Quantitative studies in the management of the banking industry in order to increase customer satisfaction and profitability (case study: Bank Mellat)

In order to provide all kinds of facilities to their customers, credit institutions need to carry out complete surveys in order to know the applicants from qualitative and quantitative aspects, in order to fully evaluate the ability to repay and calculate the probability of non-repayment of facilities and services. Financially, these surveys are generally called validation. The purpose of this research is to rank the groups of customers and determine the best parts of them so that the brokerage company can perform credit allocation in a mechanized way. For this purpose, after the initial pre-processing of the data, they are processed in the form of RFM 1 model. Then, using the SOM 2 neural network as one of the clustering algorithms, the customers will be divided into 10 clusters. In the following, using the proposed model, the clusters are ranked. The best clusters are identified and the operation of granting facilities is done for the members of these clusters. Finally, three clusters 5, 1 and 7 were determined as the best clusters, which are the target customers. The coefficient of facilities granted to these top three clusters is 0.271, 0.173 and 0.556 respectively.