In this paper Behavioral scoring model that combined analytic hierarchy process and data mining techniques is proposed to assessment Credit risk of real customers and providing organization knowledge that are helpful to decide whether or not to grant amenities to applicants. Therefore, in step one an improved data preparation method was applied to prepare and select input feuteres of behavioral scoring model. in this step we tried to extract new features that covers through interaction between customers and bank. Then analytic hierarchy process (AHP) was applied to determine the relative weights of customer’s behavioral predicators. in modeling phase, behavioral scoring of customers was defined Base on their repayment behavior and late repayments duration. The dataset that has used in this article is provided by an Iranian private bank. The proposed model show that better result and also demonstrate that the hybrid mining approach and AHP can be used to build effective behavioral scoring models better result. Therefore, finance and banking institution can utilize the novel model to identify and predict customer’s credit behavior and decrease credit risks.
