Indian Journal of Finance and Banking Vol. 9, No. 1; 2022 ISSN 2574-6081 E-ISSN 2574-609X Published by CRIBFB, USA 10 CLIENT COMPANIES’ PERCEPTION TOWARDS CREDIT RISK OF PRIVATE SECTOR BANKS WITH REFERENCE TO ICICI, HDFC, AXIS BANK, IDBI, AND YES BANK Sunitha, G PhD Research Scholar Department of Business Management KLEF (Deemed to be University), India E-mail: sunitha27.g@gmail.com https://orcid.org/0000-0002-7742-8924 Dr. Venu Madhav, V Associate Professor Department of Business Management KLEF (Deemed to be University), India E-mail: dr.v.v.madhav@gmail.com https://orcid.org/0000-0002-0089-5137 Received: October 14, 2021 Accepted: November 16, 2021 Online Published: January 15, 2022 DOI: 10.46281/ijfb.v9i1.1545 URL: https://doi.org/10.46281/ijfb.v9i1.1545 ABSTRACT The banking sector at present is facing many issues; one among them is credit risk. A Credit risk is termed as an estimate or forecast of the default of a borrower failing to recover his interest amount or borrowed amount. Currently, the banker or the lender is at risk of recovering the interest amount and principal amount, increasing their recovery costs. The present study makes an attempt to know the awareness of customers towards credit risk of private sector banks. The research objective is to analyze the significant association between Client Company’s perceptions with a view to credit risk. The study explains the major variation between client company’s perspectives towards Indian private sector. The study explains about the impact of credit risk on banks profitability. The present study helps banks to prevail over the problem of credit risk. The study analysis the objectives of research, hypothesis formulated, research methodology, findings and conclusions are discussed. The secondary sources for the study are through the websites of banks, Journals and client company’s websites. Primary data has been gathered from 285 client companies using convenience random sampling technique from private sector banks. Keywords: Private Sector Banks, Credit Risk, Customers Perception, Profitability. JEL Classification Codes: H32, Z33, E32, D21, C12, G21. INTRODUCTION Credit risk is measured as a major problem in banking sector. The factors responsible for credit risk may be financial factors, Business related factors, Government intervention and Policies of the banks etc. To decrease the non-performing assets we need to plan in advance on issuing of requested amount to the https://www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 9, No. 1; 2022 11 clients. Before issuing the loans to the customers, banks necessitate to analyze the financial background and their credit worthiness. This may help in identifying the borrowers who will settle up the borrowed sum and who defaults it. REVIEW OF LITERATURE Zergaw (2019) Author analyzed the elements that are upsetting the credit risk management practices in few Ethiopian private banks. Author evaluated the result of credit measurement, observing process and the effects of market risk, operational risk, and legal risk in credit risk management procedures of banks. The study examines the effect of creating Credit risk environments on credit risk management in accomplishment of the Banks and to evaluate credit granting procedures of the banks and its outcome on credit risk management of the banks. Yüksel et al. (2018) Authors in the present study made an effort to analyze the elements which manipulate credit risk in Azerbaijani banks. They suggested the banks to verify the ratings on customers before granting loans to them. Considering 10 banks, the analysis was done considering their asset size with 10 variables. Panel logic methodology was used for the study and analyzed that 4 independent variables like capital adequacy ratio, joblessness rate and interest rate affect credit risk of Azerbaijani banks. Zheng et al. (2018) the study tried to show the backward effect of credit defaults in bank profitability though other factors are considered with equal importance. The study chronologically showed the uni-variate to multivariate regression to determine the best executed model. The study addressed the credit risk which is the prevailing question in the contemporary time horizon. Sandada and Kanhukamwe (2016) explained the crucial elements that are affecting the credit risk of Zimbabwe banking sector. The study ascertained the impact of macro-economic, industry and bank related factors on increasing credit risk. The bank specific factors highlight the need for banking institution to take staff training on various aspects of banking operations seriously. The research findings provide a platform for further research on how to