Modeling outstanding claims in Shahr Bank using data mining (Northwestern border provinces of the country)

Considering the need to understand and recognize more about the phenomenon of increasing overdue claims in the country's banking system, the purpose of this study was to evaluate the importance and intensity of the influence of internal factors (individual characteristics of the applicants for facilities along with the characteristics of the branches paying the facilities) in the occurrence or non-occurrence of this phenomenon.

In order to achieve this goal, the data collected from nearly 110 users of Bank Shahr facilities in 11 branches of this bank in the northwestern provinces of the country, which were selected through cluster-random sampling, in the framework of the decision tree technique, which is one of the most important data mining techniques. , has been used to model overdue and overdue bank claims in the said bank.

The results of the study have indicated that the decision tree model presented in the study has a high power in correctly predicting the repayment behavior of the loan applicants in Shahr Bank, while in the following, various scenarios for the emergence or avoidance of overdue bank claims in Shahr Bank It has been discussed along with their possibilities.

When paying facilities to applicants, the branch officials should pay attention to the variables of the level of education and gender, and establish an independent validation and follow-up department for pending claims in each of the bank's branches.