Customer credit clustering for Present appropriate facilities

Credit institutions to provide variety of facilities to their customers, need to comprehensive studies by qualitative and quantitative aspects of their applicants. By this way, accomplish a complete evaluation of repay ability measure and calculate the refund facilities probability and finance services by them, these reviews generally validation name. The purpose of this study was ranking customer groups and specifies the best part of them until brokerage firm do its credit allocation process mechanically. Here, after the preprocessing of the data, they are processes in the RFM model. Then SOM neural network as one of the clustering algorithms will change customers to 10 cluster. Using the proposed model, the clusters will rank. The top clusters, identification and facilities grant operations to the members of these clusters will do. Finally, three clusters 5, 1 and 7 defines as top clusters that they are the target customers. Coefficient facilities granted to the top three clusters respectively are 0.271, 0.173 and 0.556.