Sample Business Case Studies Paper on The Effect of Credit Risk Scoring On Bank Solvency

INTRODUCTION

Background of the Study

The United States of America (US) is a home to wide array of banks. According to Yahoo Finance, 5,141 commercial banks were operating in the US in the third quarter of the year 2016. The majority (65%) of these banks was community banks with asset value below $ 300 million according to Federal Reserve Bank (June 30, 2016). One of the primary functions of the banks is to advance loan to the customers. According to the Federal Reserve Bank commercial banks, loan portfolio in the third-quarter of 2016 stood at $3.6 trillion. The top five banks accounted for about 40% of the load advances. According to World Bank, the proportion of non-performing loans in the United States has been decreasing since 2011 from 3.8% to about 1.5% in 2015. This drop indicates that the banks continue to tighten and improve their credit management strategies.  One tool that commercial banks have used for over six decades to reduce their credit risk is the credit risk scoring. Credit risk scoring is a technique used to assist financial institution makes a decision on whether to give or reject loan application made by customers. The most widely used scoring model in the US is the FICO model. The use of credit scoring technique lowers the bank credit risk and by extension share of nonperforming loans which in turn affects increase it assets or solvency.

Statement of the problem

            Commercial banks play a critical role in the US economy. They employ thousands of people, advance credit to small business and pay substantial income taxes. According to the Federal Reserve Bank, commercial banks in the US had $11.3 trillion worth of customer deposits at the end of October 2016. The top largest banks JPMorgan Chase, Bank of America and Citigroup hold about 30% of these deposits.  These three banks are too big to be allowed to collapse. The Federal Reserve Bank continuously monitors their financial health. Solvency is one of the methods used to assess the long-term sustainability of banks. While the theory of solvency is well documented, its relationship with credit scoring has not been empirically studied.

Purpose of the Study

The purpose of this study is to investigate the effects of credit risk scoring on bank solvency of US top banks. It is concerned about the nature of the relationship that exists between credit risk score and bank debt-to-equity ratio. If the relationship exists, the study is interested in establishing whether it varies by bank.

The Scope of the Study

            The study focuses on the effects of credit risk scoring on the solvency of banks. It uses secondary data from reliable online sites to investigate the nature of relationships between average credit risk scores and the debt-to-equity ratio of three top US commercial banks namely, JPMorgan, Bank of America and Citigroup. It only describes the impacts and does not venture into the causes and effect relationship between the variables.

Objectives of the Study

            The following are the objectives of the study:

1.    To determine the solvency of the US commercial banks.

2.    To investigate the effects of credit risk scoring on the solvency of US commercial banks.

3.    To examine whether the relationship between credit risk scores and solvency differ by the bank.

Research Questions

The study shall attempt to find answers to the following questions:

1.    To what extent is the US to banks solvent?

2.    What is the effect of credit risk scoring on the solvency of US commercial banks?

3.    Does the credit risk scoring have different effects on the solvency of US commercial banks?

Hypothesis

The study tests the following hypothesis at alpha level = 0.05:

Null Hypothesis (HO): The credit risk scoring has no significant effect on the solvency of the banks.

Alternative Hypothesis (H1): The credit risk scoring system has a significant impact on the solvency of the banks.

Justification of the Study

This study is important because it is the first empirical study investigating the impact of credit scoring on the solvency of the banks. It is intended to fill this research gap and provide requisite knowledge on the subject. Researchers and academicians will find the findings useful for future studies. Moreover, the results and recommendations of the study shall be used by bank managers and regulators to influence decisions concerning solvency and capital structure.

THEORETICAL BACKGROUND

Credit Risk Scoring

One of the primary roles of the banks worldwide is to provide loans to customers.  A loan is an amount of money given to a borrower to pay within a specified time plus interest (Schroeder, Tomaine & American Bar Association (2014). Credit risk assessment is the second procedure of credit risk management after risk identification (Frame, Srinivasan & Woosley, 2001). Credit scoring is part of credit risk assessment. Blanco, Pino, Lara & Rayo (2013) says that credit scoring is a technique a technique used to assist financial institution to decide whether to give a loan to customers who apply for them. Abdou & Pointon (2011) defines it as a statistical technique of measuring the chances of loan default by any entity. Einav, Jenkins & Levin (2013) say that credit risk score is a quantified summary of borrower loan history used by banks to assess his or her credit worthiness. A higher credit score implies that the borrower chances of defaulting on repayment of his or her loan obligation is low (Burton, Nesiba & Brown, 2015).  Bank use the information provided by clients on loan application forms as well as that got from credit reference bureau to gauge the credit risk of a loan applicant. A credit risk is a likelihood that a borrower shall not pay back principal amount borrowed plus interest as contained in loan agreement (Marthinsen, 2014).

