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Loan portfolio management and bank efficiency: a comparative analysis of public, old private, and new private sector banks in india.

1. Introduction

2. literature review.

• l i represents the amount of the i th loan
• r i is the rate of interest for the i th loan;
• f j refers to the funds received from the j th source;
• C j refers to the cost of funds;
• PD k is the probability of default for the k th loan;
• LGD k is the loss, given a default occurs, on the k th loan;
• EAD k is the exposure, upon default, for the k th loan.

2.1. Priority Sector Lending

2.2. secured lending, 2.3. term loans, 2.4. working capital loans, 3.1. measuring efficiency, 3.2. data envelopment analysis and choice of approach.

• θ represents the efficiency score of DMU0
• y rj is the amount of the r th output produced by the j th DMU;
• x ij is the amount of the i th input consumed by the j th DMU;
• λ j is the weight assigned to each DMU;
• yr 0 and x i 0 represent the inputs and output of DMU0.

3.3. Definitions of the DEA Variables

3.4. choice of inputs in the efficiency model, 3.5. sample and procedure, 3.6. analysis technique.

• Examination of the series for stationarity and cointegration
• The data comprised a time series, thereby necessitating a stationarity test to mitigate the risk of the findings being spurious ( Granger and Newbold 2003 ). A cointegration test was performed to assess long-term equilibrium, which was necessitated by the fact that the variables under analysis may indicate a long-term linear relationship.
• Testing for heteroskedasticity
• The study used generalized least squares regression (GLS) to assess the impact of the predictor variables on the predicted. We first tested for heteroskedasticity, as GLS is useful in instances where the error term displays heteroskedasticity. To this end, we used the modified Wald test for heteroscedasticity in the group data. This approach was deemed relevant because the panel data were related to multiple banks over a period.
• Obtain the efficiency scores
• Because the main focus is on the impact of lending variables on efficiency, it was necessary to first obtain efficiency scores using the data envelopment analysis approach.
• Hausman testing for model specification
• GLS regression can be conducted using a fixed-effects or random-effects approach, and it is important to assess which approach is the most effective. We used the specification test proposed by Hausman ( Hausman 1978 ) to identify the endogeneity of the regressors and determine the appropriate model.
• Generalized Least Squares (GLS) regression
• A GLS approach was used, considering the proportion of loans in the loan portfolio as predictors and efficiency scores as the predicted variable.

4.1. Descriptive Statistics

4.2. stationarity test and cointegration, 4.3. modified wald test for groupwise heteroskedasticity, 4.4. hausman test, 4.5. hypothesis testing, 4.6. significance level of coefficients.

• bankgeneration = public sector banks
• bankgeneration = new private banks
• bankgeneration = old private banks

5. Discussion

6. Conclusions

Implications, 7. limitations and future direction, informed consent statement, data availability statement, conflicts of interest, appendix a. output of gls regression conducted on public sector banks.

Appendix B. Output of GLS Regression Conducted on New Private Sector Banks

Appendix C. Output of GLS Regression Conducted on Old Private Sector Banks

Appendix D. Kao Panel Cointegration Test

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Share and Cite

Venugopal, S.K. Loan Portfolio Management and Bank Efficiency: A Comparative Analysis of Public, Old Private, and New Private Sector Banks in India. Economies 2024 , 12 , 81. https://doi.org/10.3390/economies12040081

Venugopal SK. Loan Portfolio Management and Bank Efficiency: A Comparative Analysis of Public, Old Private, and New Private Sector Banks in India. Economies . 2024; 12(4):81. https://doi.org/10.3390/economies12040081

Venugopal, Santhosh Kumar. 2024. "Loan Portfolio Management and Bank Efficiency: A Comparative Analysis of Public, Old Private, and New Private Sector Banks in India" Economies 12, no. 4: 81. https://doi.org/10.3390/economies12040081

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• Published: 01 November 2021

NPAs and profitability in Indian banks: an empirical analysis

• Santosh Kumar Das   ORCID: orcid.org/0000-0002-2685-3971 1 &
• Khushboo Uppal 1  

Future Business Journal volume  7 , Article number:  53 ( 2021 ) Cite this article

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As financial intermediaries, the commercial banks to a large extent depend on the performance of their lending as a critical source of earning. Due to increasing loan failures, the share of non-performing advances has increased substantially in recent years, thereby adversely impacting their profitability. The paper has examined the NPAs and profitability relationship by estimating the determinants of profitability of 39 public sector and private banks for the time period from 2005 to 2019. Using a set of bank specific and macroeconomic predictors of profitability, we found that NPA has negative impact on the rate of profit of the Indian banks. The study suggests that the banks must reduce their NPAs and operating cost to improve their profitability.

Introduction

Growing incidence of non performing advances or loans can have potential adverse impact on the performance of the banks by squeezing their earnings, thereby reducing their profitability. Typically, a loan or advance becomes non-performing assets (NPAs) when a borrower defaults on the repayment of either the principal amount or unable to serve its debt. An NPA not only makes an asset unproductive, banks also fail to recover the principal capital. On the one hand, the interest earning of the bank declines; on the other side, there is a risk of recovery of principal amount. Falling interest income while directly impacts the profitability of a bank, under recovery of principal capital can result in erosion of bank’s capital base. Beyond a threshold level, the combination of both can potentially affect the stability a bank.

