Job Summary

Credit Risk in Indian Banking: What RBI s Data Actually Shows Every risk professional in Indian banking eventually asks the same question: is credit risk actually improving, or does it just look that way in aggregate numbers Based on the Reserve Bank of India s own published data, the answer is both. System-wide asset quality has genuinely strengthened over the past five years. But the composition of that risk is shifting in a direction that deserves closer attention.

This piece works through RBI s Financial Stability Reports (FSR), sectoral credit data, and the newly finalized Expected Credit Loss (ECL) framework. Together they show what s really happening with credit risk in Indian banking between 2020 and 2025. It does not rely on a proprietary survey or projected estimates dressed up as findings. In fact, every figure below is sourced directly to a named RBI report.

That distinction matters: in a domain where regulators, auditors, and rating agencies check your numbers, credibility is the entire product.

Three questions structure the analysis. How has aggregate asset quality moved since the 2020 pandemic shock Where is risk concentrating today, even as headline numbers improve And what does the incoming ECL regime signal about how Indian banks will need to manage credit risk going forward

Methodology and Data Sources

This analysis draws on primary RBI publications, cross-checked across multiple reporting periods for consistency. RBI Financial Stability Reports (FSR): Published twice yearly. They consolidate Gross NPA (GNPA) and Net NPA (NNPA) ratios of Scheduled Commercial Banks (SCBs), capital adequacy (CRAR), bank-group-wise asset quality, and stress test results. Specifically, this piece uses FSR editions from January 2021 through December 2025.

RBI Sectoral Deployment of Bank Credit data: Monthly data on credit growth to industry, services, agriculture, and personal loans. It s sourced from 41 banks representing roughly 95% of non-food credit.

RBI s ECL framework releases: The draft ECL directions (October 2025) and final directions (April 2026). These describe the shift from incurred-loss to forward-looking PD/LGD/EAD-based provisioning, effective .

One scope note: RBI does not publish a standardized default rate by loan product table. Where this article cites loan-category or bank-group figures, they are GNPA ratios: the share of gross advances classified as non-performing. It s the metric RBI itself uses,



and the one directly comparable across periods.

Finding 1: Asset Quality Has Improved for Five Consecutive Years

SCB GNPA: from 8% to 2.1% in five years

- March 2020 GNPA: 8.4%
- September 2020 GNPA: 7.5%
- March 2024 GNPA: 2.8%
- March 2025 GNPA: 2.3%
- September 2025 GNPA: 2.1% (2.2%)
- March 2027 (projected, baseline): 1.9%

RBI s January 2021 report recorded a September 2020 GNPA ratio of 7.5%, down from 8.4% in March 2020. That was a system still absorbing the pandemic shock. GNPA had fallen to 2.8% by June 2024, then to 2.3% by March 2025. It touched a multi-decade low of 2.1% by September 2025, and RBI projects further improvement to 1.9% by March 2027 under its baseline scenario. In practice, this reflects five years of balance sheet cleanup: post-IBC resolution of legacy corporate stress, tighter underwriting after the 2PHONE_NUMBER NBFC stress episode, and stronger capital buffers overall. Meanwhile, system-wide CRAR remains comfortably above regulatory minimums, with public sector banks at 16% and private banks at 18.1% as of September 2025.

In short, aggregate GNPA is a lagging confirmation of underwriting discipline, not a leading indicator. A PD model trained mainly on 2PHONE_NUMBER stressed data will overstate current default risk. One trained only on 2PHONE_NUMBER benign data risks understating tail risk in the next downturn.

Finding 2: Improvement Isn t Even Across Bank Groups

PSBs are catching up fast

For instance, PSB GNPA fell sharply from 3.7% in March 2024 to 2.8% in March 2025. Meanwhile, private bank GNPA held roughly stable at 2.8% over the same period, and foreign banks improved from 1.2% to 0.9%. Even so, this convergence matters. For most of the post-2015 asset-quality-review era, PSB asset quality lagged private banks significantly, largely on corporate exposures. That gap has now nearly closed at the aggregate level. However, remaining risk differs by bank group, which leads to the more consequential finding below.

Finding 3: Unsecured Retail Is Where Current Risk Concentrates





The retail risk hiding inside a good headline number

This is the most important finding for practitioners, because it sits underneath the reassuring headline number. According to RBI s December 2025 FSR, roughly 53.1% of retail loan slippages now originate from unsecured products like personal loans and credit cards. At private banks, unsecured loans account for nearly 76% of fresh slippages. GNPA on unsecured retail loans stood at 1.8%, versus 1.1% for overall retail advances. In other words, the 2.1% aggregate GNPA figure blends a very clean secured/corporate book with a smaller, faster-deteriorating unsecured retail book. RBI flagged this as a fintech-adjacent risk, tied to fast credit growth in small-ticket personal loans to borrowers under 35 through digital lending channels.

This pattern, in fact, tracks with operational experience. Unsecured lending has weaker recovery mechanics (no collateral to liquidate, higher LGD), shorter behavioral history on new-to-credit borrowers, and faster origination cycles that compress underwriting review. Moreover, it is the segment where forward-looking provisioning matters most, since unsecured risk builds up quietly between formal NPA recognition points.

As a result, portfolio-level GNPA alone is no longer sufficient. Overall, segment-level GNPA and vintage curves for unsecured retail belong alongside the aggregate number in any board-level risk dashboard.

Finding 4: ECL Will Formalize This Shift

Why the 2027 ECL shift matters here

RBI has issued directions introducing forward-looking ECL provisioning, replacing the incurred-loss model. It takes effect , for scheduled commercial banks excluding RRBs, Small Finance Banks, and payments banks. ECL provisioning must be based on a bank s own historical PD and LGD data spanning at least five years, subject to RBI-specified floors. Accounts 30 90 days past due move into Stage 2, a materially earlier trigger than the current framework. Overall, the shift aligns India s prudential norms with global IFstandards. In addition, it requires closer integration between finance and risk functions, as forward-looking macroeconomic scenarios become a formal input to provisioning.

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📌 RBI Data Analysis Professionals (Gurugram)
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