Factors Affecting NPAs in Indian Banking Sector

Abstract

Banking sector is the backbone of any economy, so it is necessary to focus on its performance which is largely affected by its non-performing assets (NPAs). In the year 2018–2019, NPA of scheduled banks was Rs 355,076 Crore which is 3.7% of net advances. The purpose of this study is to identify the determinants based on analysis from previous literatures, and majorly macroeconomic and bank specific factors which are affecting NPAs using the relative weight analysis and to frame a model to predict future NPAs using multiple regression model using SPSS. The study also attempts to focus on actions and remedies that banks should make to control future NPAs. Findings of the study will act as a scaffolding for financial analysts and policymakers to prevent the conversion of its performing assets into NPAs and also help in proper management of banks and also in the recovery of economy.

Keywords

Non-performing assets predictive modelling multiple linear regression model relative weight analysis banking loans and advances

Introduction

The growth of the economy is dependent on various factors and banks are one of the crucial factors which help in mobilizing funds by converting savings into productive uses. So, it is important to have proper functioning of the banks. Improper functioning of the bank not only hampers the growth of the economy but also leads to crisis. The performance of the banks is dependent on the rates at which they get loan from central bank, the rate at which they give loan to its customers, number of defaulters (non-performing assets [NPA]), RBI guidelines to maintain proper provisions and so on (Vensel et al., 2004). NPA from a very long time interrupt the performance of the banks. It affects not only the profitability but also a major threat for the survival of the banks. In 2017–2018, Indian Banks faced a huge decline in asset quality as a result increase in provisions due to which Indian economy faced a huge loss and in the year 2018–2019 net NPAs of scheduled banks was Rs 355,076 Crore which is 3.7% of net advances. So, there is an urgent need to measure the future NPAs and to take measures to reduce it. Though government and RBI have implemented a framework of new management crisis which need not to face several challenges (Nidugala & Pant, 2017).

The structure of this article is as follows: Section 2 explains the concept of NPAs and Section 3 discuss the recent findings on NPAs and the factors affecting NPA. Section 4 discusses the objective of the study and detailed analysis of these objectives are done in Section 5.

What Are NPAs

Performing assets are those assets which generate regular income in the form of interest and principal repayment. NPAs can be defined as those loans and advances which stopped producing any income (principal repayment and interest). In other words, NPAs can be defined as loans and advances that are in arrears for a period of 90 days and banks need to make provisions for such assets and these provisions reduce the bank’s profitability (Das & Dutta, 2014). For example, a company has taken a loan of Rs 100,000 with interest payment of Rs 500 per month fails to pay this amount for the consecutive three months will be categorized as non performing asset. A loan can also be classified as NPA if a company makes all the interest payments but fails to repay principal amount. Earlier this period of 90 days was four quarters in 1993 which reduced to three quarters in 1994 and then there is a further reduction of this period to two quarters in 1995 and in 2001 this period was 180 days. Finally, in March 2004 the period declared for an asset to become NPA is 90 days (Rajan & Dhal, 2003). NPAs can further be classified on the basis of time period; (a) assets which remained unpaid or overdue for a period of 12 or less than 12 months (Sub-standard assets), (b) those assets which remained in the category of substandard assets over a period of 12 months (doubtful assets), (c) those assets which are identified by banks or RBI as assets with losses which needs to be written off (loss assets) (Kakker, 2004). NPAs can be classified as gross NPA which is the sum total of all the assets which are under the category of sub-standard, doubtful and loss assets as on the balance sheet date and net NPA which is the amount left after deducting the provisions for NPAs. In India, banks have to mandatory make the provisions according to the RBI guidelines which is quite large and this is the reasons why there is a huge difference between gross NPA and net NPA (Pradhan, 2012). They need to make provisions on the NPA’s as follows: Loss Assets: banks are required to make provision of 100% on the overdue amount; Doubtful Assets: for unsecure portion: 100% and for secured portion: provision for 25% (up to 1 year), 40% (from 1 year to 3 year) and 100% (for more than 3 years) and for sub-standard assets: Unsecured portion: 25% and secured portion: 15%. For standard assets banks are still required to make provisions on such assets. Provision percentages are as follows: 1% (commercial), 0.25% (SME and agriculture), 0.75% (commercial residential) and 0.40% (others).

