MODELLING OF CREDIT RISK MANAGEMENT IN COMMERCIAL BANKS - A STUDY WITH REFERENCE TO BENGALURU

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1 MODELLING OF CREDIT RISK MANAGEMENT IN COMMERCIAL BANKS - A STUDY WITH REFERENCE TO BENGALURU Dr.P.Pinakapani Professor, Dept of Management studies Auroa s PG College, Ramanthapur, Hyderabad Sunitha Y K Research Scholar Rayalaseema University, Kurnool- A P Ajatashatru Samal Research Scholar Rayalaseema University, Kurnool- A P ABSTRACT Risk management includes identification, measurement, matching mitigators and monitoring and control of credit risk exposures. To study credit risk management a well drafted questionnaire was administered as schedule and data collected was presented in the form of tables and suitable quantitative metrics are used to test the variability of data scientifically. Risk exposure in commercial banks increased on account of severe competition, changing socio-economic pattern, market flexibility and ever growing foreign exchange business. These developments gave the way for emerging of innumerable risks. Consequently the banking industry in India is undergoing a sea of change as far as risk management is concerned. The banks ability to gauge those risks and persue appropriate steps will be the key to success. The risk manager should possess better knowledge gauging these risks and potential risks. Expertise knowledge to gauge the variety of fraudulent innovative activities is essential and credit risk managers should tackle through weather eye which unsolve some problems. Key words: Weather eye, prediction / gauge, emerging risks, expertise knowledge, Basel II, risk modelling. Introduction Credit risk simply refers to non payment or delayed payment of loan borrowed. Risk arises on account of lending to individual, firm, corporate bank or financial institution. Effective monitoring of credit risk is quintessential of survival of a bank since lending is major business of a bank. The emerging innovative fraudulent activities of some individuals, and failure to repay the loan in time creates risk and consequently non performing assets increases. The performance of banks is questioned by many experts and mountaining NPAs are a problem and fund transfer to various corporate bodies is becoming common despite exercising maximum care and diligence by bank risk credit exports. Since risk is associated in every lending hence lending and risks are twin born with banking system. A cry for privitatisation of all commercial banks is catching the attention of financial experts since credit risk is not properly managed and the intention of nationalisation of banks is not properly managed. Exposure risk which arises on account of cash credit facility, over draft balancing is very high and hence needs to be tackled properly. Further exposure risk may also occur on account of dealing in derivatives since the values of derivatives depends upon the movement and changes constantly. 474

2 Statement of the problem It is significant to analyse the specific risk models of credit risk management and portray the same to be followed by commercial banks. Basel II is a specific and suitable model of risk management. The main function of banks are mobilisation of funds and lending of mobalised savings to the needy systematically. Banks performance will be better till the borrowers pay their dues in time, and the loan if unpaid becomes risky for the banks to recover the loans and advances made. The emerged loss may be either due to outright default or changes in portfolio value that arises due to deterioration in credit quality. Credit risk is associated also with market risk variables. The objective of credit risk management is to minimise the risk and maximise the banks risk adjusted rate of return. A proper monitoring of credit makes retail banking sector more particular and manages the quality efficiently and NPAs. The present study touches upon credit management practices and awareness among sample respondents. Review of literature Krisha A. Gopal, et al., (2010) have stated that the task of integrating Basel II is challenging. Indian banks have come a long way since independence and more so after LPG era, and however the authors said that in the coming days banking system will be bench marked with the best banks globally. Swaranjeet Arora (2013) made an attempt to recognise the factors that contribute to credit risk management in Indian banks and to compare credit and risk analysis practice followed by Indian public and private banks. This study explored the phenomenon from different perceptives and indicated that credit worthiness analysis and collateral requiring are the two important factors for analysing credit risk. Yogieta S. Mehra (2010) study reveals that a conclusive evidence of heightened awareness and due importance given to operational risks by Indian banks. Usha, Janakiramani (2008) have assessed the status of operational risk in detail in the context of Basel II. The survey results show clearly that the process of designing the framework for operational risk has just begun. The author stated that Basel II regulatory compliance and desire to establish and good controls emerged as two major drivers of operational risk management. Fatemi A., and Fooladi, I. (2006) have stated that in the context of USA, identifying counterparty default risk is the single most important purpose served by the credit risk models utilised. The paper is highly useful to the treasurers intending to better understand the current trends in credit risk management, and to academics intending to carry out research in the field. Objectives of the study 1) To study the demographic profile of respondents. 