The Reserve Bank of India (RBI) has defined the NPAs as those assets for which principal or interest payment remains overdue for a period of ninety days. The RBI has classified three types of assets within the category of NPAs—substandard assets, doubtful assets, and loss assets [ 24 ]. A substandard asset is one if it remains as an NPA for a period less than or equal to 12 months. Similarly, a doubtful asset is defined as an asset which has remained as an NPA for a period of more than 12 months. In case of loss asset, the loss has already been identified and the amount is not written off. The combination of the above three types of assets forms total NPAs in a bank. The NPAs reduce the profitability of banks due to increase in operating costs and decline in their interest margins [ 7 , 19 ]. Studies have shown that a bank with high level of NPAs generally incurs ‘carrying costs’ on non-performing assets that reduces their profitability [ 4 ]. Also, a rise in NPA is likely to cause adverse impact on the profitability of the banks due to huge amount of provisioning requirements out of operating profits, which acts as a drain on profitability of banks. Thus, provisioning and carrying costs of NPAs act as drain on the profitability of the banks. Berger and Young [ 7 ] examined the relationship between bad loans and bank efficiency. They found that increasing incidence of loan failures forces banks to incur extra operating costs in the form of increased spending on monitoring of such assets and selling off of these loans. The banks are preoccupied with recovery procedures instead of concentrating on expanding their business. Higher the bank operating costs, lower will be the cost efficiency of banks and thus lower will be the profits. Operating costs include wages and salaries of employees and costs of running branch offices. These costs have an adverse impact on profitability of banks [ 30 ].

There are several factors, including non-performance of loans that can potentially affect the profitability of the banks. It can broadly be categorised into the bank specific, and macroeconomic factors. The bank-specific factors include non-performing advances [ 7 , 19 ], deposits [ 20 , 25 ], non-interest income [ 30 ] (Harbi 2019), interest income [ 5 ], operational efficiency [ 1 , 17 ], and capital adequacy [ 6 , 11 ]. The macroeconomic factor includes GDP growth [ 11 , 30 ], rate of inflation [ 9 ], and interest rate [ 8 , 11 , 29 ].

The present paper empirically analyses the impact of NPAs on the profitability of Indian public sector and leading private banks. Accordingly, the determinants of profitability have been estimated. The paper spreads over five sections. The introduction section has provided the background of the paper. The methodology section elaborates on the empirical strategy, data, variables and estimation model. The findings of the empirical exercise have been presented in the results section. In the discussion section, the findings of the study have been discussed. The concluding remarks have been presented the conclusion section.

Literature review

Previous studies, those have examined the relationship between the non-performance of loans and profitability of banks, have overwhelmingly concluded that NPAs have adverse impact on the profitability of the banks. There are several other factors, including NPAs that affect profitability which have been discussed in the literature.

In a study of banking sector of the US, for the period between 1970 and 1976, Martin [ 18 ] concluded that a rise in NPAs hurt the earnings of the banks, which reduces the profitability of banks. Masood and Ashraf [ 19 ] studied 25 Islamic banks from 12 countries from the Middle East, East Asian, African and South Asian regions for the period from 2006 to 2010. They found that non-performing loans negatively affects the bank performance and profitability. Ongore and Kusa [ 21 ] studied commercial banks in Kenya for the period from 2001 to 2010 and found a negative relationship between bank profitability and non-performing loans. Al-Jafari and Alchami [ 2 ] in their study of 17 Syrian banks, from 2004 to 2011, found a negative relationship between credit risk, as represented by loan loss provision, and bank profitability.

Cucinelli [ 10 ] using a sample of 488 listed and unlisted Italian banks over a period from 2007 to 2013 found that an increase in credit risk by either a rise in the non-performing loans ratio or a fall in credit portfolio quality as represented by a rise in loan loss provision ratio leads to banks to decrease their lending activity, which in turn can negatively impact their profitability. Higher NPAs results in lower bank profitability as higher NPAs require increased provisioning which eats into the profits of banks. Duraj and Moci [ 12 ] in their study of studied 16 Albanian banks between 1999 and 2014 found this negative relationship.

A study by Islam and Nishiyama [ 15 ], using data for 259 commercial banks in South Asian countries including India, for the period from 1997 to 2012, found that there is a negative relationship between non-performing loans and bank profitability. Similarly, Hashem [ 14 ] in his study of Egyptian banks for the period from 2004 to 2014 reported that higher loan loss provisions represent higher credit risk and hence lowers asset quality of banks which badly affects bank profitability. Bace [ 3 ] used data for 13,000 deposit taking institutions around the world for the period from 2014 to 2015 and found negative relationship between the NPAs and bank profitability. Similarly, a study by Etale et al. [ 13 ] that investigated the relationship between the non-performing loans and bank profitability for the period between 1994 and 2014, found a negative relationship between the two. Ozurumba [ 23 ], in his study of Nigerian commercial banks, concluded that the non-performing loans had an adverse impact on the profitability of banks for the period between 2000 and 2013. A study by Ozgur and Gorus [ 22 ] using data for Turkish banks for the period from 2006 to 2016 reported a negative relationship between non-performing loans and bank profitability. Previous studies have used the following dependent and explanatory variables for the empirical analysis.

Profitability

In the literature, usually the Return on Assets (ROA) is taken as a proxy for profitability, which measures the percentage of profits that a bank earns with respect to its total assets [ 15 , 17 , 27 ]. We have used ROA as a proxy for profitability as it reflects the average asset value during a fiscal year [ 15 ].

