Resolution of Non-performing Assets of Commercial Banks: The Evidence from Banker’s Perspective in Indian Banking Sector

Abstract

The present article draws on the banker’s perspective and extracts some practical insights about the factors behind specific NPAs resolution strategies. Based on the thorough review of the perspective, conceptual and empirical literature, and using exploratory factor analysis (EFA), the study has identified 21 dimensions for ‘management of NPAs’. The empirical analysis of these dimensions has extracted 7 factors for management to be significant. A structured questionnaire has been developed and data has been collected from officers in different banks in India, especially working in the credit department. The questionnaire has been empirically tested for reliability and validity using confirmatory factor analysis (CFA) and also Z-test for checking the significance of the explored and confirmed factors. The present research work offers pragmatic suggestions for banking regulators, on improving the asset quality of banks in India and also throws new insights on effective credit management in banks.

JEL Codes: G01, G21, G32, E44

Keywords

Non-performing assets factor analysis banker’s perspective management of NPAs exploratory factor analysis confirmatory factor analysis

I. Introduction

Accumulation of non-performing loans (NPLs) is a recurring attribute of financial crises and financial stress as NPLs typically grow when a credit boom turns to bust (Aiyar et al., 2015; Borio, 2014). Investigating the issue of NPL is of considerable significance for the regulators as the intensification of the same has always been the precursor to the banking crisis and banking failures, worldwide (Barr & Siems, 1994; Gup & Kolari, 2005; Reinhart & Rogoff, 2011; Samad, 2012). The delinquent asset quality of a bank is the actual barometer of the vulnerability of a financial system (Sorge, 2004). Timely reports relating to NPAs work as a useful tool in examining the asset quality of banks (Meeker & Gray, 1987). The primary concern of the Central bank or any other banking monitoring authority is to ensure a stable and efficient financial system to protect the interest of all stakeholders. Woo (2000) stated that if the problem of NPLs is left unresolved, that can intensify the severity and duration of financial crises.

Deteriorating asset quality is emerging as a notable threat to the banking industry in India as well (Gopalakrishnan, 2004; Heid & Krüger, 2011). Bhide et al. (2002), examining the effect of financial sector reforms on the Indian banking system, asserted that in most emerging economies the banking sector has to face difficult challenges such as identifying newer risks, eschewing harmful incentives, and strengthening the banking sector along with keeping pace ever-changing technology. During the post-liberalisation era, the banking sector in India has to face twin prime banking crisis episodes, the period from 1996–1997 to 2002–2003 has been considered as 1st Phase of Banking Crisis (Resultant of post-reform systemic modifications and Asian financial crisis), while the period from 2003–2004 to 2008–2009 has been considered as The Recovery Phase (Outcome of favourable macroeconomic conditions and economic growth) and the period from 2009–2010 to present as the 2nd Phase of Banking Crisis resultant of Global financial crisis and excessive lending during the boom period (Chawla & Rani, 2019). The strain due to NPAs in the banking system in India has become increasingly perceptible in the years beginning from 2012 as the stressed assets were 9.8% in March 2012 which increased severely to 14.5% in the last of December 2015 (Mundra, 2016). The magnitude of NPAs in India is comparatively higher in public sectors banks than private sector banks (Singh, 2013). Misra & Verma (2016) and Chandrasekhar and Ghosh (2018) stated that mounting NPAs in the banking sector in India, explain a significant portion of the variability of stress in the financial sector.

Hence, managing the NPAs in banks holds immense significance in the light of reviving financial stability along with confidence in financial markets in India and the findings of the present study can contribute significantly to resolving the aberrant situation of NPAs. The present study is an endeavour to explore the measures to be adopted to manage the menace of NPAs, considering the perspective of the bankers who have been directly involved in lending decisions and credit risk management in banks. Moreover, insights can be gained about future levels of problem loans and further investigation of the crisis effects could be worthy of study.

The rest of the article has been organised into six sections. Section 2 explains an overview of the problem in the Indian banking sector and Section 3 presents a review of the literature. Section 4 explains the methodology followed by Section 5 in which results of factor analysis and Z-test for the management of NPAs are presented and discussed. And the last Section 6 epitomises the conclusion of the study.