2) To analyse the awareness of Basel II and developing a base for application of the findings in terms of implications of the study. 3) To analyse mitigation of credit risk management by banks. 4) To analyse the objectives of credit risk management. 5) To suggest ways and means of credit risk management system in commercial banks. 1) The demographic profile of respondents is not supporting modeling of credit risk management. 2) It is not easy to develop or retain a model for studying risk management and a base for application of findings in terms of implication of the study. 3) There are no mitigation factors which helps effective credit risk management. 4) There are no objectives behind credit risk management. 5) Ways and means to suggest improvement cannot be suggested. Research Methodology Universe of the study The study is confined to Bengaluru Urban only. Bengaluru urban is selected since it is nicknamed as silicon valley, global IT hub, pensioners paradise, investment safe center etc., Bengaluru is the happening city as said by Pandit Jawaharalal Nehru and the fastest growing centre in Asia. Sample of the study Bill Godden s suggest formula has been adapted to fix the sample where universe is greater than population. SS = z 2 x p x (1-D)/c 2 475

3 Where SS = Sample size Z = Z value A (e.g for 95% confidence level) p = percentage of population picking a choice expressed as decimal B Az = Values (Cumulative normal probability table) = 90% Confidence level 1.96 = 95% Confidence level = 99% Confidence level SS = x 0.5 x 0.5 / = or 600 Thus a sample of 600 is considered and taken for the present study. Sampling technique The division of 600 sample is presented below Type of respondents Farm house proprietors Government service employees Private sector employees Self employed Business Professionals House makers Data collection: Both primary and secondary data sources were amply used in the present study. Convenient sampling technique was adopted for collection of data. Samples were interviewed and questionnaire was administered as schedule in order to avoid delay, non respondents and transport problem. Secondary sources includes, journals, books, financial statements of commercial banks. Total questionnaires received was 655 and 55 not usable and 600 were usable ones and yields a response rate of 91%. Credit risk management model Banks of International Settlement (BIS) in order to recognise the different kinds of risks faced by banks and to reduce the credit and operational risks faced by banks, set up Basel committee on banking supervision in 1988, which gave guidelines for updating risk managemnt. These guidelines aims at protecting depositors / share holders of the bank. As per the guidelines issued, capital adequacy was considered as solution for risk managment and all banks were advised to have Capital Adequacy Ratio (CAR) 8%. In Janurary 1999, the Basel committee proposed a new capital accord known as Basel II. Basel III is an improvement order Basel II. Since Basel III is an improvement over Basel II is considered studied in the present study. Structure of Basel II Accord Pillar-I Pillar-II Pillar-III (Minimum Capital Requirement) (Supervisory Review) (Market Discipline) Capital for credit risk Capital for operations risk Capital for Market risk Standardised Approach Basic Indicator Approach (BSA) Standardised Duration Appration (SDA) Internal Rating based (IRAF) Approach The standardised Approach (TSA) Modification Approach (MA) Internatl Rating based Approach (IRAA) Advanced Measurement Approach(AMA) Limitations of the study The study is confined only to Bengaluru urban. The conclusion drawn based on the opinions expressed by different respondents. The model depends upon the accurate information supplied by the customer. Further any generalisation about modeling of risk management in commercial banks at Bengaluru requires an in-depth study. Study findings Table-1 show the data pertaining to demographic profile of respondents. There are 480 males and the remaining 120 are females. The performed x 2 test fails to accept the null hypotheses that there is no significant variations in the gender data and accepts the alternative that there exists variations in the data. Out 600 respondents 520 forming 87% are married and the remaining are single. Chi-square test fails to accept the null hypotheses that there is no significant variations in the data since the calculated value being higher than the TV and accepts the alternative that there exist significant variation in the data. There are 210 degree holders, 130 post graduate degree holders, 100 studied up to PUC, 70 respondents possesed professional degree. Chi-square analytical tool fails 476

