Households- Select Indicators (Amount in ` Crore) Item Jun-2022 Sep-2022 Dec-2022 Mar-2023 Financial Assets (a+b+c+d+e+f+g+h) 25621348.1 26423992.1 27187715.6 27844981.1 Per cent of GDP 102.8 102.6 103.3 103.5 (a) Bank Deposits (i+ii) 11843527.1 12143059.7 12399459.4 12707326.2 i. Commercial Banks 10987692.1 11288257.2 11536717.0 11821685.0 ii. Co-operative Banks 855834.9 854802.6 862742.4 885641.2 (b) Non-Bank Deposits of which: Other Financial Institutions 216170.0 218246.7 226328.2 228562.2 i. Non-Banking Financial Companies 74794.2 78061.4 81308.3 85254.0 ii. Housing Finance Companies 141375.8 140185.3 145020.0 143308.2 (c) Life Insurance Funds 5325967.3 5559681.9 5786592.6 5795430.6 (d) Currency 2950343.2 2895763.9 2972524.0 3121514.1 (e) Mutual funds 2048097.3 2260209.7 2355315.8 2367792.5 (f) Public Provident Fund (PPF) 851913.4 858591.1 864730.6 939449.0 (g) Pension Funds 744459.2 796454.0 853412.0 898343.0 (h) Small Savings (excluding PPF) 1640870.6 1691985.1 1729352.9 1786563.5 Financial Liabilities (a+b) 8911860.9 9246740.6 9700657.2 10217744.7 Per cent of GDP 35.8 35.9 36.9 38.0 Loans/Borrowings (a) Banking Sector 7095467.7 7358918.0 7729700.9 8113544.1 of which: i. Commercial Banks 6620073.1 6881338.5 7249643.0 7580934.1 ii. Co-operative Banks 473897.0 476024.8 478486.9 530915.0 (b) Other Financial Institutions 1816393.1 1887822.6 1970956.3 2104200.7 of which: i. Non-Banking Financial Companies 869174.9 905825.3 961617.0 1056182.3 ii. Housing Finance Companies 835181.3 868212.5 893115.8 929861.7 iii. Insurance Corporations 112036.9 113784.8 116223.5 118156.7 174 RBI Bulletin September 2025CURRENT STATISTICS No. 50 (b): Stocks of Financial Assets and Liabilities of Households- Select Indicators (Contd.) (Amount in ` Crore) Item Jun-2023 Sep-2023 Dec-2023 Mar-2024 Financial Assets (a+b+c+d+e+f+g+h) 28754605.9 29637615.0 30737884.8 32025210.0 Per cent of GDP 104.2 104.4 105.0 106.3 (a) Bank Deposits (i+ii) 12960330.3 13462098.8 13739530.7 14130251.1 i. Commercial Banks 12065518.9 12567779.6 12847876.2 13231336.9 ii. Co-operative Banks 894811.4 894319.2 891654.5 898914.3 (b) Non-Bank Deposits of which: Other Financial Institutions 228076.8 120094.7 125432.4 127257.3 i. Non-Banking Financial Companies 91373.3 96155.6 101051.4 102994.3 ii. Housing Finance Companies 136703.5 23939.1 24381.0 24263.0 (c) Life Insurance Funds 6064436.9 6255801.1 6553726.0 6820611.8 (d) Currency 3072877.9 3036177.0 3092896.0 3239539.8 (e) Mutual funds 2626046.1 2829859.3 3156299.3 3387208.3 (f) Public Provident Fund (PPF) 955060.6 960343.6 964851.5 1051376.5 (g) Pension Funds 970016.0 1017975.0 1091276.0 1172651.0 (h) Small Savings (excluding PPF) 1877761.2 1955265.4 2013872.8 2096314.2 Financial Liabilities (a+b) 10539264.5 11065280.7 11575797.1 12096410.5 Per cent of GDP 38.2 39.0 39.6 40.2 Loans/Borrowings (a) Banking Sector 8327150.3 9196024.2 9598671.3 9991001.8 of which: i. Commercial Banks 7788960.6 8664614.6 9054512.6 9437070.5 ii. Co-operative Banks 536409.2 529527.7 542240.6 551852.1 (b) Other Financial Institutions 2212114.2 1869256.5 1977125.7 2105408.7 of which: i. Non-Banking Financial Companies 1137631.1 1197314.8 1282346.6 1383183.0 ii. Housing Finance Companies 953645.7 549351.7 570585.1 596438.0 iii. Insurance Corporations 120837.4 122590.0 124194.0 125787.7 RBI Bulletin September 2025 175CURRENT STATISTICS No. 50 (b): Stocks of Financial Assets and Liabilities of Households- Select Indicators (Concld.) (Amount in ` Crore) Item Jun-2024 Sep-2024 Dec-2024 Mar-2025 Financial Assets (a+b+c+d+e+f+g+h) 33253098.6 34421189.5 34532805.6 35264710.9 Per cent of GDP 107.9 