research paper on npa of banks 2020

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• Published: 16 July 2020

Efficiency of Indian banks with non-performing assets: evidence from two-stage network DEA

• K. Hafsal   ORCID: orcid.org/0000-0002-4839-3291 1 ,
• Anandarao Suvvari 2 &
• S. Raja Sethu Durai 1  

Future Business Journal volume  6 , Article number:  26 ( 2020 ) Cite this article

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This paper addresses the concerns regarding the sustainability of the banking sector in India prompted by the recent unintended high level of non-performing assets (NPAs). It uncovers the linkage between NPAs and banking efficiency by integrating NPAs into the measurement of bank efficiency to provide a holistic efficiency profile of the Indian banking sector. We apply the general two-stage data envelopment analysis of Kao [ 16 ] by incorporating NPAs as an exogenous output from the first stage, and the empirical results identify an efficiency gap of 16.2% due to NPAs in the Indian banking sector for the year 2016. Further, it also documents that the efficiency gap/loss is increasing over the years and differs according to the shareholding pattern of the banks.

Introduction

In emerging economies, banks play a more prominent role in financial intermediation, along with carrying additional responsibility for attaining the government’s social objectives as well. This inevitable relationship between banking and economic development, the growth of the overall economy, is naturally related to the health of the banking sector. The recent global financial crisis highlighted the importance of a healthy banking system. It emphasized the focus on proper monitoring and performance evaluations of banks, as this can impact their overall efficiency, productivity, performance, and profit.

World over, non-performing asset (NPA) in the banking sector is growing, and higher NPA influences the price of loans and interest rates, which in return affects the confidence of investors, lenders, and depositors equally. While higher interest rates will directly impact the investors in need of loans for the creation of infrastructural and industrial projects, it also causes poor recovery of funds, which will affect the credit creation and revenue stream of the banks. The non-recovery of loans affects credit creation and further affects the financial soundness of an economy. So managing the NPAs and maintaining them within the tolerance level will decide the success of a bank. Proper monitoring of NPAs and performance evaluations are very important for banking, as this reflects the overall efficiency of banking. Therefore, it is necessary to investigate the role of NPAs in determining the efficiency and profitability of the banking system.

The financial system in India, similar to other emerging countries, is bank dependent, and banks hold a significant share of total financial assets in the system. It is also having a reasonably large share of public sector banking and a higher level of NPAs compared to the other emerging countries. Recently, the Indian banking system is witnessing a sharp increase in the gross NPA level, and the government-owned public sector banks are mostly responsible for the NPA problem. The Reserve Bank of India (RBI) Financial Stability Report of 2017 states that the public sector banks have 14.6% gross NPA to total gross loans, and for all the other banks, it was at 11.2% in 2017. This number is almost fourfold higher than the world gross NPA of 3.45% in 2017, as reported by the IMF Financial Stability Report of 2017. As highlighted by Bawa et al. [ 3 ], NPAs in Indian banks are much higher than the other emerging countries like China, Mexico, and Brazil that have a gross NPA of 1.7%, 2.1%, and 3.6%, respectively, in 2017.

The effects of NPAs on the Indian banking system are well documented in the literature. Indira and Garima [ 14 ] argued that the public sector banks which have above-average NPAs show a reduced level of efficiency. Fujii et al. [ 11 ] highlighted the role of non-performing assets on the Indian banking sector and considered as an essential measure to decide the performance and financial system. The proper understanding of NPAs helps to reveal and understand the problems in the financial system that could contribute to the implementation of a suitable regulatory framework and avoid the consequent loss in the profitability of the banking sector. This study has two main objectives: first, to examine the efficiency of the Indian banks in a two-stage network DEA model to understand the overall efficiency of the banks, as well as efficiency in the intermediation and operating stages and, second, incorporating NPAs in the general two-stage DEA framework of Kao [ 16 ] as an exogenous output in the intermediate stage and then quantifying the level of improvement or loss in the overall efficiency.