US Financial Crisis 2008 is one of the reasons for today’s NPA in the Indian banking sector (Shukla, 2014). Neglecting standards, role of credit rating agencies, Wall street’s risky behaviour, buy and sell strategy through securitization were some of the factors in causing NPA crisis. When the loopholes in the systems revealed and the financial firms faced a run, then US Government decided to bail out. As a result, banks were not lending to each other, markets seized up and there were bank runs. The financial crisis of US reached the Indian markets and impacted its markets very badly. Decline in equity market by 50%, seizing up of money markets forced the Reserve Bank of India to step in and infuse liquidity through various new policies and programmes. These crises affected the Indian economy and its GDP growth fell to below 6.5% in 2008 as compared to 9% in the previous year. India’s government seeking the revival of the economy, pressurized the public sector banks to give loans to big infrastructural companies. Such kinds of measures help the economy to get back in the position as before the crisis. Then came the doomsday of NPA. New entrepreneurs failed because of the inflated cost of the projects, high leverage, collapse of international commodity prices and so on. In 2013, the new governor of Reserve Bank of India ordered the Asset Quality Review in 2015–2016 which resulted in banks to have more provisions for NPA and had a significant impact on the bank’s capital adequacy ratio The other reasons for the asset to become non-performing asset is when the assets are not taken seriously by the borrowers as well as banks. Internal factors such as defective lending, poor credit rating, no diversification of funds, irregular visits by bank officials, mismanagement by employees and so on and external factors such as wilful defaults by the borrowers, fluctuating economic conditions, stringent RBI guidelines, poor legal framework to recover NPA such as SARFAESI Act 2002 and poor entrepreneurial skills are the reasons for conversion of good loans and advances in to bad loans and NPAs. Other reasons could be liberalization and globalization as they create high competition in the market make the borrowers unable to survive with their products as there are greater choices with low prices.

There are different acts introduced by Government of India along with RBI for speedy recovery and to reduce NPAs of different banks. Some of them are Securitization and Reconstruction of Financial Assets and Enforcement of Securities Interest Act, 2002 (SARFAESI Act), debt recovery tribunal, corporate debt recovery and Lok Adalat. SARFAESI Act helps the banks to recover those assets which are declared as NPA. The Act is applied on secured loans, loans above Rs 100,000 and overdue amount is above 20% of principal. The objective of this act is to enhance rapid and efficient recovery of NPAs. For recovery of NPA this Act provides securitization, asset reconstruction and enforcement of security without intervention of court. Financial assistance is provided to the borrowers and against that bank takes the security as a collateral security. They can take possession of collateral security and sell the same without the permission of the tribunal if the asset is declared as NPA. Asset reconstruction companies (ARCs) were set up so that banks can sell their NPAs to ARCs. ARCS then find the qualified buyers and transfers the security receipts to them. DRTs help banks and other financial institutions to recover their loans from their customers. In 1993, after passing RDBBFI (Recovery of Debts due to Banks and Financial Institutions Act) Debt Recovery Tribunals were set up. DRTs takes cases of those loans whose values are above 10 lakhs. For quick settlement of disputed loans government in 2014 has set up six new DRTs. But there are some issues in DRTS such as less DRTs to handle such large number of NPA cases, no timely appointment of DRTs official and cases are being settled very late. Corporate debt recovery is the scheme introduces by RBI to help banks in improving their performances by reducing NPAs. In these banks and financial institutions can realize their debts. It is a voluntary agreement between debtors and creditor or between creditors. It takes care of only multiple bank accounts and syndicate accounts where total outstanding amounts is Rs 100 million. State government has created Lok Adalat which are voluntary agencies to take care of the small loans. Loans of less than 5 lakhs can be settled in Lok Adalat. In future progress can be seen through this channel.

