A Study of Credit Risk Management Practices of Public and Private Banks in India

(1)

A STUDY OF CREDIT RISK MANAGEMENT PRACTICES OF PUBLIC AND PRIVATE BANKS IN INDIA

Thesis Submitted to the GOA UNIVERSITY For the award of the degree of DOCTOR OF PHILOSOPHY

in

COMMERCE

by

MRS. RESHMA D. PRABHU VERLEKAR

Under the guidance of

PROF. MANOJ S. KAMAT

(VVM’s Shree Damodar College Research Centre)

Principal

D.P.M.’s Shree Mallikarjun and Shri Chetan Manju Desai College Canacona - Goa.

March 2020

(2)

DEDICATION

This Thesis is dedicated to

Late Shri Datta R. Prabhu Verlekar (Father)

Late Shri Tulshidas V. Kholkar (Father-in-law)

(3)

DECLARATION

I hereby declare that the thesis titled “A Study of Credit Risk Management Practices of Public and Private Banks in India” submitted to the Goa University, Goa for the award of the degree of Doctor of Philosophy in commerce is an outcome of original and independent research work done by me during the period 2014 to 2020 under the supervision and guidance of Prof. Manoj S. Kamat, Principal - Shree Mallikarjun and Shri Chetan Manju Desai College Canacona - Goa, and also that it has not formed the basis for award of any degree, diploma, associateship, fellowship or similar title to any candidate of this or any other University.

Date: ____________ MS. RESHMA D. PRABHU VERLEKAR

Place: Goa University

(4)

CERTIFICATE

This is to certify that the Ph.D. thesis titled “A Study of Credit Risk Management Practices of Public and Private Banks in India” is a bonafide record of the research done by “Ms. Reshma D. Prabhu Verlekar” under my supervision and guidance, at the Goa Business School - Goa University. This dissertation or a part thereof has not formed the basis for the award of any degree, diploma, associateship, fellowship or similar title of this or any other University

.

Date: ______________

Prof. Manoj S. Kamat

Place: Goa University

Research Guide Principal

D.P.M.’s Shree Mallikarjun and Shri Chetan Manju Desai College of Arts and Commerce Canacona - Goa.

(5)

ACKNOWLEDGEMENT

It is a genuine pleasure to express my deep sense of gratitude and immeasurable appreciation to many personalities who are directly and indirectly connected with this research work.

I am greatly indebted to Professor Manoj S. Kamat, Principal of Shree Mallikarjun and Shri Chetan Manju Desai College, Canacona - Goa for being my guide and contributing immensely with his valuable suggestions, advice, motivation, encouragement and mentoring throughout the course. It is sir’s moral support, valuable guidance, constructive criticism and ongoing evaluation of my research work, without which it would not have been possible for me to complete this task. He has been like a lighthouse to steer me through all the phases of this research work and always encouraged me to present the research findings with clarity. It was a great privilege and honour to work and study under his guidance.

My sincere thanks goes to Dr. Sriram Padyala - Faculty of Goa Business School, Goa University, for tirelessly witnessing my research work, periodically reviewing my progress as an expert on the Faculty Research Committee and providing me his opinions and advice throughout my research work.

I wish to express my gratitude to Prof. Dr. Y. V. Reddy, the Registrar of Goa University for his wholehearted support and advice during the course of this study. I thank Prof. Dr.

K.B. Subhash, the former Dean and Head of Department of Commerce - Goa University for providing necessary assistance and conducting the research methodology classes for the research scholars. I am very much pleased to thank Prof. Dr. Anjana Raju – Programme Director of Research for her valuable suggestions from time to time to improvise my research work. I am thankful to Prof. Dr. B. Ramesh for his encouragement and suggestions. I wish to express my gratitude to Dr. Dayanand M. S. - Vice-Dean (Research), Goa Business School - Goa University for support and encouragement.

(6)

I am very thankful to the Principal of our college - Dr. Santosh Patkar, Sridora Caculo College of Commerce & Management Studies, Mapusa for his continuous encouragement and support. I am indebted to the Management of Saraswat Education Society for sanctioning my study leave. I am indebted to Teaching , Administrative and Library staff of our college for their valuable assistance and moral support throughout my research.

I am profoundly thankful to the Principal of my Research Centre, Dr. Prita Mallya - V.V.M’s Damodar College of Commerce and Economics. I express my gratitude to the Librarian – Ms. Manasi Rege and the other library staff of research centre for providing me required source of secondary data. My sincere thanks to teaching and administrative staff of the research centre for their support and help in the process of my research work.

I am thankful to the Director of Higher Education- Shri Prasad Lolayekar for timely grant of study leave, which was of immense help in the successful completion of this research.

My special thanks and appreciation also goes to my colleague Mr. Kedar Phadke - Faculty of National Institute of Construction Management and Research for giving a patient hearing to my uncounted doubts and willingly helping me out with his abilities in deciding the right approach towards selecting the statistical tools for my data analysis. His experience and critical comments have been of immense help to me during my research work.

My indebtedness is due to, the Librarian of Goa University Dr. Gopakumar V. and the staff of ARPG section of Goa University for providing necessary assistance. I am thankful to Dr. Manasvi M. Kamat for her inspiration, assistance and advice.