II. Overview of NPAs in the Indian Banking System

With the initiation of the reform process in India, the problem of NPAs was identified as a National Priority by the policymakers. More recently, Beltrame et al. (2018), based on a sample of 97 European banks over the period from 2005 to 2016, concluded that the non-performing asset coverage ratio has a strong and significant relationship with systematic risk. The Gross NPAs (GNPA) Ratio came down constantly from 15.7% in March 1997 to 2.4% in March 2010 as shown in Table 1. The sharp decline was because of the adoption of reforms, an improvement in the credit appraisal process adopted by SCBs, implementation of new legal initiatives coupled with greater provisions and write-offs (Pandey et al., 2013). However, this trend again started mounting from March 2011 onwards and again reached 11.18% in March 2018, due to the high extension of loans during the recovery phase (Table 1). Another reason for the sudden rise in GNPAs was reported to be shifting to a system-based recognition of NPAs from a manual one (Pandey et al., 2013). The year-on-year growth rate of GNPAs reflected that the growth of these NPAs is much higher in the 2nd phase of the banking crisis (up to 89.15%), whereas the GNPAs ratio was higher in the 1st phase of the banking crisis (up to15.7%).

Table 1.

Trends of Gross NPAs in Indian Banking Sector (1997–2019).

Period	GNPAs to Gross Advances Ratio	Year on Year Growth of NPAs
1st Phase of Banking Crisis
March 1997	15.7	0.00
March 1998	14.4	7.43
March 1999	14.7	15.56
March 2000	12.7	2.87
March 2001	11.4	5.52
March 2002	10.4	11.17
March 2003	8.8	–3.03
The Recovery Phase
March 2004	7.2	–5.68
March 2005	5.2	–8.39
March 2006	3.3	–13.94
March 2007	2.5	–1.20
March 2008	2.3	11.53
March 2009	2.3	21.34
2nd Phase of Banking Crisis
March 2010	2.4	23.96
March 2011	2.5	15.59
March 2012	3.1	45.97
March 2013	3.2	35.79
March 2014	3.8	35.72
March 2015	4.3	22.77
March 2016	7.6	89.15
March 2017	9.6	29.21
March 2018	11.18	31.12
March 2019	9.3	–9.90

Source: RBI, financial stability reports (various issues) and the authors’ calculations.

Further, looking ahead at the bank group level, Figure 1 depicts the share of GNPAs in the Gross advances of different bank groups over time. Initially, the GNPAs ratios of public sector banks (PSBs) and other bank groups exhibited a diverse trend. Up till 2003, the trend of GNPAs ratio for PSBs went down gradually, albeit the same has surged in the case of other bank groups. Further, from 2003 to 2006, the same ratio exhibited a secular downward trend in the case of all bank groups. Then, from 2007 to 2009 (the period of inception of the global financial crisis) the GNPAs ratios of all groups disentangled as this ratio heightened sharply in the case of foreign banks and private sector banks whereas these ratios kept declining for PSBs. However, such a diverse trend signifies a marked variation in managing the NPAs across banks in India and, notably, from the global financial crisis to the present phase of the banking crisis, the apprehensions on NPAs are majorly confined to the PSBs.

Figure 1.

Source: RBI; Statistical tables relating to banks in India, various years.

Being concerned for managing the NPAs in the present crisis phase, many therapeutic efforts have already been made by the Regulator for recognising the true picture of NPAs in the bank books and to provide for the same such as Asset Quality Review was initiated (April 2015), the Indradhanush Programme was pioneered by Government of India (August 2015), to strengthen the process of appointment of top management in the PSBs, a Bank Board Bureau (BBB) was set up (April 2016). The government has also started the process of mergers of weak banks into strong banks and formed Insolvency and Bankruptcy Code-2016, to accelerate the exercise of recovering the debts from insolvent and bankrupt corporate houses (Sengupta & Vardhan, 2017). However, it was found that the execution processes are filled with many weaknesses as the banks try to defer the recovery process and depend more upon regulatory concessions.

