literature review on risk management in banking sector in india

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• Published: 03 September 2022

A literature review of risk, regulation, and profitability of banks using a scientometric study

• Shailesh Rastogi 1 ,
• Arpita Sharma 1 ,
• Geetanjali Pinto 2 &
• Venkata Mrudula Bhimavarapu   ORCID: orcid.org/0000-0002-9757-1904 1 , 3  

Future Business Journal volume  8 , Article number:  28 ( 2022 ) Cite this article

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This study presents a systematic literature review of regulation, profitability, and risk in the banking industry and explores the relationship between them. It proposes a policy initiative using a model that offers guidelines to establish the right mix among these variables. This is a systematic literature review study. Firstly, the necessary data are extracted using the relevant keywords from the Scopus database. The initial search results are then narrowed down, and the refined results are stored in a file. This file is finally used for data analysis. Data analysis is done using scientometrics tools, such as Table2net and Sciences cape software, and Gephi to conduct network, citation analysis, and page rank analysis. Additionally, content analysis of the relevant literature is done to construct a theoretical framework. The study identifies the prominent authors, keywords, and journals that researchers can use to understand the publication pattern in banking and the link between bank regulation, performance, and risk. It also finds that concentration banking, market power, large banks, and less competition significantly affect banks’ financial stability, profitability, and risk. Ownership structure and its impact on the performance of banks need to be investigated but have been inadequately explored in this study. This is an organized literature review exploring the relationship between regulation and bank performance. The limitations of the regulations and the importance of concentration banking are part of the findings.

Introduction

Globally, banks are under extreme pressure to enhance their performance and risk management. The financial industry still recalls the ignoble 2008 World Financial Crisis (WFC) as the worst economic disaster after the Great Depression of 1929. The regulatory mechanism before 2008 (mainly Basel II) was strongly criticized for its failure to address banks’ risks [ 47 , 87 ]. Thus, it is essential to investigate the regulation of banks [ 75 ]. This study systematically reviews the relevant literature on banks’ performance and risk management and proposes a probable solution.

Issues of performance and risk management of banks

Banks have always been hailed as engines of economic growth and have been the axis of the development of financial systems [ 70 , 85 ]. A vital parameter of a bank’s financial health is the volume of its non-performing assets (NPAs) on its balance sheet. NPAs are advances that delay in payment of interest or principal beyond a few quarters [ 108 , 118 ]. According to Ghosh [ 51 ], NPAs negatively affect the liquidity and profitability of banks, thus affecting credit growth and leading to financial instability in the economy. Hence, healthy banks translate into a healthy economy.

Despite regulations, such as high capital buffers and liquidity ratio requirements, during the second decade of the twenty-first century, the Indian banking sector still witnessed a substantial increase in NPAs. A recent report by the Indian central bank indicates that the gross NPA ratio reached an all-time peak of 11% in March 2018 and 12.2% in March 2019 [ 49 ]. Basel II has been criticized for several reasons [ 98 ]. Schwerter [ 116 ] and Pakravan [ 98 ] highlighted the systemic risk and gaps in Basel II, which could not address the systemic risk of WFC 2008. Basel III was designed to close the gaps in Basel II. However, Schwerter [ 116 ] criticized Basel III and suggested that more focus should have been on active risk management practices to avoid any impending financial crisis. Basel III was proposed to solve these issues, but it could not [ 3 , 116 ]. Samitas and Polyzos [ 113 ] found that Basel III had made banking challenging since it had reduced liquidity and failed to shield the contagion effect. Therefore, exploring some solutions to establish the right balance between regulation, performance, and risk management of banks is vital.

Keeley [ 67 ] introduced the idea of a balance among banks’ profitability, regulation, and NPA (risk-taking). This study presents the balancing act of profitability, regulation, and NPA (risk-taking) of banks as a probable solution to the issues of bank performance and risk management and calls it a triad . Figure  1 illustrates the concept of a triad. Several authors have discussed the triad in parts [ 32 , 96 , 110 , 112 ]. Triad was empirically tested in different countries by Agoraki et al. [ 1 ]. Though the idea of a triad is quite old, it is relevant in the current scenario. The spirit of the triad strongly and collectively admonishes the Basel Accord and exhibits new and exhaustive measures to take up and solve the issue of performance and risk management in banks [ 16 , 98 ]. The 2008 WFC may have caused an imbalance among profitability, regulation, and risk-taking of banks [ 57 ]. Less regulation , more competition (less profitability ), and incentive to take the risk were the cornerstones of the 2008 WFC [ 56 ]. Achieving a balance among the three elements of a triad is a real challenge for banks’ performance and risk management, which this study addresses.

Triad of Profitability, regulation, and NPA (risk-taking). Note The triad [ 131 ] of profitability, regulation, and NPA (risk-taking) is shown in Fig.  1

Triki et al. [ 130 ] revealed that a bank’s performance is a trade-off between the elements of the triad. Reduction in competition increases the profitability of banks. However, in the long run, reduction in competition leads to either the success or failure of banks. Flexible but well-expressed regulation and less competition add value to a bank’s performance. The current review paper is an attempt to explore the literature on this triad of bank performance, regulation, and risk management. This paper has the following objectives:

To systematically explore the existing literature on the triad: performance, regulation, and risk management of banks; and

To propose a model for effective bank performance and risk management of banks.

Literature is replete with discussion across the world on the triad. However, there is a lack of acceptance of the triad as a solution to the woes of bank performance and risk management. Therefore, the findings of the current papers significantly contribute to this regard. This paper collates all the previous studies on the triad systematically and presents a curated view to facilitate the policy makers and stakeholders to make more informed decisions on the issue of bank performance and risk management. This paper also contributes significantly by proposing a DBS (differential banking system) model to solve the problem of banks (Fig.  7 ). This paper examines studies worldwide and therefore ensures the wider applicability of its findings. Applicability of the DBS model is not only limited to one nation but can also be implemented worldwide. To the best of the authors’ knowledge, this is the first study to systematically evaluate the publication pattern in banking using a blend of scientometrics analysis tools, network analysis tools, and content analysis to understand the link between bank regulation, performance, and risk.

This paper is divided into five sections. “ Data and research methods ” section discusses the research methodology used for the study. The data analysis for this study is presented in two parts. “ Bibliometric and network analysis ” section presents the results obtained using bibliometric and network analysis tools, followed by “ Content Analysis ” section, which presents the content analysis of the selected literature. “ Discussion of the findings ” section discusses the results and explains the study’s conclusion, followed by limitations and scope for further research.

Data and research methods

A literature review is a systematic, reproducible, and explicit way of identifying, evaluating, and synthesizing relevant research produced and published by researchers [ 50 , 100 ]. Analyzing existing literature helps researchers generate new themes and ideas to justify the contribution made to literature. The knowledge obtained through evidence-based research also improves decision-making leading to better practical implementation in the real corporate world [ 100 , 129 ].

As Kumar et al. [ 77 , 78 ] and Rowley and Slack [ 111 ] recommended conducting an SLR, this study also employs a three-step approach to understand the publication pattern in the banking area and establish a link between bank performance, regulation, and risk.

Determining the appropriate keywords for exploring the data

Many databases such as Google Scholar, Web of Science, and Scopus are available to extract the relevant data. The quality of a publication is associated with listing a journal in a database. Scopus is a quality database as it has a wider coverage of data [ 100 , 137 ]. Hence, this study uses the Scopus database to extract the relevant data.

For conducting an SLR, there is a need to determine the most appropriate keywords to be used in the database search engine [ 26 ]. Since this study seeks to explore a link between regulation, performance, and risk management of banks, the keywords used were “risk,” “regulation,” “profitability,” “bank,” and “banking.”

Initial search results and limiting criteria

Using the keywords identified in step 1, the search for relevant literature was conducted in December 2020 in the Scopus database. This resulted in the search of 4525 documents from inception till December 2020. Further, we limited our search to include “article” publications only and included subject areas: “Economics, Econometrics and Finance,” “Business, Management and Accounting,” and “Social sciences” only. This resulted in a final search result of 3457 articles. These results were stored in a.csv file which is then used as an input to conduct the SLR.

Data analysis tools and techniques

This study uses bibliometric and network analysis tools to understand the publication pattern in the area of research [ 13 , 48 , 100 , 122 , 129 , 134 ]. Some sub-analyses of network analysis are keyword word, author, citation, and page rank analysis. Author analysis explains the author’s contribution to literature or research collaboration, national and international [ 59 , 99 ]. Citation analysis focuses on many researchers’ most cited research articles [ 100 , 102 , 131 ].

The.csv file consists of all bibliometric data for 3457 articles. Gephi and other scientometrics tools, such as Table2net and ScienceScape software, were used for the network analysis. This.csv file is directly used as an input for this software to obtain network diagrams for better data visualization [ 77 ]. To ensure the study’s quality, the articles with 50 or more citations (216 in number) are selected for content analysis [ 53 , 102 ]. The contents of these 216 articles are analyzed to develop a conceptual model of banks’ triad of risk, regulation, and profitability. Figure  2 explains the data retrieval process for SLR.

Data retrieval process for SLR. Note Stepwise SLR process and corresponding results obtained

Bibliometric and network analysis

Figure  3 [ 58 ] depicts the total number of studies that have been published on “risk,” “regulation,” “profitability,” “bank,” and “banking.” Figure  3 also depicts the pattern of the quality of the publications from the beginning till 2020. It undoubtedly shows an increasing trend in the number of articles published in the area of the triad: “risk” regulation” and “profitability.” Moreover, out of the 3457 articles published in the said area, 2098 were published recently in the last five years and contribute to 61% of total publications in this area.

Articles published from 1976 till 2020 . Note The graph shows the number of documents published from 1976 till 2020 obtained from the Scopus database

Source of publications

A total of 160 journals have contributed to the publication of 3457 articles extracted from Scopus on the triad of risk, regulation, and profitability. Table 1 shows the top 10 sources of the publications based on the citation measure. Table 1 considers two sets of data. One data set is the universe of 3457 articles, and another is the set of 216 articles used for content analysis along with their corresponding citations. The global citations are considered for the study from the Scopus dataset, and the local citations are considered for the articles in the nodes [ 53 , 135 ]. The top 10 journals with 50 or more citations resulted in 96 articles. This is almost 45% of the literature used for content analysis ( n  = 216). Table 1 also shows that the Journal of Banking and Finance is the most prominent in terms of the number of publications and citations. It has 46 articles published, which is about 21% of the literature used for content analysis. Table 1 also shows these core journals’ SCImago Journal Rank indicator and H index. SCImago Journal Rank indicator reflects the impact and prestige of the Journal. This indicator is calculated as the previous three years’ weighted average of the number of citations in the Journal since the year that the article was published. The h index is the number of articles (h) published in a journal and received at least h. The number explains the scientific impact and the scientific productivity of the Journal. Table 1 also explains the time span of the journals covering articles in the area of the triad of risk, regulation, and profitability [ 7 ].

