Designing an Intelligent Model for Segmenting Corporate Customers in Financial and Banking Institutions Using Multi-Layer Distributed Neural Network Architectures Based on Big Data Intelligent Processing

This research presents an approach based on customer lifetime value (CLV) and artificial neural networks (ANN) to classify bank corporate customers. CLV in banking means how much financial value each customer creates for the bank. The research is of an applied and quasi-experimental type and the statistical population includes 127,672 corporate IDs in the Tejarat Bank Corporate Customer System. Data are collected by scanning customer files over a six-month period. Sampling is by enumeration. K-means algorithms and artificial neural networks are used to cluster customers. Simulations showed that the artificial neural network algorithm provides more accurate results than the K-means algorithm. This approach can effectively classify customers into three clusters. The clusters were reviewed based on the opinions of banking experts; in order to conduct a deeper analysis. Based on the data of Tejarat Bank's corporate customers, these categories were analyzed in terms of CLV. Marketing and sales strategies were developed for each customer cluster. The approach proposed in this research can help banks improve their customer segmentation process and ultimately increase profitability and customer retention.