Identification and scoring of Corporate client applying for credit In terms of credit risk by using hybrid ANP-Promethee method (Case Study Refah K. Bank)

Bank lending is one of the most important Bank activities but they have many problems by Customer credit risks or inability to repay the principal and interest on loans granted. Thus the design of scoring models to manage and control credit risk known as one of the largest bankruptcy factor of banks is necessary. Various methods such as parametric models as logistic regression and nonparametric methods as neural networks have been used to rank banking customers until now, but in this paper provide a model in scoring of Corporate client applying for credit In terms of credit risk by using hybrid ANP-Promethee method that one the hand need not to get the distribution and assumptions such as parametric models and unlike methods such as neural networks and logistic regression ranked Bank Customer by using actual data and in the other hand consider impact of expert opinions. The application of the proposed method22 index criteria is extracted 5C model validation and the weight of each was determined using ANP and the end step scoring on a sample of 25 corporate customers bank were being Promethee method.