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The novel coronavirus (COVID-19) has caused financial stress and limited their lending agility, resulting in more non-performing loans (NPLs) and lower performance during the II wave of the coronavirus crisis. Therefore, it is essential to identify the risky factors influencing the financial performance of Indian banks spanning 2018–2022.

Our sample consists of a balanced panel dataset of 75 scheduled commercial banks from three different ownership groups, including public, private and foreign banks, that were actively engaged in their operations during 2018–2022. Factor identification is performed via a fixed-effects model (FEM) that solves the issue of heterogeneity across different with banks over time. Additionally, to ensure the robustness of our findings, we also identify the risky drivers of the financial performance of Indian banks using an alternative measure, the pooled ordinary least squares (OLS) model.

Empirical evidence indicates that default risk, solvency risk and COVAR reduce financial performance in India. However, high liquidity, Z-score and the COVID-19 crisis enhance the financial performance of Indian banks. Unsystematic risk and systemic risk factors play an important role in determining the prognosis of COVID-19. The study supports the “bad-management,” “moral hazard” and “tail risk spillover of a single bank to the system” hypotheses. Public sector banks (PSBs) have considerable potential to achieve financial performance while controlling unsystematic risk and exogenous shocks relative to their peer group. Finally, robustness check estimates confirm the coefficients of the main model.

This study contributes to the knowledge in the banking literature by identifying risk factors that may affect financial performance during a crisis nexus and providing information about preventive measures. These insights are valuable to bankers, academics, managers and regulators for policy formulation. The findings of this paper provide important insights by considering all the risk factors that may be responsible for reducing the probability of financial performance in the banking system of an emerging market economy.

The empirical analysis has been done with a fresh perspective to consider unsystematic risk, systemic risk and exogenous risk (COVID-19) with the financial performance of Indian banks. Furthermore, none of the existing banking literature explicitly explores the drivers of the I and II waves of COVID-19 while considering COVID-19 as a dependent variable. Therefore, the aim of the present study is to make efforts in this direction.

By incorporating the role of nonperforming loans (NPLs), the study aims to assess the impact of global financial crisis (GFC) on the intermediation efficiency of Indian banks for the period of 1998/99 to 2016/17.

To obtain efficiency level of Indian banks, this study applied sequential data envelopment analysis (DEA) based directional distance function (DDF) approach, which performed simultaneous expansion of desirable output and reduction of undesirable output in the bank's loan production structure. Additionally, using fixed effect regression approach in the panel data framework, this study assesses both the phenomenon of σ- and unconditional β-efficiency convergence in public sector banks (PSBs), private banks (PBs), foreign banks (FBs) and overall scheduled commercial banks (SCBs) during the pre-crisis, crisis and post-crisis years in India.

Irrespective of the bank's production model, the evidence suggests that the accounting NPLs as an undesirable output significantly deteriorating the intermediation technical efficiency levels of Indian banks, especially after the crisis years until the last year of the study period. This reflects that Indian banks failed more to achieve their financial intermediation objective in the post-crisis years as compared to the crisis and pre-crisis years. In-depth, statistical evidence of commercial bank ownership groups reveals that public sector banks exhibit a higher level of efficiency in pursuance of traditional loan-based activity followed by private and foreign banks. The study also found the existence of sigma convergence in technical efficiency levels of Indian banks and ownership groups as well.

This study is perhaps the first one, which present the robust evolution of Indian banks intermediation efficiency by taking into account both endogenous (i.e. NPLs as an undesirable output and equity as a quasi-fixed input in the bank production process) crisis and exogenous (i.e. global financial and economic stress) crises. Moreover, none of the existing studies have conducted sub-period wise analysis to show the apparent occurrence of both convergence properties in technical efficiency, adding novelty in the literature.

Comparing conventional data envelopment analysis (DEA) model with contemporary Seiford and Zhu model, this study aims to evaluate the technical efficiency (TE) of Indian banks from 1998/99 to 2016/17 in the presence of non-performing loans (NPLs).

To examine TE, this study has considered a novel approach, i.e. linear monotone decreasing transformation as suggested by Seiford and Zhu (2002), which treats undesirable output as a desirable output in the framework of Charnes, Cooper and Rhodes (CCR)-based output-oriented DEA approach. In particular, to remove the biasness from the estimated efficiency scores, Simar Wilson (1998, Algorithm #1) has been applied, which is perhaps the first attempt in this kind of literature till now. This study further tries to investigate the notion of sigma and unconditional β-convergence in TE using two-step system generalized method of moments model in dynamic panel framework.

Treatment of NPLs using conventional DEA model misinterprets the TE scores, while a true picture emerges when the NPLs are correctly accounted as an undesirable output in banks’ loans production process. Efficiency has declined during the crisis years, but it recovered immediately after the crisis years in India. However, a sudden and steep deterioration in efficiency scores has been seen from 2013 till the most recent study period. Public sector banks and old private banks have reported higher average efficiency scores than new private banks (NPBs) and foreign banks (FBs) in India. However, FBs are the only commercial banks that maintained their efficiency levels during crisis years in India. This study also saw the persistence and presence of σ-convergence phenomena in TE for Indian banks, reflecting the ability to reach up to “Catch-up” phenomenon owing to the lower dispersion and persistence of convergence in TE by the Indian banks.