I am thankful to all the Respondents who contributed to the major part of my research work by sparing their valuable time and by providing me the required information. I express my sincere gratitude to the Resource Persons of various workshops.

(7)

I am grateful to all the fellow researchers, at the Goa University – Dr. Ujwalah Hanjunkar, Ms. P.S. Devi, Dr. Meera Mayekar, Dr. Paresh Lingadkar, Dr. Harsha Talaulikar , Dr. Anjali Sajilal and Mr. Kavir Shirodkar for their support and timely assistance. My sincere thanks to the faculties of Department of English – Ms. Priyanka Pednekar and Mr. Merwyn Miranda, Purushottom Walawalkar Higher Secondary School for the grammar check of my thesis.

I would fail in my duty, if I fall short of thanking and appreciating the valuable sacrifice of my son Mast. Mehul Kholkar in seeing me achieve my research dream. My wholehearted gratitude goes to my mother – Smt. Sulekha Prabhu Verlekar and my spouse Mr.

Mahesh Kholkar for supporting me in all the endeavours of my life. Also I express my thanks to my sibling - Ms. Disha Panandikar for the assistance provided at appropriate time. I am also indebted to my brother in law, Mr. Siddesh Kenkre for sharing his valuable expertise in computer skills with me.

Above all, I pay my homage to the Almighty God for giving me the courage, patience and health as well as surrounding me with people emitting positive vibes.

Reshma Prabhu Verlekar

THANK YOU.

(8)

238 A.16 NPA to Equity of 21 public banks for 13 years for 2005-2017 238 A.17 NPA to Equity of 21 public banks for 13 years for 2005-2017 239 A.18 CAR of 21 public banks for 13 years for 2005-2017 239 A.19 CAR of 18 private banks for 13 years for 2005-2017 240 A.20 ROA of 21 public banks for 13 years for 2005-2017 241 A.21 ROA of 18 Private Banks for 13 years for 2005-2017 242 A.22 Liquidity Analysis of Indian banks using Proxy L1 (WC/TA) for time

series data from 2005 2017

243 A.23 Liquidity Analysis of Indian banks using Proxy L1 (WC/TA) for

Cross-sectional data from 2005-2017

244 A.24 Liquidity Analysis of Indian banks using Proxy L2 (Current Ratio)

for time series data from 2005-2017

244 A.25 Liquidity Analysis of Indian banks using Proxy L2 (Current Ratio)

for Cross-sectional data from 2005-2017

245 A.26 Profitability Analysis of Indian banks using Proxy P1 (EBIT/TA) for

time series data from 2005-2017, N=273

244 A.27 Profitability Analysis of Indian banks using Proxy P1 (EBIT/TA) for

246

(16)

A.28 Profitability Analysis of Indian banks using Proxy P2 (ROA) for time series data from 2005-2017, N=273

246 A.29 Profitability Analysis of Indian banks using Proxy P2 (ROA) for

247 A.30 Profitability Analysis of Indian banks using Proxy P3 (PBIT/CL) for

time series data from 2005-2017

248 A.31 Profitability Analysis of Indian banks using Proxy P3 (PBIT/CL) for

248 A.32 Profitability Analysis of Indian banks using Proxy P4 (TI/TA) for

249 A.33 Profitability Analysis of Indian banks using Proxy P4 (TI/TA) for

249 A.34 Leverage Analysis of Indian banks using Proxy LV1 (RE/TA) for

250 A.35 Leverage Analysis of Indian banks using Proxy LV1 (RE/TA) for

251 A.36 Leverage Analysis of Indian banks using Proxy LV2 (MVE/TL) for

time series data from 2005-2017,

251 A.37 Leverage Analysis of Indian banks using Proxy LV2 (MVE/TL) for

252 A.38 Leverage Analysis of Indian banks using Proxy LV3 (TD/TA) for

253 A.39 Leverage Analysis of Indian banks using Proxy LV3 (TD/TA) for

253 A.40 Result of Altman Z-score of 21 public banks for 2005-2017 254 A.41 Result of Altman Z- score of 18 Private Banks for 2005-2017 255 A.42 Result of Altman Z-score of five NWB from 2005-2013 255 A.43 Result of Springate Model of 21 public banks for 2005-2017 255 A.44 Result of Springate Model of 18 private banks for 2005-2017 256 A.45 Result of Springate Model NWB banks from 2005-2014 257 A.46 Result of Zmijewski model for 21 public banks from 2005-2017 257 A.47 Result of Zmijewski model for18 Private banks from 2005-2017 258 A.48 Results of Zmijewski Model for NWB from 2005-2014 258 A.49 Result of Grover model of 21 Public Sector banks from 2005-2017 259 A.50 Result of Grover model for 18 Private Banks from 2005-2017 259 A.51 Result of Grover model for NWB banks from 2005-2014 260 A.52 Result of Recalibrated Altman Model of 21 Public and 18 Private

Banks

260

(17)

A.53 Result of Recalibrated Springate Model of 21 Public and 18 Private Banks

261 A.54 Result of Recalibrated Zmijewski Model of 21 Public and 18 Private

Banks

262 A.55 Result of Recalibrated Grover Model of 21 Public and 18 Private

Banks

263 A.56 Questionnaire (Part 2): Preparedness in Basel III Implementation 264

A.57 Result of Tier I Capital Ratio from 2009-2018 267

A.58 Result of Tier II Capital Ratio from 2009-2018 268

A.59 Result of CAR from 2009-2018 269

A.60 Result of Leverage Ratio from 2009-2018 271

(18)

