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Balancing Innovation and Prudence- AI’s Role in India’s Financial Future1
Distinguished guests, participants, ladies and gentlemen, a very good evening.
I am delighted to address this august gathering of distinguished persons, and key
stakeholders across the financial spectrum in the banking transformation summit on
the theme of ‘Banking That Builds Bharat: AI-Powered, Credit-Driven’, which is
extremely contextual and relevant and encapsulates the spirit of Viksit Bharat.
Introduction
2. The Indian banking system has time and again exhibited its capability to embrace
and adapt to newer technologies. Beginning from the early days of computerization in
the 1980s, to spread of ATMs in the 1990s, the expansion of internet and mobile
banking in the 2000s, interoperable infrastructure for payments, data repositories and
data sharing since 2010s, the banking sector has adapted successfully to the
requirements of the changing times and has over the period made banking more
efficient and inclusive. Banking has now entered into a new phase of evolution driven
by digital democratization. A prime example is the homegrown Unified Payments
Interface (UPI), which has made India a global leader in digital payments which
reinforces our belief that responsible innovation can be a powerful driver of progress.
3. The Government of India has articulated an ambitious vision of “Viksit Bharat” i.e.,
transforming India into a developed country by 2047. This sets the tone for the next
step in our technological journey of financial sector and more specifically, in the
banking sector. Under Viksit Bharat, the goal is for every adult to not only have a bank
account, a target largely achieved under Jan Dhan Yojana2, but also have access to
affordable credit, insurance, and investment options. The endeavour of banking sector
should be to ensure that benefits of banking, more so of credit accessibility, is made
available to customers across all segments on a fair, transparent and affordable basis.
1 Keynote Address delivered by Shri M Rajeshwar Rao, Deputy Governor, Reserve Bank of India on September
16, 2025 at 3rd edition of the CNBC-TV18 Banking Transformation Summit on ‘Banking That Builds Bharat: AI-
Powered, Credit-Driven’ in Mumbai. Inputs provided by Chandni Trehan Saluja and Abhishek Kumar Narwal are
gratefully acknowledged.
2 Accounts have grown from 14.72 crore in 2015 to over 56.16 crore by August 2025, with around 67% in rural/semi-
urban areas and 33% of Jan Dhan accounts were opened in urban/ metro -
https://www.pib.gov.in/PressNoteDetails.aspx?NoteId=155102&ModuleId=3 areas.
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Measures taken by RBI
4. In last five years, between 2019-20 to 2024-25, the bank credit growth has been
averaging around 10.5 percent3. It is also seen that the share of retail credit has grown
to 33 per cent4 while the share of credit to Micro, Small & Medium Enterprises (MSME)
sector has been growing steadily, forming 18 per cent of total bank credit as of March
2025. Before delving into the role of new technologies, let me briefly first touch upon
the steps taken by RBI over the years to increase flow of credit.
5. The RBI has undertaken several measures to expand both the reach and depth of
credit by reducing friction, increasing access points, and lowering the cost of financial
service delivery. Key initiatives include Aadhaar-based KYC, the Central KYC
Registry, the revised Priority Sector Lending norms, Partial Credit Enhancement
Guidelines, and the Account Aggregator framework5. In response to the growing
digitalisation of credit, the Digital Lending Guidelines6 were introduced to ensure
transparency, fairness, data privacy, while enabling fund flow to wider sectors. The
RBI has also enabled formal credit access through frameworks like Trade Receivables
e-Discounting System (TReDS) for MSMEs, co-lending models between banks and
NBFCs, and targeted refinancing schemes.
