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Purpose: Worldwide economies have been shattered by the alarming increase in Non-Performing Assets (NPAs) in Banking Sector. In India, the rise in NPA levels gives a clear insight into the health of industry and state. This study aims to determine how NPAs in India impact the profitability of eight banks chosen from the public and private sectors; specifically: Punjab National Bank (PNB), Bank of India (BOI), UCO Bank, Punjab and Sind Bank (PSB), HDFC Bank, Axis Bank, ICICI Bank, and Yes Bank; during the period 2009/2010 to 2017/2018.

Design/methodology/approach: The study utilised IBM SPSS version 20 application to carry out our statistical analysis of measures of central location (mean and median), measures of dispersion (standard deviation), to carry out the Kolmogorov–Smirnov test to check the normality of data, the Mann–Whitney U test (for two groups) for median comparison between private and public sector banks and the Kruskal–Wallis test (for more than two groups) for median comparison for more than two banks. p ≤0.01 and p ≤0.05 were the two-tailed significance level used for determining the significance of all statistical tests.

Findings: Trend analysis and statistical tests show that the trend in public sector banks to have NPAs is higher compared to private sector banks, and losses arising from NPA impact the banks’ profitability.

Practical implications: It is apparent that NPAs are a large threat to banks in India as it reflects the state of the Indian economy. The growth of the economic cycle is predominantly dependent on the smooth and profitable functioning of private and public sector banks. This current study focusses on and compares the impact of NPAs on the profitability of public and private sector banks. NPAs have grown exponentially more in the case of public sector banks than private sector banks, which has affected the former banks’ financial health and performance. Increases in the level of NPAs adversely affect the working style and long-term stability of public and private sector banks in the economy.

Social Implications: NPAs have a negative influence on the profitability of the banks as well as on the economic growth of the country too. However, it is recommended that management in the banking sector, particularly the public banks, should use various preventive and recovery strategies to reduce the risk of failure and to keep track of NPAs to stay safe.

Originality/value: This study aims to determine how NPAs in India impact the profitability of eight banks chosen from the public and private sectors; specifically: PNB, BOI, UCO Bank, PSB, HDFC Bank, Axis Bank, ICICI Bank, and Yes Bank; during the period 2009/2010 to 2017/2018.

This paper aims to analyse trends and determinants of NPAs in India's banks. It has empirically examined the bank-specific determinants of NPAs.

An FE panel estimation of a sample of 44 banks was carried out for the post-crisis time period, from 2010 to 2020 to identify the bank-specific determinants of NPAs. The sample of 44 banks includes 20 PSBs, 19 private banks and 5 foreign banks. Separate FE estimation was also carried out to identify the drivers of NPAs in PSBs.

The determinant of NPAs during the post-crisis period suggests that faulty earning management and deterioration in loan quality have resulted in high NPAs in India's banks. The result is similar for PSBs as well.

The findings of the study suggest that the banks, especially the Public Sector Banks (PSBs) need to revisit their earning management strategies to maximise income and improve their loan quality in order to reduce the incidence of loan failure.

The paper contributes by empirically analysing the determinants of NPAs during the recent decade, between 2010 and 2020. Separate estimations have been carried out to understand whether the drivers of NPAs differ in the case of PSBs.

The issue of mounting non-performing assets (NPAs) in Indian banking industry is serious and attracting attention of academia and policy planners. Thus, the purpose of this paper is to test the hypothesis whether NPAs in Indian commercial banking have reached at alarming state where they start affecting the technical efficiency levels adversely or not.

The efficiency score have been computed using case model (model with NPAs as bad/undesirable output) vs control model (model without NPAs as bad/undesirable output) methodology under meta-frontier data envelopment analysis framework.

It has been noticed that the effect of NPAs on overall technical efficiency and its various components is insignificant. The comparison of the case models (i.e. model with NPAs as bad output) with the control models (i.e. model without NPAs) reveals insignificant difference in average efficiency scores and rank distribution of commercial banks. The major source of inefficiency is technology gap (i.e. structure, setup and objectives of banking) among public, domestic private and foreign private categories of banks.

Though NPAs are increasing in Indian banking industry and specifically in Indian public sector banks because of their compulsory lending to priority sector yet the banks have huge scope to extend credit to priority sector as the NPAs have not reached at alarming stage where they start affecting adversely the efficiency performance.