List of Abbreviations

AB Anticipated Benefit NPA Non-Performing Asset

AC Anticipated Cost NSFR Net Stable Funding Ratio

AI Anticipated Impact NWB Non - Working Banks

AR Availability of Resources OLS Ordinary Least Square BP Basel III Preparedness

PBIT/TA

Profit before Interest and Tax to Total Asset (PBIT/TA)

CA/CL

Current assets / Current

Liabilities PBT/CL

Profit Before Tax to Current Liabilities

CAR Capital Adequacy Ratio PCA Principal Component Analysis CCB Capital Conservation Buffer RAA Risk Assessment Analysis CCCB Counter Cyclical Capital Buffer RBI Reserve Bank of India

CRA Credit Risk Analysis RE/TA Retained Earnings to Total Asset CRAA Credit Risk Assessment and

Analysis

RI Risk Identification CRI Credit Risk Identification RMC Risk Monitoring Control CRMC Credit Risk Monitoring Control RMP Risk Management Practices CRMP Credit Risk Management

Practices ROA Return on Asset

CRU Credit Risk Understanding RWA Risk Weighted Assets EBIT/CL

Earnings Before Interest and

Tax/Current Liability SEBI

Security Exchange Board of India

EBIT/TA

Earnings Before Interest and Tax

to Total Asset TD/TA Total Deposit to Total Asset EC Expected Challenge TI/TA Total Income / Total Asset,

FD Financial Distress TL Tolerance Level

LCR Liquidity Coverage Ratio TL/TA Total liabilities / Total Assets

LR Leverage Ratio URM Understanding Risk Management

MVE/TA

Market Value of Equity to Total

Asset VIF Variable Impact Factor

NI/TA Net Income / Total Assets WC/TA Working Capital to Total Asset

(19)

ABSTRACT

Credit Risk is a significant threat for banks as the value of any organisation is measured by its creditworthiness. Given the above, effective Credit Risk Management Practice (CRMP) needs to be followed by banks to reduce this risk. The CRMP of public and private sector banks in the Indian banking sector significantly differs as shown by past studies. In the spirit of these studies, the present research assesses and compares the CRMP of the public and the private banks in India, to check the extent and magnitude of variations. It is crucial to study the lending and financial ratios of the banks as piling of NPAs adversely affects the financial performance of the banks. Therefore the present study measures and compares the credit risk management practices of public and private banks using lending ratio and financial ratios.

Since ineffective CRMP may result in the bankruptcy of banks, the prediction of bankruptcy by applying the bankruptcy model in the Indian banking context plays a significant role. Due to some criticism of the models and the error rates that occurred in the application of the model in present study, the current study aims at recalibration of the applied models. Effective CRMP is possible due to precise control exercised by regulations and supervision (Basel III norms). These controls pose challenges to bankers, which drives the attention of bankers towards adequate preparedness needed for Basel III implementation. Therefore the present study assesses the level of preparedness and compliance of Basel III norms by public and private sector banks.

The present study used Principal Component Analysis (PCA) to determine the factors influencing Credit Risk management Practices (CRMP) of banks. Descriptive statistics were used to provide a summary of variables (CRMP determinants and Basel III dimensions). In ratio analysis, lending and financial ratios were used to measure the credit risk. Pearson’s Correlation Coefficient is used to find the relationship between financial variables such as liquidity, profitability and leverage. An independent sample t-test is conducted to compare the difference in credit risk determinants of CRMP, lending and

(20)

financial ratios, bankruptcy scores, Basel III preparedness and the Tier I, Tier II and CAR between public and private banks in India. The test of ANOVA, along with a post hoc test is conducted to find which bank reveals a statistically significant difference in the group.

One sample t-test and paired t-test was used on Basel III ratios. The robust test is conducted to measure the financial health of the bank in the year 2017.

Regression Analysis such as, Ordinary Least Square (OLS) regression is used to determine the statistical impact of the credit risk determinants on CRMP. Multiple linear regression is used to measure the factors influencing the impact of the Basel III preparedness. Present study applied Altman Z-score, Springate, Grover, and Zmijewski's model for assessing the bankruptcy risk and also recalibrated these models by changing its coefficients through multiple linear regression analysis.

In measuring the impact of the credit risk determinants on their respective CRMP for public and private banks, regression results reveals that explanatory variable Credit Risk Understanding and Credit Risk Assessment and Analysis (CRAA) among the four components are most influential in the contribution of CRMP of private banks. The regression model fits the data well for private banks compared to public banks in India. The results of the Independent sample t-test show a significant difference in the credit risk determinants between public and private banks.

In measuring the CRMP using lending ratios, it is observed that the certain lending ratios are high in case of public banks. In measuring the CRMP using financial ratios, the result states that both public and private banks experience low liquidity ratios and the leverage ratios are lower for public banks and higher for private banks. The profitability ratios of private banks show a fluctuating trend, and public banks show a decreasing trend.

