An application of logistics regression model to determining the credit suitability and impacting factors in a special bank branch

Abstract

There are quite complicated rules and constraints that can be imposed by thebank when the loan issued. Bank branches, which play a direct role in the credit,must accurately determine the customer's credit request to eliminate thesedifficulties and create an effective payment system according to the customer. Inthe study, 100 random loan applications made in 2016 of a bank branch operatingin the Black Sea Region were examined. These customer demands are affectingcustomer characteristics. The "Logistic Regression (LR) Model" was created topredict creditworthiness according to the identified fugitives. In the model,customer age, education, marital status, debt grade, credit card debt, otherdebts, cross product are the variables. These are statistically significant interms of marital status, gender, cross product, or creditworthiness. However,various variables such as debt income ratio, credit card debt, and other debtsare statistically significant and affect credibility to negatively. In addition,occupational, income and educational constraints were found to be meaningless.With this model, the factors affecting the credit were evaluated. As a result ofthe study, the bank branch will benefit from the statistical model in which it iscreated, to evaluate according to the customer characteristics in its portfolio,and to give more credit to branch customers.