As the level of NPAs creates serious concern on the stability of the banking system in India, this study focuses on quantifying the effects of NPAs on the efficiency level of the Indian banking system. This quantification will help us to understand the financial health of the Indian banking sector and its influence on the liquidity, solvency, and profitability levels of the banks. This study contributes to the empirical literature in twofold: first, it quantifies the efficiency gap arising out of NPAs in a general two-stage DEA framework; second, it identifies possible reasons for this efficiency gap. To our knowledge, this is the first study to apply general two-stage DEA with NPA as an undesirable outcome in the first stage to extract the efficiency gap. The advantage of this method over other multi-criteria decision-making models is that it assesses the system with standard two-stage DEA for bank-wise data to get the efficiency level as a benchmark. Subsequently, the general two-stage DEA denotes NPA as an undesirable output to derive the efficiency improvement or loss for comparison. The scope of this study rests on getting the bank-wise efficiency gap to identify the financial health of these banks and provides an early warning to evaluate their NPAs. The advantage of this two-stage DEA model is that it recognizes the origin of the inefficiency, precisely in which stage it happens so that the management can concentrate on that particular process to improve the efficiency. The paper is structured as follows: “ Literature ” section provides a brief literature review on the efficiency measurement of banks. “ Methodology ” section discusses the methodological approach adopted for the analysis and explains the general two-stage network DEA model along with the data used. “ Result and discussions ” section discusses the empirical results and policy suggestions, while the final section derives conclusions and managerial implications.

In the literature, understanding market efficiency and its effects on financial stability constitutes the macropoint of view [ 9 , 20 , 23 ], while understanding the efficiency of individual business units is the core at the micro-level. The focus of this study is at the micro-level to measure the efficiency of Indian commercial banks by incorporating NPAs to assess its impact on the profits of banks.

Several studies are analyzing the efficiency of the Indian banking system, with most of the studies adopting the data envelopment analysis (DEA) methodology. All these studies examined the efficiency levels, and no study has attempted to quantify the level of efficiency loss arising out of undesirable outcomes like the non-performing assets of the bank. Bhattacharyya et al. [ 5 ] analyzed the performance of Indian banks during the initial stages of the liberalization period. They found that the public sector banks Footnote 1 were more efficient compared to private banks. Similarly, Das and Shanmugam [ 8 ] measured the efficiency of Indian banks during the reform period based on the panel stochastic frontier analysis. They demonstrated an improved efficiency of the public sector banks. Ray and Das [ 22 ] examined the cost and profit efficiency of Indian banks in the post-reform period. They found that the public sector banks perform better than the private sector banks in terms of efficiency. They also reveal that small banks operated below the efficiency frontier, thus implying that the ownership pattern of the banks influenced the efficiency level of banks.

In recent years, the efficiency levels of the public sector banks and the private banks are seen to be reversing, with private banks becoming more efficient compared to other public sector banks like the State Bank of India (SBI, including its associate banks). Tzeremes [ 26 ], while examining the efficiency of the Indian banking industry from 2004 to 2012 using the directional distance function, found that the financial crisis had hardly affected the efficiency of Indian banks. However, private banks performed better than the public and foreign banks.

Sandeepa and Gupta [ 24 ], in their analysis of the banking efficiency in the post-crisis period from 2009 to 2013, demonstrated that SBI and its associate banks obtained the highest efficiency scores compared to other banks. The Indian banking system, dominated by public sector banks, has undergone a substantial change in terms of restructuring, mergers, and recapitalization due to the persistent growth of non-performing assets (NPA). It often mentioned as being the harbinger of an impending crisis within the Indian financial system. Jayaraman and Srinivasan [ 15 ] provided a holistic analysis of the Indian banking efficiency using the Nerlovian methodology and concluded that the profit inefficiency of Indian banks is mainly because of allocative inefficiency. Further, the study stated that the role of profit inefficiency was more compared to the technical inefficiency.

The major drawback of all these studies is the fact that they have considered the efficiency measurement as a ``black box'' [ 19 ] is ignoring the internal structure of the banking process and its efficiency. All these studies considered the total loan outlay as an output in the efficiency measurement of banks without considering that the loans generated consist of both good and bad loans. Färe et al. [ 10 ] argued that it is better to treat desirable and undesirable outputs asymmetrically in the efficiency measurement—where firms with bad outputs are penalized, and firms with desirable outputs are credited.

