Home India Ministry of Finance Parliament Question: Methodology adopted for CIBIL Score...
Date: 2026-08-10 Category: LOKSABHA_QNA State: Union Government Country: India

Parliament Question: Methodology adopted for CIBIL Score

Issued by Ministry of Finance · Not Applicable

Research with AI Agent Chat with Document Generate Summary Translate Helpful Share Add to Project Create Task
Official Source Record View Original Source →
See Full Document Text
GOVERNMENT OF INDIA MINISTRY OF FINANCE DEPARTMENT OF FINANCIAL SERVICES LOK SABHA UNSTARRED QUESTION NO- 3588 ANSWERED ON MONDAY, 10 AUGUST, 2026/SHRAVANA 19, 1948 (SAKA) Methodology adopted for CIBIL Score 3588. SHRI SHAFI PARAMBIL: Will the Minister of FINANCE be pleased to state:- (a) whether the Government has reviewed the methodology adopted by Credit Information Companies, including Credit Information Bureau (India) Limited (CIBIL), for assigning credit scores, if so, the details thereof; (b) whether the Government has received representations regarding irrational or disproportionate decline in credit scores despite timely repayment of loans, if so, the details thereof; (c) the details of complaints received and disposed of during the last five years; and (d) whether the Government proposes to introduce a regulatory framework to enhance transparency, accountability, consumer safeguards and independent review of credit scoring practices and if so, the details thereof? ANSWER THE MINISTER OF STATE IN THE MINISTRY OF FINANCE (SHRI PANKAJ CHAUDHARY) (a) and (b): Credit Information Companies (CICs) function under the extant framework of Credit Information Companies (Regulation) Act, 2005 (CICRA), Credit Information Companies Rules, 2006 (CIC Rules) and Credit Information Companies Regulations, 2006 (CIC Regulations). CICs are regulated and licensed by the Reserve Bank of India (RBI) under the CICRA. RBI has informed that the credit scoring models used by the CICs are proprietary in nature which are based on their respective experience in the business of credit information. CICs generate credit score of a borrower considering multiple factors, including but not limited to, recency and frequency of delinquency, debt level and payment history. RBI has not prescribed any methodology to be adopted by CICs for calculating credit scores. However, with a view to facilitate the understanding and interpretation of credit scores in an easy and consistent manner, the extant RBI directions mandate that credit scores shall be calibrated from 300 to 900 by a CIC to enable common classification of credit scores. Further, in order to sensitize the public CICs have also uploaded on their website the knowledge material for improving the credit score. Score-related queries and complaints have been received by CICs from borrowers due to borrower's incomplete understanding regarding the methodology used to calculate credit scores. Borrowers may expect a higher credit score based primarily on timely repayments, however the credit score reflects a broader assessment of multiple credit-related factors and their interaction over time, including inter alia credit utilisation, credit exposure, balances, overdue, and historical credit behaviour.(c): As per RBI inputs, 15,093 complaints were received against the CICs during the period from FY 2022-23* to FY 2025-26. As on date, all such complaints have been disposed under the Reserve Bank Integrated Ombudsman Scheme, 2021 (RB-IOS, 2021). (*CICs were brought under the purview of RB-IOS, 2021 with effect from 1.9.2022) (d): In order to strengthen and enhance transparency, accountability, consumer safeguards and independent review of credit scoring practices provided by the credit institutions (CIs) and CICs, RBI has stipulated that the following measures be put in place by CIs and CICs. (i) All CIs are required to be members of all CICs and submit data to all of them. (ii) Standardized data formats have been prescribed for reporting of credit information to CICs by CIs under each of the three reporting segments – consumer, commercial and microfinance. (iii) With effect from 1.7.2026, RBI Directions require the CIs to submit credit information with greater frequency i.e. four times a month as against fortnightly reporting mandated earlier to CICs or at such shorter intervals as mutually agreed upon between the CI and CIC. (iv) CICs to send alerts through SMS/email to customers when their CIR is accessed by the Specified Users, wherever mobile number/email ID details of the customers are available. CIs to send alerts through SMS/email to customers while submitting information to CICs regarding default/days past due in existing credit facilities, wherever the mobile number/email ID details are available. (v) CIs to have a dedicated nodal point / official of contact for CICs for redressal of customer grievances. (vi) CIs to inform the customers regarding the reasons for rejection of their request for data correction, if any, to enable such customers to better understand the issues in the CIR. (vii) CIs to undertake Root Cause Analysis of the customer grievances at least on a half yearly basis. (viii) With a view to enable proactive detection of errors in credit reports and credit scores, if any, RBI has also mandated the CICs to provide access, upon request and after due authentication of the requester, to one free full credit report including credit score, once every year. (ix) RBI has also prescribed a compensation framework for delayed updation/rectification of credit information by CIs/CICs wherein complainants would be entitled to a compensation of Rs. 100 per day for delayed resolution beyond 30 days of filing the complaint. Further, RBI has issued a draft on “Guidance on Regulatory Principles for Model Risk Management (MRM), 2026” on 24.6.2026, which lays down a principle-based approach to MRM for all models used by Regulated Entities (REs), including CICs. It provides that an RE is accountable for the outcomes of all models used by it and covers aspects such as Board-approved MRM Framework, independent validation and audit, risk tiering, consumer protection and Artificial Learning/Machine Learning specific guidance. *****

Continue your research