Bank specific determinants of profitability

Net Non-Performing Advances (NNPA) : The higher the portion of income generating assets among total bank assets, the higher would be the interest income of the banks. When NPAs increase, the proportion of interest earning assets falls, which leads to a fall in interest income, and hence ROA declines. Thus, NPAs and ROA have a negative relation; as NPA rises, return on assets (ROA) of banks falls [ 5 ]. Masood and Ashraf [ 19 ] and Berger and Young [ 7 ] have used non-performing loans to total assets as a measure of non-performing assets.

Deposits are the principal and the cheapest source of funds for banks. Therefore, the more deposits a bank collects, higher will be the availability of funds for generating loans and for other profitable uses such as investments, higher will be the bank profitability. Thus, a positive relationship between deposits and profitability is expected [ 20 , 25 ].

Non-interest income

The non-interest income is the income of banks from sources other than interest bearing assets. It is an indicator of bank’s off-balance sheet business and fee income, that is non-traditional activities. Non-interest income consists of commission, service charges, and fees, guarantee fees, net profit from sale of investment securities, and foreign exchange profit. Higher the bank’s non-interest income, higher will be the profits [ 30 ] (Harbi 2019). We have used the ratio of non-interest income to total income as the variable for non-interest income.

Interest Income: Net Interest Margin (NIM)

Interest income is the difference between the interest rate a bank pays to its depositors and the interest rate it charges to its borrowers. It is measured as a ratio of Net Income to Total Assets. NIM represents income of the banks from its ‘core lending business’. NIM is adversely affected by NPAs, because when an asset becomes an NPA, it stops generating interest income and hence, interest earned by banks reduces, while the bank still has to pay interest on deposits [ 5 ]. The profitability of a bank increases with increase in net interest earning.

Capital adequacy

High capital reserve requirement leads to higher profitability for banks because of lower costs of financial risk for banks. Lower financial risks attract higher deposits and boost the banking busies, thereby leading to higher rate of profit. Several studies have found a positive relation between capital and profitability of banks [ 1 , 6 , 11 , 19 ] (Harbi 2019). We have used Tier 1 capital ratio as prescribed by the Basel Committee as the variable for capital adequacy.

Operating costs

It is the total amount of wages and salaries of bank employees and the cost of running branch office facilities. Higher the operating costs, lower will be the profits. Sufian and Habibullah [ 30 ] used the ratio of overhead expenses to total assets as a measure of overhead expenses. Al-Homaidi et al. [ 1 ] used ratio of operating expenses to interest income as a measure of operating efficiency and argued that lower the ratio, higher will be the management efficiency and higher will be the profits of banks, whereas Kohlscheen et al. [ 17 ] took the ratio of operational expenses to gross revenues as the measure of operating efficiency.

Macroeconomic determinants of profitability

Gdp growth rate.

It is the value of all final goods and services produced in a country in a given period of time. During higher economic growth, profitability of banks would be higher because it encourages banks to lend more and charge higher interests [ 11 , 30 ].

It is the rate at which general price level of goods and services rises and the purchasing power of currency falls. Studies have found that profitability of banks will be higher with inflation. It has been used by prior studies on banks’ profitability [ 1 , 9 , 11 , 19 ].

Interest rate

There has been mixed evidence with respect to the relationship between interest rate and profitability. Low interest rates along with stiff competition among banks put pressure on interest margins of banks and hence negatively affect bank profitability (Trujillo-Ponce 2013). Studies such as Demirguç-Kunt and Huizinga [ 11 , 29 ], Bourke [ 8 ] have found a positive relationship between interest rates and bank profitability. The repo rate has been used as it reflects the lending rate of banks.

There are very few studies that cover current phase of NPAs with the revised definition while analysing the NPAs and profitability in Indian banks. The present study not only covers the recent phase of NPAs crisis, but also covers the time period with revised or new definition of NPAs. The definition of NPAs in the present study follows uniformity.

In this study, we have drawn a sample of 39 scheduled commercial banks, out of which 20 are Public sector Banks (PSBs) and 19 are domestic private banks. As per the recent data, these 39 banks constitute more than 90 percent of the banking operation in terms of assets, and close to 95 percent in terms of deposits and credit disbursement in India. In case of Public Sector Banks (PSBs), the overall management responsibility lies with the Government, as it remains the majority stakeholder. The PSBs are governed by specific acts (banking acts) passed by the parliament. On the other side, the private banks are registered under the Companies Act and governed as per that act. Their management lies with the majority promoters or shareholders. In terms of NPA volume, it is largely the PSBs and some private banks that have been badly affected by the NPA crisis. Few small private banks were dropped from the analysis due to unavailability of data. The time period of the study is from 2005 to 2019. The period of the study has been chosen as the definition of NPA underwent a change in 2004, and the NPA data from 2005 onward follow uniformity with the new definition. Annual data for the sample of 39 banks was collected from a Reserve Bank of India (RBI) publication—Statistical Tables Relating to Banks in India. The bank specific determinates or factors that potentially explain the profitability of banks were obtained the above report. The data for macroeconomic variables were collected from the Handbook of Statistics on Indian Economy—a publication of the RBI.