Figure  4 depicts the network analysis, where the connections between the authors and source title (journals) are made. The network has 674 nodes and 911 edges. The network between the author and Journal is classified into 36 modularities. Sections of the graph with dense connections indicate high modularity. A modularity algorithm is a design that measures how strong the divided networks are grouped into modules; this means how well the nodes are connected through a denser route relative to other networks.

Network analysis between authors and journals. Note A node size explains the more linked authors to a journal

The size of the nodes is based on the rank of the degree. The degree explains the number of connections or edges linked to a node. In the current graph, a node represents the name of the Journal and authors; they are connected through the edges. Therefore, the more the authors are associated with the Journal, the higher the degree. The algorithm used for the layout is Yifan Hu’s.

Many authors are associated with the Journal of Banking and Finance, Journal of Accounting and Economics, Journal of Financial Economics, Journal of Financial Services Research, and Journal of Business Ethics. Therefore, they are the most relevant journals on banks’ risk, regulation, and profitability.

Location and affiliation analysis

Affiliation analysis helps to identify the top contributing countries and universities. Figure  5 shows the countries across the globe where articles have been published in the triad. The size of the circle in the map indicates the number of articles published in that country. Table 2 provides the details of the top contributing organizations.

Location of articles published on Triad of profitability, regulation, and risk

Figure  5 shows that the most significant number of articles is published in the USA, followed by the UK. Malaysia and China have also contributed many articles in this area. Table 2 shows that the top contributing universities are also from Malaysia, the UK, and the USA.

Key author analysis

Table 3 shows the number of articles written by the authors out of the 3457 articles. The table also shows the top 10 authors of bank risk, regulation, and profitability.

Fadzlan Sufian, affiliated with the Universiti Islam Malaysia, has the maximum number, with 33 articles. Philip Molyneux and M. Kabir Hassan are from the University of Sharjah and the University of New Orleans, respectively; they contributed significantly, with 20 and 18 articles, respectively.

However, when the quality of the article is selected based on 50 or more citations, Fadzlan Sufian has only 3 articles with more than 50 citations. At the same time, Philip Molyneux and Allen Berger contributed more quality articles, with 8 and 11 articles, respectively.

Keyword analysis

Table 4 shows the keyword analysis (times they appeared in the articles). The top 10 keywords are listed in Table 4 . Banking and banks appeared 324 and 194 times, respectively, which forms the scope of this study, covering articles from the beginning till 2020. The keyword analysis helps to determine the factors affecting banks, such as profitability (244), efficiency (129), performance (107, corporate governance (153), risk (90), and regulation (89).

The keywords also show that efficiency through data envelopment analysis is a determinant of the performance of banks. The other significant determinants that appeared as keywords are credit risk (73), competition (70), financial stability (69), ownership structure (57), capital (56), corporate social responsibility (56), liquidity (46), diversification (45), sustainability (44), credit provision (41), economic growth (41), capital structure (39), microfinance (39), Basel III (37), non-performing assets (37), cost efficiency (30), lending behavior (30), interest rate (29), mergers and acquisition (28), capital adequacy (26), developing countries (23), net interest margin (23), board of directors (21), disclosure (21), leverage (21), productivity (20), innovation (18), firm size (16), and firm value (16).

Keyword analysis also shows the theories of banking and their determinants. Some of the theories are agency theory (23), information asymmetry (21), moral hazard (17), and market efficiency (16), which can be used by researchers when building a theory. The analysis also helps to determine the methodology that was used in the published articles; some of them are data envelopment analysis (89), which measures technical efficiency, panel data analysis (61), DEA (32), Z scores (27), regression analysis (23), stochastic frontier analysis (20), event study (15), and literature review (15). The count for literature review is only 15, which confirms that very few studies have conducted an SLR on bank risk, regulation, and profitability.

Citation analysis

One of the parameters used in judging the quality of the article is its “citation.” Table 5 shows the top 10 published articles with the highest number of citations. Ding and Cronin [ 44 ] indicated that the popularity of an article depends on the number of times it has been cited.

Tahamtan et al. [ 126 ] explained that the journal’s quality also affects its published articles’ citations. A quality journal will have a high impact factor and, therefore, more citations. The citation analysis helps researchers to identify seminal articles. The title of an article with 5900 citations is “A survey of corporate governance.”

Page Rank analysis

Goyal and Kumar [ 53 ] explain that the citation analysis indicates the ‘popularity’ and ‘prestige’ of the published research article. Apart from the citation analysis, one more analysis is essential: Page rank analysis. PageRank is given by Page et al. [ 97 ]. The impact of an article can be measured with one indicator called PageRank [ 135 ]. Page rank analysis indicates how many times an article is cited by other highly cited articles. The method helps analyze the web pages, which get the priority during any search done on google. The analysis helps in understanding the citation networks. Equation  1 explains the page rank (PR) of a published paper, N refers to the number of articles.

T 1,… T n indicates the paper, which refers paper P . C ( Ti ) indicates the number of citations. The damping factor is denoted by a “ d ” which varies in the range of 0 and 1. The page rank of all the papers is equal to 1. Table 6 shows the top papers based on page rank. Tables 5 and 6 together show a contrast in the top ranked articles based on citations and page rank, respectively. Only one article “A survey of corporate governance” falls under the prestigious articles based on the page rank.

Content analysis

Content Analysis is a research technique for conducting qualitative and quantitative analyses [ 124 ]. The content analysis is a helpful technique that provides the required information in classifying the articles depending on their nature (empirical or conceptual) [ 76 ]. By adopting the content analysis method [ 53 , 102 ], the selected articles are examined to determine their content. The classification of available content from the selected set of sample articles that are categorized under different subheads. The themes identified in the relationship between banking regulation, risk, and profitability are as follows.

Regulation and profitability of banks

The performance indicators of the banking industry have always been a topic of interest to researchers and practitioners. This area of research has assumed a special interest after the 2008 WFC [ 25 , 51 , 86 , 114 , 127 , 132 ]. According to research, the causes of poor performance and risk management are lousy banking practices, ineffective monitoring, inadequate supervision, and weak regulatory mechanisms [ 94 ]. Increased competition, deregulation, and complex financial instruments have made banks, including Indian banks, more vulnerable to risks [ 18 , 93 , 119 , 123 ]. Hence, it is essential to investigate the present regulatory machinery for the performance of banks.

There are two schools of thought on regulation and its possible impact on profitability. The first asserts that regulation does not affect profitability. The second asserts that regulation adds significant value to banks’ profitability and other performance indicators. This supports the concept that Delis et al. [ 41 ] advocated that the capital adequacy requirement and supervisory power do not affect productivity or profitability unless there is a financial crisis. Laeven and Majnoni [ 81 ] insisted that provision for loan loss should be part of capital requirements. This will significantly improve active risk management practices and ensure banks’ profitability.

Lee and Hsieh [ 83 ] proposed ambiguous findings that do not support either school of thought. According to Nguyen and Nghiem [ 95 ], while regulation is beneficial, it has a negative impact on bank profitability. As a result, when proposing regulations, it is critical to consider bank performance and risk management. According to Erfani and Vasigh [ 46 ], Islamic banks maintained their efficiency between 2006 and 2013, while most commercial banks lost, furthermore claimed that the financial crisis had no significant impact on Islamic bank profitability.

Regulation and NPA (risk-taking of banks)

The regulatory mechanism of banks in any country must address the following issues: capital adequacy ratio, prudent provisioning, concentration banking, the ownership structure of banks, market discipline, regulatory devices, presence of foreign capital, bank competition, official supervisory power, independence of supervisory bodies, private monitoring, and NPAs [ 25 ].

Kanoujiya et al. [ 64 ] revealed through empirical evidence that Indian bank regulations lack a proper understanding of what banks require and propose reforming and transforming regulation in Indian banks so that responsive governance and regulation can occur to make banks safer, supported by Rastogi et al. [ 105 ]. The positive impact of regulation on NPAs is widely discussed in the literature. [ 94 ] argue that regulation has multiple effects on banks, including reducing NPAs. The influence is more powerful if the country’s banking system is fragile. Regulation, particularly capital regulation, is extremely effective in reducing risk-taking in banks [ 103 ].

Rastogi and Kanoujiya [ 106 ] discovered evidence that disclosure regulations do not affect the profitability of Indian banks, supported by Karyani et al. [ 65 ] for the banks located in Asia. Furthermore, Rastogi and Kanoujiya [ 106 ] explain that disclosure is a difficult task as a regulatory requirement. It is less sustainable due to the nature of the imposed regulations in banks and may thus be perceived as a burden and may be overcome by realizing the benefits associated with disclosure regulation [ 31 , 54 , 101 ]. Zheng et al. [ 138 ] empirically discovered that regulation has no impact on the banks’ profitability in Bangladesh.

Governments enforce banking regulations to achieve a stable and efficient financial system [ 20 , 94 ]. The existing literature is inconclusive on the effects of regulatory compliance on banks’ risks or the reduction of NPAs [ 10 , 11 ]. Boudriga et al. [ 25 ] concluded that the regulatory mechanism plays an insignificant role in reducing NPAs. This is especially true in weak institutions, which are susceptible to corruption. Gonzalez [ 52 ] reported that firm regulations have a positive relationship with banks’ risk-taking, increasing the probability of NPAs. However, Boudriga et al. [ 25 ], Samitas and Polyzos [ 113 ], and Allen et al. [ 3 ] strongly oppose the use of regulation as a tool to reduce banks’ risk-taking.

Kwan and Laderman [ 79 ] proposed three levels in regulating banks, which are lax, liberal, and strict. The liberal regulatory framework leads to more diversification in banks. By contrast, the strict regulatory framework forces the banks to take inappropriate risks to compensate for the loss of business; this is a global problem [ 73 ].