III. Literature Survey

The recent Global Financial Crisis has ignited a curiosity in recognising the drivers of NPLs across the world and managing the same. NPLs have been quoted with the name of ‘financial pollution’, in various studies, because of their unpredictable economic consequences and researchers have proposed to incorporate an early alert mechanism for the incipient financial crisis scenario (Barseghyan, 2010; Gonzales-Hermosillo, 1999; Zeng, 2012). Different distressed debt cycles have been faced by varied economies and a probe has been made both in macro-economic as well as bank-specific factors of NPLs. As demonstrated in Table 2, some studies have exclusively investigated the single category of probable determinants of NPLs (Al-Khazali & Mirzaei, 2017; Beck et al., 2015; Jimenez & Saurina, 2006; Keeton & Morris, 1987; Khan et al., 2020; Klein, 2013; Lee & Rosenkranz, 2020; Nkusu, 2011; Podpiera & Weill, 2008). On the other hand, some studies have focused on the interaction between systemic (macroeconomic) and idiosyncratic (bank-specific) factors to draw an exhaustive picture of NPLs (Arpa et al., 2001; Fofack, 2005; Ghosh, 2015; Louzis et al., 2012; Makri et al., 2014; Pain, 2003; Salas & Saurina, 2002).

Table 2.

Literature Survey of Studies Relating to Determinants of NPLs.

Sr. No.	Authors	Period	Country	Methodology	Results
1	Keeton & Morris (1987)	1979–1985	USA	Dynamic panel data methods	Economic environment, bank capitalisation, the lending policy as the determinants of NPLs
2	Arpa et al. (2001)	1990–1999	Austria	Single equation time series model	Risk provisions to grow with the decrease in real GDP growth, and increase with real estate prices, consumer prices and operating income
3	Salas & Saurina (2002)	1985–1997	EU (Spain)	Panel data	GDP growth level, price levels, bank capitalisation, and adverse selection at the bank level to be the factors determining NPLs
4	Shu (2002)	1995–2002	Hong Kong	Single equation time series model	NPLs to decline with GDP growth, consumer price inflation, and property prices growth and increase with nominal interest rate and bankruptcies
5	Gerlach et al. (2005)	1994–2002	Hong Kong	Regression analysis	NPL ratio increases with the number of bankruptcies and nominal interest rate but decreases with inflation and economic growth
6	Hoggarth et al. (2005)	1988–2004	UK banks	Stress testing using VAR	Retail price inflation and nominal interest rate adversely affect bank loan portfolio quality captured through loan losses
7	Jimenez & Saurina (2006)	1984–2002	EU (Spain)	GMM estimations	Rapid credit growth, adverse selection, weak supervision and corruption as the determinants of default risk
8	Bofondi & Ropele (2011)	1990–2010	Italian banks	Single equation time series model	The new bad loans ratio or NBL ratio for households decreases with real GDP growth and housing prices, while the same increase with the unemployment rate and short-term nominal interest rate
9	Nkusu (2011)	1998–2009	26 advanced economies	panel regressions and panel VAR	Adverse macroeconomic factors namely GDP growth, unemployment and asset prices to be associated with high NPLs
10	De Bock & Demyanets (2012)	1996–2010	25 emerging market economies	Panel regressions and Panel VAR	Credit quality worsens with deteriorating GDP growth, depreciating exchange rate, weaker terms of trade, and fall in debt—creating capital inflows. Capital inflows had the biggest impact
11	Louzis et al. (2012)	2003–2009	Data of 9 largest Greek banks	Dynamic panel data methods	Real GDP growth rate, the unemployment rate, the lending rates and public debt have a strong effect on the level of NPLs. Moreover, bank-specific variables such as performance and efficiency also predict the NPLs
12	Klein (2013)	1998–2011	EU (CESEE)	Dynamic panel data methods	Unemployment rises, the exchange rate depreciates, high inflation rate, GDP growth, global risk aversion and excessive risk-taking as determinants of NPLs
13	Makri et al. (2014)	2000–2008	EU	Dynamic panel data methods	Public debt, unemployment, the annual percentage growth rate of GDP, capital adequacy ratio, and ROE as the determining factors
14	Ghosh (2015)	1984–2013	USA	Dynamic panel data methods	Greater capitalisation, liquidity risks, poor credit quality, greater cost inefficiency, higher state real GDP and real personal income growth rates to be the determinants of NPAs
15	Konstantakis et al. (2016)	2001–2015	EU (GREECE)	Vector autoregression	Macroeconomic and financial factors to have a significant impact on NPAs
16	Lee & Rosenkranz (2020)	2000–2019	UK and Australia	Fully modified OLS and dynamics OLS estimation	The stricter capital ratio increases operator earnings of banks which ultimately decreases bank profitability and efficiency.
17	Khan et al. (2020)	2005–2017	Pakistan	Regression modelling	NPLs of commercial banks listed in Pakistan Stock Exchange to be negatively associated with operating efficiency

Source: The authors.