In this study, we have estimated the determinants of profitability of Indian Scheduled Commercial Banks. The dependent variable is profitability, which is determined by a set of bank specific and macroeconomic factors (Table 1 ). In the study, the Return on Assets (ROA) has been used as the variable for profitability. In literature, the ROA is widely used as indicator or proxy for bank profitability. It is an appropriate indicator of profitability, as it measures the earnings of a bank in relative to its total assets. Therefore, it has been used as the dependent variable. We have used the following bank specific explanatory variables like Net NPA, total deposit, interest income, non-interest income, operational efficiency and capital adequacy. The study has used the following macroeconomic predictors of bank profitability—economic growth, inflation and interest rate to estimate the determinants of profitability.

To understand how NPAs impact the profitability, we have estimated the determinants of profitability of Indian scheduled commercial banks. We have employed the panel data estimation procedure to estimate the factors that have affected the profitability of banks in India. The following functional relationship has been employed to analyse the determinants of profitability.

where i  = bank, 1,….0.39, and t  = time, 1,….,15. \({\varepsilon }_{i,t}\) is the error term.

In the above equation, six bank specific factors and three macro-economic factors combined determine the profitability of a bank. In the paper, we have employed both the fixed and random effect approach to estimate the determinants of bank profitability. By using fixed effect (FE) model, the impact of variables those are time variant can be analysed. The FE estimation also controls for all time invariant heterogeneity among the sample banks. It therefore is likely to produce unbiased coefficient estimates due to omitted time invariant characteristics [ 31 ]. The general form of the fixed effects model can be expressed in the following equation [ 32 ].

In Eq. ( 2 ), the dependent variable ‘profitability’ is \({P}_{i,t}\) for i-th bank and t -th year. The dependent variable \({P}_{i,t}\) is determined by a set of exogenous regressor that includes both the bank specific and macroeconomic variables, \({X}_{i,t}\) , for i -th bank and t -th year; and \(\beta s\) are model parameters. Beta value in regression is the estimated coefficients of the independent or explanatory variables. It indicates a change in the dependent variable as a result of a unit change in explanatory variables keeping other independent or explanatory variables constant. The unobserved individual bank effect is \({\mu }_{i}\) , and the random error is, \({u}_{i,t}\) .

Unlike the fixed effects model, in the random effects (RE) model, it is assumed that the error term is uncorrelated with the explanatory variables. It allows the time invariant variables to act as similar to the predictors in the model. The benefit of RE is that the inferences can be generalised, beyond the sample drawn in a model [ 31 ]. The general form of the RE model can be expressed in the following equation [ 32 ].

In Eq. ( 3 ), the random error, \({\varepsilon }_{i,t}\) is with in entity error term and \({u}_{i,t}\) is between entity error term. \(\mu\) is the bank specific random effect. Random effect model assumes that the unobservable individual-specific effects (unobserved heterogeneity) are distributed independently of the explanatory variables or independent variables. More clearly, it assumes that the unobserved heterogeneity is uncorrelated with each explanatory variable across in all time period. Then, if the random effect model is significant, it indicates that the unobserved individual (cross-sectional) effects are uncorrelated with all the explanatory variables across all time-period.

The following fixed effects (FE) model has been estimated to analyse the determinants of profitability.

where i  = bank, 1,….0.39, and t  = time, 1,….,15.

In Eq. ( 4 ), the dependent variable is \(\text{ROA}_{i,t}\) . It is determined by a set of exogenous regressors that includes both the bank specific and macroeconomic variables. The unobserved individual bank effect is \({\mu }_{i}\) , and random error is \({u}_{i,t}\) . It is assumed that the set of explanatory variables is uncorrelated with the error term \({u}_{i,t}\) , and the error term is normally distributed, \({u}_{i,t}\) ~ N (0, \({\sigma }_{u}^{2}\) ), where \({\sigma }_{u}^{2}\) is > 0.

We have estimated the following random effect (RE) model to analyse the determinants of profitability in Indian scheduled commercial banks.

The descriptive statistics of the variables that has been used in the estimation of determinants of profitability is presented in Table 2 . The descriptive statistics of both the dependent and explanatory variables for the time period between 2005 and 2019 is presented in the form of mean, standard deviation, minimum and maximum. The results show that the return on profitability (ROA) ranges from − 5.49 to 2.13, with a mean ROA value of 0.65. Similarly, the minimum and maximum values of the explanatory variables range low to high. The mean and standard deviation values of the variables suggest that there is variation between the two.

The correlation matrix with correlation coefficients of the variables used is presented in Table 3 . The results suggest that there is no multicollinearity problem in the data. The results show a negative association of ROA with NNPA and CapT1. The rest of the explanatory variables exhibit positive association with ROA.

We have estimated both the fixed effect (Eq.  4 ) and random effect (Eq.  5 ) models to analyse the determinants of profitability in Indian scheduled commercial banks. The estimation result of the FE model shows that there is an inverse relationship between the rate of profit (ROA) and non-performing loans (NNPA), and the association is statistically significant (Table 4 ). Non-interest income (NII), interest income (II), capital adequacy (CAPT1) and GDP growth (GDPGr) are found to be positively associated with the rate of profit (ROA). The estimates are found to be statically significant. Ratio of operating cost to interest income (OCTII) shows negative relationship with profitability (ROA). The other macroeconomic variables like rate of inflation and interest rate show negative and positive associations, respectively. However, their association is not statistically significant.