6. More recently, the RBI has, in collaboration with its subsidiary RBI Innovation Hub
(RBIH), tested a prototype of the Public Tech Platform for Frictionless Credit7 to enable
seamless digital data flow to lenders. Building on this, the Unified Lending Interface
(ULI)8 is being developed to transform credit delivery by integrating access to both
financial and non-financial data such as digitised land records, Goods and Services
Tax Network (GSTN) data, property records, and satellite data along with services like
e-KYC, the Account Aggregator framework, and Credit Guarantee Fund Trust for
Micro and Small Enterprises (CGTMSE). This ecosystem aims to make lending faster,
3 Handbook of Statistics on the Indian Economy, 2024-25 - Table 44 - Scheduled Commercial Banks - Select
Aggregates - Adjusted Bank Credit (excluding the impact of a merger of non-bank with a bank since July 28, 2023).
4 Handbook of Statistics on the Indian Economy, 2024-25 - Table 45 - Sectoral Deployment of Non-Food Gross
Bank Credit - Share of Non-Adjusted Personal Loans
5 https://rbi.org.in/web/rbi/-/notifications/master-direction-non-banking-financial-company-account-
aggregator-reserve-bank-directions-2016-updated-as-on-december-29-2022-10598
6 https://rbi.org.in/web/rbi/-/notifications/reserve-bank-of-india-digital-lending-directions-2025
7 https://rbi.org.in/web/rbi/-/press-releases/reserve-bank-of-india-to-launch-the-pilot-project-for-public-tech-
platform-for-frictionless-credit-56200
8 https://rbihub.in/unified-lending-interface/
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cheaper, and more accessible to underserved segments. Together, this digital public
infrastructure and an enabling regulatory framework would help to drive inclusive credit
growth.
Gaps in access to formal credit
7. Having said that, a lot of distance still needs to be traversed, as the gap in credit
penetration persists. Even today, only around 25 per cent of India’s adult population
have formal access to institutional credit9 while in the MSME sector (which forms ~30
per cent of GDP)10, only part of their credit needs is met by formal institutions11. While
the Financial Inclusion Index developed by RBI has shown steady improvement, rising
from 53.9 in March 2021 to 67.0 in March 202512 reflecting the growth in account
ownership and credit access, scope remains in effecting improvement in the usage
and quality of services delivered. The gap between inclusion and credit access is a
challenge and an opportunity to banking fraternity. The credit needs to not only grow
in terms of volume and numbers but also needs to be directed towards productive,
sectors that deliver higher multipliers such as MSMEs, infrastructure, informal sectors,
and rural population, to achieve not just the goal of “Viksit Bharat” but to have a
“Samaveshi Viksit Bharat”.
The new wave in banking- Artificial Intelligence
8. Driving the next credit revolution will require harnessing new technologies. Over
time, the role of technology in finance has shifted from improving operational efficiency
to fully automating and centralizing previously manual, fragmented processes. Among
emerging technologies, Artificial Intelligence (AI) stands out for its vast potential from
strengthening internal operations and risk management to delivering faster, more
seamless customer experiences. Reports indicate that nearly 70% of Banking,
Financial Services, and Insurance (BFSI) organisations in India have an enterprise
level AI strategy in FY 202413. An RBI study of banks’ annual reports also shows a
9 https://newsroom.transunioncibil.com/more-than-160-million-indians-are-credit-underserved/
10 https://www.pib.gov.in/PressReleasePage.aspx?PRID=2142170
11
https://www.sidbi.in/uploads/Understanding_Indian_MSME_sector_Progress_and_Challenges_13_05_25_Final.p
df
12 https://rbi.org.in/web/rbi/-/press-releases/financial-inclusion-index-for-march-2025 dated July 22, 2025
13 NASSCOM AI Adoption Index – India]
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sharp rise in references to AI, underscoring its growing strategic importance14. While
both demand-side factors (profitability, competition, compliance efficiency) and
supply-side drivers (tech advances, data growth, new business models) influence AI
adoption, supply-side forces remain the primary catalyst.
