Customer Ranking Based on Credit Risk Using MCDM Methods

Banks are one the most important foundations of each country`s economy all around the world. One of the most important activities of the banking system is facility of payment which due to inefficiencies in credit risk management has caused crises in banking system and money market in recent years. Since from theoretical point of view the elimination of the risks is impossible, thus identifying and managing them has been considered as the only possible solution. Applying scientific decision making, evaluation, assessment and optimization of information are the most important principles in improving risk management in any organization. This research aims to rank customers and is looking for an improvement method in decision making about grant credit to customers. In this study, the main indicators in the field of real and legal customers has been identified using bank experts opinion, and AHP model, the coefficient of each through pairwise comparisons has been obtained. Then using TOPSIS credit ranking, the credit applicants of Refah bank has been ranked and the results of this method were compared with the logistic regression model. The studies show that the credit risk of different groups of customers has different priorities. There is no significant difference between new customers and existing customers in case of ranking in the case of study branches. Also there is no significant difference between real customers of Yazd Refah Bank ranking based on credit risk by TOPSIS and logistic regression methods.