The risk of bankruptcy of public and private banks was analysed by applying four bankruptcy models. It is found that the results are not appropriate in case of two models.

The ranking results as per bankruptcy models shows that Bank of Baroda and Nainital Bank

(21)

are in the first position (most efficient, and credit-risk safe) among public and private bank respectively.

In measuring the statistical impact of factors of Basel III Preparedness, the regression results shows that the factor ‘Anticipated Benefit’ is the most crucial followed by

‘Anticipated Impact’ in Basel III preparedness. The result of one-sample t-test shows that private sector banks commenced disclosure towards compliance (minimum common equity Tier I) ratio from 2018 while the public sector banks started compliance from 2014. The private as well as public banks showed higher compliance of the Tier 1 capital ratio and capital adequacy ratio, compared to a limit prescribed by Basel III norms.

Based on these findings, a broad conclusion can be drawn that private banks are generally more efficient in terms of following credit risk management practices. The lending ratios are low for private banks, indicating a better financial position. The bankruptcy study concludes that the recalibrated Altman and the Grover model perform better than the original model. In contrast, the original Springate and the Zmijewski model showed improved accuracy over the recalibrated model. Further, it can be concluded that Indian banks are well prepared to implement Basel III norms, as the Basel III ratios are maintained by the public and private banks are at a much higher level than prescribed by RBI.

The study contributes to the literature in terms of the determination of credit risk determinants of CRMP using the Principal Component Analysis (PCA) technique and recalibration of bankruptcy models. The uniformity assumption of Institutional theory does not apply in our study on account of differences in the understanding among employees on the different aspects of credit risk and in the assessment and monitoring and control policies of credit risk followed by the public and private banks in India. The private banks show a negative relationship of factor, ‘Anticipated Cost’ with Basel III preparedness. These results support the Legal Theory of Finance.

(22)

Chapter I Introduction 1.1. Background of the Study

The health of a nation’s economy is largely dependent on a sound financial system. The financial system provides necessary funds for the nation’s growth and development. In the financial system, the banking sector occupies the most important position as it holds the savings of the public provides a means of payment for goods and services and finances the growth of business. The banks also play a major role in planning and implementing a country’s financial policy (Itanisa 2016). Thus, a banking sector needs to be strong and resilient for the strong economy of nation.

The word bank is derived from the French word banco or "bancus" or banc which means a bench (Jhingan, 2001). The early Jews in Lombardy conducted the banking business by sitting on benches. The banking is as old as the authentic history and the origins of modern commercial banking are traceable in ancient times. According to Shekhar and Shekhar (2008), in ancient Greece around 2000 B.C., the famous temple of Ephesus was used as depositories for people’s surplus fund and these temples were the centres of money lending transactions. The Priest of these temples acted as financial agent.

The period of banking reforms began in the year 1992, which provided a basis for looking into the future of the Indian banking system (Kumar, 2014). During the last two decades, there have been several reforms at the global and national levels that had an impact on the operating environment of the banks. These reforms are in the form of, the introduction of new players, new institutions, and new instruments. The introduction of new financial products and services and the huge strides in technology and communication have brought significant changes in the working of banks (Mallya 2012). These changes are gradual liberalization of Indian banking, deregulation of interest rate, global competition, new financial reforms, virtual banking, diversification of activities, retail character of banking, liberalization of Foreign Direct Investment (FDI), active role of banks in upliftment of

(23)

socio-economic development of the country, implementation of new Basel requirement etc. Given these above growing complexities in the bank’s business and the dynamic operating environment, has led the banks to face a new or higher risk.

Risk is understood as “anything that can create hurdles in the way of achievement of certain goals”. Therefore, the requirement of the risk management perceives a paramount importance for the banks. According to Rao and Rentala (2012) the dynamic operating environment of banks, have pushed the risk management to the forefront of the financial landscape.

Risk management is understood as “adopting a practice of identifying the potential risk in advance, analysing them and taking precautionary steps to reduce the risk”. Risk management is significant because, today’s banks run its operation with two goals in mind i.e. to generate profit and to stay in the business (Marision 2005), In other words, although avoiding failure is the principal reason for managing risk but financial institution also have broader objective of maximizing Risk Adjusted Rate of Return on Capital (RAROC).

According to Rao and Rentala (2012), the importance of risk management in banks is because of their effect on the financial crisis and determining its role in the survival growth and profitability of the bank. The risk management provides reasonable assurance of achieving firm’s goals, compliance with applicable laws and regulations. It also helps in encouragement of specific investment and decreasing the earnings volatility.

The risk management in banks witnessed a substantial change, due to emergence of regulations evolved from the global financial crisis. As per these regulations the banks need to retain higher standards for capital, leverage, liquidity, funding requirements and risk reporting (Phillipe 2015). Although a variety of regulatory and supervisory authorities have set norms for managing risk, still it remains a challenge for banks, as managing risk depends on bank’s ability and stability. The process of risk management is complex and difficult due to absence of risk culture in many banks. As the bank moved into a new high powered world of financial operations and trading, with a new risk, there is a need for the more sophisticated and versatile instrument for risk assessment, monitoring and controlling risk exposure (Nandi and Chaudhary, 2011). The banks are expected to perform risk management practice by designing effective risk management policies, designing a

(24)

procedure for identification and assessment of risk etc. Indian banking industry requires a combination of new technologies, better processes of credit and risk appraisal, treasury management, product diversification, internal control and external regulation for better risk management (Adamson 2012).