Literature Review

Properly developed financial system ease the flow of savings and investments and thus helps in reviving the economic growth (King & Levine, 1993). Banks and other financial institutions help in channelizing these savings and investments and thus their performance need to be properly managed (Shukla, 2014). Although their performances are based on number of factors but past researches have shown that major factor is NPAs. With the increase in profits, NPA of the banks increases due to the mismanagement done by the banks. This mismanagement is more in public sector banks which not only impact the economy development but also forced the government to provide funds to bail out these banks along with budgetary provisions and as a result cost is to be borne by the taxpayers. According to sectors, public sectors, priority sectors, banking sectors, and other sectors have significant difference between the values of advances and in the growth rate of these advances, there is a fluctuating trend except in 2011–2012 (Thangam et al., 2019). Public banks to compete in the Indian banks try to adopt cost reduction and assets quality strategies. In terms of recovery, performances of small individual borrowers are better as compared to institutional borrowers as big corporate borrowers do not have any fear of banks as there are no laws to recover bad loans and take actions against defaulters. Also, wrong customer choices by banks make them unable to provide loans to new clients which lead to poor domestic growth and slow recovery ultimately giving rise to NPAs (Rathore et al., 2016). Insider lending is also a cause of increasing non-performing loans and biasness in giving loans to some borrowers leads to more NPAs as these loans are given without giving attention to credit rating of these borrowers. Credit quality affects banks performance and ultimately leads to their collapse (Gaur & Mohapatra, 2020). Most of the time Banks officers prefer out of court settlement than sue the defaulter and this takes more time as a result of greater NPAs (Pradhan, 2012). Majorly, NPAs are affected by three important factors—macroeconomic fluctuations, credit terms, bank risk preference and out of the three credit terms significantly affect the banks non-performing loans as compared to macroeconomic fluctuations and bank risk preferences (Rajan & Dhal, 2003). Among the macroeconomic factors, construction expenditure, stock market index and growth rate in per capita income are significantly affecting NPAs and have negative relation with it (Prasanna et al., 2014). On the other hand, under bank specific variables capital adequacy ratio and return on assets affect NPA significantly (Patra & Padhi, 2016). As macro-economic factors are common for all and back specific factors are unique to each bank which cause different behaviour of NPAs on change in macroeconomic variables (Rema & Karunakaran, 2019). Among all the factors CPI, GPD and loans and advances are the major factors affecting NPA (Vallabh et al., 2016). One important driver of NPA is economic-political environment that enhances the factors like adoption of best international practices and affects the ownership patterns, among others (Rizvi et al., 2019). At the same time depreciation of domestic currency leads to higher NPAs which shows, ratio of foreign currency denominated loans, that is, unhedged and total loans is high. So, policymakers should make efforts towards not only appreciating the domestic currency but also to increase the share of hedged borrowings in total foreign currency borrowings (Mishra et al., 2020). Leverage is not significantly affecting the bank’s credit risk in several economies. Also, in developing countries credit risk is more as compared to developed countries. As in developing countries with the slow economic growth, fall in stock market index and poor performance of private corporate sector will lead to more NPAs than developed economies (Mohanty et al., 2018).

So, there should be a proper check and timely recognition of bad debts and bad loans so that timely actions can be taken. Accountability should be fixed by the banks for top-level management, auditors and board of directors; extra vigilant towards high value account having signals of delinquency; no political interference in the operations of banks; clear guidelines for settlement and improvement in corporate governance for better credit decision (Sharma et al., 2020). For this, we need good banking governance and regulations. This requires a proper information technology (IT) strategy as with the increase in investments on IT leads to increase in deposits, profitability and hence reduction in net NPA ratio. Machine learning in data analytics and in various services of banks help in increasing banks performance and better insights. But artificial intelligence (AI) in India is used by very few private sector banks and this is also restricted to very few services and operations related work. More importance should be given to asset management companies and independent goals should be given to these asset management companies so that better recovery of non-performing loans can be done. Also, international best facilities and practices should be adopted for better results. Along with this, incentives and legal enforcement powers should be given to asset management companies.