4 to accept the null hypotheses that there is no significant variations in the data and accepts the alternative that there exist significant variation in the data. The age data reveals that there are 230 respondents falling in the age group years followed by 130 between years, 100 between years, 80 between years and finally 60 respondents age lies between 60 years and above. Chi-square tool fails to accept the null hypotheses that there is no significant variation in the age data and accepts the alternative that there exist significant variations in the age data since the calculated value is higher than the TV. There 190 respondents getting a monthly income in the range of Rs. 50K - 60K followed by 130 in the range of 40K to 50K, 100 getting a monthly income in the range of 30K - 40K, 80 above 60K, 60 between 20K - 30K. Chi-square test fails to accept the null hypotheses that there exist no significant variations in the data since the calculated value being higher than the TV and accepts the alternative that there exist significant variations in the income data. Table-2 highlights data about respondents of three pillars of Basel-II. 324 respondents forming 54% are strongly agree about their awareness level of Basel-II followed by 201 or 34% agree and 75 some what agree. ANOVA quantitative metric fails to accept the null hypotheses and accepts alternative. Therefore it is concluded here that there exist significant variations in the data. Hence respondents are aware of Basel-II. Table-3 reveals about drivers of risk mitigation and its awareness. There are 300 respondents forming 50% who are strongly agree the statements driving the mitigation process followed by 220 or 37% agree and only 80 somewhat agree. ANOVA test fails to accept the null hypotheses and accepts the alternative. Therefore it is concluded here that there exists significant variations in the drivers of risk mitigation. Table-4 highlights data about objectives of credit risk management. 280 respondents strongly agree about factors driving objectives of risk management, followed by 220 agree and 100 somewhat agree ANOVA fails to accepts the null hypotheses and accepts the alternative. Therefore it is concluded here that there exist significant variations in the data. Table-5 reveals data about ways and means of improving credit management system in commercial banks at Bengaluru. 320 respondents highly suggested various means and ways of improving credit management system. These suggestions vary from credit managers training to penalising the officers who violates norms at the time of sanction of loan. Further, 210 respondents agree over the same and 70 some what suggested. ANOVA test fails to accept the null hypotheses and accepts the alternative. Therefore it can be concluded that there exist significant variations in the suggestions offered. Table - 1 : Demographic Profile of Respondents A. Gender No. of respondents % x 2 Males Females * * 5% level x 2 (0.05; df=1) B. Marital Status No. of respondents % x 2 Married Single * * 5% level x 2 (0.05; df=1) C. Qualification No. of respondents % x 2 10th Standard PUC Degree BA, BSc, BBA Professional Degree * ITI and others Post Graduate (MA, MSc. MCom, MFA) * 5% level x 2 (0.05; df=5) D. Age in years No. of respondents % x * & above * 5% level x 2 (0.05; df=4)

5 E. Monthly income No. of respondents % x 2 10k - 20k k - 30k k - 40k * 40k - 50k k - 60k k & above * 5% level x 2 (0.05; df=5) Source: Field survey Table 2 - Respondents awareness of three pillars of Basel-II Awareness level SA A SWA T Pillar I : Minimum capital requirement Pillar II : Supervisory Review Pillar III : Market discipline Total Source : Field Survey Note : SA - Strongly Agree, A - Agree, SWA - Some What Agree H0 : Modelling of risk management in Commercial Banks at Reject Bengaluru is not positively related to respondents awareness of three pillars of Basel and there is no significant variations in the awareness level. H1 : Modelling of risk management in Commercial Banks at Accept Bengaluru is positively related to respondents awareness of three pillars of Basel and there is significant variations in the awareness level. ANOVA Table Source of variation SS df MS F-ratio 5% F-Limit (From F Table) Between the sample (3-1)= /2 5167/ =5167 = Within the sample (9-2)=7 258/7 F(2, 7) = =4.74 Total (9-1)=8 Source: Field Survey ANOVA Analysis The calculated value being higher than the TV = 5% level of significance with df = v1 = 2 and v2 = 7 fails to accept null hypotheses and accept the alternative. Table 2 reveals data about the respondents awareness of three pillars of Basel II. 324 respondents strongly agree, followed by 201 agree and 75 somewhat agree. Out of the 324 who have strongly agree 115 said about pillar II supervisory review, 110 about pillar I, minimum capital requirement and 99 about peillar III, market discipline. Further the table reveals that 201 respondents who have agree about awarenes level, 71 said that pillar III, market disciplines, 70 about pillar II - supervisory review and 60 about minimum capital requirement. ANOVA quantitative metric fails to accept the null hypotheses and accepts the alternative. Therefore it can be concluded here that respondents are aware of three pillars Basel II. 478