Banks face three types of risk i.e. credit risk, market risk, and operational risk. Among these risks, credit risk is the most important because it has a substantial effect on the return on investment of the banks. According to Ngoroge and Ngahu (2017) Credit risk is a significant threat for the banks as the value of any organization is measured by its creditworthiness. Given the above, effective Credit Risk Management Practice (CRMP) needs to be followed by banks to reduce this risk. The CRMP assumes establishing a suitable credit risk management environment by identifying, measuring and managing credit risk and covers all aspects such as identification of credit risk, understanding what determines it and its measurement using various tools. The CRMP of public and private sector banks in the Indian banking sector significantly differ as shown by the studies of Thiagarajan (2011), Goel and Rekhi (2013), Kattel (2016) and Pourkeiki (2016). In the spirit of these studies, the present research assesses and compares the CRMP of the public and the private banks in India, to check the extent and magnitude of variations.

An ineffective CRMP may result in the bankruptcy of banks therefore, the prediction of bankruptcy may help stakeholders to obscure themselves from the risks arising there from.

There are many models available for bank failure predictions. Literature survey shows that the majority of international bank failure prediction employs the Altman-Z score model, Grover model, Springate model and Zmijewski model. The present research aims to apply all these models in the Indian banking context. Some critics of the above mentioned models opine that when the original model is applied to a more recent sample, its predictive power may be deficient and the risk of bankruptcy may be over predicted. Due to this fact, the present study aims at the recalibration of the above models and compares the results from the recalibrated and the original model, to judge its accuracy.

The effective CRMP is possible due to precise control by authorities in terms of regulations and supervision. The concern regarding credit risk management at an international level

(25)

began in 1974, with the creation of the committee for regulation and supervision of banking practices known as the Basel Committee on Banking Supervision (BCBS). Due to this concern, the formal framework for bank’s capital structure was evolved in 1988 with an introduction of Basel I. Basel I primarily focused on credit risk and appropriate Risk Weighted Asset (RWA). The RBI implemented Basel I in the year 1992 in India. Basel I was criticized for its rigidity of the "One Size Fit" approach and the absence of risk sensitivity in capital requirements (Jayadev 2013). Due to these deficiencies in Basel I accord, the BCBS introduced Basel II. Basel II is a much more comprehensive framework of banking supervision when compared to Basel I (Sarma 2007). It was built on three mutually reinforcing pillars such as Minimum Capital Requirements (Pillar1), Supervisory Review (Pillar 2) and Market discipline (Pillar 3). Basel II was criticized for not considering the liquidity and leverage risk in capital regulation and also failed to address the systemic risk (Jayadev 2013). It was also criticized for its inability to prevent the financial crisis; hence in response to the 2007-09 global financial crisis, BCBS issued Basel III norms.

Basel III focused on four vital banking parameters viz. capital, leverage, funding, and liquidity. Basel III introduced different aspects to control risk such as Counter Cyclical Capital Buffers (CCCB), Capital Conservation Buffer (CCB), Leverage Ratio (LR), Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR). Basel III poses several challenges to the bankers; risk managers, finance managers, and Basel III program managers are under pressure to implement Basel III. The key challenges for these managers will be deciding how best to implement a solution that allows them to comply with Basel III (Whitepaper). These challenges drive the attention of bankers towards the adequate preparedness needed for Basel III implementation. This prompts the present study to assess the level of preparedness by public and private sector banks of Basel III norms.

India should transit to Basel III because of several reasons. By far the most important reason is that, as India integrates with the rest of the world, as increasingly Indian banks go abroad and foreign banks come on to our shores, we cannot afford to have a regulatory deviation from global standards. Also, as per RBI guidelines, Basel III need to be

(26)

implemented in phases, due to this Basel III is not implemented fully by some of the banks.

This drives the attention of the researcher, to study the compliance of Basel III.

1.2. Statement of Problem

Over the current years, there has been an increased number of credit-related problems in the banks due to increased Non-Performing Assets (NPAs). Moreover, the piling of NPAs of public and private banks adversely affects the profitability, liquidity and leverage position of banks. It is a worrying fact that public sector banks report higher NPAs than the private banks. This NPA problem may be the result of ineffective CRMP. The result of ineffective CRMP may lead to the breakdown of the whole banking system (Ishitiq 2015).

The other problem that guides this study is that recognising of exact determinants of CRMP is difficult given the different factors that govern CRMP in different countries. The factors applicable in some context/nations may not apply to Indian banks due to the difference in the economic background and the Central Bank’s policies of the country.

The credit risk management is also a significant challenge for an Indian banking system due to the constant heat of competition that banks face from foreign banks. This fact compels the banks to look into its profitability, liquidity and leverage position, as the financial position is positively affected by these three variables, or one is achieved at the cost of others. Since the available literature state an inverse relationship between liquidity and profitability and also between profitability and leverage, an effort is made to judge the relation in the Indian context.