deal with the credit risk problems. The research gap identified is that there are very less studies which showed an influence of Client Company’s perception towards credit risk. No study was carried out on analyzing the significant association of Client Company’s perception towards credit risk. The current study recovered the research gap. The current study makes an attempt to minimize the consequences of credit risk on banks profitability and suggests suitable methods to overcome credit with more samples. After studying various research papers and articles, besides the above mentioned, the following significant remarks with regard to the study were made. OBJECTIVES OF THE STUDY  To analyze the significant influence of client companies perception towards credit risk.  To evaluate the significant association of client companies perception towards credit risk.  To study the impact of credit risk on banks profitability.  To suggest suitable methods to overcome credit risk. SCOPE OF THE STUDY The scope is limited to analyzing the customer’s perception towards credit risk of private sector banks. Primary data has been gathered from 285 client companies. STATEMENT OF THE PROBLEM Credit risk is the reason for economic decline as banks fail because of default risk from client companies, which has had a negative impact on the economic development of many nations around the world. RESEARCH METHODOLOGY Research Design The study uses descriptive research design with a survey method by convenience random sampling technique to assemble the facts. The analysis was conducted to analyze the client company’s perception https://www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 9, No. 1; 2022 12 towards credit risk for the solutions like Credit risk management, Fraud management, and better loan securitization etc. Sample Size The present study collected samples from 285 client companies of private sector banks using convenience random sampling technique. Questionnaire was forwarded to 1880 companies all over the country. But the responses were received from 285 companies. Statistical Tools Few statistical tools like percentage analysis, ANOVA, Chi-Square test and Regression were used to examine the collected data to evaluate the customer’s perception towards credit risk using SPSS. HYPOTHESIS OF THE STUDY  There is no significant influence of customer’s perception towards credit risk.  There is no significant association of customer’s perception towards credit risk.  There is no significant impact of credit risk on banks profitability. RESULTS AND DISCUSSIONS Table 1. Tabular representation of Demographic factor of the client companies Type of Business Number Percentage Agricultural based 23 8 Manufacturing 56 20 Construction 38 13 Health care 44 15 Education 26 10 Petroleum 32 11 Others 66 23 Total 285 100 Annual Turnover Number Percentage Below 100 crores 18 6 101 crores to 200 crores 5 2 201 crores to 300 crores 58 20 301 crores to 400 crores 70 25 401 crores to 500 crores 55 19 501 crores and above 33 12 Others 46 16 Total 285 100 Place of the organization Number Percentage Andhra Pradesh 35 12 Arunachal Pradesh 15 5 Assam 6 2 Bihar 11 4 Madhya Pradesh 19 7 Uttar Pradesh 9 3 Gujarat 9 3 Goa 14 5 Kerala 15 5 https://www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 9, No. 1; 2022 13 Karnataka 18 6 Meghalaya 15 5 Odisha 11 4 Mizoram 9 3 Nagaland 11 4 Maharashtra 15 5 Himachal Pradesh 16 6 Tripura 14 5 Rajasthan 20 7 Uttarakhand 10 4 Telangana 13 5 Total 285 100 Bank Number Percentage ICICI 57 20 HDFC 53 18 Axis bank 57 20 IDBI 59 21 Yes bank 59 21 Total 285 100 Source: Authors own work The data was gathered from 285 client companies, the details of the companies are explained here. Considering the type of business other business are 23%, manufacturing companies are 20% and Health care are 15%. Other type of businesses like Construction, Education and Petroleum contributed fewer shares. The companies having an annual turnover of 301crores-400 crores are 25%, 201 crores- 300 crores are 20% and 401 crores- 500 crores are 19%. Questionnaire was forwarded to few companies from all the states. The questionnaire was forwarded to 1880 companies all over the country. But the responses received are 285, out of which the responses from Andhra Pradesh are 12%, Madhya Pradesh is 7% and Rajasthan are 7%. The responses from other states are very less. The banks opted by the client companies are IDBI and Yes bank are 21%, ICICI and Axis bank are 20% and HDFC are 18%. Table 2. Tabular representation of influence of customer perception towards credit risk Test of Homogeneity of Variances Credit Risk Levene Statistic df1 df2 Sig. 3.472 10 274 .000 ANOVA Credit Risk Sum of Squares Df Mean Square F Sig. Between Groups 19.022 10 1.902 15.453 .000 Within Groups 33.727 274 .123 Total 52.749 284 Source: Authors own work https://www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 9, No. 1; 2022 14 ANOVA is a test of hypothesis that is suitable to compare means of a permanent variable in two or more independent comparison groups. Here, Customer perception has been altered in relation to Credit risk. Since the p values are 0.010 that are commenced to be less than 0.05, the null hypothesis is discarded and alternative hypothesis is established at 5% level of significance, therefore the strength of association between variables is very strong. There is a significance difference on the Customer perception towards Credit risk. Table 3. Tabular representation of significant association of consumer’s perception towards credit risk Chi-Square Tests Value Df Asymp. Sig. (2-sided) Pearson Chi-Square 213.145a 80 .000 Likelihood Ratio 212.411 80 .000 Linear-by-Linear Association 71.790 1 .000 N of Valid Cases 285 a. 83 cells (83.8%) have expected count less than 5. The minimum expected count is .01. Symmetric Measures Value Approx. Sig. Nominal by Nominal Phi .865 .000 Cramer's V .306 .000 N of Valid Cases 285 a. Not assuming the null hypothesis. b. Using the asymptotic standard error assuming the null hypothesis. Source: Authors own work Chi-Square is a single value that explains about how much difference exists between the observed counts and the expected count. For testing Chi-square, a p-value that is smaller than or equal to the significance level explains that there is satisfactory facts to terminate that the new distribution is not the same as the conventional distribution. Therefore we can conclude that a relationship exists between the variables. A low value for chi-square means there is a high correlation between two sets of data. Since the p values are 0.000 which are less than 0.05. The null hypothesis is rejected and alternative hypothesis is accepted at 5% level of significance, therefore the strength of association between variables is very strong. There is a significance association of Consumer perception towards Credit risk. Table 4. Tabular representation of significant impact of credit risk on banks profitability Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .568a .323 .321 .31953 .323 135.130 1 283 .000 a. Predictors: (Constant), Credit Risk ANOVAa Model Sum of Squares Df Mean Square F Sig. 1 Regression 13.797 1 13.797 135.130 .000b Residual 28.894 283 .102 https://www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 9, No. 1; 2022 15 Total 42.691 284 a. Dependent Variable: Banks profitability b. Predictors: (Constant), Credit Risk Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1.909 .191 9.990 .000 Credit Risk .511 .044 .568 11.625 .000 a. Dependent Variable: Banks profitability Source: Authors own work Regression is a reliable method of identifying which variables have impact on a particular topic. The process of performing a regression allows you to confidently determine which factors matter most, which factors can be ignored, and how these factors influence each other. R-squared (R2) is a statistical measure that elaborates the ratio of the variation for a dependent variable that's explained by an independent variable or variables in a regression model. The p-value is tested for all the terms, and results illustrates that the null hypothesis that the coefficient is equal to zero (no effect). A low p-value (< 0.05) illustrates that you can reject the null hypothesis. Typically, you use the coefficient p-values to determine which terms to keep in the regression model. As the R-Square value should be between 0 and 1, here it is 0.323 and the p value is 0.000 which is less than 0.05, therefore null hypothesis is rejected and alternate hypothesis is accepted. Therefore changes in Credit risk are associated with Bank profitability. FINDINGS  Demographic factors explains that other type of businesses are more than the businesses like Manufacturing, Health care, Construction and Petroleum.  The annual turnover of majority of the Client companies is 301-400 crores, which is a good indication of growth of income of the organizations.  People from Andhra Pradesh have responded more to the questionnaire when compared to other states.  The percentage of banks selected by the respondents is nearly equal. Maximum percent of the respondents have opted to all the selected banks.  There is a significant influence of Client Company’s perception towards credit risk. This represents that Client companies have comprehensive particulars on the basis for the increase of credit risk like Customers default, Banks internal rating, inappropriate credit policies and Volatile interest rates.  