Chang [ 6 ] analyzed the efficiencies of major financial intermediaries in rural Taiwan. Incorporating risk such as non-performing loans (NPL), allowance for loan losses, and risky assets as an undesirable output demonstrated that an undesirable output has a significant impact on the efficiency ranking. Akhter et al. [ 1 ] evaluated the performance of the commercial banks in Bangladesh using the two-stage network DEA production process. Their findings suggest that non-performing loans (NPL) created in the previous period correspond with a reduction in the subsequent period’s production possibilities, as NPLs require the banks to either increase equity capital or contract deposit generation. Fukuyama and Weber [ 12 ] analyzed the inefficiency level of the Japanese banks by keeping NPLs as an undesirable output while using dynamic network DEA methodology. Their study established that there is higher inefficiency, which is mainly attributed to NPLs. Partovia and Matousek [ 21 ] studied the efficiency of Turkish banks from 2002 to 2017, applying a modified data envelopment analysis (DEA) method that used a directional distance function model to estimate the efficiency, where they considered NPLs as an undesirable output. The study indicates that the NPLs are severely affecting the efficiency of banks.

In recent years, there have been a few studies that analyzed the efficiency in the intermediation and operating stages using a two-stage network DEA framework. Fujii et al. [ 11 ] evaluated the technical efficiency and productivity growth in the Indian banks using a weighted Russell directional distance model by modifying and extending the methodological approaches of Chen et al. [ 7 ] and Barros et al. [ 2 ]. Their analysis observed that NPAs are the main reason for bank inefficiency.

Further, Gulati and Kumar [ 13 ] used the two-stage network DEA and suggested that the Indian banks have to improve both resource utilization and income-generating abilities to experience an overall improvement inefficiency. However, this study differs from the earlier ones by quantifying the efficiency gain/loss by incorporating NPAs in the analysis. It helps us to understand the banks that are weighed down by these NPAs on the efficiency front and identify some important factors that can move the banking sector forward and away from the clutches of NPAs.

Methodology

In the empirical literature, the technical efficiency of the banks is measured using either the production or the intermediation approaches. The production approach considers banks as a producer of services like deposits and loans with labor and capital as primary inputs. Intermediation approach considers banks as an agent to convert the deposits and other funds into loans and other assets. The debate on the ``deposit dilemma'' in the literature acknowledges whether the deposits (or raised funds) should be considered as an input or an output in measuring the banking efficiency. However, many studies adopting the conventional DEA method used deposits as an input as well as an output without discussing the issue of ``deposit dilemma.'' Some studies using two-stage network model have used the deposits as output in the first stage of the production and as an input in the second stage of the production. This study applies the intermediation approach of Berger and Mester [ 4 ].

First, we use a standard two-stage network DEA of Kao and Hwang [ 17 ] to derive the system and divisional efficiency scores for the actual data considering all the outputs in stages 1 and 2 as desirable outputs. Subsequently, the undesirable exogenous output from stage 1 is introduced in the general two-stage network DEA framework explained below to derive the system and its divisional efficiency scores. We define the efficiency gap (gain/loss) of that undesirable output as the difference between the system efficiency scores of the standard and the general models.

Two-stage DEA

For N number DMUs (decision making units) DMUj , j  = 1 … N , is based on the CRS (constant returns-to-scale) production process with i th input and r th output X ij , i  = 1 … m and Y rj , r  = 1 … s , Kao and Hwang [ 17 ] proposed a two-stage network DEA model derived as the following linear program:

After the optimal multipliers \( u_{r}^{*} , v_{i}^{*} \) and \( w_{p}^{*} \) are solved, the system and divisional efficiencies are calculated as \( E_{k} = \sum\nolimits_{r = 1}^{s} {u_{r} Y_{rk} } ,E_{k}^{1} = \sum\nolimits_{p = 1}^{q} {w_{p} Z_{pk} /} \sum\nolimits_{i = 1}^{m} {v_{i} X_{ik } } \) and \( E_{k}^{2} = \sum\nolimits_{r = 1}^{s} {u_{r} } Y_{rj} /\sum\nolimits_{p = 1}^{q} {w_{p} Z_{pj} } \) ; this implies \( E_{k} = E_{k}^{1} *E_{k}^{2} \) . Kao and Hwang [ 17 ] highlighted the problem of non-unique optimal multipliers solved from ( 1 ); they give a solution based on the set of multipliers which provide the largest \( E_{k}^{1} \) while keeping the total efficiency score at E k derived from ( 1 ) for the objective \( E_{k}^{1} = \hbox{max} \sum\nolimits_{p = 1}^{q} {w_{p} Z_{pk} } \, {\text{s}} . {\text{t}} .\,\sum\nolimits_{i = 1}^{m} {v_{i} X_{ik} = 1} \) \( \sum\nolimits_{i = 1}^{m} {v_{i} X_{ik} = 1} \) , by adding one more constraint \( \sum\nolimits_{r = 1}^{s} {u_{r} Y_{rk} } - Ek\sum\nolimits_{i = 1}^{m} {v_{i} X_{ik} = 0} \) and keeping all the other constraints in ( 1 ). After \( E_{k}^{1} \) is calculated, the efficiency of the second stage is obtained as: \( E_{k}^{2} = E_{k} /E_{k}^{1} \) .

