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 now the core of the business world, today the electronic portals of banks using various information exchange networks such as Satna, Paya, Chakavak, Sayad and. . . They are connected to each other. The most important interbank network used in Iran is Shetab network.In this study, data mining techniques were used to classify and rank customers in the Shetab network using an improved RFM-based data mining model so that banks could analyze and evaluate their customers' behavior in this network and formulate Deal with effective policies in dealing with customers.For this purpose, the required data were extracted from the bank card switch database and the required analyzes and data mining operations were performed on it by CRISP method. This data is available in the database of the Welfare Bank and was extracted from the database of the institution using DML commands. 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 by using this model, banks can identify customers active in the interbank exchange network (acceleration) and expensive customers and communication channels. Recognize in terms of fees and demographic information