There is a significant association of Client Company’s perception towards credit risk. Here, the factors like Consumers failure to repay the loan amount, inappropriate rating from rating agencies and Banks internal rating are considered to evaluate the Client Company’s perception towards credit risk.  There is an impact of credit risk on banks profitability. The factors like Bank size, Capital ratio, Deposits ratio, Liquidity ratios are considered to analyze the impact of credit risk on Bank profitability. SUGGESTIONS  To improve banks internal rating.  Inspection from banks is to be done accordingly.  To advise the customers or the businesses who took loan when needed. https://www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 9, No. 1; 2022 16  To provide proper rating from credit rating agencies.  Banks should limit the borrowing amount, as the high the borrowed sum higher would be the credit risk.  Banks have to evaluate the financial arrangement of the firm thoroughly before granting loan to them. CONCLUSION Credit risk of private sector banks can be slowly but surely decreased by following few suggestions mentioned in this paper resembling improving internal rating from banks and external rating from credit rating agencies. Banks can advise and provide training to the customers when needed. It assists the client companies to sustain in his business environment. As these recommendations are constructed on the perception of Client companies, there may be probably other factors which are responsible for increase of credit risk. Banks have to take precautionary measures to overcome these issues. When banks credit risk is decreased, the profitability of the banks increases. AUTHOR CONTRIBUTIONS Conceptualization: V. Venu Madhav Data Curation: Sunitha, G Formal Analysis: Sunitha, G Funding Acquisition: Sunitha, G Investigation: Sunitha, G Methodology: Sunitha, G Project Administration: V. Venu Madhav Resources: Sunitha, G Software: Sunitha, G Supervision: V. Venu Madhav Validation: Sunitha, G, V. Venu Madhav Visualization: Sunitha, G Writing – Original Draft: Sunitha, G Writing – Review & Editing: Sunitha, G, V. Venu Madhav CONFLICT OF INTEREST STATEMENT The author declare that they have no competing interests. ACKNOWLEDGEMENT All authors contributed equally to the conception and design of the study. REFERENCES Sandada, M., & Kanhukamwe, A. (2016). An analysis of the factors leading to rising credit risk in the Zimbabwe banking sector, 12(1), 80-94 Yüksel, S., Mukhtarov, S., Mammadov, E., & Özsarı, M. (2018). Determinants of profitability in the banking sector: an analysis of post-soviet countries. Economies, 6(3), 41. https://doi.org/10.3390/economies6030041 Zergaw, F. (2019). Factors affecting credit risk management practices, the case of selected private commercial banks in Ethiopia, International journal of advanced research, 7(1), 811-849. http://dx.doi.org/10.21474/IJAR01/8392 Zheng, C., Sarker, N., & Nahar, S. (2018). Factors affecting bank credit risk: An empirical insight. Journal of Applied Finance and Banking, 8(2), 45-67. http://dx.doi.org/10.21474/IJAR01/8392 https://www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 9, No. 1; 2022 17 APPENDICES 1. Name of the company: 2. Type of business ___________ 1. Agricultural based [ ] 2. Manufacturing [ ] 3. Construction [ ] 4. Health care [ ] 5. Education [ ] 6. Petroleum [ ] 7. Others [ ] 3. Annual Turnover: 1. Below 100 crores 2. 101 crores to 200 crores 3. 201 crores to 300 crores 4. 301 crores to 400 crores 5. 401 crores to 500 crores 6. 501 crores and above 4. What is your place of organization __________________  list of states[ ] 5. Name of the bank where you have your account 1. ICICI [ ] 2. HDFC [ ] 3. Axis bank [ ] 4. IDBI [ ] 5. Yes bank [ ] 6. Credit risk SD Disagree Neutral Agree SA 1. Customer default 2. Banks internal rating 3. Inappropriate credit policies 4. volatile interest rates 7. Banks profitability SD Disagree Neutral Agree SA 1. Bank size 2. Capital ratio 3. Deposits ratio 4. Liquidity ratio 8. Customers perception on Credit risk SD Disagree Neutral Agree SA 1. Consumer's failure 2. Inability to repay 3. Banks internal rating 4. Inappropriate rating from rating agencies https://www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 9, No. 1; 2022 18 9. Suggestions to overcome credit risk SD Disagree Neutral Agree SA 1. Improve banks internal rating 2. Inspection from banks 3. Advise customers when needed 4. Proper ratings from rating agencies Copyrights Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0)