multiple logistic regression

The purpose of this research is to provide a decision support system to assess the credit risk of real bank customers. For this purpose, firstly, using the library method, the effective indicators on the credit risk assessment of real customers were identified and then, using the fuzzy Delphi method, the indicators were screened. The community of this section was formed by banking experts (Bank Mellat) who were selected by the snowball method. In the following, The decision support system was designed and implemented to evaluate the credit risk of real bank customers in SPSS Modeler software. The final data related to the indicators included the files of 7318 real customers of Bank Mellat during the years 2013-2016.The results of the research showed that the decision support system consisting of support vector machine and random forest correctly predicted the status of customers in the four categories of on-time delivery, past due, overdue and questionable delivery by 67.09% and 65.10%, respectively. Meanwhile, the combined support vector machine-random forest model has provided a better prediction with a rate of 77.17%.

The purpose of this study is to validate the real customers of Bank Mellat using fuzzy Delphi approach and multiple logistic regression. For this purpose, first, the effective indicators on assessing the credit risk of real customers were identified by library method and then, the fuzzy Delphi method was used and the indicators were screened. The community of this section was formed by banking experts (Bank Mellat) who were selected by snowball method. Then, the final data related to the indicators, including the files of 7318 real customers of Bank Mellat during the years 2014-2020, were collected and analyzed by multiple logistic regression method in four categories of timely receipt, past due date, delinquent and doubtful receipt. The results showed that the loan amount, loan repayment time, installment interval, installment amount, loan extension, previous loan, real estate collateral, inventory average, facility interest rate and education level have a significant effect on credit risk of real customers. Also, the significance of gender, age and occupation indices was not confirmed.