




































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 



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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 



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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 



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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 

 



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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   



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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. 



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 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


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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           



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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) 

 