Capital regulation reduces banks’ risk-taking [ 103 , 110 ]. Capital regulation leads to cost escalation, but the benefits outweigh the cost [ 103 ]. The trade-off is worth striking. Altman Z score is used to predict banks’ bankruptcy, and it found that the regulation increased the Altman’s Z-score [ 4 , 46 , 63 , 68 , 72 , 120 ]. Jin et al. [ 62 ] report a negative relationship between regulation and banks’ risk-taking. Capital requirements empowered regulators, and competition significantly reduced banks’ risk-taking [ 1 , 122 ]. Capital regulation has a limited impact on banks’ risk-taking [ 90 , 103 ].

Maji and De [ 90 ] suggested that human capital is more effective in managing banks’ credit risks. Besanko and Kanatas [ 21 ] highlighted that regulation on capital requirements might not mitigate risks in all scenarios, especially when recapitalization has been enforced. Klomp and De Haan [ 72 ] proposed that capital requirements and supervision substantially reduce banks’ risks.

A third-party audit may impart more legitimacy to the banking system [ 23 ]. The absence of third-party intervention is conspicuous, and this may raise a doubt about the reliability and effectiveness of the impact of regulation on bank’s risk-taking.

NPA (risk-taking) in banks and profitability

Profitability affects NPAs, and NPAs, in turn, affect profitability. According to the bad management hypothesis [ 17 ], higher profits would negatively affect NPAs. By contrast, higher profits may lead management to resort to a liberal credit policy (high earnings), which may eventually lead to higher NPAs [ 104 ].

Balasubramaniam [ 8 ] demonstrated that NPA has double negative effects on banks. NPAs increase stressed assets, reducing banks’ productive assets [ 92 , 117 , 136 ]. This phenomenon is relatively underexplored and therefore renders itself for future research.

Triad and the performance of banks

Regulation and triad.

Regulations and their impact on banks have been a matter of debate for a long time. Barth et al. [ 12 ] demonstrated that countries with a central bank as the sole regulatory body are prone to high NPAs. Although countries with multiple regulatory bodies have high liquidity risks, they have low capital requirements [ 40 ]. Barth et al. [ 12 ] supported the following steps to rationalize the existing regulatory mechanism on banks: (1) mandatory information [ 22 ], (2) empowered management of banks, and (3) increased incentive for private agents to exert corporate control. They show that profitability has an inverse relationship with banks’ risk-taking [ 114 ]. Therefore, standard regulatory practices, such as capital requirements, are not beneficial. However, small domestic banks benefit from capital restrictions.

DeYoung and Jang [ 43 ] showed that Basel III-based policies of liquidity convergence ratio (LCR) and net stable funding ratio (NSFR) are not fully executed across the globe, including the US. Dahir et al. [ 39 ] found that a decrease in liquidity and funding increases banks’ risk-taking, making banks vulnerable and reducing stability. Therefore, any regulation on liquidity risk is more likely to create problems for banks.

Concentration banking and triad

Kiran and Jones [ 71 ] asserted that large banks are marginally affected by NPAs, whereas small banks are significantly affected by high NPAs. They added a new dimension to NPAs and their impact on profitability: concentration banking or banks’ market power. Market power leads to less cost and more profitability, which can easily counter the adverse impact of NPAs on profitability [ 6 , 15 ].

The connection between the huge volume of research on the performance of banks and competition is the underlying concept of market power. Competition reduces market power, whereas concentration banking increases market power [ 25 ]. Concentration banking reduces competition, increases market power, rationalizes the banks’ risk-taking, and ensures profitability.

Tabak et al. [ 125 ] advocated that market power incentivizes banks to become risk-averse, leading to lower costs and high profits. They explained that an increase in market power reduces the risk-taking requirement of banks. Reducing banks’ risks due to market power significantly increases when capital regulation is executed objectively. Ariss [ 6 ] suggested that increased market power decreases competition, and thus, NPAs reduce, leading to increased banks’ stability.

Competition, the performance of banks, and triad

Boyd and De Nicolo [ 27 ] supported that competition and concentration banking are inversely related, whereas competition increases risk, and concentration banking decreases risk. A mere shift toward concentration banking can lead to risk rationalization. This finding has significant policy implications. Risk reduction can also be achieved through stringent regulations. Bolt and Tieman [ 24 ] explained that stringent regulation coupled with intense competition does more harm than good, especially concerning banks’ risk-taking.

Market deregulation, as well as intensifying competition, would reduce the market power of large banks. Thus, the entire banking system might take inappropriate and irrational risks [ 112 ]. Maji and Hazarika [ 91 ] added more confusion to the existing policy by proposing that, often, there is no relationship between capital regulation and banks’ risk-taking. However, some cases have reported a positive relationship. This implies that banks’ risk-taking is neutral to regulation or leads to increased risk. Furthermore, Maji and Hazarika [ 91 ] revealed that competition reduces banks’ risk-taking, contrary to popular belief.

Claessens and Laeven [ 36 ] posited that concentration banking influences competition. However, this competition exists only within the restricted circle of banks, which are part of concentration banking. Kasman and Kasman [ 66 ] found that low concentration banking increases banks’ stability. However, they were silent on the impact of low concentration banking on banks’ risk-taking. Baselga-Pascual et al. [ 14 ] endorsed the earlier findings that concentration banking reduces banks’ risk-taking.

Concentration banking and competition are inversely related because of the inherent design of concentration banking. Market power increases when only a few large banks are operating; thus, reduced competition is an obvious outcome. Barra and Zotti [ 9 ] supported the idea that market power, coupled with competition between the given players, injects financial stability into banks. Market power and concentration banking affect each other. Therefore, concentration banking with a moderate level of regulation, instead of indiscriminate regulation, would serve the purpose better. Baselga-Pascual et al. [ 14 ] also showed that concentration banking addresses banks’ risk-taking.

Schaeck et al. [ 115 ], in a landmark study, presented that concentration banking and competition reduce banks’ risk-taking. However, they did not address the relationship between concentration banking and competition, which are usually inversely related. This could be a subject for future research. Research on the relationship between concentration banking and competition is scant, identified as a research gap (“ Research Implications of the study ” section).

Transparency, corporate governance, and triad

One of the big problems with NPAs is the lack of transparency in both the regulatory bodies and banks [ 25 ]. Boudriga et al. [ 25 ] preferred to view NPAs as a governance issue and thus, recommended viewing it from a governance perspective. Ahmad and Ariff [ 2 ] concluded that regulatory capital and top-management quality determine banks’ credit risk. Furthermore, they asserted that credit risk in emerging economies is higher than that of developed economies.

Bad management practices and moral vulnerabilities are the key determinants of insolvency risks of Indian banks [ 95 ]. Banks are an integral part of the economy and engines of social growth. Therefore, banks enjoy liberal insolvency protection in India, especially public sector banks, which is a critical issue. Such a benevolent insolvency cover encourages a bank to be indifferent to its capital requirements. This indifference takes its toll on insolvency risk and profit efficiency. Insolvency protection makes the bank operationally inefficient and complacent.

Foreign equity and corporate governance practices help manage the adverse impact of banks’ risk-taking to ensure the profitability and stability of banks [ 33 , 34 ]. Eastburn and Sharland [ 45 ] advocated that sound management and a risk management system that can anticipate any impending risk are essential. A pragmatic risk mechanism should replace the existing conceptual risk management system.

Lo [ 87 ] found and advocated that the existing legislation and regulations are outdated. He insisted on a new perspective and asserted that giving equal importance to behavioral aspects and the rational expectations of customers of banks is vital. Buston [ 29 ] critiqued the balance sheet risk management practices prevailing globally. He proposed active risk management practices that provided risk protection measures to contain banks’ liquidity and solvency risks.

Klomp and De Haan [ 72 ] championed the cause of giving more autonomy to central banks of countries to provide stability in the banking system. Louzis et al. [ 88 ] showed that macroeconomic variables and the quality of bank management determine banks’ level of NPAs. Regulatory authorities are striving hard to make regulatory frameworks more structured and stringent. However, the recent increase in loan defaults (NPAs), scams, frauds, and cyber-attacks raise concerns about the effectiveness [ 19 ] of the existing banking regulations in India as well as globally.

Discussion of the findings

The findings of this study are based on the bibliometric and content analysis of the sample published articles.

The bibliometric study concludes that there is a growing demand for researchers and good quality research

The keyword analysis suggests that risk regulation, competition, profitability, and performance are key elements in understanding the banking system. The main authors, keywords, and journals are grouped in a Sankey diagram in Fig.  6 . Researchers can use the following information to understand the publication pattern on banking and its determinants.

Sankey Diagram of main authors, keywords, and journals. Note Authors contribution using scientometrics tools

Research Implications of the study

The study also concludes that a balance among the three components of triad is the solution to the challenges of banks worldwide, including India. We propose the following recommendations and implications for banks:

This study found that “the lesser the better,” that is, less regulation enhances the performance and risk management of banks. However, less regulation does not imply the absence of regulation. Less regulation means the following:

Flexible but full enforcement of the regulations

Customization, instead of a one-size-fits-all regulatory system rooted in a nation’s indigenous requirements, is needed. Basel or generic regulation can never achieve what a customized compliance system can.

A third-party audit, which is above the country's central bank, should be mandatory, and this would ensure that all three aspects of audit (policy formulation, execution, and audit) are handled by different entities.

Competition

This study asserts that the existing literature is replete with poor performance and risk management due to excessive competition. Banking is an industry of a different genre, and it would be unfair to compare it with the fast-moving consumer goods (FMCG) or telecommunication industry, where competition injects efficiency into the system, leading to customer empowerment and satisfaction. By contrast, competition is a deterrent to the basic tenets of safe banking. Concentration banking is more effective in handling the multi-pronged balance between the elements of the triad. Concentration banking reduces competition to lower and manageable levels, reduces banks’ risk-taking, and enhances profitability.

No incentive to take risks

It is found that unless banks’ risk-taking is discouraged, the problem of high NPA (risk-taking) cannot be addressed. Concentration banking is a disincentive to risk-taking and can be a game-changer in handling banks’ performance and risk management.