9. AI as a discipline has evolved over decades and has seen rise of machine learning
systems which can learn from historical data to make decisions, often with high
accuracy. Indian banks and non-banks, embarked on AI journey nearly a decade ago,
mainly handling at that time, large volume of information and supplementary analysis
which added value without displacing established systems. The adoption has since
moved from deployment of AI for back-office functions for efficiency enhancements to
more varied use cases such as in the areas of fraud risk management, optimising IT
operations, facial recognition for KYC, credit scoring, claim processing, and customer
focused services. As observed in the surveys conducted by RBI in 2023 and 202415,
more than three-fourth of the banks have deployed AI-powered chatbots for customer
service. This marks a fundamental shift in which AI is no longer just an enabler but a
part of the decision-making process, product design, and customer engagement.
AI across the credit lifecycle
10. Though, the banks have been using AI in some areas of lending, there is potential
use cases for its usage across other areas of the credit lifecycle. I would like to
highlight a few of them:
(i) Credit Inclusion
11. An important use case would be in the way credit is assessed and distributed. This
would require supplementing existing assessment methods with broader data based
and more refined analysis, whereby financial institutions could gain additional
perspectives on risk and capability of the potential borrowers. This would be a
significant game changer to address the credit requirement of the millions of “credit
invisibles”, i.e., people with no formal credit history or new to credit customers. Given
the availability of a variety of digital footprints which customers have, AI can be
leveraged to peruse alternative data sets, to assess their creditworthiness. This would
14 RBI Bulletin – How Indian Banks are Adopting Artificial Intelligence? [https://rbi.org.in/web/rbi/-/publications/rbi-
bulletin/how-indian-banks-are-adopting-artificial-intelligence-27941]
15 RBI Trends and Progress of banking in India – 2023-24 (Para 10 under Chapter IV)
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mean a paradigm shift from asset-based lending to cash-flow and alternate data-
based lending, promoting a journey towards inclusive digital finance. Government of
India also has recognised the potential of alternate data to drive financial inclusion and
has started initiatives such as the “Grameen Credit Score”16 which aims to provide
underserved communities with formal credit access by analyzing alternative financial
data, including UPI transactions, government subsidy receipts, and utility payments.
(ii) Turnaround Time
12. AI enables banks to process large volumes of customer data quickly, accelerating
credit decisions and service delivery. This is especially valuable in time-sensitive
sectors like MSME working capital, where AI can analyse diverse datasets like bank
statements, payment histories, GST filings, e-invoices, TReDS receivables, and public
records. It also aids lenders in assessing seasonality, supply chains, inventory cycles,
customer concentration, and overall creditworthiness of MSMEs.
(iii) Credit Appraisals
13. The banks can embrace AI algorithms for leveraging behavioural analytics by
analysing vast amounts of transactional data for detecting patterns indicative of
creditworthiness and stable financial behaviour, leading to more accurate credit
assessments and improved decision-making processes.
(iv) Customized Credit Solutions
14. AI’s ability to dynamically assess customer preferences and behaviour can
empower banks to offer customized credit solutions tailored to an individual’s financial
capacity. This not only improves loan accessibility but also ensures that borrowers
receive fair and structured financial products that align with their needs.
(v) Early Warning Signals and Provisioning
15. AI-based early warning systems can help lenders monitor credit portfolios more
effectively through dynamic risk scoring and real-time default probability tracking. By
flagging early signs of financial stress, these systems protect the lender’s balance
sheet while giving borrowers a chance to course-correct. The goal, ultimately, is not
just to lend more but to lend better.
16 https://www.pib.gov.in/PressReleasePage.aspx?PRID=2112198
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(vi) Document Management
16. AI also plays a key role in automating document verification by using techniques
like Optical Character Recognition (OCR) to read and process information from
documents – especially handwritten documents which are generally unstructured,
allowing for automated extraction of data thus reducing rejections. It can also examine
the visual characteristics of the documents to detect forgery, tampering, or
modifications and accurately process large volumes of records, classifying, extracting,
and validating key data, thereby improving efficiency and minimizing errors in the
lending process.