Further, there is one more category of factors which are known as borrower/loan-specific factors where it has been observed that excessive lending, ineffective monitoring, and unhealthy competition give borrowers, a chance to relax and leads to higher NPLs (Aballey, 2009; Agresti et al., 2008; Cheriye, 2013; Kangimba, 2010; Sinkey & GreenWalt, 1991). Muniappan (2002) stated that diversion of funds, investing money in new projects, time cost overrun, strained labour relations, inefficient management, and technical glitches at the business level are the main internal reasons for deteriorating asset quality in banks. In India, the Central Bank has directed banks to term bad loans as wilful defaulters, and such borrowers, despite being capable to pay, do not pay the same knowing the weak legal and governance framework while in the US, such defaulters are termed as strategic defaulters (Gerardi et al., 2015). McGoven (1998) while studying the loan losses relating to US banks, asserted that Character is the prime factor for deciding lending credit and found that the banks in the US faced loan losses due to lenient lending, credit without guarantee, and also the perception and culture of the borrowers. Similarly, Pagano et al. (2014), Jayadev and Padma (2020), also suggested that to prevent the NPLs, the banks must strengthen the ‘bank governance model’ by instituting tighter loan accords, strict monitoring, and effective governance, and exploit transaction data available through payment channels. Ugoani (2016) taking a sample of Nigerian banks suggested that the bank regulators have to upgrade the micro and macro-prudential specifications for banks. Mora-Sanguinetti et al. (2017) found that during recessionary times, strict legal enforcement plays a vital role in the case of NPLs. Also, the out-of-court measures (Garrido, 2012) work as an alternative way out, which is encouraged to a great extent. Such reformatory measures would assist banks to write off bad loans.

The above discussion suggests that, although there have been significant studies for the effective management of NPAs, however, there is a dearth of literature investigating the measures to be adopted to manage the NPAs from the perspective of the bankers. Moreover, the majority of the literature discusses the macro-economic and bank-specific variables of bad loans (Manz, 2019), and the least heed is given to micro-level variables which could be loan/asset or borrower specific. Therefore, the present analysis desires to fill this void by understanding that bankers’ perspectives can well explain the issues relating to NPAs at the borrower level as well as at the macro level and suggest mitigation strategies for resolving the same.

IV. Materials and Methods

The research methodology is a procedure to fathom a research problem systematically or we can say that it chalks out the framework based on which the entire research is completed. The present study is explorative natured as it examines the different aspects of NPAs using primary data analysis by collecting data from the officers of different banks in India.

Research Instrument

A structured questionnaire, drafted based on extant literature and also consulting the experts in the field, has been used to collect primary data. The questionnaire is divided into two parts. The first part (Part-A) includes the questions relating to the demographic profile of the respondents. The second part (Part-B) contains questions concerning the perception of bankers towards the management of NPAs.

Scope and Sample Size

Data have been collected from officers working in different banks in India (PSBs, private sector banks and foreign banks) especially from the persons employed in the credit division of the banks. Multi-stage sampling has been used to collect the data. Initially, the top 11 banks have been selected (based on gross advances extended by the banks) as a sample from the entire population of Indian Scheduled Commercial Banks (5 Public, 5 Private and 1 foreign sector bank). These are State Bank of India, Punjab National Bank, Bank of Baroda, Canara Bank, Bank of India, HDFC Bank Ltd, ICICI Bank Limited, Axis Bank Limited, Yes Bank Limited, IDBI Bank Limited, and Standard Chartered Bank. The selected 11 banks contributed 58.69 % out of total NPAs of the Indian Scheduled Commercial Banks (₹5,479,000 million out of ₹9,336,090 million) and possess a 64% market share in terms of gross advances (RBI, 2019). The respondents have been asked to express their opinion towards various dimensions drawn for managing the NPAs. A total of 350 bankers from 40 major bank branches belonging to the top 11 selected banks have been approached, out of which only 303 respondents, relating to 35 bank branches, responded properly. A good response rate (87%) is due to constant follow-up through personal contact with the bankers or mail for the same.