Research on the risk and performance of banks reveals that the existing regulatory and policy arrangement is not a sustainable proposition, especially for a country where half of the people are unbanked [ 37 ]. Further, the triad presented by Keeley [ 67 ] is a formidable real challenge to bankers. The balance among profitability, risk-taking, and regulation is very subtle and becomes harder to strike, just as the banks globally have tried hard to achieve it. A pragmatic intervention is needed; hence, this study proposes a change in the banking structure by having two types of banks functioning simultaneously to solve the problems of risk and performance of banks. The proposed two-tier banking system explained in Fig.  7 can be a great solution. This arrangement will help achieve the much-needed balance among the elements of triad as presented by Keeley [ 67 ].

Conceptual Framework. Note Fig.  7 describes the conceptual framework of the study

The first set of banks could be conventional in terms of their structure and should primarily be large-sized. The number of such banks should be moderate. There is a logic in having only a few such banks to restrict competition; thus, reasonable market power could be assigned to them [ 55 ]. However, a reduction in competition cannot be over-assumed, and banks cannot become complacent. As customary, lending would be the main source of revenue and income for these banks (fund based activities) [ 82 ]. The proposed two-tier system can be successful only when regulation especially for risk is objectively executed [ 29 ]. The second set of banks could be smaller in size and more in number. Since they are more in number, they would encounter intense competition for survival and for generating more business. Small is beautiful, and thus, this set of banks would be more agile and adaptable and consequently more efficient and profitable. The main source of revenue for this set of banks would not be loans and advances. However, non-funding and non-interest-bearing activities would be the major revenue source. Unlike their traditional and large-sized counterparts, since these banks are smaller in size, they are less likely to face risk-taking and NPAs [ 74 ].

Sarmiento and Galán [ 114 ] presented the concerns of large and small banks and their relative ability and appetite for risk-taking. High risk could threaten the existence of small-sized banks; thus, they need robust risk shielding. Small size makes them prone to failure, and they cannot convert their risk into profitability. However, large banks benefit from their size and are thus less vulnerable and can convert risk into profitable opportunities.

India has experimented with this Differential Banking System (DBS) (two-tier system) only at the policy planning level. The execution is impending, and it highly depends on the political will, which does not appear to be strong now. The current agenda behind the DBS model is not to ensure the long-term sustainability of banks. However, it is currently being directed to support the agenda of financial inclusion by extending the formal credit system to the unbanked masses [ 107 ]. A shift in goal is needed to employ the DBS as a strategic decision, but not merely a tool for financial inclusion. Thus, the proposed two-tier banking system (DBS) can solve the issue of profitability through proper regulation and less risk-taking.

The findings of Triki et al. [ 130 ] support the proposed DBS model, in this study. Triki et al. [ 130 ] advocated that different component of regulations affect banks based on their size, risk-taking, and concentration banking (or market power). Large size, more concentration banking with high market power, and high risk-taking coupled with stringent regulation make the most efficient banks in African countries. Sharifi et al. [ 119 ] confirmed that size advantage offers better risk management to large banks than small banks. The banks should modify and work according to the economic environment in the country [ 69 ], and therefore, the proposed model could help in solving the current economic problems.

This is a fact that DBS is running across the world, including in India [ 60 ] and other countries [ 133 ]. India experimented with DBS in the form of not only regional rural banks (RRBs) but payments banks [ 109 ] and small finance banks as well [ 61 ]. However, the purpose of all the existing DBS models, whether RRBs [ 60 ], payment banks, or small finance banks, is financial inclusion, not bank performance and risk management. Hence, they are unable to sustain and are failing because their model is only social instead of a much-needed dual business-cum-social model. The two-tier model of DBS proposed in the current paper can help serve the dual purpose. It may not only be able to ensure bank performance and risk management but also serve the purpose of inclusive growth of the economy.

Conclusion of the study

The study’s conclusions have some significant ramifications. This study can assist researchers in determining their study plan on the current topic by using a scientific approach. Citation analysis has aided in the objective identification of essential papers and scholars. More collaboration between authors from various countries/universities may help countries/universities better understand risk regulation, competition, profitability, and performance, which are critical elements in understanding the banking system. The regulatory mechanism in place prior to 2008 failed to address the risk associated with banks [ 47 , 87 ]. There arises a necessity and motivates authors to investigate the current topic. The present study systematically explores the existing literature on banks’ triad: performance, regulation, and risk management and proposes a probable solution.

To conclude the bibliometric results obtained from the current study, from the number of articles published from 1976 to 2020, it is evident that most of the articles were published from the year 2010, and the highest number of articles were published in the last five years, i.e., is from 2015. The authors discovered that researchers evaluate articles based on the scope of critical journals within the subject area based on the detailed review. Most risk, regulation, and profitability articles are published in peer-reviewed journals like; “Journal of Banking and Finance,” “Journal of Accounting and Economics,” and “Journal of Financial Economics.” The rest of the journals are presented in Table 1 . From the affiliation statistics, it is clear that most of the research conducted was affiliated with developed countries such as Malaysia, the USA, and the UK. The researchers perform content analysis and Citation analysis to access the type of content where the research on the current field of knowledge is focused, and citation analysis helps the academicians understand the highest cited articles that have more impact in the current research area.

Practical implications of the study

The current study is unique in that it is the first to systematically evaluate the publication pattern in banking using a combination of scientometrics analysis tools, network analysis tools, and content analysis to understand the relationship between bank regulation, performance, and risk. The study’s practical implications are that analyzing existing literature helps researchers generate new themes and ideas to justify their contribution to literature. Evidence-based research knowledge also improves decision-making, resulting in better practical implementation in the real corporate world [ 100 , 129 ].

Limitations and scope for future research

The current study only considers a single database Scopus to conduct the study, and this is one of the limitations of the study spanning around the multiple databases can provide diverse results. The proposed DBS model is a conceptual framework that requires empirical testing, which is a limitation of this study. As a result, empirical testing of the proposed DBS model could be a future research topic.

Availability of data and materials

SCOPUS database.

Abbreviations

Systematic literature review

World Financial Crisis

Non-performing assets

Differential banking system

SCImago Journal Rank Indicator

Liquidity convergence ratio

Net stable funding ratio

Fast moving consumer goods

Regional rural banks

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‘SR’ performed Abstract, Introduction, and Data methodology sections and was the major contributor; ‘AS’ performed Bibliometric and Network analysis and conceptual framework; ‘GP’ performed citation analysis and discussion section; ‘VMB’ collated data from the database and concluded the article. All authors read and approved the final manuscript.

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Rastogi, S., Sharma, A., Pinto, G. et al. A literature review of risk, regulation, and profitability of banks using a scientometric study. Futur Bus J 8 , 28 (2022). https://doi.org/10.1186/s43093-022-00146-4

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• Bank performance
• Profitability
• Bibliometric analysis
• Scientometric analysis

Does credit risk persist in the Indian banking industry? Recent evidence

Asian Journal of Economics and Banking

ISSN : 2615-9821

Article publication date: 2 August 2021

Issue publication date: 4 August 2022

This study aims to capture the “persistence effect” of credit risk in Indian banking industry using the bank-level data spanning over the period of 19 years from 1998/1999 to 2016/17. Alongside, the study explored how the bank-specific, industry-specific, macroeconomic variables alongside regulatory reforms, ownership changes and financial crisis affect the bank's asset quality in India.

Design/methodology/approach

Using two-step system generalized method of moment (GMM) approach, the study derives key factors that affect the bank's asset quality in India.

The empirical results confirm the time persistence of credit risk among Indian banks during study period. This reflects that bank defaults are expected to increase in the current year, if it had increased past year due to time lag involved in the process of recovery of past dues. Further, higher profitability, better managerial efficiency, more diversified income from nontraditional activities, optimal size of banks, proper credit screening and monitoring and adherence regulatory norms would help in improving the credit quality of Indian banks.

Practical implications

The practical implication drawn from the study is that nonaccumulation of nonperforming loans (NPLs), higher profitability, better managerial efficiency, more diversified income from nontraditional activities, optimal size of banks, proper credit screening and monitoring and adherence regulatory norms would help in improving the credit quality of Indian banks.

Originality/value

This study is probably the first one that identifies in addition to the current year, whether lag of bank industry-macroeconomic affects the level of NPLs of Indian banks. So far, such an analysis has received less attention with respect to Indian banking industry, especially immediate aftermath of the global financial crisis.

• Credit risk
• Persistence effect
• Dynamic estimation
• Indian banks

Goswami, A. (2022), "Does credit risk persist in the Indian banking industry? Recent evidence", Asian Journal of Economics and Banking , Vol. 6 No. 2, pp. 178-197. https://doi.org/10.1108/AJEB-01-2021-0006

Emerald Publishing Limited

Copyright © 2021, Anju Goswami

Published in Asian Journal of Economics and Banking . 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 recent global financial crisis of 2007–08 has stimulated the interest of academicians, policymakers and researchers to the key consequences that banking crisis can have on to the nation's economy. The situations of financial crisis intensify the banking distress, and in the process, become one of the main obstacles to the stability of the financial system, in general, and banking system, in particular. More specifically, a rapid increases in asset prices, high leverage of borrowers and lenders, a decline in lending standards, coupled with liquidity and/or insolvency problems caused by the increase in nonperforming loans (NPL) and regulation and supervision failures may pace up the risk of an occurrence of such financial crises ( Laeven and Valencia, 2008 ; Castro, 2013 ; Claessens et al. , 2014 ). Caprio and Klingebiel (1996) concluded that such crises in the past have resulted in severe bank losses or public sector resolution costs, especially in developing countries [1] . Such a consequences of banking crises has raised the concern as to the reasons why such crises occur? The credit risk, which arises due to bubbling up of NPLs in the bank's balance sheets, generally overlays the other causes for the occurrences of banking crisis since it can seriously undermine the financial soundness of the banking sector.

The level of NPLs or impaired loans is generally used as a critical indicator to quantify the credit risk burden, which represent the risk of loss due to nonpayment by the borrower ( RBI, 2007 ) [2] . Currently, the high level of NPLs in banks has been a matter of grave concern for all nations' policymakers since it creates bottlenecks in the smooth flow of credit in the economy. This underlines the procyclical behavior of the banking system, wherein asset quality get compromised during periods of high credit growth and results in the creation of default risk for banks in the later years. In the Indian context too, the gross and net NPLs as a percentage of advances stood at 15.7 and 8.1% in 1996–1997, which later declined to 2.3 and 1.1% in 2007–08, reflecting an improvement in asset quality in post-reforms period. But, during the crisis year 2008–09, the gross NPLs ratio remained stable for Indian banks, reflecting the success of financial sector deregulation and reforms, regulatory and supervisory process. In particular, banks have made substantial progress in cleaning up the NPLs from their balance sheets during the pre-crisis period ( Reserve Bank of India, 2004 ).

