Paper Title
Provide an Intelligent Method to Identify Customer Credit in the Field of Electronic Banking using the LRFM Approach and Dual Clustering

Abstract - The LRFM model is characterized by novelty, repeatability, monetary value, and customer engagement time. In this research, customers are first divided into different categories using clustering methods. Given that a customer may fall into several categories, this study uses the c-means classifier. The new customer is then categorized using classification methods such as decision trees and neural networks. The results show that the proposed method has an acceptable detection rate in the clustering and classification phase so that the detection rate of the proposed model is 76%. According to other methods, classification is much better and has at least a 5% higher detection rate. Keywords - Customer Evaluation, Electronic Banking, Clustering, Classification, LRFM