Identification and Evaluation of the Factors Affecting Credit Risk Management Using the Multinomial Logistic Regression Model

The aim of this study is to identify and evaluate the factors that influence credit risk employing a multinomial logistic regression approach. For this purpose, in the first phase, indicators that affect credit risk assessment of natural customers were identified using documentation and library method. Then, the final data on the indicators were collected, including 7330 files of natural customers of Mellat Bank, and multinomial logistic regression was employed in studying the indicators of credit risk assessment of the bank’s natural customers in the four classes of timely receipt, overdue, deferred, and non-performing loans. The results of the estimated model show that the indicators of gender, loan value, age, installment interval, previous loan, occupation, loan repayment term, number of installments, quantity of each installment, loan extension, type of collateral, average balance, facility interest rate, type of facility, and education level have a significant impact on the credit risk of real customers.