The regression estimates of the RE model also give a similar result (Table 3 ). NPAs and operating cost (OCTII) are negatively associated with the rate of profit (ROA). Their relationship is statistically significant. On the other side, deposit (lnTD), non-interest income (NII), interest income (II), capital adequacy (CAPT1) and GDP growth (GDPGr) exhibit positive association with profitability (ROA). Their association is statistically significant. The other two macroeconomic explanatory variables, the rate of inflation and interest rate exhibit negative and positive associations, respectively. While total deposit was found to be significant in RE, it is found to be insignificant in FE model. In order to arrive at an appropriate test between FE and RE, the Hausman test was conducted. The results of Hausman test suggest that the RE estimate will be appropriate for the sample as the ‘ p ’ value is greater than 0.05 (Table 5 ).

In this paper, we have examined the impact of NPAs on the profitability of Indian banks. Using set of bank specific and macroeconomic variables, we have estimated the determinants of profitability of 39 commercial banks in India. The estimation result suggests that growing incidence of NPA is likely to reduce the profitability of the banks considerably. Results also suggest that increase in operating cost has negative impact on the profitability in Indian banks. The negative association between profitability (ROA) and NPA (NNPA); and profitability (ROA) and operating cost (OCTII) is statistically significant. The results show that there is a positive relationship between profitability (ROA), and interest earning (II) and non-interest earnings (NII). Their association is found to be statistically significant. The results further show that the volume of deposit (lnTD) is positively associated with the profitability (ROA). As financial intermediaries, commercial banks largely relay on interest earnings as their major source of income. In order to boost up their interest earnings, the banks must reduce their NPA volumes. The result suggests that Indian banks must reduce NPAs and operating cost in order to enhance their profitability.

The findings of the empirical estimation are similar to the findings of the studies by Kannan et al. [ 16 ], Sensarma and Ghosh [ 26 ], and Sinha and Sakshi [ 28 ]. A study by Kannan et al. [ 16 ], using data for 86 Indian banks, for the period from 1995–96 to 1999–2000 found that banks with higher NPAs have relatively lower profit margins. A study by Sensarma and Ghosh [ 26 ] of Indian commercial banks, for the period from 1997–98 to 2000–01, reported that a rise in NPA adversely affects the interest margins for banks and hence reduces bank profitability. Similarly, Sinha and Sakshi [ 28 ], in their study of 42 Indian commercial banks for the period from 2000 to 2013, found that higher credit risk, as measured by provision non-performing assets, negatively impacts bank profitability. Analysing NPAs in 46 Indian commercial banks from 2007 to 2014, Bawa et al. [ 5 ] found a negative relationship between NPAs and return on assets.

The paper has empirically estimated the factors that determine the profitability of Indian scheduled commercial banks, in order to understand the relationship between increasing non-performing advances and the rate of profit. The determinants of profitability have been estimated by taking a set of bank specific and macroeconomic explanatory variables. From the panel data estimation of 39 Public Sector and private banks, we found that the increase in non-performing advances has negative impact on the rate of profit. Operating cost is also found to be negatively associated with profitability. The estimates of both the FE and RE model suggest that non-interest income, interest income, capital adequacy and GDP growth rate have positively contributed to the rate of profit of the Indian banks. Given that, banks to a large extent depend on the performance of their loan assets as a critical source of income and profit, the rising NPAs is a cause of concern. It on the one hand reduces their interest earning and on the other side also affects their future deposits and increases their operating cost as the cost of recovery of NPAs will go up. The study suggests that the banks must reduce their NPAs and operating cost to improve their profitability.

Limitation of the study and future research avenues

The findings of the study are based on a sample of banks that mostly covers the PSBs and the private banks, covering the time period from 2005 to 2019. Though data for the year 2020 are available, it could not be incorporated due to recent bank mergers in India. Between 2020 and 2021, several mergers took place within the Public Sector Banks (PSBs). Post-merger, the number of PSBs has declined from 20 to 12. While it would be interesting to include the mergers into the empirical analysis, however one year is a too short time period to make any meaningful conclusion. The effect of merger in the analysis of NAPs and profitability of banks can be studied in future, with the availability of data for a longer time period.

Availability of data and materials

The data that support the findings of this study are collected from public domain resources. It is available at https://dbie.rbi.org.in/DBIE/dbie.rbi?site=publications [RBI publications/database on Indian economy].

Abbreviations

Non-Performing Assets

Gross Domestic Product

Fixed Effects

Random Effects

Reserve Bank of India

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Acknowledgements

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The paper is drawn from a research project “Performance of India’s Banking Sector: A Critical Focus on Non-Performing Advances (NPAs)”, funded by the Indian Council of Social Science Research under ICSSR-MHRD IMPRESS Scheme. The funding body has NO role in designing of the study, analysis, interpretation of the data and in writing. The research paper/study has been designed and prepared by the authors.

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Both the authors have contributed in completing the research paper/study. The paper was conceptualised by SKD. The structure of the paper was prepared by SKD in consultation with KU. KU largely contributed to the literature section and data collection. Estimation and analysis were done by SKD. Both the authors have contributed to the methodology section. Both the authors have read and approved the final manuscript.

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Das, S.K., Uppal, K. NPAs and profitability in Indian banks: an empirical analysis. Futur Bus J 7 , 53 (2021). https://doi.org/10.1186/s43093-021-00096-3

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• Bank performance
• Bank profitability
• Indian banks

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A critical review of non-performing assets in the Indian banking industry

Rajagiri Management Journal

ISSN : 0972-9968

Article publication date: 28 November 2019

Issue publication date: 13 December 2019

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.