109.6 107.2 106.6 (a) Bank Deposits (i+ii) 14385136.5 14773465.1 14914755.1 15316332.6 i. Commercial Banks 13482508.0 13872242.0 14020106.6 14415444.1 ii. Co-operative Banks 902628.6 901223.2 894648.5 900888.5 (b) Non-Bank Deposits of which: Other Financial Institutions 134718.7 137760.5 142570.3 146955.5 i. Non-Banking Financial Companies 109284.0 112514.0 116958.5 121178.5 ii. Housing Finance Companies 25434.7 25246.5 25611.9 25777.0 (c) Life Insurance Funds 7123527.6 7385938.1 7272871.3 7293099.1 (d) Currency 3273752.3 3216137.1 3286977.8 3449213.9 (e) Mutual funds 3866386.1 4291914.4 4224091.7 4128924.5 (f) Public Provident Fund (PPF) 1059829.5 1063056.1 1064212.0 1157449.2 (g) Pension Funds 1247832.0 1337535.0 1371615.0 1443509.0 (h) Small Savings (excluding PPF) 2161915.8 2215383.2 2255712.2 2329227.2 Financial Liabilities (a+b) 12384902.9 12789183.5 13207625.1 13662338.5 Per cent of GDP 40.2 40.7 41.0 41.3 Loans/Borrowings (a) Banking Sector 10196042.2 10518189.9 10837816.5 11224862.1 of which: i. Commercial Banks 9645595.7 9966837.1 10269406.4 10649262.8 ii. Co-operative Banks 548284.4 549069.4 566104.4 573131.8 (b) Other Financial Institutions 2188860.7 2270993.6 2369808.7 2437476.4 of which: i. Non-Banking Financial Companies 1448996.8 1514485.5 1590250.0 1630083.9 ii. Housing Finance Companies 611563.2 625796.8 646358.2 672115.0 iii. Insurance Corporations 128300.7 130711.4 133200.5 135277.5 Notes : 1. Data as ratios to GDP have been calculated based on the Provisional Estimates of National Income 2024-25, released by NSO on May 30, 2025. 2. Pension funds comprises funds with the National Pension Scheme. 3. Outstanding deposits with Small Savings are sourced from the Controller General of Accounts, Government of India. 4. Non-bank deposits apart from other financial institutions, comprises state power utilities, co-operative non credit societies etc. Data for outstanding deposits are available only for other financial institutions. 5. Figures in the columns may not add up to the total due to rounding off. 176 RBI Bulletin September 2025CURRENT STATISTICS Explanatory Notes to the Current Statistics Table No. 1 1.2& 6: Annual data are average of months. 3.5 & 3.7: Relate to ratios of increments over financial year so far. 4.1 to 4.4, 4.8,4.9 &5: Relate to the last friday of the month/financial year. 4.5, 4.6 & 4.7: Relate to five major banks on the last Friday of the month/financial year. 4.10 to 4.12: Relate to the last auction day of the month/financial year. 4.13: Relate to last day of the month/ financial year 7.1&7.2: Relate to Foreign trade in US Dollar. Table No. 2 2.1.2: Include paid-up capital, reserve fund and Long-Term Operations Funds. 2.2.2: Include cash, fixed deposits and short-term securities/bonds, e.g., issued by IIFC (UK). Table No. 4 Maturity-wise position of outstanding forward contracts is available at http://nsdp.rbi.org.in under ‘‘Reserves Template’’. Table No. 5 Special refinance facility to Others, i.e. to the EXIM Bank, is closed since March 31, 2013. Table No. 6 For scheduled banks, March-end data pertain to the last reporting Friday. 1.1: Notes in Circulation include CBDC-Retail (R) and CBDC-Wholesale (W). 1.4: Cash on Hand with Banks includes CBDC-W. 2.2: Exclude balances held in IMF Account No.1, RBI employees’ provident fund, pension fund, gratuity and superannuation fund. Table Nos. 7 & 11 3.1 in Table 7 and 2.4 in Table 11: Include foreign currency denominated bonds issued by IIFC (UK). Table No. 8 NM and NM do not include FCNR (B) deposits. 2 3 2.4: Consist of paid-up capital and reserves. 