In today's era, the most common problems of the Indian banking sector are bank frauds and hike in NPAs leading the banks towards state of bankruptcy. The stakeholders are concerned about forecasting the likelihood of financial distress to respond before the event takes place. Thus a variety of scoring models that can predict the bank failure have been developed. Further, although the scoring model is one of the powerful tools used for bankruptcy prediction, it also has its loopholes. Some authors question the model’s capacity for sound default detection and argue that the bankruptcy prediction model is less accurate

(27)

when it is applied to a different environment, period and industry as compared to the reason for which it was initially devised. The problem is that the prediction models used in this research might have been for other industries rather than banking or may be outdated. Thus the models may not provide credible results under all conditions and may need some kind of calibration. This may lead to another problem. There may be a difference in the predictive ability in the original and the recalibrated model, as a result both these models may not match with each other due to change in coefficients. This demands a need for comparing the results of both the models with the robust test, to decide the accuracy of the models followed.

Furthermore, due to the above loopholes in the models, the reliability of the model is doubtful; hence using a single model may not give a perfect result. Therefore, more than one model needs to be used to predict bankruptcy. According to Kleinert (2014), single bankruptcy prediction model faces limitations and use of multiple bankruptcy prediction models improved the prediction accuracy.

The Basel III implementations need proper preparations as it involves huge cost and resources in terms of capital, manpower and technology. Further, Basel III preparations are affected by several factors i.e. Anticipated Benefit (AB), Anticipated Cost (Anticipated Cost), Anticipated Impact (AI), Expected Challenge (EC), Availability of Resources (AR), Education Level (EL) etc. Thus, there is a need to find the most significant factors influencing its preparedness. The other research problem is related to compliance of Basel III implementation by Indian banks. RBI has given the transitional schedule for implementation of these new norms from the year 2013-2019. In this schedule, the minimum requirement for Basel III ratios (CAR, Tier I, Tier II, leverage and liquidity ratio) required to be followed by individual banks are prescribed by RBI. A reference to annual reports show that some of the banks have recently moved towards Basel guidelines and are still following standard approaches, but still many do not follow the expected advanced approaches. This explains the need to study the compliance of Basel III norms in the Indian context.

(28)

The other research problem is related to the pre and post-implementation phase of Basel III.

Capital ratios of the banks differ in the pre and post-implementation phase of Basel III.

Capital ratios of the banks need to be enhanced in the post-implementation of Basel III in order to meet the higher requirements. Therefore the present study assesses Basel III ratios in these two phases.

1.2.1 Research Questions

The research question underlying this study is recognizing the exact determinants of CRMP. These determinants are not standard and as per the literature review it is observed that, the factors governing the CRMP differs from country to country. In the past, some of the studies undertaken in one country have used three determinants whereas some other studies have used five determinants in other country. Thus, determinants applicable in other context or country may not apply to the Indian banks. Therefore, the research question addressing the above issue states, what are the determinants of Credit Risk Management Practices in India? These different determinants have certain impact on the CRMP as per the reviews of past studies. Therefore, a further question arises as, what is the impact of these determinants on the CRMP?

The primary function of public and private banks is accepting deposits for lending. In performing a lending function, both these banks undertake a heavy credit risk. Also, these banks follow an equivalent procedure and safety precautions before sanctioning a loan and are on equal foot of bearing the credit risk. Considering this fact, it is understood that there is uniformity in the risk management practices among the public and private banks.

However it is a worrying fact that, public sector banks report higher NPAs than the private banks. This triggers the researcher to find an answer to a research question such as, whether there is a difference in the credit risk determinants of CRMP between public and private banks?

Financial performance indicators such as, liquidity, profitability and leverage measured through financial ratios helps public and private banks in knowing its financial position to manage its credit risk. The financial position of both these banks may not be uniform as the pattern and extent of liquidity, profitability and leverage differs between public and private banks due to their nature of operations. Therefore, the research question arises, whether

(29)

there is significant difference in the financial ratios between the public and private banks?

The research gap from the literature review highlights the fact that the relationship between liquidity profitability and leverage has been studied in other countries, but very few similar studies have been carried out in the Indian banking sector. Therefore, the research question arises to find - Whether there exists a particular trend and relations among liquidity, profitability and leverage of Indian banks?

A study on lending ratio helps to understand the lending and NPA position of the banks.

As per the studies of Goel and Rekhi (2013) and kattel (2016) the credit risk management practices of public and private banks differ. This difference in the CRMP may be on account of various factors and one amongst them may be lending ratios. Due to this, a question arises, whether there is a significant difference in the lending ratios between public and private banks?

The bankruptcy models applied in the present study were originally developed at a different time period, with different samples and were affected by the country’s economic environment. Therefore the predictive power of these models may differ. Thus the question arises as to how accurate are the bankruptcy models? If the predictive power of the model is affected after its application to the Indian banking sector, the next question arises as to, whether there is a need for the recalibration of these models to suit the current economic environment and our specific regulatory environment of the country? After recalibration and application of these models to the Indian banking sector, the study tries to answer the question –Which financial distress model is accurate to predict bankruptcy of banks?

The financial position of the bank is assessed using bankruptcy score. Based on these bankruptcy scores which Indian banks could be ranked as the best, and the worst? Lastly, based on these scores, it further tries to answer a question whether there is significant difference in the bankruptcy score of public and private banks?

