Classification of Customer’s Credit Risk Using Ensemble learning (Case study: Sepah Bank)

Banks activities are associated with different kinds of risk such as cresit risk. Considering the limited financial resources of banks to provide facilities, assessment of the ability of repayment of bank customers before granting facilities is one of the most important challenges facing the banking system of the country. Accordingly, in this research, we tried to provide a model for determining factors affecting the credit behavior of bank customers. In this study, using real customer data in Sepah Bank in 2007, research modeling was done using the Neural Network, a fuzzy decision tree. What research innovation can be considered is the use of collective learning methods that are considered in this study to increase the accuracy of the results of the fuzzy decision tree. The results of the research show that customer's revenues, along with the customer's financial transactions, are the main factors in determining the customer's credit risk. The results also show that the fuzzy decision tree using the Begging method has higher accuracy than the neural network method and the conventional fuzzy decision tree.