The actual efficiency score can only be estimated when the NPL will be considered as an undesirable output rather than a desirable output when designing the loan production process of banks. Although the ownership clusters of all commercial banks in India need to formulate stricter policies to increase the level of assets quality and efficiency, but, NPBs need to pay some more efforts in this direction. This study’s outcome has the potential to provide useful information for regulators and policymakers, which suggests that in which direction or in which clusters improvement are needed to raise the level of asset quality and technical efficiency in the coming years.

For a long time, there has been the existence of trade-offs between researchers, like which is the best model for accounting for NPLs? Traditional or contemporary? Thus, our study aims to add knowledge to the limited stock of NPL modelling in the efficiency literature. Dynamic convergence in TE scores in Indian banks has yet not to be tested, which is another novelty of the study.

This study aims to capture the “persistence effect” of credit risk in Indian banking industry using the bank-level data spanning over the period of 19 years from 1998/1999 to 2016/17. Alongside, the study explored how the bank-specific, industry-specific, macroeconomic variables alongside regulatory reforms, ownership changes and financial crisis affect the bank's asset quality in India.

Using two-step system generalized method of moment (GMM) approach, the study derives key factors that affect the bank's asset quality in India.

The empirical results confirm the time persistence of credit risk among Indian banks during study period. This reflects that bank defaults are expected to increase in the current year, if it had increased past year due to time lag involved in the process of recovery of past dues. Further, higher profitability, better managerial efficiency, more diversified income from nontraditional activities, optimal size of banks, proper credit screening and monitoring and adherence regulatory norms would help in improving the credit quality of Indian banks.

The practical implication drawn from the study is that nonaccumulation of nonperforming loans (NPLs), higher profitability, better managerial efficiency, more diversified income from nontraditional activities, optimal size of banks, proper credit screening and monitoring and adherence regulatory norms would help in improving the credit quality of Indian banks.

This study is probably the first one that identifies in addition to the current year, whether lag of bank industry-macroeconomic affects the level of NPLs of Indian banks. So far, such an analysis has received less attention with respect to Indian banking industry, especially immediate aftermath of the global financial crisis.

This paper aims to investigate the productivity behavior of Indian banks in the presence of non-performing assets (NPAs) over the period 1999 to 2017. The study examines whether Indian banks withstand the shocks of the global financial crisis (GFC) of 2007–2009 and sustain their total factor productivity (TFP) levels in the post-crisis economic turbulent period or not.

The robust estimates of TFP and its components: efficiency change and technical change are obtained using the state-of-the-art and innovative sequential Malmquist-Luenberger productivity index (SMLPI) approach. The key advantages of this approach are that it explicitly allows the joint production of undesirable output (NPAs in our case) along with desirable inputs and outputs in the production process and precludes the possibility of spurious technical regress.

The empirical results of the study reveal that the Indian banking system has experienced a (−1) percent TFP regress, contributed solely by efficiency loss during the period under investigation. The GFC has slowed down the growth trajectory of TFP growth in the Indian banking industry. Among ownership groups, the effect of the GFC was pronounced on the public sector banks.

The practical implication drawn from the study is that the Indian banks have not been able to successfully transmit the use of installed technology in a way to generate early warning signals and mitigate the risk of defaults so as to maximize their productivity gains in the banking industry.

This study is perhaps the first one to understand the productivity dynamics of the Indian banks in response to both endogenous (i.e. NPA crisis) and exogenous (i.e. global financial and economic stress) crises. Moreover, the authors obtain the robust estimates of TFP growth of Indian banks by explicitly accounting for NPAs as an undesirable output and equity as a quasi-fixed input in the bank production process.

The lives of the poor in the urban spaces of India are filled with hardships. They live amidst poverty and struggle to survive within other problems such as insecure jobs, lack of proper housing, unsanitary conditions and low levels of health immunity. This vulnerable section of the population has been rendered furthermore vulnerable by the outbreak of the COVID-19 pandemic in ways that were never imagined before. Taking this into consideration, the purpose of this article is to examine the vulnerability of the poor in the urban settings of India with special reference to Mumbai in the context of the COVID-19 pandemic.

The methodology adopted in the study is based on the analysis of secondary data and content analysis of the existing literature. In addition to this, the study also makes use of certain narratives of the urban poor in Mumbai that have been captured by various articles, reports and blogs.

The findings of the study reveal how the urban poor of India, with special reference to Mumbai, the financial capital of India, has emerged as the worst sufferers of the socioeconomic crisis caused by the social distancing and lockdown measures imposed for combating the pandemic.

The study tries to explore the reality of the urban poor's right to the city in the wake of the pandemic.