Given the fact that the banking penetrations, structure and objectives differ significantly across ownership, separate frontiers for each ownership (public, private and foreign banks) category has been used to evaluate the technical efficiency levels of 81 commercial banks operating in India over the period 2005 to 2013.

The purpose of this paper is to examine the factors that influence the variability of loan losses (termed as non-performing advances or NPA in India) of Indian banks in the public sector during the period of five years from 2001 to 2005.

The analysis is based on a panel approach, which considers both spatial and time dimensions of observations. Panel regression was used to explore the impact of different bank-specific factors on NPAs of 27 public sector banks (PSBs). Standard tests were used to find out suitability of different models of panel data analysis. Eight bank-specific factors were identified for analysis on the basis of review of extant literature.

Certain bank-specific factors, in particular, net interest margin and capital adequacy ratio exhibit negative and significant impact on gross non-performing advances (GNPA) ratio of Indian PSBs. The results also suggest that relative quantum of sensitive sector (SEN) (comprised of commercial real estate, commodity and capital market) advances has a positive relationship with NPA ratio, and such a relationship is statistically significant.

The sample is restricted to India and may not be reflective of other countries. The study considers bank-level factors, and there are some macro factors (e.g. gross domestic product, interest rate and inflation rate) which could have explained the variability of GNPA ratio.

Provisioning against loan losses is a major issue for stability of the banking system. Identification of appropriate causes of variability of such loan losses is important for managing credit portfolio of a bank. A positive and significant relationship between SEN advances and NPA calls for a more cautionary approach toward lending to those sectors.

This paper is believed to be the first attempt to empirically examine the role of bank-specific factors. This study attempts to enrich empirical research in the field and provides an insight into the role of various bank-specific factors on loan losses in the context of Indian PSBs. The study provides contrary evidence regarding the role of priority sector advances on a GNPA ratio.

The level of non-performing assets (NPAs) best indicates the soundness of the banking sector of a country. The purpose of this study is an effort to look into the contribution of the different banks individually to the NPA in the industry by looking into its growth pattern during the period 2010-2017. Further, the study is made to look into the effect of different groups of banks, namely, State Bank of India (SBI) and its associates, nationalised banks and private sector banks on the banking industry in this regard.

The individual private sector banks, nationalised banks and SBI and its associates have been considered for the purpose of the study. The analysis is based on secondary data collected from the Reserve Bank of India website for the period 2010-2017. The geometric mean has been used as a statistical tool for arriving at the mean growth rate of gross NPAs. Further, refinement of the result is done by comparing the growth of gross NPAs of individual banks with that of the average growth rate.

The assessment of private sector banks reveals that the growth rate of NPAs is low as compared to the nationalised banks, as well as the SBI and its associates. The nationalised banks and the associate banks of SBI failed to handle the issue of poor loans effectively due to which the growth in such loans has been phenomenally high.

The research is interesting as the study period follows the financial crisis. There is no such previous study that has looked at the perspective of banking from this angle. The research is valuable from two angles. Firstly, it brings to light the situation of the different categories of banks with regard to NPAs. Secondly, the information can be useful for investors as the issue of poor loans is a relevant one for them because it has an impact on the profitability of banks and thereby the future prospects.

The purpose of this paper is to examine the impact of credit risk components on the performance of credit risk management and the growth in non-performing assets (NPAs) of commercial banks in India.

The data are obtained from primary and secondary sources. The primary data are collected by administering questionnaire among risk managers of Indian banks. The secondary data on NPAs of Indian banks are from annual reports and Prowess database compiled by the Centre for Monitoring Indian Economy. Multiple linear regression is used to estimate the models for the study.

The results suggest that the identification of credit risk significantly affects the credit risk performance. The results are robust as credit risk identification is negatively related to annual growth in NPAs or loans. There is evidence in support of a priori expectation of better credit risk performance of private banks compared to that of government banks.

The study has implications for Indian banks suffering from a high level of losses due to bad loans. In addition, it will have implications for the implementation of new Basel Accord norms (Basel III) by the Reserve Bank of India.