6 Table 3 - Respondents awarenes of mitigation of risk Drivers of Risk Mitigation SA A SWA T Risk based pricing - charging a high interest to borrowers of likely default Periodic report of financial condition, refrain from paying dividends, borrowing further Asking lenders to buy credit insurances or credit derivatives - Transfer of risk Reducing the amount of credit - distributor reduces credit payment to a troubled retailer Reduce the risk by reducing the amount of credit extended Deposit insurance Total Source : Field Survey Note: SA- Strongly Agree, A - Agree, SWA - Some What Agree. H0 : Modelling of risk management in Commercial Banks at Reject Bengaluru is not positively related to the mitigation of risk and there exists no significant variation the drivers of risk mitigation H1 : Modelling of risk management in Commercial Banks at Accept Bengaluru is positively related to the mitigation of risk and there exists significant variation the drivers of risk mitigation ANOVA Table Source of variation SS df MS F-ratio 5% F-Limit (From F Table) Between the sample (3-1)= / / = = Within the sample (18-3)= /15 F(2, 15) = = Total (18-1)=17 Source: Field Survey ANOVA Analysis The calculated value being higher than the TV = 5% level of significance with df = v1 = 2 and v2 = 15 fails to accept null hypotheses and accept the alternative. Table 3 reveals data about respondents awareness of mitigation a risk. 300 respondents out of 600 strongly aware, 220 aware and only 80 somewhat aware. Out of 300 respondents, 62 each said about periodic reporting of financial condition of borrower and reduction in the amount of credit. 58 viewed and strongly agree about lenders taking credit insurance, 50 about risk based pricing and 45 about reduce the risk by reducing the amount of credit extended. Out of the 220 respondents 46 said about reduce the risk by reducing the amount of credit extended, 38 about risk based pricing, 35 about asking lenders to buy credit insurance, 29 about period reporting of financial condition of borrower. ANOVA statistical tool fails to accept the null hypotheses and accept the alternative. Therefore it can be concluded here the respondents are factors driving mitigation of risk. Table 4 - Respondents perception about objectives of credit risk management Objectives of risk management SA A SWA T Review the exposures and performance periodically Devise suitable control/monitoring mechanisms Evolve and refine analytical tools Assessment of risk profiles for enduring healty portfolios and guarding against sickness Draw suitable strategies at the corporate level to attain the prescribed level of exposure and issue guidelines to business units. Compare the recovered percentages, NPA level etc. Total Source : Field Survey Note: SA- Strongly Agree, A - Agree, SWA - Some What Agree. 479

7 H0 : Modelling of credit risk management in Commercial Banks at Reject Bengaluru is not positively related to resdpondents perception about objectives of credit risk management and ther exist no significant variation in the respondents perception about objectives of credit risk management H1 : Modelling of credit risk management in Commercial Banks at Accept Bengaluru is positively related to resdpondents perception about objectives of credit risk management and ther exist significant variation in the respondents perception about objectives of credit risk management ANOVA Table Source of variation SS df MS F-ratio 5% F-Limit (From F Table) Between the sample 4360 (3-1)=2 4360/2 2180/21.58 =2180 = Within the sample 259 (15-3)=12 259/12 F(2, 12) =21.58 =3.88 Total 4619 (15-1)=14 Source: Field Survey ANOVA Analysis The calculated value being higher than the TV = 5% level of significance with df = v1 = 2 and v2 = 12 fails to accept null hypotheses and accept the alternative. Table 4 reveals the data about respondents perception about objectives of credit risk managemnt. 280 respondents out of 600, strongly agree, 220 agree and 100 somewhat agree. Out of 280 respondents who have strongly agree, 63 view that evolve and refine analytical tools followed by 60 review the exposures and performance periodically, 54 about drawing suitable strategies at the corporate level, 52 about device suitable control, 51 about the objective of assesment of risk profiles for enduring healthy portfolios and guarding against sickness. Further the table reveals that out of 220 respondents 50 said about the objective of review the exposures and performance periodically, 48 draw suitable strategies at the corporate level, 45 about assessment of risk profiles, 39 about devide suitable control monitoring mechanisms. 