However, the robust credit growth (of more than 30%), followed by economic expansion (of around 10%), in the Indian economy during 2006–2011, has further raised concern with regard to the credit risk. As of March 2015, gross and net NPLs for the Indian banking system as a whole rose at 4.4 and 2.4% of total advances, respectively, doubled from the 2007–08 level. Thus, due to an excessive credit lending to troubled borrowers and mismanaged information regarding borrowers, reduced the likelihood of them to repay their debts and increased the probability of defaults ( Reserve Bank of India, 2011 ). Now, the distressed asset crisis weighed heavily on credit growth in India, which stood at only 4% for public sector banks, compared with 25% for private banks as at end March 2016. The public sector banks now have no ability to take on additional credit risk which poses a serious issue for the economy. From the above discussion, it is clear that the rising NPLs cause a serious concern for the policymakers, regulators, government and the central bank. For minimization of the credit risk, the bank regulators need to undergo deeper investigation of its underlying determinants. The present study is an attempt in this direction.

Against this backdrop, the key objective of this paper is to examine the determinants of credit risk in Indian banking industry for more recent time period, i.e. 1999–2014, covering the period following the global financial crisis. This study intends to provide evidence on the factors determining the credit risk structure in the emerging nation with special reference to Indian banking industry. The contribution of our study to the existing literature on credit risk determinants is threefold. First, this study provides new and most recent evidence on the time persistence in accumulation of NPLs in the Indian banking industry. For the estimation of “persistence effect”, the study employs a two-step system generalized method of moment (GMM) estimation method on an unbalanced panel of bank-level data spanning over the period of 19 years from 1998/1999 to 2016/17. The study further provides the pooled OLS, PCSE, within group fixed effects and two-step difference GMM estimates for robustness check.

Second, this study is probably the first one that identifies in addition to the current year, whether lag of bank-industry-macroeconomic also generates a burden of credit risk in Indian banking industry. This study would perhaps be the first one to consider the role of prudential norms, crisis and ownership structure as additional factors, along with size, profitability, credit growth, diversification, market concentration, bank solvency, among others, underlying the dramatic changes in credit risk structure of Indian banking industry. I believe this study has potential to provide a clear and lag-wise scenario of bank-macro and industry-specific factors for credit risk to regulators and stakeholders of Indian banks. So that they can form the necessary strategy against those factors, which are fully responsible for the generation of NPLs in the banking industry in India.

Finally, our study provides an evidence for a single country with particular reference to the Indian banking industry. Such an analysis bear a great significance due to the fact that the Indian economy has bank-based financial system like Indonesia and Pakistan, where banks play an important role in their financial system, and any shock to banks ultimately impact the entire economy ( Demirgüç-Kunt and Levine, 1999 ). This study seems to have relevance in the current scenario due to surging bad loans in the balance sheets of Indian banks in the recent years. Furthermore, bank's NPLs in India as a percentage of gross loans has been found to be consistently far above the levels seen in other Asian economies [3] . Ahmad and Ariff (2007) concluded that the credit risk in emerging economy banks is higher than that in developed economies, and that risk is formed largely by bank-specific factors in emerging economies compared to their counterparts. In this regard, this study would try to help the bank managers in identifying the factors that may lead to deterioration in credit quality and increase the burden of default risk. So far, such an analysis has received less attention with respect to Indian banking industry, especially in the aftermath of global financial crisis.

The rest of the paper is organized as follows. Section 2 discusses credit risk scenario in in the Indian banking industry. Section 3 presents a relevant literature review on the subject matter. Section 4 encompasses description of databases, methodology and discussion on conceptual framework. Section 5 focuses on results and discussion, while Section 6 concludes the findings of the study.

2. Review of the literature

A significant body of the literature has evolved in the past which explored the determinants of credit risk in the banking sector. In particular, there exit two strands of the literature on the determinants of bank credit risk ( Castro, 2013 ; Aver, 2008 ; Ahmad and Ariff, 2007 ). The first volume of the literature focused primarily on the factors affecting systematic credit risk (e.g. macroeconomic factors, economic policies, political changes, etc.). The studies that only examined the macroeconomic factors affecting the credit risk include Baboucak and Jancar (2005) , which provide the systematic assessment of the links between loan quality and macroeconomic shocks in the Czech banking industry. They found a direct relation between NPLs, rate of unemployment and consumer inflation rate, while an inverse relation with GDP growth in the Czech economy. On the similar grounds, Nkusu (2011) analyzed the credit risk determinants across 26 advanced economies during the period spanning from 1998 to 2009. They found that NPLs were positively explained by macroeconomic variables such as the unemployment rate, policy rate of interest and lagged NPLs, while negatively explained in GDP growth rate, housing price index and equity price index. Beck et al. (2013) examined the role of macroeconomic indicators in 75 advanced and emerging economies during the period 2000–2010 and concluded that the bank asset quality significantly affected by a drop in real GDP growth, share prices, the exchange rate and the lending interest rate. Further, exchange rate depreciations have also lead to an increase of NPLs in sampled countries. Similarly, Castro (2013) analyzed the link between the macroeconomic factors and credit risk in the Greece, Italy, Portugal, Spain and Ireland by employing dynamic panel data approaches over the period 1997q1–2011q3. They conclude that the credit risk increases when GDP growth, share price indices and housing prices decrease and rises when the unemployment rate, interest rate and credit growth increase.

In contrast, the second strand of the literature has also considered the role of unsystematic risk factors (e.g. bank-specific, industry-specific, regulatory and institutional, etc.) in generating the default risk. Considering the relationship between bank's efficiency and bad loans, Berger and DeYoung (1997) performed Granger causality analysis for the period 1984–1995 and found that less cost efficient banks wind up having more problem loans. They also concluded with the importance of four hypotheses explaining the relationship between efficiency and NPLs – bad management, bad luck, moral hazard and skimping hypotheses. Ahmad and Ariff (2007) explored a sample of four advanced and five developing nations and concluded that regulatory capital, management quality and loan loss provisions were significant determinants of potential credit risk. Louzis et al. (2012) explored the factors that affect NPLs from three categories of loans mortgage, business and consumer separately. The results show that, for all loan categories, NPLs in the Greek banking system have been explained mainly by GDP, unemployment, interest rates, public debt and management quality. The similar findings have been revealed by Abid et al. (2014) for Tunisian banking industry. Using the panel dataset of 80 banks in the GCC region, Espinoza and Prasad (2010) found that lower non-oil real GDP growth and higher interest rates increased the level of NPLs during the period of 1995–2008. Further, a positive relationship has been found between lagged credit growth and NPLs. Khemraj and Pasha (2009) found that real effective exchange rate and real interest rate to have a positive significant impact on NPLs, while GDP growth, loan to assets ratio and loan growth had a negative impact. Makri et al. (2014) also found strong correlations between NPL and various macroeconomic (annual GDP growth rate, public debt to GDP ratio and unemployment rate) and bank-specific factors (return on equity and capital adequacy ratio).

Using a dynamic panel analysis, Chaibi and Ftiti (2015) compared the determinants of NPLs of commercial banks in France (a market-based economy), with Germany (a bank-based economy) during 2005–2011. The empirical results reveal that credit risk in France is more susceptible to bank-specific determinants compared to Germany. Klein (2013) observed the persistence of NPLs in 16 Central, Eastern and South Eastern Europe countries during 1998–2011. Further, unemployment, inflation, exchange rate, VIX and loan growth has been found to positively explain the NPLs, while the solvency ratio, ROE and GDP growth rate had a negative association. The similar findings have been reported by Skarica (2013) . Alhassan et al. (2014) also found the persistence of NPLs in Ghanaian banking sector, with loan growth, bank market structure, bank size, inflation, real exchange rate and GDP growth to have a significant effect on banks' asset quality. Finally, Ghosh (2015) analyzed the persistence effect of credit risk in US banking sector during the period 1984–2013 using dynamic panel estimation method. The results reveal that greater capitalization, liquidity risks, poor credit quality, greater cost inefficiency and banking industry size to significantly increase NPLs, while greater bank profitability lowers NPLs.

In Indian context, Rajaraman and Vashishtha (2002) were the first one to examine the factors influencing the NPLs in the public sector banks during the period 1996–2000. They found that operating profit to working funds has a significant negative impact on asset quality of public sector banks in India. Later, Ranjan and Dhal (2003) also considered a sample of public sector banks and found that bank size in terms of assets has the negative, while in terms of capital has positive impact on gross NPLs. Das and Ghosh (2007) empirically reported the high persistence of credit risk across state-owned banks in India during the period 1994–2005. Using the balanced panel data of 19 private and 26 public sector banks operating in India during 2005–2013, Satpathy et al. (2015) found that operating inefficiency, restructured debt and inflation rate have a positive impact on NPLs, while credit growth, priority sector advances, fiscal deficit, GDP growth rate, lending rate, trade balance and advanced to sensitive sector seems to have a negative effect. Bardhan and Mukherjee (2016) find the persistence effect of NPAs in the Indian banking industry. A higher level of capitalization, profitability and GDP growth lowers NPAs level in the following years, while the lagged size of banks and inflation leads to a higher level of NPAs in the Indian banking industry. Bawa et al . (2019) find that lagged NPAs level is positively associated with the current NPAs level in the Indian banking industry during the period 2007–2014. In addition, they reveal that a higher intermediation cost and return on assets tend to reduce the level of NPAs, while aggressive asset growth and solvency induce a rise in the level of NPAs. Using a two-step system GMM approach, Gulati et al . (2019) explore the key determinants of credit risk for the period 1998/99 to 2013/14. They find a persistence effect of credit risk in the Indian banking industry.