The high and rising level of NPAs will have adverse consequences for credit flow in the economy in the absence of appropriate intervention by government and central bank in the form of changes in institutional and regulatory infrastructure. The problems in banking and financial services sector will lead to lower industrial and aggregate economic growth, and lower (or negative) growth in employment.

There is little evidence on credit risk management practices of Indian banks, and its relationship with credit risk performance and NPA growth. The need for an effective risk management system to manage credit risk assumes importance and urgency in the context of high and rising NPAs of Indian banks, and the consequences for the Indian economy.

This study aims to examine the impact and contribution of priority and non-priority sectors, as well as their sub-sectors, on the gross non-performing assets of public, private and foreign sector banks.

The Reserve Bank of India's database on the Indian economy is used to retrieve data over 13 years (2008–2021). Public sector (12), private sector (22) and foreign sector (44) banks are represented in the sample. Two-way ANOVA, multiple regression and panel regression statistical techniques are used in SPSS and EViews to examine the data. Further, the results are also validated by using robustness testing by applying the fully modified ordinary least square (FMOLS) and dynamic least square (DOLS) regression.

The results showed that, for private and foreign banks, the non-priority sector makes up the majority of the total gross non-performing assets, although both the priority and non-priority sectors are substantial for public sector banks. The largest contributors to the total gross non-performing assets in public, private and foreign banks are industries, agriculture and micro and small businesses. The FMOLS displays robustness results that are qualitatively similar to the baseline result.

Based on the study's findings about the patterns of non-performing assets originating from these specific industries, banks might improve the way in which these advanced loans are managed.

There has not been much research done on the subject of sub-sector-specific non-performing assets and how they affect total gross non-performing assets across the three sector banks. The study's primary focus will be on the issue of non-performing assets in the priority’s and non-priority’s sub-sectors, namely, agricultural, micro and small businesses, food credit, industries, services, retail loans and other priority and non-priority sectors.

In the economic literature, a crisis has been thematically defined around bank runs, failure of large financial corporations, and financial distress. Section 1 summarizes our learnings about international banking crisis, in terms of the origin and impact of such crises. This provides us an international benchmark before we delve deeper into India's banking distress, its size and trends. Section 2 focuses on the twin-balance-sheet crisis in India. On one side, corporate firms recklessly overleveraged, resulting in excess capacities and business diversification. On the other side, banks, both private and public, fell prey to excessive and procyclical credit lending and improper monitoring. Overall, too many projects were left too weakly monitored. Separately, we have focused on two subsections, first, how the financial institutions in India have overstretched their credit-disposal limit during market upturns. Second, we found absence of any theoretically grounded approaches to determine the capital-adequacy ratios (CARs) for the banks. In Section 3, we have identified the steps taken so far by the Banking regulator and the Government to resolve the crisis. Further, we critically examine the role of Korea Asset Management Corporation (KAMCO) towards a successful non-performing assets (NPAs) resolution in South Korea. Few key takeaways include, (1) establishing a public asset-management company (AMC) focused on maximization of recoveries and resolution of stressed assets, (2) well-defined governance structure for the AMC ensuring it works on market principles, shielded from political interferences, and (3) realistic asset valuation and transfer price that ensures limited downside risks for the public AMC.

The banking sector requires a major comeback with the series of bank frauds that has shook the nation. The rising non-performing assets (NPAs) and corporate frauds find their roots in the top-level management or executive levels. The purpose of this study to analyse the behavioural component with corporate governance lapses for creating a trail and to what extent it can contribute to forensic analysis to help reduce and prevent fraud in the future.

This study is investigative in nature. This study uses case study approach by taking into account the major Advance–NPA–Fraud cases over period of 2010–2022. RBI data for bank advances, NPAs and advances-relate frauds from 2005 to 2019 were studies and interpreted for creating a trend and pattern for the reduction and prevention of frauds.

The authors found that behavioural factors and personalities affect the systems and culture of the company, thereby giving a jolt to the corporate governance mechanisms along with various entities like depositors, consumers and shareholders.

Assessing the behavioural aspects for risk mitigation remains unexplored in the banking sector. The personality dimension can help in contributing to comprehending the mental aspects and the reasons behind the combination of dark triads with economic offences.

This study is beneficial to all the beneficiaries of the banking sector and the economy at large in understanding the implications of risks because of patterns formed by emotions and vulnerability towards economic and fugitive economic crimes.

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.