Design/methodology/approach

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.

Originality/value

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.

• Nationalized banks
• Non-performing assets
• Private sector banks
• SBI and its associates

Agarwala, V. and Agarwala, N. (2019), "A critical review of non-performing assets in the Indian banking industry", Rajagiri Management Journal , Vol. 13 No. 2, pp. 12-23. https://doi.org/10.1108/RAMJ-08-2019-0010

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Copyright © 2019, Varuna Agarwala and Nidhi Agarwala.

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1. Introduction

The banking sector is a keystone of any financial system. The smooth functioning of the banking sector ensures the healthy condition of an entire economy. In the process of accepting deposits and lending, loans banks create credit. The funds received from the borrowers by way of interest on loan and repayments of principal are recycled for raising resources. However, building up of non-performing assets (NPAs) disrupts this flow of credit. It hampers credit growth and affects the profitability of the banks as well. NPAs are the leading indicators to judge the performance of the banking sector. As per Reserve Bank of India (RBI) reports on November 2018, the gross amount of poor quality loans is in excess of Rs 9 lakh crores, which shows the severe impact it has on lending practices of banks and their liquidity positions. This growth is a result of quadrupling during the past five years, which shows the poor practice of banks with regard to lending.

sub-standard asset : If an asset has been non-performing for less than 12 months;

doubtful asset : If an asset has been non-performing for more than 12 months; and

loss assets : Assets where losses have been identified by the bank, auditor or inspector and have not been fully written off.

The generation of poor loans in the books of banks is not a favourable event for the banking industry as it affects the size and soundness of the balance sheet. There is an unfavourable impact on the level of return on assets as well. Large amount of profits have to be provisioned against the doubtful and bad loans, which reduces profitability. Banks are even burdened with the increasing level of carrying costs of NPA accounts, which could have been used for any other profitable purpose. The financial institutions are also desired to maintain a certain capital adequacy level to strengthen their net worth. Though this issue is bad news for the banking industry, in recent times from the newspaper reports, it is evident that this problem has taken a serious toll on the banking space. The RBI has been taking measures to control the NPA menace. Some legal measures such as debt recovery tribunals (DRTs), Lok Adalats, the SARFAESI (Securitisation and Reconstruction of Financial Assets and Enforcement of Security Interest) Act and the Insolvency and Bankruptcy Code, 2016 have been introduced for the resolution of NPAs. Recapitalisation of public sector banks, setting up of stressed asset management verticals are some other steps taken by the RBI. In recent years, a few concepts like special mention accounts (SMA) and creating categories such as SMA 0, SMA 1 and SMA 2 have been added. Moreover, the regulator has also imposed restriction on eleven public sector banks by imposing the prompt corrective action (PCA) on them. Because of these developments, the present paper aims to find out which banks have contributed to the growing menace and what has been the trend in the banking industry with regard to these poor quality loans.