Although the practice of lending money is as old as commerce, the use of credit risks scores to assess the suitability of loan applicant just about six decades old (Abdou & Pointon, 2011). FICO developed the first statistically modeled credit risk scoring system in 1958 (Davenport & Kim, 2013). The company availed the multipurpose version of the system in 1989 (Schmarzo, 2015). The system was created using data and information derived from thousands of loan applications made at various financial institutions in the US. Before then bank relied mostly on traditional interview-based underwriting methods (Einav, Jenkins & Levin, 2013). FICO-model remains the most popular credit risk scoring framework used by credit reference bureaus in the US.  However, Berger & Frame (2007) say that large banks such as Wells Fargo have created their proprietary credit risk scoring system. The FICO credit risk score range from 300 to 860 with majority individuals falling between 650 and 799 (Abdou & Pointon, 2011). Each bureau calculates a different score for every person based on different sets of data but the same model. Although the workings of FICO’s system is not well understood, Durkin (2014) hints that the company uses factors that include payment history, the length of credit history, credit utilization, recent credit search and type of credit among other determinants to calculate the credit score.

Davenport & Kim (2013) says that credit scores provide banks with fast, fair and consistent, and data-driven means of measuring borrowers’ credit risk. Abdou & Pointon (2011) say that the advantage of credit risk scoring is that it utilizes statistical variables that are linked to repayment performance. Besides, they argue that the systems are developed on a very large sample of applicant information and characteristics that are correlated with loan performance. Other benefits include faster loan processing time, minimization of credit processing cost and few errors. Hayashi & Stavins (2012) argue that the formulas used to develop the scoring system are complex and proprietary and non-standardized. Consequently, the benefits of using such systems depend on the learning curve of credit analyst and managers.  Besides, they argue that the credit risk score of a borrower can change over time even though the behavior of the customer does not change.  The scoring system has also been accused of excluding some consumer characteristics that if included may predict customer’s ability to pay (Staples, 2012). Chen (2014) says that credit scoring failed to predict the mortgage defaults that triggered financial crisis of 2008.

            Credit scoring models are by large extension built on five Cs of credit namely character, capacity to borrow, capital, collateral and economic condition. Pride, Hughes & Kapoor (2010) say that character is the willingness of the borrower to meet his or her financial obligations. It is measured using the client’s payment history. The capacity according to Chandra (2008) is the ability to the borrower to repay loans using his or her cash flows. Capital according to Pride, Hughes & Kapoor (2010) means the applicant net worth or assets. Collateral, on the other hand, is the security issued by the borrower against the loan. Bank use collateral to determine the amount of loan advanced to customers. From information theory perspective, banks are required to collect relevant and reliable information about borrowers to determine whether they are worthy of the credit.  Gakure, Ngugi, Ndwiga, & Waithaka (2012) argue that both qualitative and quantitative techniques such as credit risk scoring can be used to evaluate the lending suitability of borrowers. However, they warn that qualitative methods are subjective and so bank should assign the variable numbers to minimize biases.

Solvency

            Pratt (2010) says that solvency measures a firm’s capacity to survive over a prolonged period. It quantifies the extent to which business asset cover its liabilities. Engle (2010) adds that it shows the relationship between firm’s assets, liabilities and equity. A business is solvent when its assets can pay its debts. Solvency is measured using various financial ratios. One of the widely used financial ratios of gauging solvency is the debt-to-equity ratio.  The debt-to-equity ratio is a relative measure of capital contributed to the business the owners and outsiders or capital structure (Aylen, 2012). The ratio shows the extents to which the owners of the firm (shareholders) rely on external funding (loans) to keep the business running. A higher debt to equity ratio means the business has excessive liabilities. A firm that heavily relies on loans to finance its operations and investments bears the risk of paying interest and principal. The extent of the debts compared to equity, capital, and the asset can be used to determine the firm’s financial risk (solvency). Excessive debt means higher credit risk.

The Relationship between Credit Risk Scoring and the Solvency of Banks

            There is no empirical study on the impact of credit risk scoring on the solvency of the bank. Most studies found on journal databases, Zou & Li (2014), Hosna, Manzura & Juanjuan (2009). Kithinji (2010) and Afriyie & Akotey (2012) focused on the effect of credit risk management in general on banks profitability. Few such Frame, Padhi & Woosley (2001), Berger, Cowan & Frame (2011) investigated the effects of credit risk scoring on small business lending.  The others studied the impacts of credit scoring on payment choices, Hayashi & Stavins (2012), relationship lending Vicente (2011) and consumer lending Einav, Jenkins & Levin (2013).  Roussakis (2014) argues that because banks liabilities are expressed in fixed monetary. Consequently, their solvency is widely determined by changes in assets. Nonperforming loans reduce the value of bank assets and by extension affect their solvency. Credit risk scoring is meant to minimize the amount of nonperforming loans.