Objective of the Study

Whether different factors are affecting the NPAs or not.

Prediction of NPA by deriving a model which can predict future NPAs.

Suggest different ways through which NPAs can be reduced.

Research Methodology

The data taken for the present study is secondary and has been taken from the various reports and publications on RBI website. Various journals and newspapers are also used for the data collection. Time period for the present study is 13 years from 2006–2007 to 2018–2019.

Relative Weight Analysis using SPSS is being used to find the relevance of different factors affecting NPA. Multicollinearity between the variables are being checked by variance inflation factor (VIF) and multiple linear regression tools has been applied to predict future NPA.

Scope of the Study

Geographical Region: The study covers the Indian banking system.

Banks: The study mainly covers the data of different scheduled commercial banks.

Time Period: Time period used in the study is last 13 years from 2006–2007 to 2018–2019.

Limitation of the Study

The study is limited to Indian banking sectors an ignores the foreign banks.

Data of the study is mainly taken from RBI publications.

For forecasting NPAs, few factors under macroeconomic and bank-specific factors are considered. There could be other factors as well which are not taken and can affect the NPA.

Further performance of public and private sector banks based on NPAs can be studied.

Independent Variables Used

Following are the independent variables which are used in the current study to determine their significance in predicting the NPAs.

Macroeconomic factors

Gross Domestic Product: GDP of the economy is the sum total of the final value of all goods and services produced in the economy during a specific period of time. With the increase in GDP, demand as well as supply of loans and advances also grows and as a result NPA increases (Rey, 2016).

Unemployment Rate: Situation where a person is actively looking for employment but not able to get employed is called unemployment. However, unemployment rate is the proportion of unemployed people to total number of people in the labour force. NPA rises with rise in unemployment rate and decreases with the decrease in unemployment rate. Also, unemployment rate has not significant relation with the NPAs of Foreign banks (Surdarsan & Santosh, 2019).

Inflation: Rise in prices of goods and services is known as inflation. With the increase in inflation, prices go up as a result purchasing power reduces and therefore borrowers found it difficult to return the loan and it creates NPAS. NPAs of public sectors are more affected by the inflation as compared to private sector (Bhaarathi & Thilagvathi, 2018).

Exchange Rate: It is the value of country’s nation versus the currency of another country. In the current study exchange rate vis a vis dollar is taken.

Lending Rates: It is the rate at which banks give the loans to their customer. In other words it is the cost of borrowing. With the increase in lending rates, NPA also increases (Rey, 2016).

Repo Rates: Repo rate is the rate at which commercial banks gets the loan from central banks when there is shortfall of funds. If the repo rate is high then commercial banks will also charge high amount from its customers as interest, which affects the NPA (Prasanna et al., 2014).

Bank-Specific Factors

Return on Equity (ROE): How effectively a bank is utilizing its shareholders fund is known as return on equity (Arditti, 1967). ROE significantly affects NPA.

Return on Assets (ROA): Return on asset is efficiency in asset utilization and how much these assets are generating income. Efficiency of bank is highly correlated with the asset quality (Siraj & Pillai, 2013). Higher ROA indicates better performance as a result less chances of becoming an asset into NPA (Duraisamy, 2016).

Liquidity Ratio: It is the ratio to that measures a company’s ability to pay short term obligations. Banking sector is essential financial sector where risk of increase in NPAs will affect the profitability and liquidity of banks (Bamoriya & Jain, 2013). Increase in NPA leads to decrease in cash level as borrowers are unable to pay their debt on time which force banks to borrow from other alternate source and ultimately increase banks liability (Bawa et al., 2019). In the current study, credit deposit ratio is used as a proxy for liquidity ratio.

Loans and Advances: Increase in loans and advances increases the risk for the banks in terms of interests and loan repayments and therefore increase in NPAs.

Hypothesis

H₀: Macroeconomic and bank-specific factors are not significantly affecting NPAs.

