Comparison Between the Optimized Binary and Multinomial Logistic Regression for Credit Scoring of Real Costumers in Bank Refah Kargaran

One of the main tasks of the financial institutions is to give loan to the customers. Prediction and evaluation of the credit risks due to loan and consequently managing this risk is one of the greatest ongoing challenges for the financial institutions. The main aim of this work is to provide an optimized logistic regression model for credit scoring of real customers. Here the effects of increasing the customer’s credit classification from two (binary) to four (multinomial) distinct groups on the results of the logistic regression has been investigated. Identification of the most important parameters in prediction of the real customers’ credit scoring is the other important outcome of this work. The results of both binary and multinomial logistic regression show the relative importance of the education level and the age of the customer compared with other independent variables. The results of this work show that either increasing the number of classification types of the dependent variable, real customer’s credit, to four distinct groups has no sharp effect on the results of the optimized models or this conclusion can be due to improper distribution of the number of customers in different groups.