It is crucial for the Indian banks to have good preparations for the Basel III implementation in order to meet international standards. Therefore the question arises as to verify, whether the Indian banks are prepared for the implementation of Basel III norms? The Basel III preparations are affected by several factors as per the studies of Al- Tamimi (2008) and

(30)

Kapoor and Kaur (2015), with this raises the question as to, what are the significant factors contributing towards Basel III preparedness? The Basel III preparations of public and private banks may differ due to their nature of operation, availability of resources, etc.

Thus, further question arises whether there is statistical difference in the Basel III preparations of public and private banks?

Basel III implementation poses a variety of challenges in terms of enhanced capital requirements, design of comprehensive liquidity management framework, data acquisition and software and hardware development. Therefore, some of the banks may not follow these norms due to cost factor thus the question arises as to Whether Indian banks comply with the minimum requirements of Basel norms stipulated by the RBI? The post implementation period of Basel III requires banks to maintain higher capital to meet the norms. Therefore, the question arises as to what is the difference in the Basel III ratios of banks in the pre and post-implementation phase of Basel III? and whether banks have followed required capital requirements in the pre and post-implementation phase of Basel III?

1.3. Objectives of the Study

Following are the four objectives o this study:

1. To identify the factors (determinants) affecting the credit risk management practices, measure its individual impact and compare them between public and private banks.

2. To measure and compare the credit risk management practices of public and private banks using lending ratios and financial ratios.

3. To analyse the risk of bankruptcy of public and private banks using the bankruptcy models, judge their accuracy and rank the banks based on its score.

4. To assess the preparedness and compliance of the Basel III accord of public and private banks in India.

(31)

1.4. Scope of the Study

The present study has been undertaken primarily to examine the CRMP of public and private banks in India. Banks face three types of risk – Credit risk, Market Risk and Operational risk. Among these risks credit risk is the most important one, because it has a substantial effect on the return on investment of the bank. Therefore, present study is restricted to credit risk, ignoring the other risk. There are different methods to measure CRMP, however, the present study covers two methods i.e. credit risk measurement tools and identifying determinants of credit risk.

The current study covers banks from two categories public sector banks and private banks.

There are 27 public sector banks in India, consisting of 19 Nationalised banks , six State Bank group, Industrial Development Bank of India (IDBI) and Bhartiya Mahila Bank. The present study covers 19 nationalized banks, State Bank of India and IDBI bank making it twenty one public sector banks. There are 29 private banks (old and new private sector banks) operating in the country, however the present study covers 18 private banks. The type and the number of banks are selected based on the availability of data and the consequences of time constraints.

Present study assesses the risk of bankruptcy of public and private banks using bankruptcy models. There are many models available for bank failure predictions such as Standard and Moody’s financial ratios, Beaver model, Altman Z- score model, Ohlson’s model CAMEL model, Grover Model, Springate Model, Neural Network and Zmijewskis Model to predict the health of the bank. Among these models, the present study focuses on the most frequently used model in past studies. These models are the Altman-Z score model, Grover model, Springate model, and Zmijewski model.

The accuracy of these models could be judged based on various factors; however present study covers Type I and Type II errors, robust test and the ranks given to the individual banks. The secondary data for the bankruptcy study is collected for thirteen years period from the year 2004-5 to 2016-17. The data period consists of two phases, the pre- implementation and the post-implementation phase of Basel II and Basel III norms. This is the period where the Basel committee put norms on the banks and the effect of all these

(32)

norms is reflected in the financial statements of the banks, which are more reliable and will reduce measurement errors.

Although a study concerning Basel III norms covers many aspects such as impact, challenges, cost- benefit analysis etc, however, due to time and resource constraints present study is limited to the assessment of the preparedness and compliance of Basel III norms between select public and private banks in India.

A vast and intensive study is possible with respect to full compliance of these norms by the Indian banking sector, but based on the data availability and time factor, present study, focuses on compliance of Basel III ratios (Minimum Common Equity Tier I capital, Tier I capital, Tier II capital, Capital Adequacy Ratio and leverage ratios). The other ratios such as Liquidity ratio and Net Stable Funding ratio is not covered due to lack of data in the annual statements.

1.5. Significance of the Study

The credit risk is a significant threat for banks as it has a direct impact on the profitability of the bank. A study by Ishitiq (2015) found a positive relationship between the performance and risk management. This relationship throws light on the paramount rationale of management of credit risk. A study by Rehman (2016) states that credit risk management is getting more attention, especially after the credit crunch of 2007-2009.

According to Ngoroge and Ngahu (2017) effective risk management can bring far reaching benefits to all organisations, whether large or small, public or private sector. Therefore efficient CRMP is one of the vital strategy bank needs to make by diverting its resources for the growth and survival of banking business. This strategy of CRMP can give many advantages to the financial institution in terms of financial performance, competitive advantage, optimum utilisation of resources, improved service delivery, growth and survival, reduced waste and developing policies for regulatory bodies.

The information about CRMP will assist in developing policies for regulatory bodies.

According to Onyano (2010) policy makers in the financial industry will use this study in understanding the extent to which the banking industry is exposed to credit risks. This will

(33)

guide them in designing the best practices for the financial institution to manage credit risk.