(vii) Customer Support and Grievance Redress
17. One promising use case of AI is the development of multilingual chatbots and
voice assistants which enable customers to interact with banks in their native
language. This is a revolutionary change as by localizing the user experience, the AI
can enable more people across different languages, literacy levels, and abilities to
confidently use formal banking services.
18. Banks are also experimenting with using AI powered virtual assistants to enable
staff to respond to customer queries which can enhance customer experience. Timely
resolution of customer queries boosts trust in the banking system and encourages
deeper engagement, furthering financial inclusion. As digital access has expanded, so
has customer complaints. This highlights another use case for AI. It can efficiently
categorize, prioritize, and route complaints, enabling faster, proactive resolution by
detecting patterns and addressing root causes before issues escalate into grievances.
(viii) Loan Servicing
19. AI-powered loan servicing platforms can create personalised repayment solutions
and reduce operational errors across servicing and portfolio management. They can
also provide new ways of loan servicing by supporting collections and recovery
through prioritised outreach strategies. AI can help to strengthen compliance with
auditable trails of measures initiated for recovery.
(ix) Fraud Risk Management and Cyber Security
20. AI also holds potential in safeguarding the financial system itself. As cybercriminals
increasingly use AI for sophisticated attacks, Regulated Entities (REs) can leverage
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AI-driven tools to protect customers and detect threats. AI can monitor large
transaction volumes in real time, flagging anomalies that may assist in detecting fraud
or money laundering.
Collaboration for Credit Revolution
21. With fintechs advancing rapidly, REs are increasingly partnering with them across
the credit lifecycle. A key model—Digital Lending, involves embedding processes like
KYC, credit assessment, and collection directly into fintech platforms. While such
collaborations enhance financial inclusion and innovation, they also bring risks such
as blurred accountability, data misuse, inadequate grievance redressal, and potential
mis-selling. The guiding principle to address this remains the premise that regardless
of the model used, accountability ultimately rests with the regulated entity.
Risks and challenges of AI - New dimensions and the Ethical imperative
22. Technological advancements come with challenges that REs must recognize, as
these risks can undermine the benefits of innovation. For example, the recent
Supreme Court ruling17 on video-based e-KYC highlighted that mandatory automated
processes may create barriers for people with disabilities, underscoring the need for
fairness and accessibility in technology adoption to avoid exclusions. AI, without
proper controls, can introduce risks like algorithmic bias, lack of transparency, ethical
concerns, and systemic vulnerabilities. It can also amplify existing risks such as third-
party dependencies, concentration, model, cyber, and data privacy risks. Let me
highlight a few:
(i) Third-party dependency
23. As AI adoption grows, financial institutions increasingly rely on complex networks
of external providers, cloud platforms, AI vendors, and data aggregators. These
interdependencies create vulnerabilities where a disruption or breach in one link can
cascade across multiple institutions, disrupting critical services. The complexity and
opacity of these layers makes it difficult to identify risks, which may accumulate
unnoticed and spread rapidly during shocks.
17 https://api.sci.gov.in/supremecourt/2024/17879/17879_2024_13_1501_61229_Judgement_30-Apr-2025.pdf
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(ii) Market Correlation
24. A critical vulnerability of AI is its potential to synchronize behaviors across the
financial system. When institutions use similar models trained on overlapping data,
their decisions on asset pricing, credit assessment, trading, and others may align,
creating hidden linkages. This can amplify market stress, spread shocks rapidly,
worsen liquidity shortages, increase asset price volatility, and trigger sharp, self-
reinforcing market swings.
(iii) Cyber Risk
25. The integration of AI into financial systems—especially through new interaction
methods and increased reliance on specialized providers expands the cyber threat
landscape in unpredictable ways. AI’s strengths, such as reliance on large datasets,
open interfaces, and automated decisions, also create vulnerabilities. Malicious actors
can exploit these through adversarial attacks or compromised training data, potentially
corrupting AI outputs. Even a single breach can disrupt critical operations across
multiple REs and undermine trust in AI across the sector.