2.5: includes other demand and time liabilities of the banking system. Table No. 9 Financial institutions comprise EXIM Bank, SIDBI, NABARD and NHB. L and L are compiled monthly and L quarterly. 1 2 3 Wherever data are not available, the last available data have been repeated. Table No. 13 Data against column Nos. (1), (2) & (3) are Final and for column Nos. (4) & (5) data are Provisional. RBI Bulletin September 2025 177CURRENT STATISTICS Table No. 14 Data in column Nos. (4) & (8) are Provisional. Table No. 17 2.1.1: Exclude reserve fund maintained by co-operative societies with State Co-operative Banks 2.1.2: Exclude borrowings from RBI, SBI, IDBI, NABARD, notified banks and State Governments. 4: Include borrowings from IDBI and NABARD. Table No. 24 Primary Dealers (PDs) include banks undertaking PD business. Table No. 30 Exclude private placement and offer for sale. 1: Exclude bonus shares. 2: Include cumulative convertible preference shares and equi-preference shares. Table No. 32 Exclude investment in foreign currency denominated bonds issued by IIFC (UK), SDRs transferred by Government of India to RBI and foreign currency received under SAARC and ACU currency swap arrangements. Foreign currency assets in US dollar take into account appreciation/depreciation of non-US currencies (such as Euro, Sterling, Yen and Australian Dollar) held in reserves. Foreign exchange holdings are converted into rupees at rupee-US dollar RBI holding rates. Table No. 34 1.1.1.1.2 & 1.1.1.1.1.4: Estimates. 1.1.1.2: Estimates for latest months. ‘Other capital’ pertains to debt transactions between parent and subsidiaries/branches of FDI enterprises. Data may not tally with the BoP data due to lag in reporting. Table No. 35 1.10: Include items such as subscription to journals, maintenance of investment abroad, student loan repayments and credit card payments. Table No. 36 Increase in indices indicates appreciation of rupee and vice versa. For 6-Currency index, base year 2022-23 is a moving one, which gets updated every year. REER figures are based on Consumer Price Index (combined). The details on methodology used for compilation of NEER/REER indices are available in December 2005, April 2014 and January 2021 issues of the RBI Bulletin. Table No. 37 Based on applications for ECB/Foreign Currency Convertible Bonds (FCCBs) which have been allotted loan registration number during the period. 178 RBI Bulletin September 2025CURRENT STATISTICS Table Nos. 38, 39, 40 & 41 Explanatory notes on these tables are available in December issue of RBI Bulletin, 2012. Table No. 43 Part I-A. Settlement systems 1.1.3: Tri- party Repo under the securities segment has been operationalised from November 05, 2018. Part I-B. Payments systems 4.1.2: ‘Others’ includes e-commerce transactions and digital bill payments through ATMs, etc. 4.2.2: ‘Others’ includes e-commerce transactions, card to card transfers and digital bill payments through ATMs, etc. 5: Available from December 2010. 5.1: includes purchase of goods and services and fund transfer through wallets. 5.2.2: includes usage of PPI Cards for online transactions and other transactions. 6.1: Pertain to three grids – Mumbai, New Delhi and Chennai. 6.2: ‘Others’ comprises of Non-MICR transactions which pertains to clearing houses managed by 21 banks. Part II-A. Other payment channels 1: Mobile Payments – Include transactions done through mobile apps of banks and UPI apps. o The data from July 2017 includes only individual payments and corporate payments initiated, o processed, and authorised using mobile device. Other corporate payments which are not initiated, processed, and authorised using mobile device are excluded. 