From the above survey of literature, following observations have been made. First, it is clear that most existing studies on credit risk determinants in the banking industry relates to either those of developed nations or were conducted in cross-country settings, especially in the aftermath of global crisis. No doubt, the research efforts have also been made to investigate the factors contributing to credit risk in single-country settings, but large majority of studies have focused on European nations. Thus, among the existing studies, there exist only few one whose attention is directed to developing countries. Second, the contemporary literature proves that, in the past, most of the studies concentrated on macroeconomic linkage of credit risk, while few other studies incorporate the role of bank-specific and other factors which may be responsible for the rise in NPL levels. The large majority of studies mainly focused on the macroeconomic and bank-specific factors, but the changes in credit worthiness of borrowers, depth of information sharing, regulatory policies, governance structure which are difficult to examine and left out of consideration. Third, only a handful of studies have accounted for the persistence of credit risk in the banking sector. The large majority of research efforts were only after the global crisis of 2007–08, and that too for US and European banks. However, none of the existing studies tried to identify that whether accumulation of credit risk, bank-macro and industry-specific factors may impact the NPLs level over the last 3 decades in the Indian banking industry or not. The present study aims to attempt in this direction. I believe this study has potential to provide a clear and lag-wise scenario of bank-macro and industry-specific factors for credit risk to regulators and stakeholders of Indian banks. So that they can form the necessary strategy against those factors, which are fully responsible for the generation of NPLs in the banking industry in India.

It is obvious that there is a gap in the contemporary literature, regarding the determinants of NPLs in the developing and emerging nations, particularly India. The studies pertaining to Indian banking sector have mainly looked at the determinants of credit risk in the public sector banks only (see, Das and Ghosh, 2007 ), which currently forms only 75% of the business operations in terms of total assets in India. This study is perhaps an effort to consider full range of sample of Indian banks (including public, private and foreign banks) operating in India from 1998–99 to 2016–17. Further, it has been observed that credit risk in emerging economy banks has been found to be higher than that in developed economies ( Ahmad and Ariff, 2007 ). So, considering the above notion, our study tries to fill this gap for emerging nations by empirically investigating the determinants of credit risk across a bank-based economy like India. The study would not only analyze all the possible factors that may deteriorate the asset quality but also account for the persistence of credit risk in Indian banks.

3. Database and methodology

3.1 database.

Our study considers all the banks operating in the industry during the period from 1998/99 to 2016/17. The bank-level data pertaining to all the variables have been obtained from the various issues of “Statistical Table Relating to Banks in India”, an “ annual publication of Reserve Bank of India (RBI) ” and “ Performance Highlights of Public Sector Banks ” , “ Performance Highlights of Private Banks' and Performance Highlights of Foreign Banks' ”, an “ annual publications of Indian Banks' Association” (IBA). The real GDP growth rate (%) and inflation rate (%) for each sample year has been obtained from the World Bank database. Finally, the mergers and acquisitions, and exit of some banks from the industry have left us with the unbalanced panel of banks for the above mentioned period.

3.2 Dynamic panel model estimation

This study adopts the two-step system generalized method of moments (GMMs) technique of Blundell and Bond (1998) to test the time persistence in credit risk structure in the Indian banking industry for the following reasons: (1) in the presence of the lagged dependent variable, Y i , t − 1 , the traditional panel estimators are seriously biased (see, Baltagi, Econometric Analysis of Panel Data , 5th edition, 2013 and Roodman, D., through the looking glass, and what OLS found there: on growth, foreign aid and reverse causality. Unpublished working paper, Center for Global Development, 2008); (2) fixed effects model's accuracy deteriorates when the panels are unbalanced. Therefore, the use of system GMM method appears to outperform than the fixed effects model in the presence of endogeneity and lagged dependent variable in unbalanced panels (see, Arellano and Bond, 1991 ; Blundell and Bond, 1998 ) and (3) one-step GMM estimation can produces consistent estimates under the assumption of independent and homoscedastic residuals (both cross-sectional and over time). However, its standard error is largely downward biased in small samples. Therefore, Windemeijer's (2005) correction for small sample is applied to rectify the standard error bias. Consequently, the two-step GMM estimator is used which provides more accurate estimates than the robust one-step GMM estimator, especially for the system GMM ( Roodman, 2006 ). In addition, the study uses the Arellano and Bover (1995) forward orthogonalization procedure and collapsing method of Holtz-Eakin et al. (1988) to limit the number of instruments (for more details, see Roodman, 2009 ).

The factors that determine credit risk have been examined based on the generalized method of moments. The dynamic panel data specification used is given by: (1) Y i t = α + δ Y i , t − 1 + ∑ j = 1 J β j X i t − s j + ∑ z = 1 Z γ z X i t − s z + ∑ k = 1 K θ k X i t − s k + ∑ d = 1 D η d X i t d + μ i t where  | δ | < 1 , i = 1 , ... N , t = 1 , ... , T , s = 0,1 , ... , L where the subscripts i and t denote the cross-sectional and time-dimensions of the panel, respectively. The dependent variable, Y i t , used is the logit transformation of the net nonperforming loans to total advances, a proxy for credit risk for i th bank in the t th year. As suggested by Espinoza and Prasad (2010) , Klein (2013) , Wenzel et al. (2014) and Ghosh (2015) , such transformation ensures the dependent variable to span over the interval [+∞, −∞] and is distributed symmetrically. Further, it allows the assumption of normality in the error term and accounts for nonlinearities in a way that larger shocks to the explanatory variables may cause a large, nonlinear response in the transformed dependent variable. The value of δ lies between 0 and 1 implies persistence of credit risk. β j X i t − s j denotes bank-specific variables in t - s period, β z X i t − s z , β k X i t − s k and β d X i t − s d macroeconomic, industry-specific and dummy variables (see, section 4 for more details). Further, μ i t = η i + ν i t , where η i represents the unobserved bank-specific effects, and ν i t is the error term.

The overall validity of the instruments has been tested by using the Hansen J specification test, which under the null hypothesis of joint validity of the moment conditions (the presence of over-identification) is asymptotically distributed as chi-square ( Arellano and Bond, 1991 ; Arellano and Bover, 1995 ; Blundell and Bond, 1998 ). Furthermore, we assess the fundamental assumption of serially uncorrelated errors, i.e. ν i t using Arellano–Bond tests for Autoregression AR(1) and AR(2) by testing the hypothesis that Δ ν i t is not second order autocorrelated. The rejection of the null hypothesis of no second order autocorrelation of the differenced errors implies serial correlation for the level error term and thus, inconsistency of the GMM estimates.

4. Variable(s) specification

4.1 dependent variable.

In the present study, we use the ratios of net nonperforming loans (NNPLs) to total advances as proxies for credit risk. Much of the literature on credit risk (see, for example, Salas and Saurina, 2002 ; Das and Ghosh, 2007 ; Espinoza and Prasad, 2010 ; Klein, 2013 ) has considered the dependent variable in dynamic panel data regression using logit transformation of either GNPLs or NNPLs. Similarly, we define the dependent variable as of the following: ln   [ NNPLs i , t / ( 1 − NNPLs i , t ) ] in case of net NPLs specification. It is important to note that this transformation ensures the dependent variable to span over the interval [ − ∞ , + ∞ ] (as opposed to between 0 and 1 in case of NPLs ratio) and is distributed symmetrically.

The rest of systematic (macroeconomic) and unsystematic factors that are expected to form credit risk in the Indian banking industry are listed in Table 1 . However, the brief description of each independent variable(s) is given below.

4.2 Systematic (macroeconomic) variables

4.2.1 real gdp growth rate (rgdp).

The real GDP growth rate (RGDP) is used to control the effect of macroeconomic business activity. The literature suggest that during the periods of expansion, growth in real GDP usually increase the income which ultimately enhances the loan payment capacity of the individual and corporate borrowers which in turn contribute to lower default. As the expansion period continues, credit is then extended to lower quality debtors and subsequently results in increase in NPLs in the recession period. Thus, considering the above notion, the literature suggest that a negative relationship between economic activity and NPLs (see for, e.g. Ranjan and Dhal, 2003 ; Khemraj and Pasha, 2009 ; Nkusu, 2011 ; Beck et al. , 2013 ; Castro, 2013 ; Chaibi and Ftiti, 2015 ).

4.2.2 Inflation rate (INF)

The literature spells an ambiguity in the relationship between NPLs and inflation. The studies by Baboucek and Jancar (2005) , Klein (2013) and Alhassan et al. (2014) have found that an increase in inflation rate (INF) characterized by uncertain business conditions worsens the loan payment capacity by eroding the purchasing power of consumers and reducing the real income of borrowers, and thus reduces the debt servicing capacity resulting in increased risk of nonpayment of loans. On the contrary, a rise in inflation rate in the current period could see a reduction in the level of NPLs. This is because it can enhance the loan repayment capacity of borrower by reducing the real value of outstanding debt ( Shu, 2002 ; Khemraj and Pasha, 2009 ).

4.3 Unsystematic (bank-specific) variables

Return on assets (ROAs) is expressed as a proxy for bank's profitability. It is expected that better bank's performance in terms of profitability lowers the level of NPLs. Louzis et al. (2012) , Castro (2013) and Chaibi and Ftiti (2015) found that more profitable bank reflect better management quality in terms of efficiency in borrower's application screening and credit granting procedures, which may likely to lower the risk of defaults as supported by the “bad management” hypothesis. Thus, ROA is hypothesized to have a negative relationship with the level of NPLs. On the contrary, Rajan (1994) model suggests that higher profits may also lead to rise in NPLs. This may be due to “ liberal credit policy ” adopted by banks' management to maximize banks' earnings to maintain the short-term reputation. This view has been empirically tested by Ghosh (2015) .

4.3.1 Non-interest income (NONIT)

The ratio of non-interest income (NONIT) to total assets is used as a measure of income diversification which may expect to lower the risk from traditional lending. Banks earnings not only depend on loans and advances but also rely on NONIT like fee-paying and commission paying services, investment banking, assets management, etc. It leads to reduction in the bank credit risk from loans due to bank's diversified sources of income. Following Alhassan et al. (2014) , Chaibi and Ftiti (2015) and Louzis et al. (2012) , a negative association is hypothesized between NONIT and credit risk.

4.3.2 Credit growth (CGROWTH)

The literature suggests that growth in advances of a bank also helps in determining the credit risk. It is expected that higher loan growth leads to higher NPLs. It is argued that increase in supply of loans may reduce the credit standards, thereby increase the chances of loan defaults by borrowers ( Keeton, 1999 ). Following, Espinoza and Prasad (2010) , Messai and Jouini (2013) and Alhassan et al. (2014) , the study proxied credit growth by total loan growth, i.e. percent change in the current year loans and advances with previous year's by an individual bank.