38 about evolve and refine analytical tools. ANOVA fails to accept the null hypotheses and accepts the alternative. Table 5 Respondents opinion about ways and means of improving credit management system ien commcerial banks Suggested expressed by respondents HS S SWS T Credit managers should be well trained to detect diversion of funds by borrowers Upgrade the skill of HR so as to understood the financial data of borrowers and follow new and innovative risk strategies Banks update their data management & IT Capabilities Review thouroughly the internal audit system Rewarding credit managers should be based quality of risk assesment, mitigation and control New awareness also bring new risk and hence bank risk managers should handle properly Penalyse the officers who sanction loans against the norms and extra cautions about diversification of funds by borrowers who are ready to leave the country Total Source : Field Survey Note: HS - Highly suggested, S - Suggested, SWS - Somewhat suggested 480

8 H0 : Modelling of credit risk management in Commercial Banks at Reject Bengaluru is not positively related to the respondents suggestions to improve credit management system and there is exist no significant variations in the suggestions. H1 : Modelling of credit risk management in Commercial Banks at Accept Bengaluru is positively related to the respondents suggestions to improve credit management system and there is exist significant variations in the suggestions. ANOVA Table Source of variation SS df MS F-ratio 5% F-Limit (From F Table) Between the sample (7-1)= / /41.75 = =21.27 Within the sample (21-7)= /14 F(6, 14) =41.75 =2.85 Total (21-1)=20 Source: Field Survey ANOVA Analysis The calculated value being higher than the TV = 5% level of significance with df = v1 = 6 and v2 = 14 fails to accept null hypotheses and accept the alternative. Table 5 reveal the data about respondents suggestion to improve the risk management trend in commercial banks. 320 respondents out of 600 are highly suggested different ways and means to improve the credit management susystem at Bengaluru, followed by 210 suggested and 70 only some what suggested. Out of 320 who are highly suggested, 53 suggested that new avenues also bring new risk and hence risk managers should handle properly followed by 52 suggesting credit managers should be well trained to detect diversion of funds by borrowers, 50 strategy credit managers reward should be based on risk management approach, mitation and control, 47 about penalising the officers who grant loans against norms. 45 about upgrading the skill of HR so as to understand the financial data of borrowers and follow new and innovative risk strategies, 38 suggesting banks update their data management & IT capabilities, 35 suggesting reviewing the internal audit system. 210 respondents out of 600, who have suggested belongs to different suggestions and offered to the researcher. 37 respondents out of 210, suggested that new avenues also brings new risk and therefore credit risk managers should handle situation properly followed by 35 stating the credit managers hould be well trained, 32 about penalising the erring officers who grant loan against the norms. 28 about reviewing throughly the internal audit system, 27 stating rewarding the managers should be based on risk assesment, 26 about banks updating their data management and IT capabilities, 25 about upgrading the skill of HR so as to understand financial data. 70 respondents stated some what suggesting and includes. 18 stating credit managers should be well trained, followed by 12 about penalising the erring managers who grant loan against norms, 9 about bad updating their data management & IT capabilities, 8 each respondents stating about upgrading the skill of HR department, review thoroughly the internal audit system, and new avenues also brings new risk and hence should be handled properly. ANOVA fails to accept the null hypotheses and accepts the alternative. Therefore it can be concluded here that respondents suggested different suggestions to improve credit movement system in commercial banks. Conclusion Banks risks are expanding since there is heavy competition among themselves. Indian banks are undergoing heavy NPAs and no will to pay the loan. Delays in tackling willful defaulters should bilaterally reduced and true spirit of lending and banks should be restored. Banking industry should gain public confidence and filing to do so will be fatal to the growth and development of banking industry. References 1. Krishna A. Goyal, Sunitha Agarwal. (2010). Risk management in Indian banks. Some emergin issues, IJER, 1(1) P Swarajeet Arora. (2013). Risk analysis in Indian Commercial banks - An Empirical investigation, Asia Pacific Finance and Accounting Review, 1(2). 3. Yogieta S. Mehra. (2010). Operational risk management in Indian banks : Some emerging issues. International Journal of Economics & Research, 1(1), Usha Janakiramani. (2008). Operational risk management in Indian Banks in the context of Basel II : A survey of the state of preparedness and challenges in developing the framework, India Asia Pacific Journal of Finance and Banking Research, 2(2). 5. Fatemi, A., and Fooladi, I. (2006). Credit Risk Management, A survey of practices. Management Finance, 32(3),