RESEARCH METHODOLOGY

 Research Design

            A research design is a plan that guides a researcher in data collection, analysis and interpretation (Merrill, 2010). This study utilizes a descriptive design. It is concerned with the description of the effects of credit risk scoring on banks solvency indicated by debt to equity ratios

Sampling Technique and Size

A sample size refers to the number of subjects or observations in a given study (Kanawaty & International Labor Office, 2014). This study utilizes purposive or judgmental sampling technique to select the members of the sample. Polit & Beck (2010) argues that purposive sampling is based on the assumption that the researcher can use his or her knowledge to select sample members. Three banks, JPMorgan, Bank of America and Citigroup were chosen to represent the commercial banks in the study because their total loan advances were about one-third of the entire US commercial bank loan portfolio. The researcher recognized that the sample size was not representative enough. However, it could reliably be used to investigate the nature of the relationship between credit risk scoring and solvency of the banks. Nonetheless, the finding may lack generalizability because of small sample size used.

Data Sources

            The data used in this study was retrieved from authoritative online sources. The average customer credit scores were obtained from the Fair Isaac Corporation’s website, a firm that developed the proprietary credit risk scoring system used credit reference bureau in the United States namely Experian and Equifax. The debt-to-equity ratios were calculated using data retrieved from Yahoo Finance. 

 Data Analysis

            This study used multiple regression analysis to evaluate the effect of credit risk scoring on the solvency of the banks. Cios (2007) says that multiple regressions are statistical techniques that are used to assess the association between two or more independent variables and one dependent variable. Hair, Celsi, Money, Samouel & Page (2016) say an independent variable is that which is used to explain the changes in the dependent variable.

EMPIRICAL ANALYSIS

Sample Calculation of Debt-to-Equity Ratio

JPMorgan D/E 2013

Results

Table 1: US average credit risk scores and debt-to-equity ratios of JPMorgan, Bank of America and Citigroup from 2011 to 2016

  Debt to Equity Ratio
YearAverage Credit ScoreJPMorgan ChaseBank of AmericaCitigroup
20116881.791.782.13
20126901.481.291.54
20136911.681.271.37
20146921.611.131.34
20156951.381.031.00
20166991.360.931.03

Source Fair Isaac Corporation & Yahoo Finance

Table 2: Multiple Regression Analysis Result from Excel 2007

StatisticValue
Multiple R0.882
R Square R20.778
Adjusted R Square0.444
Standard Error2.93
Intercept713
Coefficient of Debt-to-Equity Ratio of JP Morgan-7.13
Coefficient of Debt-to-Equity Ratio of Bank of America-7.41
Coefficient of Debt-to-Equity Ratio of Citigroup-4.86
P-value of Debt-to-Equity Ratio of JP Morgan0.657
P-value of Debt-to-Equity Ratio of Bank of America0.882
P-value of Debt-to-Equity Ratio of Citigroup0.906
Significance F0.314

Empirical Model

The research employed the following regression model to investigate the effects of credit risk scoring on the effect of credit scoring on the solvency of the banks in the United States

Where:

Application:

Inserting the values obtained in the regression analysis, the model becomes:

The results of the regression analysis show that debt-to-equity, a measure of solvency is negatively correlated to the credit risk score. All the beta coefficients of the debt-to-equity ratios are negative. When the average credit risk score increases the debt-to-equity ratio reduces A one unit decrease in the debt-to-equity ratio of JPMorgan causes the average credit score of customers to increase by about seven units. The correlation can be described as strong because the correlation coefficient (R) is 0.882 Peck (2014) argues that the relationship between variables is regarded as strong when the coefficient is between 0.8 and 1 . About 77.8% (R square = 0.778) of the variations in average credit score are attributed to changes in banks debt-to-equity ratios. The impact of credit risk scoring on solvency of the bank varied with the bank depending on the value of its assets as shown by the decrease in the coefficients of debt-to-equity ratios from JPMorgan (-7.41) to Citigroup (-2.31)

Hypothesis Testing

The p-values for debt-to-equity ratios of banks, JPMorgan (0.657), Bank of America (0.852) and Citigroup (0.906) are greater than alpha level of 0.05. Consequently, the null hypothesis was not rejected. The effect of credit risk scoring on the solvency of banks was insignificant.

