This paper aims to study customer risk management in banking industry. For this purpose, Notions and backgrounds of Customer Relationship Management (CRM), Risk and Risk Management, classifying and clustering methods as well as Multiple Criteria decision Making Methods (MCDM) have been studied briefly. Since, to manage customer credit risks, recognizing and classifying them is a must, therefore 150 legal customers and 100 general customers from two private banks in Iran have been selected. K-means algorithm has been proposed for clustering both general and legal customers, moreover a WRFM model has been applied to classify general customers based on customer loyalty properties. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been used for prioritizing general customers based on loyalty properties of RFM model. On the other hand in order to calculate the relative importance coefficient or weight of loyalty properties in WRFM method, the pair wise comparison matrix based on analytical hierarchy process (AHP) has been applied.
