The objective of this study is to present a framework to increase the return and profitability and reduce credit risk of Mellat Bank customers by developing the RFM model. In this study, which was conducted as a case study in Mellat Bank of Iran, first the variables of RFM model were identified. In the next step, relevant weights of RFM variables were calculated using AHP technique. In the next step, using the K-means algorithm, customers were clustered based on weighted RFM and extended RFM. The result included customer clusters. The results indicated that the three clusters 5, 1, and 7 obtained the highest scores for receiving facilities and the coefficients for receiving facilities were equal to 0.271, 0.173, and 0.556, respectively. By determining the facility coefficient for the cluster and consequently for the customers presented in these top groups, granting facility becomes more transparent and more purposeful, and therefore, it will help the company increase profitability, reduce the churn among high-efficiency customers, and create value for customers. This research demonstrates a systematic method for granting facilities to recognize the true value based on the capability and prevention of arbitrary acts
