Validation of SADERAT Bank customers: Discrete regression scoring approach

Purpose: Evaluating the credit of customers in credit institutions is one of the most important challenges of banks today. The lack of accurate evaluation of customers can lead to delayed maturity and ultimately burn the bank's claims and increase the credit risk. Overdue claims, which are known as non-current claims in the banking literature, are the result of credit risks. The banking and economic authorities always try to minimize the credit risk and non-current claims in order to avoid bankruptcy and its negative consequences. Bank credit is an efficient scale for calculating credit risks. But the judgment method is often used to determine the credit risk of customers. This type of validation and decision-making is not as real as it should be; it has little accuracy and actually causes problems for banks in the process of handing over facilities. The purpose of this article is to present a detailed framework for the validation of bank customers, which is done on a case-by-case basis for a number of recipients of facilities from Saderat Bank in the period of 2019-2021.
Methodology: In this study, the censored multiple logistic regression technique is employed to rank customers based on creditworthiness and examine its impact on reducing non-current claims in Saderat Bank. The results obtained can facilitate the decision-making process regarding the credit rating of customers and the reduction of non-current claims in Saderat Bank. This purpose is fulfilled with discrete regression models in which the dependent variable selects binary values. A common approach in modeling binary choices is the use of multiple logistic probability models (censored regression), where it is assumed that there is an unobservable dependent variable (yi*) defined by a regression relationship.
The statistical population of the present study includes the real customers in the Saderat Bank system who have received facilities and whose facilities are now in progress or have ended. The data on these customers cover the 2000 credit facilities of people who have referred to the bank. The data were collected from Saderat Bank branches during the period of 2019-2021. Also, the statistical sample of the research was divided into two categories of customers, creditworthy and non-creditworthy.
Findings and discussion: Based on the estimation of the censored regression model, gender and age have a significant effect on the timely collection of loans. Women have performed better than men in repaying the loan, and, as age increases, the probability of not repaying the loan increases. The level of education and the repayment period also have positive effects on the timely collection of claims. Moreover, the amount of the granted loans and the interest rate of the loans have a negative and significant effect. Among the other investigated variables is the history of obtaining loans by the borrower, which has a positive and significant effect on the timely collection of loans. This factor can be a good sign of the eligibility of the borrower. The type of collateral deposited with the bank as a guarantee is another investigated variable with a significant impact on the non-repayment of loans. The loans obtained with property collaterals have better repayment conditions than joint collaterals. Finally, the average balance of the borrower when taking a loan has been found as an effective and significant factor in identifying his eligibility. On the other hand, the type of facility (capital or current) has no effect on timely collection or overdue claims.
In addition, the results obtained from the censoring model for bad credit customers, in which the deferred class and the questionable access class are considered as dependent variables, show that the factors of gender, age and loan amount have positive and significant effects on the non-repayment of the loan. The repayment period has a negative and significant effect on the non-repayment of the loan. Also, the factors of the average balance of the applicant, the type of facility and the customer's work have no significant effect on the creditworthiness of bad credit customers of Saderat Bank. With these findings on the bank, it is better to identify bad customers and focus on other study variables such as loan repayment time, installment interval, number of installments, amount of each installment per month, loan extension, history of receiving loans, real estate collateral, and interest rate of loans.
Conclusions and Policy Implications: Collection of the granted loan within the specified period of time points to the implementation of the correct methods and the use of resources to create the loans necessary for expanding economic activities and directing the bank resources to the correct investment locations. However, the implementation of inappropriate economic policies in the past years, the lack of financial disciplines, and the unfavorable economic conditions of the country have played a large role in the non-repayment of the granted loans. Therefore, the demographic and economic features of loan recipients are the key parameters for validation, playing a central role in managing non-current claims and the credit risk of banks. It is, thus, suggested that, at the micro-level in the technical and economic evaluation of the projects for granting loans, banks should take into account the trend of macroeconomic variables, so that loans can be given based on the relevant forecasts and in such a way that the original resources and expected profit of the loans can be returned. Expansion of knowledge and research in the field of customer validation and reduction of non-current claims of banks will have valuable results for organizations. Among them, one may mention the reduction of costs and the possibility of completing the money cycle in the bank and the country.