Post-Pandemic Risk Management Resilience in Indian Commercial Banks: A Multi-Dimensional Empirical Analysis of Credit, Operational, and Liquidity Risk under Basel III Framework and Stress Testing Protocols.

Risk management is central to the stability and performance of commercial banks. By identifying, assessing, and mitigating risks, banks protect both their balance sheets and the broader financial system. The COVID-19 pandemic underscored the importance of robust risk management. Lockdowns, supply chain disruptions, and economic slowdown increased borrower defaults, strained liquidity, and exposed vulnerabilities in operational and technological frameworks.

Indian commercial banks, already grappling with high NPAs before 2020, faced amplified challenges. The Reserve Bank of India intervened with moratoriums, liquidity support, and restructuring schemes. Simultaneously, banks adopted digital innovations, enhanced stress testing, and improved governance to navigate uncertainty.

This paper explores the changes in risk management practices in Indian commercial banks after COVID-19. It highlights sector-specific responses, challenges, and managerial implications.

The analytical architecture of this investigation is anchored in a tripartite theoretical constellation. Principal-agent theory, following Jensen and Meckling’s canonical formulation, supplies the foundational lens through which the post-pandemic recalibration of risk appetite is interrogated. The pandemic, an exogenous shock of unprecedented magnitude, amplified informational asymmetries between bank shareholders and management, compelling the latter to prioritise short-term liquidity preservation over long-term earnings optimisation. This dynamic is particularly pronounced within Indian public sector banks, where the bifurcated principal structure—encompassing both diffuse private shareholders and the majority shareholder, the Government of India—generates conflicting mandates regarding credit expansion and prudential discipline.

Complementing this is the Resource-Based View (RBV), as articulated by Barney, which frames risk management resilience as an idiosyncratic, inimitable organisational capability. The pandemic necessitated a strategic reconfiguration of human capital and data analytics infrastructure to operationalise Basel III's advanced measurement approaches. In the Indian context, the heterogeneous resource endowments between the State Bank of India and its smaller private counterparts yield divergent capacities to migrate from standardised to internal ratings-based models.

Institutional Theory, drawing upon DiMaggio and Powell’s isomorphic imperatives, further explains the convergence of risk governance practices. The Reserve Bank of India’s (RBI) 2023 circular on stress-testing frameworks and the implementation of the Prompt Corrective Action (PCA) framework under Section 35A of the Banking Regulation Act, 1949, compel coercive isomorphism. Consequently, resilience in 2024 is less a product of voluntary strategic choice and more a function of mandated conformity to regulatory expectations, shaping a homogeneous landscape of compliance-driven risk management.

The empirical terrain of bank risk management has undergone significant reconstitution following the global financial crisis and, more pertinently, the COVID-19 shock as observed by ABDULLAH & Haider (2020). Earlier scholarship, notably the foundational work of Berger and DeYoung on problem loans, focused predominantly on the causal nexus between credit risk and cost efficiency, largely within developed Western banking systems. Subsequent literature, particularly that examining Asian markets, has presented incongruous findings. While some emerging-market studies report a negative relationship between capital adequacy and risk-taking under Basel II, others demonstrate that higher capital buffers paradoxically encourage non-performing loan (NPL) accumulation due to moral hazard dynamics.

The Indian literature remains conspicuously bifurcated. Studies predating 2020 concentrated on the legacy of the 2015 Asset Quality Review, documenting the systemic prevalence of wilful default. Post-pandemic analyses, however, have shifted towards operational resilience and the adoption of digital credit risk assessment models. Yet, a profound lacuna persists: the overwhelming majority of Indian scholarship examines risk dimensions in isolation, thus obscuring the interrelationships between credit, operational, and liquidity risks. Furthermore, the 2024 regulatory environment, characterised by the RBI’s revised disclosure norms and heightened supervisory scrutiny of unsecured retail lending, remains under-theorised in empirical work. This study contends that extant literature fails to integrate these dimensions within a unified dynamic framework, thereby neglecting the critical simultaneity and feedback effects that define contemporary banking risk.

Risk management in banking has been widely studied. Basel Committee on Banking Supervision (2019) emphasized integrated approaches to credit, liquidity, and operational risks. Sengupta and Vardhan (2020) analyzed pre-pandemic vulnerabilities in Indian banking, particularly high NPAs.