2. Literature review

The issue of NPAs has been a major area of concern for the lenders and the policymakers. Various research studies have been made to understand the causes contributing to the rise in NPAs, measures that should be taken to resolve the issue in its nascent stage and reforms that have come into effect to reduce the piling up of NPAs. Some of the relevant studies are arranged in a chronological sequence. Karunakar et al. (2008) discuss the various factors that boost NPAs, their size, their effect on Indian banking operations and suggest measures to control the curse on the banking industry. Use of suitable credit assessment and risk management methods is the key to solve the problem of NPA accumulation. Rajeev and Mahesh (2010) , in their article deal with the issue of NPAs after the global financial crisis. They suggest that mere recognition of the problem and self-monitoring can help to manage the NPA problem to a great extent. Self-help groups can also play an important role in the recovery of the loans. Barge (2012) examines that early monitoring and management of lent funds is the necessity of the hour. The study suggests several measures like better supervision of end use of funds, information about the credit history of the borrower and assisting the borrowers to develop entrepreneurial skills to ensure that the asset does not convert into a non-performing asset. Gupta (2012) makes a comparative study of the position of NPAs of State Bank of India (SBI) and associates and other public sector banks. The researcher concludes that for evaluation of the solvency of borrowers each bank should set up a separate credit rating agency. It also suggests the need for a committee comprising of financial experts to supervise and monitor the issue of NPAs. Shalini (2013) has analysed the causes and suggested remedies for reducing NPAs in Indian public sector banks with special reference to the agricultural sector. The analysis of the different problems faced by the Indian farmers deduces the conclusion that banks should follow some measures before lending the loan. Prior collection of reports regarding the goodwill of the farmers, post sanction inspection, educating the farmers regarding the effects and consequences of defaulting are some of the suggested measures. Singh (2013) in the investigation on the position of Indian commercial banks with regard to NPAs finds that these poor quality loans are a major problem for the public sector banks, which show a consistent rise over the years. The main contribution comes from the loans directed at the micro sector and for poverty alleviation programmes. Bhaskaran et al. (2016) in their paper have compared the NPAs of public sector banks and private sector banks over a period of ten years (2004-2013). From their study, it is evident that private sector banks are performing better than public sector banks in reducing the level of NPAs. The authors propose that banks should be proactive in adopting structured NPAs management policy where prevention of NPAs receive priority. Thomas and Vyas (2016) in a recent study on loan recovery strategy of Indian banks suggests two measures, preventive and corrective. The paper also discusses several corrective measures – legal, regulatory and non-legal that are to be taken to recover the non-performing loans. Singh (2016) in another recent study on NPAs and recovery status find that the problem is more severe for the public sector banks compared to the private sector banks. The academic review points to the need to have strict lending policies for speedy recovery of loans. Meher (2017) in the post-demonetisation period looks into the impact of the government’s notebandi decision on the NPA of Indian Banks. The researcher finds both positives and negatives of the event on the banking industry. Sengupta and Vardhan (2017) have compared the two banking crisis episodes post-liberalisation- one that took place in the late 1990s and the other that commenced after the 2008 global financial crisis that raised the issue of NPAs. The authors are of the view that strong governance, proactive banking regulations and a strong legal framework for resolution of NPAs would assist in solving the problem of NPAs. On the other hand, regulatory forbearance would adversely affect the banking crisis. Mittal and Suneja (2017) have analysed the level of NPAs in the banking sector in India and the causes that have led to the rise in NPAs. They have proposed that though the government has taken a number of steps to reduce the problem of NPAs, bankers should also be proactive in adopting well-structured policies to manage NPAs. The loan should be sanctioned after considering the return on investment of a proposed project and the credit-worthiness of the customers. Sahni and Seth (2017) study the different causes responsible for rising NPAs and the impact it has on the operation of banks. The authors have mentioned several preventive and curative measures to control the NPAs. They have suggested that proper assessment regarding the credit-worthiness of the borrower should be done to ensure the speedy recovery of loans. Mishra and Pawaskar (2017) have recommended that banks should have a good credit appraisal system so as to avoid NPAs. They point out that the problem of NPAs can be solved if there is a proper legal structure to support the banks in recovery of debt. Banerjee et al. (2018) have examined the status of gross NPAs and net NPAs in private sector banks and public sector banks to study their effect on the asset quality of the banks. Deliberate loan defaults, poor credit management policies, sanctioning of loans without analysing the risk-bearing capacity of the borrowers are the main reasons for piling up of NPAs. The banks should stress on better strategy formulation and its proper execution as well. Stringent provisions by the government could help in reducing the level of NPAs. Mukhopadhyay (2018) , in his paper, has discussed about finding solutions to India’s NPA woes. He has suggested that to resolve the problems of NPAs the RBI should not abide by a single model, instead, an innovative and flexible approach is needed for each affected bank, which should differ on case-by-case basis. Kumar (2018) , in her study has found that NPAs have a serious negative impact on the profitability and liquidity of the banking sector. According to her if the issue of NPAs is managed efficiently, then many microeconomic issues such as poverty, unemployment, imbalances of balance of payments can be reduced, the money market can be strengthened, and thus, the image of Indian banking system can be improved in the international market. Sharma (2018) emphasises the role of the banking sector as an instrument of economic growth and development. The paper discusses how banks are burdened due to growing NPAs especially in case of public sector banks. The author states a number of preventive measures that would curtail the level of NPAs. Viable regulatory standards and timely implementation of them could pave the way for a strong financial sector in India. Dey (2018) in a very recent research paper looks at the recovery aspect of recovery of poor loans of the Indian commercial banks. The author finds the role of DRTs to be much better compared to the recovery through Lok Adalats and SARFASEI Act. Kumar et al. (2018) make an interesting study to find out the main reasons behind accumulating NPAs. They find the main reasons to be industrial sickness, change in government policies, poor credit appraisal system, wilful defaults and defect in the lending process.

2.1 Research gap

Thus, an overview of the above literature shows that there are quite a few studies in the field of non-performing assets in the banking industry. However, there are no studies that look at the data till 2017, which is important and pertinent because the major piling up has been taking place after 2011 in the aftermath of the financial crisis of 2008. Moreover, the major focus of the paper is not only on groups of banks but also individual banks. This is done to identify those banks, which have been contributing more to the NPA menace in the banking space. Hence, the article is not only relevant but also addresses a contemporary issue like NPAs. The research adds new knowledge to the banking literature, which will help readers to comprehend the position of banks in a better way.

3. Objectives of the study

to determine the mean growth rate for different groups of banks and individual banks; and

to make comments relating to the growth pattern of Gross NPAs.

4. Research design

Sample : the individual private sector banks, the nationalised banks and SBI and its associates have been considered.

Data period : the analysis is based on data for the period 2010-2017.

Nature of the data and source : The investigation is based on secondary data, which is collected from the RBI website.

Variable of interest : gross NPAs.

Research methods : in this article, the statistical tool that the researchers have used is the geometric mean for arriving at the mean growth rate and then the growth of individual banks has been compared with the average growth rate.

5. Analysis and findings

The details of the analysis are presented in the sub-section below.

5.1 Assessment of private sector banks

The position of the private sector banks with regard to the movement of gross NPAs during the study period is discussed below.

5.1.1 Assessment at the individual level.

An examination of the gross NPA position of the banks in the private sector shows that the growth rate (calculated using Geometric Mean) is quite low in the initial years of the study period (the lowest being 3 per cent in the year 2011-2012), but it goes on increasing thereafter. The overall position of NPAs of the private sector goes up to a maximum of 72 per cent in the year 2016-2017. Majority of the private sector banks show a sharp rise in the NPA growth rates after the year 2015-2016. This sudden rise may have been the result of “asset quality review” conducted by the RBI in the year 2015. The inspection carried out by the RBI highlighted the under-reporting of NPAs in the private sector banks. Big lenders like Axis Bank, Yes Bank and ICICI Bank reveal high growth rate of NPAs during the latter years of the study period. Axis Bank experienced a significant rise in the gross NPAs of close to 250 per cent in 2016-2017 followed by Karur Vysya Bank (190 per cent) and Yes Bank (170 per cent) ( Table I ).