From the perspective of many previous researchers, the thumb rule is that the sample size should be five times the number of items (Hair et al., 1995). For measuring of sampling adequacy or whether data could factor well, Hair et al. (2010), Pallant (2000), Tabachnick et al. (2007) suggested that if the Kaiser-Meyer-Olkin (KMO) is greater than 0.6 and Bartlett’s Test of Sphericity (BTS) is significant at α<.05 then factorability of the correlation matrix is assumed. In the present study, KMO is 0.673, higher than the acceptable value of 0.5 (Kaiser & Rice, 1974). Moreover, BTS is significant at p<.05, which also assures the suitability of the sample for factor analysis (Bartlett, 1950) as per Table 3. Hence, it justifies that the collected data set is appropriate for further analysis.

Table 3.

KMO and Bartlett’s Test.

Management of NPAs	Kaiser-Meyer-Olkin Measure of Sampling Adequacy.	0.673
	Bartlett’s Test of Sphericity	χ2	1917.672
		df	210
		Sig.	0

Source: Compiled from primary data analysis.

Data Analysis Tools

To further work on the objectives, the data collected have been analysed using SPSS 20, Microsoft Excel, and AMOS 26. Demographics are examined based on descriptive statistics such as average and percentage. A total of twenty-one statements were drafted on a five-point scale. The bankers are asked to express their level of agreement/disagreement on a five-point Likert Scale ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (5). Exploratory Factor Analysis (EFA) has been used to reduce the data and identify the factors for managing NPAs. Principal component analysis along with orthogonal rotation (Varimax) has been performed to extract the factors. It is the most commonly applied method, in the extant literature, to extract factors (Henson & Roberts, 2006; Tabachnick et al., 2007; Thompson & Daniel, 1996). Further, Confirmatory Factor Analysis (CFA) has been applied using AMOS 26, which is a type of structural equation modelling and also known as a suitable statistical technique, dealing with measurement models (Sureshchandar et al., 2001). Moreover, to validate the significance of various constructs, identified and confirmed through EFA and CFA, Z-value is used.

V. Results and Discussions

In this article, the internal consistency has been measured using the reliability test Cronbach’s alpha (Cronbach, 1951). Many researchers have revealed that Cronbach’s alpha scores of 0.60 be an acceptable reliability coefficient (Nunnally, 1978; Straub et al., 2004). Cronbach’s alpha for the overall data has been calculated, which stands at a value of 0.707.

Exploratory Factor Analysis

An EFA has been performed to confirm the factorial stability of the factors by utilising a maximum likelihood extraction method. Fabrigar et al. (1999) asserted that EFA allows for the computation of a wide range of indices of the goodness of fit of the model. At the preliminary stage of the analysis, using EFA, all the 21 statements were examined (as Management for NPAs), to assess the pattern of loadings of the statements under consideration of their resultant constructs. However, the results elucidated the 7 factors solution, along with the standard criterion for keeping the factors demonstrating the Eigenvalues larger than 1.00. Table 4 presents a synopsis of the results of EFA relating to the management of NPAs, depicting factor loadings, Eigenvalues, percentage of variance explained, cumulative % variance of the various factors, and Cronbach’s alpha values of the individual constructs. The factor loadings for all the items ranged from 0.661 to 0.895, therefore verifying that the constructs are one-dimensional and factorially idiosyncratic (Hair et al., 2010; Hulland, 1999; Truong & McColl, 2011). Distinct factors have been allotted appropriate labels depending upon the factor loading of the variables loaded on that specific factor, which are Staff Incentivising, Regulatory Intervention, Robust Appraisal and Assessment, Debtor Mortifying, Quick Resolution, and Disposal, Borrower Incentivising, and Monitoring and Follow up.

Table 4.

Mean Importance, Percentage of Variance Explained, Eigen Values and Cronbach’s Alpha for Management of NPAs.