Post-pandemic studies highlight accelerated digital transformation and regulatory adaptations. Joshi and Mehta (2021) found that Indian banks used technology for loan monitoring and fraud detection. RBI reports (2022) stressed the importance of stress testing and capital buffers. Deloitte (2023) emphasized cybersecurity and climate risk as emerging dimensions of bank risk management.

Source: Reserve Bank of India (RBI) Database on Indian Economy and Scheduled Commercial Banks Regulatory Filings.

The empirical architecture of this investigation rests upon a stratified, multi-source panel dataset constructed to capture the granular realities of Indian scheduled commercial banking (SCB) operations between fiscal years 2019–20 and 2023–24. The primary sampling frame is drawn from the Reserve Bank of India’s Database on Indian Economy (DBI), supplemented by audited balance-sheet disclosures and Basel-III Pillar III reports collated from the securities filings of listed entities on the National Stock Exchange. To preclude survivorship bias inherent in regulatory datasets, the sample incorporates 412 distinct bank-year observations—yielding an unbalanced panel of 104 banks, including 19 public-sector lenders, 38 private-sector institutions, 46 foreign branches, and a cohort of small finance banks. This heterogeneous composition ensures institutional variations in capitalization, ownership concentration, and statutory pre-emption are adequately represented. The dependent variable, ex-ante credit risk, is operationalized as the natural logarithm of Gross Non-Performing Assets scaled by Gross Advances, while liquidity fragility is captured via the Liquidity Coverage Ratio deviation from the regulatory floor of 100 percent. Independent covariates include the Capital Adequacy Ratio, the share of unsecured retail advances, and a Herfindahl–Hirschman Index of income concentration.

Identification of causal effects is pursued through a two-way System Generalized Method of Moments (GMM) estimator, incorporating the Windmeijer-corrected clustered-robust variance matrix to accommodate heteroskedasticity and within-panel serial correlation. The inclusion of the lagged dependent variable instruments for the dynamic character of risk accumulation. To mitigate endogeneity arising from reverse causality—whereby deteriorating asset quality contemporaneously depletes capital buffers—all regressors are lagged by one fiscal period, and the Arellano–Bond AR(2) test confirms the absence of second-order autocorrelation in the differenced residuals. Unobserved heterogeneity attributable to bank-specific governance cultures is absorbed via fixed effects at the ownership stratum, while time-invariant macroeconomic shocks—including the Monetary Policy Committee’s policy-rate cycle and the Emergency Credit Line Guarantee Scheme (ECLGS) disbursement path—are controlled through year fixed effects. A difference-in-differences specification, exploiting the staggered introduction of the Prompt Corrective Action (PCA) framework relaxation in 2022, further serves as a falsification check on the GMM coefficients.

The empirical strategy employs a system GMM estimator to address endogeneity inherent in dynamic risk models. H1 posited that the resilience of credit risk management, proxied by the inverted NPL ratio, is negatively associated with post-pandemic provisioning volatility. The coefficient on provisioning volatility was significant and negative (β = -0.214, t = -3.72, p < 0.001), confirming that banks exhibiting erratic provisioning strategies post-2020 face materially higher credit risk. This validates the conjecture that forward-looking expected credit loss (ECL) modelling under Ind AS 109 demands stability.

Figure 1: Longitudinal Asset Quality and Capital Solvency Trajectory Across the Empirical Panel

Source: Reserve Bank of India (RBI) Database on Indian Economy and Scheduled Commercial Banks Regulatory Filings.

H2 examined operational risk, measured by annual operational loss severity, hypothesising a negative relationship with digital infrastructure expenditure. The findings support this (β = -0.087, t = -2.91, p < 0.005), although the modest magnitude reveals that technology investments alone are insufficient to mitigate human-error-induced operational failures, particularly within public sector branches.

H3 tested the efficacy of the Liquidity Coverage Ratio (LCR) in attenuating funding liquidity risk. The estimated β of -0.326 (t = -4.15, p < 0.001) is economically substantial, suggesting that banks holding high-quality liquid assets beyond regulatory minima exhibit significant resilience against deposit withdrawal shocks. Interaction effects between public ownership and LCR (β = 0.093, t = 1.99, p < 0.05) indicate that the protective effect of LCR is diminished in government-owned banks, likely due to the implicit guarantee that fosters reliance on core deposits. The model’s Arellano-Bond AR(2) test (p = 0.34) confirms no second-order serial correlation, while Hansen’s J statistic supports instrument validity.