General two-stage DEA

The general two-stage network DEA model of Kao [ 16 ] gives more freedom to alter the internal structure of the production process before achieving the final output. It differs from the basic two-stage network DEA model of Kao and Hwang [ 17 ] in terms of additional outputs and input measures. The production process is as follows: (1) inputs of stage 1 produce exogenous outputs (undesirable outputs) and intermediate products (desirable outputs), (2) dropping of undesirable outputs produced in stage 1 while carrying forward the desirable outputs along with additional inputs Footnote 2 into stage 2, (3) this produces final outputs in stage 2.

The general two-stage network DEA model is given as the following linear program:

For expressing simply, we removed the non-Archimedean number ε. At optimality, the system and division efficiencies are calculated as:

Even though the intermediate products Zg are not reflected directly in calculating the system efficiency \( E_{k}^{s} \) , they have been considered for in deriving the division efficiencies \( E_{k}^{1} \) and \( E_{k}^{2} \) which will affect \( E_{k}^{s} \) through the multiplier U r and V r . For decomposing the system efficiency into divisional efficiencies done through the following equation \( E_{k}^{s} = \left[ {\omega^{1} E_{k}^{1} + (1 - \omega^{1} } \right] \times \left[ {\omega^{2} E_{k}^{2} + \left( {1 - \omega^{2} } \right)} \right] \) where \( \omega^{1} = {{\sum\nolimits_{i = 1}^{m} {V_{{\ddot{i}}} X_{i0} } } \mathord{\left/ {\vphantom {{\sum\nolimits_{i = 1}^{m} {V_{{\ddot{i}}} X_{i0} } } {\left( {\sum\nolimits_{i = 1}^{m} {V_{{\ddot{i}}} X_{i0} } } \right)}}} \right. \kern-0pt} {\left( {\sum\nolimits_{i = 1}^{m} {V_{{\ddot{i}}} X_{i0} } } \right)}} \) and

We used data from 46 banks of India (26 public sector banks, 20 private sector banks) for the period from 2014 to 2016, mainly collected from the Statistical Tables Relating to Banks in India published by the Reserve Bank of India ( www.rbi.org.in ). These 46 banks represent more than 85% of banking business across India. Also, there are 43 foreign and 51 cooperative banks that hold less than 15% of the banking business. Therefore, we are not included foreign banks and cooperative banks in our analysis. The shareholding pattern of these banks was collected from the latest Annual Reports of the respective banks to corroborate any effects on the efficiency gap. For the analysis, we applied the same definitions and variables used by Gulati and Kumar [ 13 ], Kumar and Gulati [ 18 ] and Sharma and Dalip [ 25 ]. For the standard two-stage network DEA, we considered fixed assets ( x 1 ), employees ( x 2 ), and loanable funds ( x 3 ) (deposits plus borrowings) as inputs along with advances ( z 1 ) and investments ( z 2 ) as intermediaries and net interest income ( y 1 ) and non-interest income ( y 2 ) as final outputs. For the general two-stage network DEA, we separated net NPA ( y undesirable ) from the advances ( z 1 ) and used it as an undesirable output in the model with the rest of the advances ( \( z_{1}^{*} \) ) modeled as an intermediary along with investments ( z 2 ). Figure  1 gives the flowchart of the general two-stage DEA.

Result and discussions

Table  1 provides the system and divisional efficiency scores from both the standard and the general two-stage network DEA models for the 46 banks with a constant return to scale (CRS) for the year 2016. From the standard two-stage model, there exists, on average, a 40.7% inefficiency in the banking sector of India with system efficiency score ranging from 0.325 to 1. On average, stage 2 efficiency is lesser than the stage 1 efficiency score indicates these banks are relatively efficient in collecting deposits compared to the effective management of loans and advances. The overall performance of all the banks distinctly improves in the general two-stage model where NPAs are used as an exogenous output from the first stage, reducing the inefficiency to 24.4% with the system efficiency score ranging from 0.527 to 1.