Sr. No	Factor-wise Dimensions	Mean Importance	Factor Loading	Eigen Value	% of Variance Explained	Cumulative % of Variance	Cronbach’s Alpha
Label	F₁: Staff incentivising	3.73		2.652	12.627	12.627	0.821
B1	Incentivising the credit department staff	3.53	0.854
B2	Adding more staff at credit appraisals	3.95	0.83
B3	Frequent training of the staff of the credit department	3.66	0.750
B4	Engaging industrial bodies while making decisions on credit	3.79	0.746
Label	F₂: Regulatory intervention	4.20		2.592	12.341	24.968	0.793
B5	KYC norms and early warning system	4.31	0.839
B6	By encouraging corporate governance practices in entities financed by bank	4.14	0.768
B7	Credit rating agencies could be a great help	4.23	0.758
B8	Diversification of income	4.21	0.715
Label	F₃: Robust appraisal and assessment	3.95		2.023	9.633	34.601	0.730
B9	Improving loan appraisal system	3.78	0.816
B10	Thorough knowledge of the client’s profile	3.89	0.791
B11	Promptness in the review of credit	4.17	0.753
Label	F₄: Debtors mortifying	3.56		1.915	9.118	43.719	0.705
B12	Publicising the defaulters list	3.42	0.831
B13	Adoption of naming and shaming policy	3.53	0.808
B14	Involvement of external auditors and regulators in recovery	3.72	0.725
Label	F₅: Quick resolution and disposal	3.94		1.914	9.113	52.832	0.660
B15	Debt recovery tribunals has been able to speed up adjudications of matters	4.05	0.814
B16	The out of court measures like corporate debt restructuring and the Joint Lenders Forum has enhanced the credit discipline	3.73	0.738
B17	The SARFAESI Act, 2002 has been a success in empowering banks regarding recovering NPAs without court intervention	4.05	0.734
Label	F₆: Borrower incentivising	4.03		1.497	7.131	59.962	0.604
B18	Allowance for the moratorium period	4.00	0.895
B19	Waiving of interest part of the loan	4.06	0.661
Label	F₇: Monitoring and follow up	3.82		1.446	6.885	66.847	0.571
B20	Frequent visits to borrower units	3.74	0.857
B21	Continuous review of existing loan portfolios	3.90	0.786

Source: Compiled from primary data analysis.

Confirmatory Factor Analysis

After EFA was performed and good loadings of items were retained, a CFA was further employed and a model was developed as shown in (Figure 2). In Figure 2, the rectangles portray the observed factors and the unobserved variables are portrayed in the ovals. The correlations and co-variances are depicted by the double-headed arrows among the variables which are unobserved and the factor loadings relating to observed variables have been depicted by the straight-headed arrows. The error factors, also termed by the name of standard error squared multiple correlations, are depicted in the small circles.

Figure 2.

Source: Primary data analysis.

The Reliability of the items was scrutinised using composite reliability (CR), the standard threshold value for the same is 0.70 (Hair et al., 2010). Table 5 depicts the results of CR values for all the variables is above the threshold limit and thus verifying that strong reliability among the measures had been achieved. Further, the Content validity has been ensured by extant literature, pilot survey, and interaction with the experts in the field while framing the questionnaire. The convergent validity of the construct measures has been assessed through the average variance extracted (AVE). Table 3 depicts that AVE values were 0.5 or more than that, as suggested by Fornell and Larcker (1981), which implies that each latent variable explains equally or above 50% of the variance of its respective indicators.

Table 5.

Composite Reliability and Average Variance Explained (Management of NPAs).

Sr. No.	Factors	CR	AVE
1	Staff incentivising	0.8	0.5
2	Regulatory intervention	0.8	0.5
3	Robust appraisal and assessment	0.7	0.5
4	Debtor mortifying	0.7	0.5
5	Quick resolution and disposal	0.7	0.5
6	Borrower incentivising	0.7	0.5
7	Monitoring and follow up	0.6	0.5

Source: Compiled from primary data analysis.

The regression output of CFA (Table 6) depicted that all the factors proved significant and well explain its statements as the p-value is less than the .05 in all the cases. The model fit is studied from proprietary indicators and goodness to fit indices. Table 6 reveals that the χ2 of the model is 375.620 with 166 degrees of freedom (χ2/df = 2.263) which is well below the threshold limit of three. The fit indices values for the comparative fit index (CFI) is equal to 0.881, the Tucker-Lewis index (TLI) is 0.849, the goodness to fit index (GFI) is equal to 0.904, the adjusted goodness to fit index (AGFI) is equal to 0.866 and all the CFI, GFI, TLI values are close to 0.9 which shows a relatively good fit (Bentler, 1990; Kim et al., 2016; Mulaik et al., 1989). Further, the badness to fit index Root Mean Square Error of Approximation (RMSEA) is equal to 0.065 and Root Mean Square Residual (RMR) is 0.033, which is below the threshold limit of 0.08 (Bentler, 1990; Byrne, 2001; Fabrigar et al., 1999), revealing a good model fit.