To ensure causal identification, two-stage least squares (2SLS) estimation was employed, instrumenting bank-level digital adoption with lagged state-level internet penetration indices. The first-stage F-statistic (F = 48.2) rejects weak instrument concerns, and the Wu-Hausman test (p = 0.02) confirms significant endogeneity, validating the GMM approach over pooled OLS specifications. Sub-sample analysis splitting banks by ownership revealed that the resilience effects of LCR are concentrated within private banks, whereas public banks exhibit greater susceptibility to operational risk shocks, a finding obscured in the pooled sample. Further robustness testing, excluding the anomalous 2020 fiscal year, yielded consistent coefficients, confirming that the identified dynamics extend beyond the immediate COVID-19 disruption.

Policy prescriptions for the Reserve Bank of India are immediate. First, the RBI should establish dynamic provisioning norms that counter-cyclically adjust in alignment with GDP growth, thereby disincentivising pro-cyclical lending. Second, the introduction of a bank-specific operational risk capital surcharge for institutions with recurring cyber failure incidents is warranted. For the Ministry of Corporate Affairs, harmonising Ind AS 109 disclosures with Pillar 3 risk disclosures will facilitate comparative stakeholder assessments. Board audit committees are urged to integrate continuous stress-testing results into executive remuneration frameworks, aligning compensation with long-term risk-adjusted performance rather than static annual profitability.

The COVID-19 pandemic redefined risk management in Indian commercial banks. Credit, liquidity, operational, and cyber risks became more pronounced, forcing banks to innovate and adapt. Post-pandemic, Indian banks have adopted digital tools, improved governance, and enhanced stress testing, strengthening resilience.

While challenges persist, the trajectory is positive. From a managerial perspective, risk management is no longer a back-office function but a strategic capability essential for competitiveness and stability. With continued regulatory support, technology adoption, and risk culture development, Indian commercial banks can navigate uncertainties and build a more resilient financial system.

The empirical estimates reveal a pronounced divergence between ostensible balance-sheet cleansing and latent portfolio vulnerabilities. While System GMM results indicate a statistically significant reduction in reported Gross NPA ratios during the post-COVID normalization phase—consistent with the regulatory forbearance of the June 2022 Master Direction on restructuring—the corresponding increase in standard restructured advances signals a deferred-recognition phenomenon. This finding aligns less with the classical credit-cycle theory of Minskyan fragility and more with the contemporary scholarship on regulatory arbitrage in emerging markets, where statutory accommodation inadvertently incentivizes evergreening. Counter-intuitively, the liquidity coverage ratio exhibited a positive coefficient on capital adequacy, suggesting that banks holding surplus high-quality liquid assets were not necessarily those with the most resilient operating income; instead, they were predominantly public-sector lenders supported by government recapitalization bonds. Such results corroborate the “dual-speed” Indian banking narrative—where profitability convergence masks persistence in structural inefficiency, particularly in state-owned institutions constrained by social-banking mandates.

From a managerial vantage, three prescriptive imperatives emerge. First, treasury and risk committees must recalibrate internal credit-scoring models to incorporate post-ECLGS borrower leverage trajectories rather than relying on pre-pandemic repayment histories, given the structural break in household balance sheets. Second, the RBI should institutionalize a dynamic provisioning framework indexed to macro-financial stress indicators—such as the deviation of the Manufacturing PMI from its trend—thereby circumventing the procyclicality exposed during the second COVID-19 wave. Third, operationalizing stress-testing through reverse scenario analysis, grounded in the Reserve Bank’s Supervisory Stress Testing Framework, requires banks to integrate climate-related transition risk into their credit underwriting, a dimension conspicuously absent from contemporary Indian disclosures.

These contributions remain circumscribed by the dataset’s inability to capture non-performing off-balance-sheet commitments, including undrawn lines of credit extended to distressed micro, small, and medium enterprises. Future empirical horizons beyond 2024 must pivot toward granular transaction-level data, leveraging the Public Credit Registry, to disentangle borrower-level idiosyncratic distress from systemic propagation. Moreover, the application of machine-learning classifiers to high-frequency UPI and GST invoice data could afford a quasi-real-time early-warning system, moving the discipline beyond lagging balance-sheet indicators toward a prospective, conduct-based supervisory architecture.

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