A Study on Credit Risk Assessment Strategies at Punjab National Bank (PNB)

Abstract

This paper examines the credit risk assessment strategies employed by Punjab National Bank (PNB), India\'s second-largest public sector bank, over the period FY2019–FY2025. The study traces PNB\'s recovery from the landmark ?14,000 crore fraud of 2018 through a comprehensive redesign of its credit risk infrastructure. Drawing on PNB\'s annual reports, Pillar 3 disclosures, and RBI publications, the paper empirically analyses key credit quality indicators including Gross NPA, Net NPA, Provision Coverage Ratio (PCR), Capital to Risk-Weighted Assets Ratio (CRAR), and Credit Cost. Principal findings indicate a dramatic improvement in asset quality — Gross NPA fell from 15.50% in FY2019 to 3.95% in FY2025 — alongside a restoration of capital adequacy (CRAR: 9.73% to 17.01%) and a return to sustained profitability (Net Profit: ?16,630 Cr in FY2025). The paper also evaluates PNB\'s technological interventions, including its Early Warning System (EWS), machine learning-based MSME credit scoring, and Account Aggregator integration. Strategic recommendations are offered across six domains: IRB transition, ML production deployment, advanced stress testing, credit culture reform, legacy NPA resolution, and ESG credit risk integration.

Introduction

The study analyzes Punjab National Bank (PNB)’s credit risk management and financial recovery following major banking failures, especially the 2018 fraud incident. It explains that banking crises typically arise from poor credit risk management, where bad loans are not properly identified or provisioned. PNB, one of India’s largest public sector banks, underwent major structural reforms after the fraud, including system integration improvements, merger with other banks, and strengthened risk frameworks.

The research focuses on credit risk concepts such as Probability of Default (PD), Loss Given Default (LGD), and Expected Credit Loss (ECL), along with regulatory frameworks like Basel III and RBI norms. It reviews existing academic models for credit risk prediction and highlights that while advanced machine learning methods improve accuracy, explainability and practical implementation remain challenges.

Using secondary data from FY2019–FY2025, the study shows strong financial recovery: Gross NPAs fell sharply from 15.5% to 3.95%, net NPAs reduced to 0.4%, capital adequacy improved to 17.01%, and PNB returned to strong profitability by FY2025. The main drivers of improvement include recapitalization, better risk controls, reduced credit costs, and stronger resolution mechanisms.

Conclusion

Six years is long enough to see whether a recovery is real. PNB entered FY2019 in a genuinely precarious position — the aftermath of the largest banking fraud in Indian history, NPAs at 15.50%, capital barely above the regulatory minimum, and a merger with two other distressed banks looming. By FY2025, the picture looks substantially different: Gross NPA at 3.95%, CRAR at 17.01%, and net profit of ?16,630 crore — more than double the previous year. The evidence suggests the institutional changes are real, even if incomplete. The SWIFT-CBS integration closed a control gap that should never have existed. The Early Warning System\'s NPA prediction accuracy going from 62% to 84% is a meaningful operational improvement. Post-2018 credit vintages are performing materially better than pre-2018 ones at comparable ages. These are not just numbers — they reflect a different way of managing credit. The challenges that remain are also real: sectoral concentration in residual NPAs carries tail risk that headline ratios understate; the technology gap relative to private sector peers requires sustained investment to close; and credit risk culture across 1,03,000 employees cannot be changed by a policy document or a rating model revision alone. What PNB\'s recent history demonstrates — perhaps more clearly than any other single institution — is that even after a genuine crisis, recovery is possible when reforms are substantive rather than cosmetic.

References

[1] E. I. Altman, \"Financial ratios, discriminant analysis and the prediction of corporate bankruptcy,\" Journal of Finance, vol. 23, no. 4, pp. 589–609, 1968. [2] R. C. Merton, \"On the pricing of corporate debt: The risk structure of interest rates,\" Journal of Finance, vol. 29, no. 2, pp. 449–470, 1974. [3] J. A. Ohlson, \"Financial ratios and the probabilistic prediction of bankruptcy,\" Journal of Accounting Research, vol. 18, no. 1, pp. 109–131, 1980. [4] M. E. Zmijewski, \"Methodological issues related to the estimation of financial distress prediction models,\" Journal of Accounting Research, vol. 22, pp. 59–82, 1984. [5] S. Lessmann, B. Baesens, H. V. Seow, and L. C. Thomas, \"Benchmarking state-of-the-art classification algorithms for credit scoring,\" European Journal of Operational Research, vol. 247, no. 1, pp. 124–136, 2015. [6] P. M. Addo, D. Guegan, and B. Hassani, \"Credit risk analysis using machine and deep learning models,\" Risks, vol. 6, no. 2, p. 38, 2018. [7] R. Rajan and S. C. Dhal, \"Non-performing loans and terms of credit of public sector banks in India: An empirical assessment,\" RBI Occasional Papers, vol. 24, no. 3, pp. 81–121, 2003. [8] V. Kumar and P. Kishore, \"Macroeconomic and bank specific determinants of non-performing loans in Indian PSBs,\" Journal of Emerging Economies & Islamic Research, vol. 7, no. 1, pp. 1–17, 2019. [9] Basel Committee on Banking Supervision, Basel III: A global regulatory framework for more resilient banks and banking systems, Bank for International Settlements, 2010. [10] Reserve Bank of India, Master Circular on Prudential Norms on Income Recognition, Asset Classification and Provisioning, RBI/2023-24/53, Mumbai, 2023. [11] Punjab National Bank, Annual Reports FY2019 through FY2025, including Pillar 3 Disclosures, PNB Investor Relations, New Delhi, 2019–2025. [12] B. M. Misra and S. Dhal, \"Pro-cyclical management of banks\' non-performing loans by the Indian public sector banks,\" BIS Asian Research Papers, Bank for International Settlements, 2010. [13] Government of India, The Insolvency and Bankruptcy Code, 2016, Ministry of Law and Justice, New Delhi. [14] KPMG India, Banking on India: Annual Banking Survey 2024, KPMG Advisory Services, Mumbai, 2024.

Copyright

Copyright © 2026 Priyanshu Kumari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.