Table 6.

Fit Indices of Structure Model (Management of NPAs).

Propriety Indicators	Research Findings
χ2	375.620
DF	166
P	0.000
Normed χ2	2.263
Goodness of fit
GFI	0.904
AGFI	0.866
CFI	0.881
TLI	0.849
Badness of fit
RMR	0.033
RMSEA	0.065

Source: Compiled from primary data analysis.

Significance of the Different Constructs Explored as Measures for Management of NPAs

The exploratory and confirmatory factor analysis identified seven factors as Management of NPAS. By applying Z-value, the statistical significance of the same has been examined. For this very purpose, the hypothesis applied is as follows:

H₀: The Mean Score is = 3.75

H₁: The Mean Score is > 3.75

Decision rule when α = .05

Table 7 demonstrates the results of the Z-test on the confirmed measurement items of CFA relating to the Management of NPAs. The first factor for the management of NPAs is Staff Incentivising. Sometimes staff shortage is considered as one of the major reasons for delinquent assets in a bank or it is assumed that if the staff is incentivised, they can be boosted to work well. But in the opinion of the bankers, it is the skills and not the financial boost, which improves the asset quality in a bank (Chakraborty, 2012; Sanjeev, 2007). Another factor for the management of NPAs is the Regulatory Intervention, which states that as per the notion of prevention is better than cure, banks should exercise due diligence while sanctioning credit. As per their perception, KYC norms have proved to be very much fruitful in containing NPAs. Singh et al. (2016) also asserted that such strong laws prevent fraudulent financial reporting and help in managing NPAs. Bankers should also educate borrowers to promote corporate governance practices in their entities. The credit rating agencies could be a great help as it disseminates the client’s information among banks and can support in managing NPAs. Existing literature also reveals that banks can take advantage of income diversification as it always has a positive effect on bank profitability (Meslier et al., 2014; Sanya & Wolfe, 2011). The next factor for the management of NPAs is Robust Appraisal and Assessment. If tough decisions are taken while scrutinising, the banker will not have to end up chasing delinquent loans or running from pillar to post in the name of contracted legal suits (Gandhi, 2015). Also if the disbursement of loans is expedited by promptly executing the legal documents, the financed projects can bring timely fruit. The next factor for the management of NPAs is Debtor Mortifying, which means declaring non-cooperative borrowers as ‘Wilful Defaulters’, publishing defaulter’s names. The involvement of auditors or regulators, and so on, can also help in effectively recovering NPAs (Attigeri et al., 2019; Jayadev & Padma, 2020). Another factor for managing NPAs is Quick Resolution and Disposal, which states that regulatory bodies involved in the recovery process can be a great help in managing NPAs. The Securitisation and Reconstruction of Financial Assets and Enforcement of Security Interest Act, 2002 has proved to be a great success in the management of NPAs in India as it enables the banks to auction properties when borrowers are not able to repay the borrowed money and this way helps banks to decrease their delinquent assets, by the adoption of recovery measures (Alamelumangai & Sudha, 2019; Garrido, 2012). As per the perception of the bankers out of court, measures like one-time settlements (OTS), and so on, are the most effective techniques for recovering and managing NPAs. Following more, such plans of action must also incorporate initiatives to utilise existing legal machinery to speed up the recovery process with the help of the debt recovery tribunal. The next factor for the management of NPAs is borrower incentivising, which states that banks usually execute policies like providing moratorium periods for bad loans or settling the shivering loans by waiving off the interest, and so on, to incentivise the borrowers to service their promised loans (Rizvi et al., 2019). Such boosts sometimes work wonders in the case of NPAs management. The next factor for the management of NPAs is monitoring and Follow up. Bankers believe that if the performing assets are monitored meticulously and vigorous follow-up, the NPA position would be improved. Moreover, the literature on NPAs emphasised reviewing the existing loan portfolio by frequently visiting the premises of borrowers (Jayadev & Padma, 2020). Bawa et al. (2019) found that business per employee is a significant indicator, believing that every staff member is associated with the loan generation and monitoring, and NPAs were a function of the bank agents’ bad assessment of lending.

Table 7.

Significance of Different Factors Explored and Confirmed as Management of NPAs (Hypothesised Mean = 3.75 & α = 0.05).