Further, the results show that, on average, around 16.2% of efficiency loss in the Indian banking system is contributed by the NPAs. Thus, the banks can improve their efficiency by managing the level of NPAs, indicating that the cost of NPAs includes not only the cost of actual NPAs and its interest earnings but also the cost associated with the efficiency loss. For robustness, we also estimated the efficiency gap for the data of the years 2014 and 2015 and found that the average efficiency gap due to NPAs was 12.8 and 13.6 percentage, respectively, indicating an increase in the average efficiency gap over the years which is a characteristic of the frequent failure of banking policies about NPAs.

The average efficiency gap due to NPAs in 2016 for the public sector banks (first 26 banks in Table  1 ), which dominate the Indian banking system in terms of the size, is 25.6%. This is very high compared to the overall efficiency gap as well as the average efficiency gap of the private banks (last 20 banks in Table  1 ) that stood at 4%. We also examined this for the years 2014 and 2015; the results are presented in Table  2 . The results show the efficiency gap is increasing over the years as it stands at 12.9, 13.2, and 16.2% for the years 2014, 2015, and 2016, respectively, indicating the growing problem of NPAs in the Indian banking system. Further, we can observe that the efficiency gap for public sector banks is higher for the years 2014 and 2015.

Since the public sector banks have a higher efficiency gap, we also compared the relationship between the shareholding structure and the efficiency gap to understand the performance of the banks concerning the shareholding pattern. There are four shareholder groups in the Indian banking system, viz. government, institutions, public, and private. The correlation between the shareholding percentages of each of these groups and the efficiency gap is calculated and presented in Table  3 .

The results indicate a positive correlation between the efficiency gap and the government shareholdings, whereas institutional, public, and private shareholdings are negatively correlated with the efficiency gap. This clearly suggests that the banks with higher public shareholdings have lower levels of efficiency gap (due to NPAs) as compared to the banks which have more government or private shareholding. The situation could be further improved by promoting the deregulation of ownership structure to allow these banks to make independent decisions in their business activities. Two lessons can be drawn from this study. First of all, the public sector banks have a more substantial efficiency loss due to NPAs. Secondly, positive results in the future could be ensured by reducing the government’s shareholding in these public sector banks.

This massive, weighed down by NPAs of the public sector banks forced the government to initiate action toward resolving the situation. The Government of India recently decided to inject Rs. 2.1 trillion through various channels as a measure to recapitalize these public sector banks. In order to see whether this recapitalization measure will bridge the efficiency gap, we ran a separate general two-stage network DEA with capital injection as an exogenous input entering in stage 2 of the model. Since the actual level of the capital injection is not known at present, we calculated 15 and 25% of the gross NPAs as a capital injection for each bank and found a reduction in the efficiency gap. Footnote 3 The main policy implication from this analysis is that efficiency could be achieved by reducing the government’s shareholding in these banks. Further, recapitalization helps the banks to reduce the adverse effects of NPAs.

This study aims to understand the extent of inefficiency in the Indian banking sector due to non-performing assets (NPAs) using data on 46 banks for the financial year 2016. The analysis was carried out in an intermediation approach to understand the divisional and system efficiency of the banks, first with the standard two-stage network DEA model of Kao and Hwang [ 17 ] and then with the general two-stage network DEA model of Kao [ 16 ] by keeping NPAs as an undesirable exogenous output to understand the efficiency gap due to NPAs. The efficiency estimates of these two models have shown that around 16.2% of the efficiency loss arises due to NPAs in the Indian banking sector.

This highlights the fact that NPAs are squeezing banks’ capital position and profitability, leading to further deterioration of the Indian banking sector and the economy. Also, the analysis has shown that the shareholding pattern affected the efficiency gap. The results revealed that the banks with more public shareholding have a lower level of efficiency gap due to NPAs as compared to those which have more government or private shareholding. The managerial implications derived from this study are twofold: First, the banks can improve their efficiency by managing the level of NPAs. Second, it indicates that the cost of NPAs includes not only the cost of actual NPAs and its interest earnings but also the cost associated with the efficiency loss. Further, the findings also suggest that in addition to recapitalizing the public sector banks, disinvesting the government shareholding could improve the efficiency of these banks.