(iv) Model Risk
26. Unlike traditional models built on clear rules and well laid out assumptions, AI
models operate through dense, opaque algorithms, which we also refer to as “black
boxes” and evolve with the data they consume. This introduces the risk of prejudice
within models where biased data, opaque design, or untested assumptions may lead
to biased outcomes of model. Such distortions can lead to unfair credit assessment,
excluding deserving segments, or conversely extending credit where risks are
understated.
(v) Data Risk
27. AI is only as strong as the data that shapes it and this leads to a host of
vulnerabilities emanating from data quality. Currently, while most of the financial data
is structured, much of it is fragmented across systems, often in inconsistent formats,
sometimes incomplete or outdated, or skewed by historical biases. When such data
flows into AI models, it can produce results that may appear authentic, but suboptimal
and in some cases may produce wrong outputs. Over-reliance on such models can
quietly turn data gaps into large-scale misjudgments and wrong business decisions.
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(vi) Legal Certainty and Intellectual Property Right issues
28. AI models are often trained on publicly available data, like news stories, articles,
and explainer videos, etc., and may lead to intellectual property and copyright
infringements.
(vii) Concentration Risk
29. Reserve Bank’s Financial Stability Report18 has pointed out the high market
concentration in critical third-party providers of cloud/ AI services, noting that heavy
reliance on a small number of tech players could create single points of failure. Such
concentration risks are compounded by the vertical integration of certain providers,
who supply not just models but also the underlying infrastructure and datasets.
(viii) Frauds and disinformation
30. While AI is transforming many of the processes of financial institutions, the rise of
Generative AI has also lowered the barriers for fraud, putting powerful deception tools
in the hands of malicious actors. Deepfakes can mimic voices, faces, and documents
with unsettling accuracy, while AI-generated phishing lures, fake identities, and forged
credentials can slip past traditional checks.
31. In light of these multi-faceted risks, some of which I have touched upon, it becomes
crucial that adoption of AI in banking sector must be done in a responsible and
measured manner. The excitement around AI’s benefits should not overshadow
prudent risk management. In this context, the following aspects become even more
crucial.
(i) Governance
32. A robust governance is indispensable for ensuring the integrity of data, the reliability
of models, and mitigating the risks associated with adoption of AI. The financial
institutions should have in place a comprehensive strategy for AI adoption. It should
be accompanied by clear policies, risk appetites, criticality, and impact assessments
as well as ethical standards that cascade through the organisation. Robust monitoring
and reporting mechanisms should be put in place to ensure alignment between
18RBI FSR - June 2025 (Para 1.42 under Chapter 1)
https://rbi.org.in/documents/87730/39711208/FSR_JUNE_2025.pdf
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innovation goals and institutional stability. Further, in a regulated industry like banking,
it is essential to understand how a model arrives at its decisions, making explainability
a critical requirement. Thus, there is a need for financial institutions to invest in
Explainable AI frameworks that provide clear, auditable reasons for loan decisions.
Strong governance is central to managing AI-driven model risk.
(ii) Human-in-the-loop
33. While AI can automate and recommend, the humans should be responsible for the
decisions. The financial institutions while adopting AI for business processes should
implement the principle of human-in the-loop to ensure that AI is leveraged as a tool
to support and enhance human decisions and not replace them.
(iii) Maintaining Data quality and security
34. High-quality data is the backbone of safe and effective AI in finance. While the RBI
already collects data through supervisory reports, regulatory returns, and surveys, the
introduction of model risk guidelines, aligned with global best practices, will soon
extend this scope to include data on AI models used by regulated entities. Financial
institutions should therefore adopt robust data strategies, incorporating diverse,
reliable indicators that reflect both the scale of AI adoption and associated
vulnerabilities.