The findings will further enhance the managers understanding of the key systems that will improve the banks performance. Proper CRMP is also important because Ishitiq (2015) found in its study that, there is positive relationship between the performance and risk management. In addition to the regulatory requirement, the CRMP is valuable and relevant in order to increase the value of the firm (Ishitiq 2015).The value of financial institution is judged based on its financial performance. The credit risk management is found to be one of the determinants of returns of banks stock (Hussain and Ajmi, 2012). Thus the credit risk management will maximize the banks risk adjusted rate of return by adjusting credit risk exposure within acceptable limit.

It is crucial to study the lending and financial ratios of the banks as piling NPAs adversely affects the financial performance of the banks. A study on measurement of credit risk using lending and financial ratios is significant to managers, investors, creditors, government and regulators. Managers can depend on these ratios to have the right choice in the decision- making process and efficiently adopt new policies and a new management system. These ratios help the investors to predict the future situation, earning capacity of their invested companies and the safety of their investments. The ratio analysis assist the creditors in knowing the ability of a company to pay off its debt and the company's potential in the future to keep lending it. The ratios aid government to compare individual bank with the industry ratio to provide financial support for the weaker banks and could help the bank regulators to evaluate and compare banks performance.

The bank health needs attention because banking is a business entity that collects funds from the public in the form of savings and Chanel’s to the community in the form of credit.

This process attaches the risk that the borrower may default on his application. This uncertain economic environment during recent years has stressed the importance bankruptcy prediction. In such a condition it becomes necessary to assess the financial state of the banks using distress prediction model to estimate the business risk. This study will help the banks to understand its financial position much in advance and to avoid financial distress.

(34)

A proper prediction of bank bankruptcies is significant as the financial sector is very crucial for a growing economy, and any variations in its performance can affect the economy either way (Chaudhary and Nandi, 2011). The bankruptcy information is useful to the management to undertake precautions and essential to the auditor to report on the viability of the firm. Investors are interested in the bankruptcy model to avoid the risk of loss from an unsuccessful investment, and the lenders are interested in taking decisions on the granting of loans to the bank. The government agency has an interest in seeing the signs of bankruptcy to provide support for its revival. Score value calculated using models is useful to banks to demand loans from RBI or any other funding agency as per Pradhan (2014).

Thus the financial health of banks can help each beneficiary and it can be applied in a wide variety of situations. Bankruptcy study will provide information on variables that will influence the health of a bank (Stingar and Warstuti 2014). In total, if bankruptcy could be predicted with reasonable accuracy ahead of time banks could better protect their business and could take action to minimize risk and loss of business perhaps even prevent bankruptcy (Ramage and Pongstal 2004).

The progress report on the implementation of Basel III regulatory framework (2013) reflects that RBI has taken the steps for convergence of the International Basel standards and the Indian banks need to transit to Basel III because of several reasons. The most important reason being, the Indian banks make it presence in the world and foreign banks come on to our shores. Hence the Indian banks cannot afford to have a regulatory deviation from global standards. Considering this fact, preparedness of Indian banks for the implementation of Basel III norms assumes a high level of significance. Further, it is also significant because it poses a variety of challenges to the banks. These challenges drive them to develop technological infrastructure and human resources to cope up with risk environment.

A major challenge for the Indian banks is the implementation of regulations as per the transitional schedule specified by RBI. In the post-implementation period of Basel III norms, the banks need to infuse an additional capital, to maintain quantity, quality, consistency, and transparency of capital as per set norms. According to Athira and Shanti (2014), banks require additional capital of 5 lakh crores and further the public sector banks

(35)

require common equity of 1.4 - 1.5 trillion to meet Basel III requirements. Apart from this, Basel III norms require banks to follow the Capital Conservation Buffer and Counter Cyclical Capital Buffer for the better risk management framework. All these stipulations trigger the banks to enhance their capital base by balancing the profitability aspects of banks. These facts trigger the significance of Basel III preparations.

Due to the above challenges, RBI introduced phase in arrangement for the implementation of Basel III norms from 2013-14 to 2018-19, which will also align full implementation of Basel III in India closer to the internationally agreed date of January 1, 2019. Thus, all banks are proceeding at their own pace for meeting the Basel III requirements. Therefore, a study on the compliance of Basel III norms as per the transitional schedule specified by RBI perceives paramount importance.

The evolution of Basel III norms has taken place due to the impact of the financial crisis in 2008. This crisis affected the U.S. economy and other economies very badly as the capital was dried up during this crisis. However, the impact of the same was less on the Indian economy, maybe due to the better capital position of the Indian banks in this period. This period was known as the pre-implementation period of the Basel III norms and in this period the capital position of the Indian banks was sufficient to face the financial crisis. The post-implementation period of Basel III norms shows an improvement in the minimum core capital stipulations. Thus there is difference in the capital ratios in the pre and post- implementation period of Basel III norms. Therefore, a comparative study on the Basel III capital ratios of the public and private banks in the pre-implementation phase and also in the post-implementation phase achieves due importance.

The present study is also significant as it makes valuable contribution to the current academic literature in the area of banking and also proposes methodologies as well as practical additions to the key area. Furthermore, it will act as a source of reference and background for other research in this area plus findings will stimulate the future research.

A Study of Credit Risk Management Practices of Public and Private Banks in India