H₁: Macroeconomic and bank-specific factors are significantly affecting NPAs.

To test the above hypothesis data of GDP, unemployment rate, inflation, exchange rates, lending rates, repo rate, ROE, ROA, liquidity ratio and NPA of commercial schedule banks from 2006–2007 to 2018–2019 has been taken.

There are more than one independent variables (Table 1) so, we need to first check the Multicollinearity between these independent Variables. The same has been checked by VIF (Table 2). All the VIF values except ROA and ROE are between 1 to 10. So, this implies that multicollinearity problem exists.

To know the relative contribution of each predictor used in the study in explaining the variance in the criteria variable, we have used relative weight analysis as determining with the help of P values may not give reliable results because of multicollinearity problem. Relative weight analysis helps us in determining the proportionate contribution of each predictor in total predicted criterion variance by taking into account a variable’s own contribution and also with other predictor variables (Johnson & LeBreton, 2004). Table 3 shows the relative importance of each predictor in determining the NPA. It shows that out of macroeconomic factors, GDP, lending rates and exchange rates are contributing majorly in the prediction of NPA where as in bank specific factors return on assets and return on equity majorly affecting NPA.

R-square which is 0.965 indicates that 96.5% of variance in NPA is being explained by all the predictor variables used in the study (Table 4). So, it can be implied that regression line is best fitted into the model.

Table 1.

Data of Scheduled Commercial Banks.

Year	NPA (As Percentage of Gross Advances)	Macro-Economic Variables						Bank-Specific Factors
Year	NPA (As Percentage of Gross Advances)	GDP	Unemployment Rate	Inflation	Exchange Rate vis-a-vis Dollar	Lending Rates	Repo Rates	ROE	ROA	Liquidity Ratio (Used CDR)
2006–2007	2.5	7.66	5.32	6.37	2.204479	14.75	7.1875	15.51	1.05	73.46
2007–2008	2.3	3.09	5.28	8.35	–11.0251	15.75	7.75	15.98	1.12	74.61
2008–2009	2.3	7.86	5.57	10.88	14.2387	16.75	7.25	15.44	1.13	73.83
2009–2010	2.5	8.5	5.64	11.99	3.152633	15.75	4.8125	14.31	1.05	73.66
2010–2011	2.4	5.24	5.64	8.86	–3.9641	9.5	6.125	14.96	1.1	76.52
2011–2012	2.9	5.46	5.65	9.31	5.180345	10.75	8.1875	14.6	1.08	78.62
2012–2013	3.2	6.39	5.67	10.91	13.53633	10.25	7.875	13.84	1.04	79.14
2013–2014	3.8	7.41	5.61	6.35	11.19649	10.25	7.625	10.69	0.81	78.93
2014–2015	4.3	8	5.57	5.87	1.060628	10.25	7.875	10.42	0.81	78.32
2015–2016	7.5	8.26	5.51	4.94	7.073349	9.7	6.875	3.58	0.4	78.24
2016–2017	9.3	7.04	5.42	2.49	2.449269	8.2	6.375	4.16	0.36	73.04
2017–2018	6.1	6.12	5.33	4.86	–3.90193	7.95	6.0625	–2.81	–0.15	74.16
2018–2019	6.4	4.18	5.36	7.66	8.483451	8.55	6.375	–1.85	–0.09	75.09

Source: Handbook of Statistics on Indian Economy, RBI Bulletin.

Table 2.

Unstandardized Coefficients.

Model		Unstandarized Coefficients
Model		B	Std. Error
1	(Constant)	81.002	39.752
	GDP	.096	.203
	Unemployment	–5.877	6.881
	Inflation	–.220	.167
	Exchange Rate	.042	.066
	Lending Rate	–.335	.253
	Repo Rate	.102	.576
	ROE	–2.480	.780
	ROA	36.357	12.257
	Liquidity Rate	–.436	.270

Source: The author.

Table 3.

Relative Weight Analysis.