2: Internet Payments – includes only e-commerce transactions through ‘netbanking’ and any financial transaction using internet banking website of the bank. Part II-B. ATMs 3.3 and 4.2: only relates to transactions using bank issued PPIs. Part III. Payment systems infrastructure 3: Includes ATMs deployed by Scheduled Commercial Banks (SCBs) and White Label ATM Operators (WLAOs). WLAs are included from April 2014 onwards. Table No. 45 (-) represents nil or negligible The table format is revised since monthly Bulletin for the month of June 2023. Central Government Dated Securities include special securities and Sovereign Gold Bonds. State Government Securities include special bonds issued under Ujwal DISCOM Assurance Yojana (UDAY). Bank PDs are clubbed under Commercial Banks. The category ‘Others’ comprises State Governments, DICGC, PSUs, Trusts, Foreign Central Banks, HUF/ Individuals etc. Data since September 2023 includes the impact of the merger of a non-bank with a bank. RBI Bulletin September 2025 179CURRENT STATISTICS Table No. 46 GDP data is based on 2011-12 base. GDP for 2023-24 is from Union Budget 2023-24. Data pertains to all States and Union Territories. 1 & 2: Data are net of repayments of the Central Government (including repayments to the NSSF) and State Governments. 1.3: Represents compensation and assignments by States to local bodies and Panchayati Raj institutions. 2: Data are net of variation in cash balances of the Central and State Governments and includes borrowing receipts of the Central and State Governments. 3A.1.1: Data as per RBI records. 3B.1.1: Borrowings through dated securities. 3B.1.2: Represent net investment in Central and State Governments’ special securities by the National Small Savings Fund (NSSF). This data may vary from previous publications due to adjustments across components with availability of new data. 3B.1.6: Include Ways and Means Advances by the Centre to the State Governments. 3B.1.7: Include Treasury Bills, loans from financial institutions, insurance and pension funds, remittances, cash balance investment account. Table No. 47 SDF is availed by State Governments against the collateral of Consolidated Sinking Fund (CSF), Guarantee Redemption Fund (GRF) & Auction Treasury Bills (ATBs) balances and other investments in government securities. WMA is advance by Reserve Bank of India to State Governments for meeting temporary cash mismatches. OD is advanced to State Governments beyond their WMA limits. Average amount Availed is the total accommodation (SDF/WMA/OD) availed divided by number of days for which accommodation was extended during the month. - : Nil. Table No. 48 CSF and GRF are reserve funds maintained by some State Governments with the Reserve Bank of India. ATBs include Treasury bills of 91 days, 182 days and 364 days invested by State Governments in the primary market. --: Not Applicable (not a member of the scheme). The concepts and methodologies for Current Statistics are available in Comprehensive Guide for Current Statistics of the RBI Monthly Bulletin (https://rbi.org.in/Scripts/PublicationsView.aspx?id=17618) Time series data of ‘Current Statistics’ is available at https://data.rbi.org.in. Detailed explanatory notes are available in the relevant press releases issued by RBI and other publications/releases of the Bank such as Handbook of Statistics on the Indian Economy. 180 RBI Bulletin September 2025RREECCEENNTT PPUUBBLLIICCAATTIIOONNSS Recent Publications of the Reserve Bank of India Name of Publication Price India Abroad 1. 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