Sr. No.	Factors	Mean	Standard Dev.	Mean Score (%)	Z-Value	Significance
1	Staff incentivising	3.732	0.735	74.64	–0.430	Non-significant
2	Regulatory intervention	4.199	0.475	83.98	16.44	Significant
3	Robust appraisal and assessment	3.945	0.577	78.9	5.880	Significant
4	Debtor mortifying	3.56	0.841	71.2	–3.931	Non-significant
5	Quick resolution and disposal	3.944	0.565	78.88	5.971	Significant
6	Borrower incentivising	4.031	0.610	80.62	8.026	Significant
7	Monitoring and follow up	3.818	0.672	76.36	1.773	Significant

Source: Compiled from primary data analysis.

However, the results of computed Z-value revealed that the factors namely Regulatory intervention, Robust appraisal and assessment, Quick resolution and disposal, Debtor incentivising, Monitoring, and Follow up proved to be the significant factors for the management of NPAs as the Z-value of these all is greater than the table value, whereas the twin factors namely Staff Incentivising, Debtor Mortifying proved non-significant factors for managing NPAs (Table 7).

VI. Conclusions, Implications and Directions for Future Research

In the Indian banking system, the problem of NPAs has been persisting and lingering on for a long haul, obstructing the routine bank lending and gets hard to circumvent. Therefore, it is the need of the hour to restore the soundness of the banking system instantly so that the NPAs in the times to come can be curtailed from taking shape of another crisis that can hamper the economic activity. The present study has tried to explore and confirm the various factors, leading to NPAs management so that problem can be understood from the banker’s perspective and mitigation strategies can be framed thereof. The study is delivering a holistic approach concerning constructs for NPAs management, as bankers’ perspectives can significantly address this issue at the grass-root level. In the present study, as per the perception of the bankers, the analysis revealed that the NPAs, primarily, can be curtailed if the authorities at the Regulator level intervene and implement a system of early warning or whistleblowing by having a frequent tap with that of credit rating agencies. For that already the Prudential Framework for Resolution of Stressed Assets, 2019 has been framed for providing a plan for early recognition, reporting, and time-bound resolution of stressed assets. Moreover, the bankers perceive that the borrowers can be boosted to embody the corporate governance practices in their entities, so that the sudden corporate failures/scandals affecting their loan servicing capacity, can be mitigated. Further, a strong assessment system solves the half problem of asset delinquencies at the initial stage as the well-assessed projects do payback and so as the loan repayments. Prompt extension of credit plays a much more important role than the money sanctioned when the opportunity is partly lost. Furthermore, the bankers strongly perceive that strengthening the recovery measures is the best possible way to decrease the NPAs ratio and ensure a robust financial environment in the economy. Further, at the borrower level, bankers opine that most of the time borrowers start with positive intentions of repaying timely, but due to unavoidable circumstances, they fail in their financed ventures, in such situations, bankers should cooperate with them by giving a moratorium period or waiving off some of the interest portions just to avoid a complete loss. Last but not the least, at the banker level, also irrespective of the collateral security concerned, the banker should not fail in its strict monitoring duty. Hence, it can be concluded that timely regulatory intervention, disbursement of funds according to the requirements of the project, effective supervision along with quick resolution and disposal, and timely follow-up based on updated knowledge of NPA accounts and implementing corporate governance practices at the borrowers’ end can also equally play a vital role in containing the same.

Looking ahead, the results of the study have implications for regulation and supervision of NPAs in the Indian banking system as high levels of NPAs distract the attention of the bank staff from routine banking activities that might have the repercussions of missed business opportunities. Since non-performing assets are very time-sensitive, the banks ought to plan strategically as to which model for the management of NPAs fits their bank the best, strengthen the competencies, and build up resources well beforehand. This necessitates stringent actions to manage the issue at hand expeditiously. A bank, that tackles its NPAs problem first in the market, will have comparatively strong chances of coming out of the crisis, recuperate more rapidly than the other banks in competition, and experience more approach to capital.

Future avenues of research can benefit by having a holistic apprehension of the generation of NPAs and their management, for which an interdisciplinary effort is required which can analyse beyond macroeconomic and bank-specific factors. To conclude, as long as NPAs persist as a permanent attribute of banks, efforts should be made to avoid sharp increases that ignite the negative feedback loops at the micro-levels. Therefore, one can try to investigate feedback loops at the microeconomic level in future studies.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Note

ORCID iDs

Sonia Chawla

Seema Rani

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