Knowing customers and identifying profitable services is of great importance due to the diversity of bank customers and the variety of services in the country's banking system. Customer relationship management is currently the core of the business world, the most important interbank network used in Iran is the Shetab network. In this research, data mining techniques are used to segment and rank customers in the Shetab network, using an improved data mining model based on recent purchasing, purchase frequency and amount spent on purchases so that banks can behave in this network. Analyze and evaluate your customers and formulate effective policies in dealing with customers. Also, in order to review similar studies and increase information through library and internet studies, information related to the model was collected. Finally, R + FMW presented a model for clustering bank customers and their transactions. The results showed that the developed R + FMW model has a higher accuracy than the basic RFM model, and using this model, banks can identify customers active in the interbank exchange network (acceleration) and customers and costly communication channels. In terms of fees and demographic information.