Variables	Raw Relative Weight	Rescaled Relative Weight
GDP	0.1411983	20.83856
Unemployment rate	0.0486912	8.2376
Inflation	0.0276598	1.030244
Exchange rate	0.0876872	15.23418
Lending rate	0.1145429	18.333124
Repo rate	0.0287526	2.876984
ROE	0.1154398	13.302322
ROA	0.0986774	16.961224
Liquidity rate	0.3023508	3.185762

Source: The author.

Table 4.

Model Summary.

Model	R	R-Square	Adjusted R-Square	Std. Error of the Estimate
1	.982^a	0.965	0.859	0.86902

Source: The author.

Note: ^aPredictors: (Constant), Liquidity Rate, GDP, ROE, Exchange Rate, Repo Rate, Inflation, Lending Rate, Unemployment, ROA.

Table 5.

Coefficients of Independent Variables used in the study.^a

Model		Unstandardized Coefficients		Standardized Coefficients
Model		B	Std. Error	Beta
1	(Constant)	81.002	39.752
	GDP	0.096	0.203	0.069
	Unemployment	–5.877	6.881	–0.365
	Inflation	–0.220	0.167	–0.362
	Exchange rate	0.042	0.066	0.132
	Lending rate	–0.335	0.253	–0.455
	Repo rate	0.102	0.576	0.042
	ROE	–2.480	0.780	–0 7.282
	ROA	36.357	12.257	7.269
	Liquidity rate	–0.436	0.270	–0.446

Source: The author.

Note: ^aDependent Variable: NPA.

Final Forecasting of NPA

From the above results, the following forecasting equation has been derived:

Y = 80.002 + (0.96 × GDP) – (5.877 × Unemployment rate) − (0.22 × Inflation) + (0.042 × Exchange Rate) – (0.335 × Lending Rate) + (0.102 × Repo Rate) – (2.48 × ROE) + (36.357 × ROA) – (0.436 × Liquidity rate)

Based on the above equation, banks can easily forecast their NPA and this early detection helps them in controlling the poor loans. The equation has a constant with the value 80.002. Also, GDP, exchange rate, repo rate and return on asset have positive relation with NPA, on the other hand unemployment rate, inflation, lending rate and return on equity have negative relation with the NPA. NPA of 2020 can be found using the above equation.

Y = 80.002 + (0.96 × 3.1) – (5.877 × 6.52) – (0.22 × 5.52) + (0.042 × 1.33) – (0.335 × 4.29) + (0.102 × 4.4) – (2.48 × 2.34) + (36.357 × 0.2) – (0.436 × 76)

= 8.16%

So, the NPA of 2020 will be 8.16% approx.

Suggestions and Solutions

One Time Settlement Scheme (OTS)

RBI mandates the banks to have OTS in their schemes. In order to reduce or recover NPAs, banks offer the borrowers to settle their dues through OTS. However, it may affect the profitability of banks as they have to accept less amount. Wilful defaults are not allowed for OTS. Borrowers’ first need is to explain the reasons for defaults and banks, if they find it genuine then bank accepts the OTS. Sometimes, banks if holding borrower’s security find it sufficient to recover its full dues from it then he may reject the request of the borrower for OTS. OTS helps in increasing liquidity, quick recovery of NPAs and so on.

Defaulters List

RBI every year on 31 March releases a list of wilful defaulters of different banks and financial institutions. RBI also releases the list of those defaulters with overdue amount of 1 crore or above, against whom the case has been filed in the court. It contains the detailed information of these defaulters so that cautions can be taken by other banks with these defaulters.

Recovery Camps

Recovery camps have been found very successful in recovering the NPAs. A place that is convenient to both the parties, that is, banks’ officials and borrowers is suitable for such camps. Such camps are normally held in public places and government’s building. People with small loans are more found in rural areas than in urban areas.

Early Warning Signals

Banks should focus on the following early warning signals to prevent an asset to become NPA:

Operational signals: By checking the past sales, bad debts amount, profitability, plant performances, dividend policies of the borrowers, banks can detect the credibility of borrower.