4.3.3 Bank size (SIZE)

This variable is proxied by natural logarithm of bank's total assets. Empirical evidences on the relationship between NPLs and bank size (SIZE) is ambiguous. The large banks are assumed to have better risk management techniques, which ensure proper screening of loan applicants and lower default rate and better diversification opportunities. In this line of research, Salas and Saurina (2002) , Ranjan and Dhal (2003) and Alhassan et al. (2014) reported a negative impact of SIZE on asset quality. Some of the empirical studies that have argued that as banks become too large, monitoring and evaluation become difficult as they take on increased risk and may lead to “too big to fail” ( Louzis et al. , 2012 ).

4.3.4 Inefficiency (INEFF)

The credit risk may also be determined by bank's inefficiency (INEFF) defined by a ratio of total operating expenses to total assets, i.e. intermediation cost of bank. The empirical literature suggests an ambiguity in the relationship between INEFF and NPLs. Berger and DeYoung (1997) argued that problem loans may arise either due to the events beyond the bank's control (“bad luck”) or management's INEFF to control lending risk (“ bad management ”). Either of the two situations will lead to increase future NPLs, implying a negative effect of INEFF on NPLs (see for, e.g. Chaibi and Ftiti, 2015 for French banks, Ghosh, 2015 ; Louzis et al. , 2012 ; Podpiera and Weill, 2008 ). On the contrary, the “skimping hypothesis” of Berger and DeYoung (1997) suggest that defaults are likely to increase with cost efficiency. This may be due to the fact that banks decide not to spend sufficient resources to ensure higher loan quality would appear to be efficient. This view has been empirically supported by Chaibi and Ftiti (2015) for German banks. Thus, the effect of inefficiency on NPLs may be expected to be negative or positive.

4.3.5 Bank solvency (SOLVENCY)

Following the Louzis et al. (2012) , Klein (2013) , Makri et al. (2014) , Chaibi and Ftiti (2015) and Ghosh (2015) , this study determines the effect of bank's solvency on asset quality by using a ratio of bank's equity to total assets. The literature suggests that managers of thinly capitalized banks have moral hazard incentives to engage in risky lending practices, along with poor credit screening and monitoring of borrowers ( Keeton and Morris, 1987 ). The inverse relation between solvency and NPLs validates the existence of “ moral hazard ” hypothesis in the Indian banking industry.

4.4 Industry-specific variable

4.4.1 concentration ratio (cr 10 ).

Only few studies have determined the impact of bank concentration on the credit risk. This variable measures a concentration of top ten banks in terms of advances in the industry during a particular year. The literature suggests that a higher concentration in lending by top ten banks increases the likelihood of credit risk. It is argued that banks with high degree of concentration may aggressive lend to specific sectors (such as agriculture and commerce) as a strategic choice to gain market power and earn higher profits which lead to high level of NPLs in future. Following Louzis et al. (2012) , we hypothesized the concentration to have a positive impact on credit risk.

4.5 Dummy variables

4.5.1 prudential norms (pnorms).

This variable is included in the econometric model as a dummy variable for a policy change. It represents a role of prudential norms in the improving the assets quality across Indian banks. The Reserve Bank of India has implemented a reform measure pertaining to classification of an asset as nonperforming and defined an asset to be a nonperforming when it remained not paid for 90 days, as on end of 2004. It is hypothesized that regulatory reforms has led to the improvement in the asset quality of Indian banks.

4.5.2 Ownership dummy (PUBLIC or PRIVATE)

The study estimates the differences in level of credit risk across distinct ownership groups using two ownership dummies – PUBLIC and PRIVATE. Higher coefficient value of PUBLIC relative to PRIVATE reflects greater credit risk among public sector banks.

4.5.3 Financial crisis (FINCRISIS)

In addition, we also incorporated the dummy to capture the influence of global financial crisis of 2007–09 on the credit risk structure of Indian banks.

5. Empirical results

5.1 descriptive statistics and preliminary evidences.

Table 2 reports the descriptive statistics of the sample data set. For the estimation purpose, the study used net NPLs to net advances as a proxy for credit risk. The dependent variable, the logit transformed ratio of net nonperforming loans to net loans (NNPLs), reports a mean value of −1.83, respectively. The negative mean values indicate that there has been a decline in impaired loans after write-offs over time. The average equity to total assets ratio is about −1.06, and log of total assets is about 4.89, respectively. The mean NONIT to total assets is approximate at 0.0085, and average ROA is 0.004 with SD 0.009. Broadly similar mean values have been observed for all the macroeconomic and industry-specific variables. The SWILK and SFRANCIA tests of normality indicate that all the variables are not normally distributed at the 1% level of significance.

Table 3 shows the cross-correlations between all the independent variables which are used in the study for estimation purpose. The results indicate that, except inflation and CR10, no significant indication of multicollinearity is observed among the independent variables [4] . Following the empirical literature, we also performed unit root tests for individual variables using the Fisher Augmented Dickey-Fuller (ADF) and the Phillips-Peron (PP) tests to establish the degree of data integration. Assuming the individual unit root process, the results reported in Table 4 reveals that all the individual variables are stationary at level.

5.2 Dynamic estimation

As noted above in Section 3 , the study employs dynamic panel estimation method to account for “persistence effect” in credit risk along with the set of potential systematic and unsystematic factors responsible in the formation of credit risk in Indian banking industry. For the estimation purpose, we employed two-step system GMM approach and presented the empirical findings in Table 5 .

5.2.1 Persistence effect

In order to account the persistence of credit risk in Indian banking industry, we included the first lag of NNPLs in the econometric model. The empirical findings, as reported in Table 5 , reveals the existence of “persistence effect” in credit risk among Indian banks with persistence coefficient ( δ ) to vary from 0.15 to 0.18% across different model specifications. This confirms that bank defaults are expected to increase in the current year, if it had increased past year due to time lag involved in the process of recovery of past dues. The results thus clearly provide an evidence of time persistence in accumulation of bad loans in the Indian banking industry. Further, the effect on NPLs has prolonged in the aftermath of the financial crisis of 2007, and it would take time to reduce at a significant level. The plausible reason for this may be that the Indian bank has followed a procyclical pattern of credit growth (during 2004–2007), in which they gave aggressive loans to stressed sectors (namely, infrastructure, coal mining and aviation, etc.), which grossly compromised their credit quality in 2011 due to economy slowdown and ultimately contributed to higher defaulters. The significant positive effects of lagged NNPLs in all the estimated models in Table 5 are similar to the findings of Louzis et al. (2012) , Ghosh (2015) and Bardhan and Mukherjee (2016) .

5.2.2 Bank-specific effects

Bank's profitability (ROA) : On discussing the effect of profitability on bank's asset quality, we note that current year rise in ROA by 1% leads to decline in risk of future accumulation of NPLs by (−) 0.0079 to (−) 0.0120%. This suggests that if the profitability of Indian bank(s) increases, they engage themselves in more prudent lending, with more careful screen and monitors the borrowers, which may lead a reduction in the risk of defaults. This empirical finding is consistent with Ghosh (2015) and validates the existence of “ moral hazard ” hypothesis in Indian banking industry. If one period lag of ROA is considered, the sign of the coefficient changes significantly. It indicates that past year's profitability of Indian banks on an average generate 0.0018–0.028% higher level of NPLs, signifying the fact that Indian banks have not followed prudent lending practices in the past years. This may be due to “ liberal credit policy ” adopted by banks' management to increase the credit supply and maximize banks' earnings, thus supporting “ bad management ” hypothesis. This finding of our study is consistent with Makri et al. (2014) , Messai and Jouini (2013) , Abid et al . (2014) , Chaibi and Ftiti (2015) and Klein (2013) . Further, it has been noted that in many developing countries, accounting standards have not been rigorous enough to prevent banks and their borrowers from concealing the true size of their NPAs portfolio. Most often, bad loans were made to look good by additional lending to troubled borrowers (“ ever-greening ”) ( Reserve Bank of India, 1999 ).

Surprisingly, the current year's 1% rise in NONIT to total assets ( NONINs ), increases the default risk by 0.0032–0.0047%, indicating that a higher the share of NONIT of banks, higher the risk for banks. This reflects risk-taking behavior of banks where they rely more on other risky investment portfolios with a view to diversify source of income rather to still depend upon the interest income incurred from loan repayment. This is also found by Ghosh (2015) . As expected, the previous year coefficient of NONIT has been found to be negative, implying that if past years' investment portfolio of banks generate good source of income from nontraditional activities then banks rely less on the interest income from loan repayment, which ultimately leads to reduction in the bank credit risk ( Louzis et al. , 2012 ; Ghosh, 2015 ; Alhassan et al. , 2014 ; Chaibi and Ftiti, 2015 ).

The large sized banks, on an average, generate higher NPLs by 0.753–1.186% in Indian banking industry. The other studies suggesting the positive relation between size and risk are Khemraj and Pasha (2009) , Louzis et al. (2012) and Chaibi and Ftiti (2015) . This reflects that large banks take excessive risk and extend their credit without proper screening and monitoring of the borrower's creditworthiness. This is also supported by an incident happened in the year 2010–11, where State Bank of India, the India's biggest lender bank, extended loans to troubled corporate borrower(s) which in turn led to deterioration in the asset quality of this bank. The lagged size effect has been found to be significantly negative in all the models (similar to Alhassan et al. , 2014 ; Ghosh, 2015 ). This shows that the smaller bank may have greater managerial efficiency than larger banks in terms of screening and monitoring of loans, leading to lower defaults.

The intermediation cost found to have expected negative sign for gross NPLs (see, Table 5 ), suggesting that Indian Banks had been very economical in making expenses on credit screening and monitoring to remain cost efficient, but it led to rise in gross NPLs in future. However, the inefficiency does not seem to have any significant impact on net NPLs adjusted for provisions.

5.2.2.1 Industry-specific effects

The impact of bank's concentration in terms of advances (CR10) in terms of market power is positively significant positive on asset quality. This is in contrast with the prediction of “ tight control ” hypotheses ( Louzis et al. , 2012 ). As the market concentration increases, the market power of top ten concentrated banks will also increase and they make more lending mainly to the stressed sector may be due to political or regulatory pressures. This is evident from the fact that Indian banks had high levels of stressed assets from five stressed sub-sectors including infrastructure, iron and steel, textiles, mining (including coal) and aviation, resulting in increased chances of future defaults ( Reserve Bank of India, 2014 ).