Conclusion and Recommendations

The study proved that the credit scoring system had effects on the bank solvency. Increasing credit risk score lowers the debt-to-equity ratio of the bank making it more solvent. Nonetheless, the impact is insignificant. Consequently, banks cannot rely on the adjustment of credit risk scores to improve their solvency or the extent to which their assets can cover their liabilities. The credit scores should be used purposely for assessing the creditworthiness of the borrower. Further research is required on the subject using large and more representative sample size. It is recommended that banks should use credit risk scoring together with other proven methods of improving solvency such as reducing liabilities to achieve greater impact. 

References

Abdou, H. A., & Pointon, J. (2011). Credit scoring, statistical techniques and evaluation criteria: A review of the literature. Intelligent Systems in Accounting, Finance and Management, 18(2-3), 59-88.

Aylen, J. (2012). Starting and Running a Small Business For Canadians For Dummies All-in-One. Hoboken: Wiley.

Berger, A. N., & Frame, W. S. (2007). Small business credit scoring and credit availability. Journal of small business management, 45(1), 5-22.

Blanco, A., Pino-MejíAs, R., Lara, J., & Rayo, S. (2013). Credit scoring models for the microfinance industry using neural networks: Evidence from Peru. Expert Systems with applications, 40(1), 356-364.

Burton, M., Nesiba, R. F., Brown, B., & Nesiba, R. F. (2015). An introduction to financial markets and institutions. New York: Routledge

Chandra, P. (2008). Financial management: Theory and practice. New Delhi: Tata McGraw-Hill Pub.

Chen, S. (2014). The credit cleanup book: Improving your credit score, your greatest financial asset. ABC-CLIO.

Cios, K. J. (2007). Data mining: A knowledge discovery approach. New York: Springer.

Cole, R. A. (2014). Credit Scores and Credit Market Outcomes: Evidence from the Survey of Small Business Finances and the Kauffman Firm Survey.

Davenport, T. H., & Kim, J. (2013). Keeping up with the quants: Your guide to understanding and using analytics. Harvard Business Review.

Durkin, T. A. (2014). Consumer credit and the American economy. London: Oxford University Staff.

Einav, L., Jenkins, M., & Levin, J. (2013). The impact of credit scoring on consumer lending. The RAND Journal of Economics, 44(2), 249-274.

Engle, C. R. (2010). Aquaculture economics and financing: Management and analysis. Ames, Iowa: Wiley-Blackwell.

Federal Reserve Bank (2016). Assets and Liabilities of Commercial Banks in the United States. Retrieved from https://www.federalreserve.gov/releases/h8/Current/

Frame, W. S., Srinivasan, A., & Woosley, L. (2001). The effect of credit scoring on small-business lending. Journal of Money, Credit and Banking, 813-825.

Gakure, R. W., Ngugi, J. K., Ndwiga, P. M., & Waithaka, S. M. (2012). Effect 0f Credit Risk Management Techniques 0n The Performance 0f Unsecured Bank Loans Employed Commercial Banks In Kenya. International journal of business and social research, 2(4), 221-236.

Hair, J. F., Celsi, M., Money, A. H., Samouel, P., & Page, M. J. (2016). Essentials of business research methods. Routledge.

Hayashi, F., & Stavins, J. (2012). Effects of credit scores on consumer payment choice. Federal Reserve Bank of Kansas City Working Paper, (12-03).

Kanawaty, G., & International Labour Office. (2014). Introduction to work study. Geneva: International Labour Office.

Marthinsen, J.E. (2014). Managing in a Global Economy: Demystifying International. London: Cengage Learning.

Merrill, R. M. (2010). Principles of epidemiology workbook: Exercises and activities. Sudbury, Mass: Jones & Bartlett.

Peck, R. (2014). Statistics: Learning from Data. London: Cengage Learning.

Polit, D. F., & Beck, C. T. (2010). Essentials of nursing research: Appraising evidence for nursing practice. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins.

Pratt, S. P. (2010). The lawyer’s business valuation handbook: Understanding financial statements, appraisal reports, and expert testimony. Chicago, Ill: Section of Family Law, General Practice, Solo and Small Firm Section, American Bar Association.

Pride, W. M., Hughes, R. J., & Kapoor, J. R. (2010). Business. Mason, Ohio: South-Western Cengage Learning.

Roussakis, E. N. (2014). Commercial banking in an era of deregulation. Westport, Conn: Praeger.

Schmarzo, B. (2015). Big data MBA: Driving business strategies with data sciences. Indianapolis, IN: Wiley.

Schroeder, G. J., Tomaine, J. J., & American Bar Association. (2014). Loan loss coverage under financial institution bonds. Chicago: American Bar Association.

Staples, W. G. (2006). Encyclopedia of privacy: 1. Westport, Conn. [u.a.: Greenwood Press.

World Bank (2016). Banks Nonperforming Loans Retrieved from http://data.worldbank.org/indicator/FB.AST.NPER.ZS?locations=US