Financial warning signals: These signals can be detected by checking the financial books of the borrowers such as balance sheet, cash flow statements and so on.

Fee-Based Income

In India, banks are more dependent on lending and borrowing income where as in developed countries banks are more dependent on fee-based incomes. So in case of NPAs, banks have sufficient money to substitute for losses.

Sector-Wise Planning

Some sectors need to be examined more carefully than others. Sectors which are more prone to make defaults are infrastructure, iron and steel, and textiles. So banks can form teams which can monitor different sectors’ conditions and can take decisions accordingly.

Self-Help Groups

A small number of people usually 10 to 20 who come together jointly to help each other mutually by pooling their small savings are called self-help groups. Banks avoids giving loans to small borrowers as there are more chances of becoming those assets as NPAs. But these small borrowers are able to repay informal loans even at high rates of interest. This proves that small borrowers are able to repay so self-help groups are made in the small regions to help these borrowers who are exploited by the informal sectors.

Effect of COVID-19 on NPAs of Indian Banks

The impact of COVID-19 financially and economically on Indian economy has been largely disruptive. With the large population, it is difficult for India to revive early from its impact.

Not only the well-being is the major issue, financial and economic issues and get out of these issues is a major challenge for India. This pandemic is creating a serious impact on the asset quality and credit delivery. Indian banks are already showing signs of distress even before COVID-19 because of slow economy. Although six-month loan moratorium relieves the pain of Covid-affected borrowers but financial analysts raised threat signals of hidden stress for the banks. If the NPAs of certain banks continue to rise like this, the gross NPAs of banks will be higher by 19–60 basis points.

Due to the pandemic, RBI allowed the relaxations in the term loan repayment until 31 August 2021 and side by side mentioned in the latest RBI financial stability repot that in July percentage of retail loans that were under moratorium is 56.2 as on 30 April while for corporate loans it is 39.1%. High restructuring before Covid contributed to hidden NPAs. There are chances that moratorium may extended for two more years because of the restructuring facility under Covid up to two years. By analysing the current stress under Indian banking system, financial analysts estimate a percentage of 5–8 of total loan will go under restructuring.

According to RBI, the current ratio of NPA will increase up to 15% by March 2022. Necessary measures need to be taken by the RBI to decrease the NPA ratio so that along with the well-being, financial stability of the country is also maintained.

Conclusion

NPAs being a major factor in determining performance of the bank need to be controlled and measures should be taken by these banks and RBI to prevent further NPAs. For Indian banks apart from maintaining profitability, they have to focus on the ways to decrease and maintain their NPAs while expanding their credit portfolio. In today’s environment, focus on asset quality is a major task as it is a basic functional challenging task for the banks. So, to reduce the NPA ratio, banks should introduce AI in its operations so as to have effective monitoring and credit appraisal system. The above study provided the reasons for NPAs and suggests some measures to reduce it.

The study concludes that both macro-economic and bank-specific factors are significantly affecting the NPAs of Indian banks, specifically GDP, lending rates, exchange rates, return on assets and return on equity.

The current study also prepares a model to forecast future NPAs using multiple linear regression which helps the banks and other financial institution in early detection of poor loans and helps in taking timely actions.

Footnotes

Declaration of Conflicting Interests

Funding

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

ORCID iD

Nitya Garg

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Factors Affecting NPAs in Indian Banking Sector

Abstract

Keywords

Introduction

What Are NPAs

Literature Review

Objective of the Study

Research Methodology

Scope of the Study

Limitation of the Study

Independent Variables Used

Hypothesis

Data of Scheduled Commercial Banks.

Unstandardized Coefficients.

Relative Weight Analysis.

Model Summary.

Coefficients of Independent Variables used in the study.a

Final Forecasting of NPA

Suggestions and Solutions

One Time Settlement Scheme (OTS)

Defaulters List

Recovery Camps

Early Warning Signals

Fee-Based Income

Sector-Wise Planning

Self-Help Groups

Effect of COVID-19 on NPAs of Indian Banks

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

References

Coefficients of Independent Variables used in the study.^a