35. When AI is used for credit decisioning or financial inclusion, especially through
alternative data, customer data becomes central, making privacy and security
paramount. The Digital Personal Data Protection (DPDP) Act, 2023 provides the legal
framework for responsible data use, and financial institutions must ensure compliance
through consent-based, privacy-first data handling practices.
(iv) Research and Development
36. Continued investment in research and development is critical for advancing the
capabilities of AI in lending lifecycle for unlocking new opportunities. Research efforts
of the financial institutions should focus on improving data quality and accessibility,
developing novel AI algorithms for enhancing credit inclusion, and addressing key
challenges related to bias, fairness, and interpretability in credit evaluation as well as
enhancement of in-house capabilities to manage concentration risk of providers.
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(v) Industry Collaborations
37. Collaboration and knowledge-sharing among industry stakeholders, including
financial institutions, fintech companies, and academic institutions is essential for
driving innovation and addressing common challenges in AI-driven credit processes.
This can foster the development of best practices, standards, and frameworks for
responsible AI use, promoting transparency, fairness, and accountability in credit
evaluation. Some of the initial areas where the industry can collaboratively work is
harmonising AI taxonomies and developing common benchmarks and metrics.
Regulatory guardrails for new technologies
38. As AI adoption gains traction, regulatory oversight is crucial in ensuring an efficient,
responsible and fair adoption. Recognising the increasing usage of model-driven credit
assessments and decision-making in REs, the RBI had issued a draft circular on
model risk management in credit19, setting out expectations on governance, validation,
monitoring, and accountability. Building on this foundation and recognising the
increasing usage of models by the REs, not only for credit functions but also for wide
spectrum of processes across functional and operational domains, the Bank is in the
process of expanding the scope of these guidelines and would be issuing overarching
Model Risk Management Guidelines applicable across all models. As technologies like
AI are generally not adopted uniformly across the sector and owing to presence of a
varied type of entities with different scales, the principle of proportionality has to be
also factored in. The objective would be to ensure that all REs can adopt technologies
best suited to their business models and customer needs, while effectively managing
risks such as explainability, algorithmic bias, resilience, and over-automation20. In
continuation of this approach, the recently released report of the Committee on
Framework for Responsible, Efficient, and Ethical AI (FREE-AI)21, has laid out seven
guiding sutras for trustworthy AI, and emphasized the need for a robust model risk
management framework by REs.
19 https://rbi.org.in/en/web/rbi/-/regulatory-principles-for-management-of-model-risks-in-credit
20 RBI Annual Report 2024-25: VI.19 under Chapter VI (Regulation, Supervision and Financial Stability)
21 FREE-AI Committee Report
[https://rbi.org.in/documents/87730/30842423/RBI+FREE-AI+Committee+Report_13082025.pdf]
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Conclusion
39. Over the decades, Indian banking sector has exhibited its ability to integrate
meaningful technological advancements. As AI transforms financial services, it's clear
this is a development which is not a mere upgrade but a major shift impacting products,
processes, and operations. From the risk perspective, the long-term implications of AI
adoption on the financial system remain uncertain but exhibit potentially far-reaching
consequences. It is therefore imperative for the financial sector to approach AI
adoption with foresight, investing not just in innovation, but also in resilience by
building strong governance structure, diversifying dependencies, engaging in
continual assessment of emerging risks, and ensuring their AI strategies align with
long-term safety and sustainability of the financial system. Ensuring that AI-driven
decisions are ethical, unbiased, and transparent will be paramount in building a
sustainable, AI-powered financial future. This calls for “optimistic vigilance” wherein AI
and other technologies in banking are neither feared nor embraced blindly but
“navigated”. The RBI, on its part, will continue to provide an enabling regulatory
environment so that together we can build a banking system that truly builds Bharat,
and not just builds, but transforms Bharat.
40. Let me sign off with the thought “Trust is the currency of banking”. Even as we
broaden the credit coverage using algorithms and digital interfaces, maintaining the
trust will be our biggest challenge and also our biggest responsibility.
Thank you.
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