5.2.2.2 Macroeconomic effects

Our results suggest that lower probability of risk of default during the periods of inflation in Indian banking industry. This may be due to adjustments in policy rates by the central bank as a step to contain inflation which reduces the real value of outstanding loans and make debt servicing easier for the borrowers. This is line with Chaibi and Ftiti (2015) , Khemraj and Pasha (2009) , and Makri et al. (2014) . Finally, the coefficient estimate of RGDP has not shown any significant impact of economic activity during the analyzed period.

5.2.2.3 Dummies effect

The implementation of prudential regulatory reforms in 2004–05 has revealed a significant decline in nonperforming loans. On an average, net NPLs have lowered by (−)0.6866% annually during the sample period. Further, the study also examined the time-specific effects by including yearly dummies on NPLs in the model 5. We note a significant decline in NPLs due to implementation of prudential norms. An attempt has also been made to ascertain the diversify behavior of NPLs across distinct ownership groups. This ownership effect is captured by including PUBLIC and PRIVATE dummies in the model. It was found that risk of defaults is significantly lower in case of private banks and foreign banks as compared with public sector banks due to effective write-off (see, Table 5 ).

This study has empirically tested the overall validity of the instruments using the Hansen J specification test, i.e. to test the null hypothesis of joint validity of the moment conditions (the presence of over-identification), is asymptotically distributed as chi-square ( Arellano and Bond, 1991 ; Arellano and Bover, 1995 ; Blundell and Bond, 1998 ). The test is based on null hypothesis, i.e. whether all the instruments are valid in the panel data model or not?, which is consistent with the empirical findings of Table 5 and confirmed the acceptability of two-step system GMM model in the dynamic panel framework.

Furthermore, we also assess the fundamental assumption of serially uncorrelated errors ν i t in Table 5 , using Arellano–Bond tests for Autoregression. The test statistics are reported of AR(1) and AR(2) in Table 5 , test the null assumption that Δ ν i t are not first and second order autocorrelated. The rejection of the null hypothesis in first and second order autocorrelation in the differenced errors, implying no serial correlation for the level error term and thus again support the consistency of the GMM estimates.

5.3 Robustness check

To test the sensitivity of two-step system GMM estimates, we have also obtained pooled OLS (POLS), panel corrected standard error (PCSE) and fixed effects (FE) estimates. The results are reported in Table 6 . We note that the empirical results obtained using POLS, PCSE and FE confirms the findings of the two-step system GMM estimation. It has been restated that larger the size of bank, more engagement of bank in nontraditional activities, lower profitability and higher concentration of banks' in lending in the current year seems to increase the risk of defaults in future. Some additional findings of POLS, PCSE and FE estimates include (1) equity to total assets ratio exhibits a negative and significant impact on NPLs, especially in case of pooled OLS and fixed effects estimations which are in parallel to the findings of Chaibi and Ftiti (2015) , Klein (2013) and Louzis et al. (2012) . This suggest that low capitalized bank face increased credit risk and validates the “solvency” hypothesis in Indian banking industry and (2) previous year credit growth seems to have a significant positive impact on asset quality (as consistent with Ghosh, 2015 ; Espinoza and Prasad, 2010 ; Klien, 2013 ). It supports the “pro-cyclical” [5] nature, wherein credit quality can get compromised during the periods of high credit growth which lead to the creation of NPLs for banks in the future years. The macroeconomic variable INFLATION too exhibits the same sign and significance in case of POLS, PCSE and FE estimation as the system GMM. However, surprisingly current year's RGDP shows positive significant impact on NPLs. It may be due to poor credit standards adopted by Indian banks during the boom period (as supported by Beck et al. , 2013 ). Finally, a clear comparison of the expected sign between different estimation methods used in the present study are reported in Table 6 . It is observed that the results are similar for different estimation methods. The results thus provide strong justification for the use of two-step system GMM estimation as the results are over estimated when OLS is applied and underestimated for fixed effects estimation.

6. Conclusion and policy implications

In order to enhance the banking stability, it is vital to monitor the deterioration in credit quality which may increase the risk of defaults in the economy. With this, the present study is an effort to capture the “persistence effect” of credit risk and assess the factors that influence the asset quality in the Indian banking industry. In particular, we test for the persistence effect of credit risk in Indian banking industry during the period 1999–2014. To achieve this objective, the study employs two-step system GMM estimation approach and explored how the bank-specific, industry-specific, macroeconomic variables alongside regulatory reforms, ownership changes and financial crisis affects the bank's asset quality in India. Such an analysis would help the policymakers to clearly quantify the degree of credit risk persistence and identify the key factors which might be responsible in the formation of credit risk in Indian banks.

Following observations have been made from the empirical results. First, the study found the persistence in credit risk among Indian banks during 1999–2014. This confirms that bank defaults are expected to increase in the current year, if it had increased past year due to time lag involved in the process of recovery of past dues. Second, higher the profitability of Indian bank(s), lower is a risk of defaults in the current year. However, the past year's lower profitability, on an average, generate higher level of NPLs, signifying the fact that Indian banks may have not followed prudent lending practices in the past years. This may be due to “liberal credit policy” adopted by banks' management to increase the credit supply and maximize banks' earnings, thus supporting “ bad management ” hypothesis. Third, with the higher share of income from nontraditional activities in the past year, the probability of default risk gets lowered for Indian banks. This is due to the fact that if past years' investment portfolio of banks generate good source of income from diversified sources then banks rely less on the interest income from loan repayment. Fourth, large banks found to have taken excessive risk and extended their credit without proper screening and monitoring of the borrower's creditworthiness. This finding is also supported by the concentration effect. As the market concentration increases, the market power of concentrated banks will also increase, and they make more lending mainly to the stressed sector may be due to political or regulatory pressures which increases the risk of default. Fifth, probability of risk of default declines during the periods of inflation in Indian banking industry. Sixth, regulatory reforms in terms of prudential norms found to have improved the asset quality in Indian banks. However, the financial crisis of 2007–08 had no significant impact on credit quality of Indian banks. This might have been due to effective write-off done by the banks under distinct ownership groups, especially new private and foreign banks.

In all, the empirical results suggest that both systematic (macroeconomic) and unsystematic (bank-specific and regulatory factors) have been found to be crucial in monitoring the level of credit risk and preventing the deterioration in the asset quality. Further, higher profitability, better managerial efficiency, more diversified income from nontraditional activities, optimal size of banks, proper credit screening and monitoring, and adherence regulatory norms would help in improving the credit quality and minimizing the likelihood of default risk. The study found significant time persistence in the accumulation of NPLs, so adequate attention is required to these bank-specific factors to solve the problem of rising future NPLs. Further, to combat the impact of inflation on NPLs, regulatory authorities need to adjust the real value of outstanding loans, so that borrowers can easily repay back their dues on time.

Specification of variable(s)

Note(s): (1) Figures in parentheses are robust standard errors, (2) AR(1) and AR(2) are the Arellano-Bond test for first and second order autocorrelation of the residuals, (3) in case of AR(1), AR(2) and BP-CW Hettest, we reported the p -values and (4) ***, ** and * denotes significance levels at 10, 5 and 1%, respectively

Source(s): Author's calculations

It has been observed that such costs amounted to 10 percent or more of GDP in more than a dozen of developing country episodes during the past 15 years ( Reserve Bank of India, 1999 ).

Reserve Bank of India (2015) defined non-performing loans as a loan or an advance where interest and/or installment of principal remain overdue for a period of more than 90 days in respect of a term loan.

The information has been reported based on the ratio of non-performing loans to gross loans of banks across Asian countries. According to the IMF data as of 2015, NPLs in India are around 6% of gross loans followed by Thailand (under 3%) and Indonesia (a little over 2%).

According to Kennedy (2008) and Alhassan et al. (2014) , correlation coefficients of below 0.70 represents weaker relationship associated among variables.

It is noteworthy that in the year 2009–10, the growth in NPAs of Indian banks has largely followed a lagged cyclical pattern with regard to credit growth.

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Corresponding author

About the author.

Anju Goswami is currently working as an Assistant Professor at the Depertment of Economics and International Business, University of Petroleum and Energy Studies, Dehradun, India. She received the MHRD Assistantship to pursue her doctoral research. During her doctoral research, she is trained with the skills in constructing models using mathematical programming approaches to benchmark the performance of decision-making units. Currently, her key research interest includes efficiency and productivity analysis and banking institutions.

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Risk Analysis And Management In Indian Banking Sector: An Overview

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An Analysis of Total Risk Management in Performances of Public Sector Banks in India

• Conference paper
• First Online: 27 September 2019
• Cite this conference paper

• Anita Nandi 7 ,
• Madhabendra Sinha 8 ,
• Abhijit Dutta 9 &
• Partha Pratim Sengupta 8  

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 159))

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1 Citations

The effect on risk management on enterprise performance has been noted as a vital one nowadays. In the face of uncertainties that are prevalent in the Indian banking sector, it is imperative to study the role of total risk management on the performance (financial health) of the banks in India. This study tries to understand the effect of total risk management on the performance (financial health) of selected public sector banks and come to a conclusion that there is a significant relationship between the performance (financial health) of the banks and its total risk management which can be used to fend these banks.

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Department of Management Studies, NSHM Knowledge Campus Durgapur, Durgapur, West Bengal, 713212, India

Anita Nandi

Department of Humanities and Social Sciences, National Institute of Technology Durgapur, Durgapur, West Bengal, 713209, India

Madhabendra Sinha & Partha Pratim Sengupta

Department of Commerce, Sikkim University, Gangtok, 737102, India

Abhijit Dutta

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Correspondence to Anita Nandi .

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School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

Suresh Chandra Satapathy

Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, Uttar Pradesh, India

Vikrant Bhateja

School of Computer Applications, KIIT University, Bhubaneswar, Odisha, India

J. R. Mohanty

School of Computer and Information Science, University of Hyderabad, Hyderabad, Telangana, India

Siba K. Udgata

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Nandi, A., Sinha, M., Dutta, A., Sengupta, P.P. (2020). An Analysis of Total Risk Management in Performances of Public Sector Banks in India. In: Satapathy, S., Bhateja, V., Mohanty, J., Udgata, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 159. Springer, Singapore. https://doi.org/10.1007/978-981-13-9282-5_7

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DOI : https://doi.org/10.1007/978-981-13-9282-5_7

Published : 27 September 2019

Publisher Name : Springer, Singapore

Print ISBN : 978-981-13-9281-8

Online ISBN : 978-981-13-9282-5

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