5.1.2 Comparing performance against the mean.

If we consider the growth rates of NPAs of each private sector bank with respect to the average growth rate of the banks in the private sector as a whole, we find that most of the banks have a growth rate less than the average growth rate (27 per cent). The performance of DCB is a commendable one as it shows an overall decline in the level of poor loans, which is an exception in the banking landscape. It points to a sound NPA management process in the bank. On the other hand, Yes Bank, which is among the big brands in the industry recorded the highest growth rate of 65 per cent followed by Axis Bank (49 per cent) ( Table II ).

5.2 Performance assessment of SBI and its associates

5.2.1 assessment at the individual level..

Next, we analyse the position of SBI and the SBI Group as a whole (note that the SBI Associates do not separately exist now as they have been merged with SBI in 2017). An analysis of the gross NPA position shows that the initial spurt in NPA growth took place in 2011-2012 followed by 2015-2016. This observation is the same as what is seen in the case of the nationalised banks. Of the entire SBI Group the State Bank shows the minimum average growth of 28 per cent. The associate banks show a poor performance in terms of the overall rise in NPAs during the period. Calculations show that State Bank of Hyderabad shows a growth of 61 per cent, which is closely followed by State Bank of Patiala (51 per cent), State Bank of Bikaner and Jaipur (50 per cent), State Bank of Mysore (49 per cent) and State Bank of Travancore (45 per cent). It is evident from the computations that with the SBI giving more focus towards NPA management rather than business expansion, fruitful results are reflected in 2016-2017 with respect to the previous year, a rise of only 14 per cent. For the remaining associate banks, it seems that the top management has not taken the issue of NPAs very seriously, due to which in 2016-2017 the year on year growth rate exceeds 160 per cent for all the banks. This might be the possible reason apart from generating economies of scale behind mergers of the associate banks with the parent bank ( Table III ).

5.2.2 Comparing performance against the mean.

The table below gives an idea about the growth position in NPAs of the individual banks against the average performance of the group ( Table IV ).

5.3 Performance assessment of nationalised banks

5.3.1 assessment at the individual level..

As per the computation, the position of Gross NPAs with respect to the growth rate during the period 2010-2011 and 2016-2017 is extremely bad, which is the reason behind the growing worry of the apex bank. If we look into specific banks and look at the growth rate during the study period we find the banks, which show the maximum rate are Andhra Bank, Punjab and Sindh Bank and IDBI Bank, which show the mean growth rate (in terms of geometric mean) to be 67, 63 and 55 per cent, respectively. In fact, the overall position of the nationalised banks taken together shows that the growth rate has risen at a high pace after the financial crisis started showing its effect in 2010. Of the 20 nationalised banks, 40 per cent show a mean growth rate of atleast 50 per cent. If we compare the growth rate of banks with respect to the average growth rate of the nationalised banking group taken together, it is evident that 50 per cent of the banks grow at a rate, which is more than the mean rate of 46 per cent. Some of the prominent names include Punjab National Bank, Andhra Bank, IDBI Bank (in which LIC has recently taken a stake of 51 per cent). For those banks in which the NPA rose by less than the average, the geometric mean lies in the range of 30 per cent (for Vijaya Bank) and 46 per cent (for Bank of Maharashtra).

If we analyse the pattern of growth (year-on-year), we find that there has been a spurt in the NPA growth of nationalised banks during 2011-2012 and 2013-2014. The second shock in terms of poor quality norms took place in 2015-2016 when the overall nationalised banks grew 104 per cent over the previous year. After the RBI came up with the concept of prompt corrective action, and looked at the problem with more diligence, some positive results (though not satisfactory) is seen in 2016-2017. It is evident from the calculations that the growth of NPAs in 2016-2017 is 21 per cent, which is the least during the study period ( Table V ).

5.3.2 Performance of banks against average.

The table below shows the categorisation of the banks into two categories, which are “more than average” and “less than average” ( Table VI ).

6. Conclusion

The overall findings point to a worrisome situation for the banking sector as a whole. An analysis of the growth rate in the NPA level shows that the problem is evident not only with small-sized banks but also with big names in the banking space. Hence, the entire sector is gripped in the crisis. The poor asset for the banks is a problem because as per the guidelines, given by the RBI, banks are required to keep some amount as provision depending on their asset quality thereby leading to declining profitability of the banks. Hence, it impacts not only the profitability level of these banks but also affects the shareholders’ wealth. Thus, the time is apt that the RBI has been coming up with very stringent norms so that the growth in these assets can be put under control. The Insolvency and Bankruptcy Code of 2016 is playing an important role with regard to recovery of assets of those creditors whose case has been filed with the National Company Law Tribunal. In fact, figures are given by the RBI point to a declining phase in the NPA growth rate, which is a positive development. But, there is still a lot to be done. Only time will say how successful has the RBI been in controlling the NPA growth in the sector. It is necessary to pull the trigger hard as these poor loans are having a severe impact on the liquidity position of banks and even the banks have been asked to go slow with regard to lending, which is ultimately having an impact on the economic growth, which has been slow during the past few quarters.

Year on year growth rate in gross NPAs in private sector banks