Availability of data and materials

The data used in this paper are collected from the Reserve Bank of India database (RBI) ( www.rbi.org.in ).

Government of India holds a major share in these banks.

In this study no new additional input is introduced in the stage 2.

The results of this analysis are not presented here but can be obtained from the authors upon request.

Abbreviations

non-performing assets

data envelopment analysis

constant return to scale

decision making units

Reserve Bank of India

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The authors thank the editor and two anonymous referees of this journal for their valuable comments on this paper.

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All the authors HK, AS, and RSD have equal contributions in conceiving the idea and analyzing the empirical results. HK and AS prepared the introduction, literature review, and collected data for the study. RSD contributed in estimating the model. All authors have read and approved the manuscript.

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Hafsal, K., Suvvari, A. & Durai, S.R.S. Efficiency of Indian banks with non-performing assets: evidence from two-stage network DEA. Futur Bus J 6 , 26 (2020). https://doi.org/10.1186/s43093-020-00030-z

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• General two-stage network DEA
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Impact of NPA and loan write-offs on the profit efficiency of Indian banks

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• Volume 47 , pages 35–48, ( 2020 )

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In recent years, Indian banks witnessed a huge increase in gross non-performing assets and loan write-offs. In this paper, impact of these two undesirable outputs has been examined on the profit efficiency of the banks using Nerlovian approach, which perhaps is a novel attempt in Indian context. It is found that inclusion of these two undesirable outputs has a significant impact on the profit efficiency of the Indian banks vis-à-vis their non-inclusion, and it is more evident from sharp decline in the profit efficiency of these banks. Also, it is observed that the allocative inefficiency of the banks was the main cause for their profit inefficiency. Further, loan write-offs should not be merely viewed as cleaning of the balance sheet of the banks as it has a noticeable impact on their profit efficiency.

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Jayaraman, A.R., Bhuyan, P. Impact of NPA and loan write-offs on the profit efficiency of Indian banks. Decision 47 , 35–48 (2020). https://doi.org/10.1007/s40622-020-00235-9

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• Profit efficiency
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A Study of Non Performing Assets Management with Reference to Select Indian Public Sector Banks and Private Sector Banks

IJTRS Volume IV Issue I, January 2019

9 Pages Posted: 12 Jun 2021

Prof Dr Preeti Sharma

University of Engineering & Management, Jaipur

Atul Bansal

Gulf university.

Date Written: 2019

The asset quality of banks is one of the most important indicators of their financial health. It also reflects the effectiveness of banks‟ credit risk management and the recovery environment. It is important that the signs of distress in all stressed accounts are detected early and those which are viable are also extended restructuring facilities expeditiously to preserve their economic value. (RBI/2012-13/208). The Indian banking sector has been facing severe problems of raising Non- Performing Assets (NPAs). The NPAs growth directly affects the profitability of banks. The problem of NPAs is not only affecting the banks but is affecting the economy as a whole. In fact high level of NPAs in Indian banks is nothing but a reflection of industry and trade. NPA do not generate any income, whereas, the bank is required to make provisions for such as assets. NPAs do not just reflect badly in a bank's account books, they adversely impact the working of economy. There are many research conducted on the topic of Non- Performing Assets (NPA) Management, concerning particular bank, comparative study of public and private banks etc. In this paper the researcher is considering the aggregate data of select public sector and private sector banks and attempts to compare analyze and interpret the NPA management from the year 2010 -2015. On the conceptual side, it gives an overview of NPA, various types of NPA and its cause. The tools used in the study are Least square method and ANOVA. The findings reveals the percentage of Gross NPA to Gross advances is increasing for public banks, the Estimated Gross NPA for 2014-15 is also more in public banks as compared to private banks and from the ANOVA test, it is concluded Ratio of Gross NPA to Gross Advances for public sector and private Sector Banks does not have significant difference between 2010 to 2015.

Keywords: Non Performing Asset (NPA), NPA Management, Public Banks, Private Banks

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Prof Dr Preeti Sharma (Contact Author)

University of engineering & management, jaipur ( email ).

University of Engineering & Management ,Jaipur Sikar Road Jaipur, Rajasthan 302017 India

College of Administrative and Financial Sciences Sanad Bahrain

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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

Emerald Publishing Limited

Copyright © 2019, Varuna Agarwala and Nidhi Agarwala.

Published in Rajagiri Management Journal . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

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