![]() Further-more, the results of this study were supported by real customer data of one of the largest banks in the MENA region. Thus, this study provides a sto-chastic framework for customer segmentation and allocates appropriate marketing promotion strategies. The main contribution of the study is the explicit calculation of individual customer’s lifetime value in the banking industry. The presented framework provides a dynamic model for calculating the individual customer’s lifetime value. The presented framework provides a beneficial way for future research and valu-able insight for allocating promotional marketing strategies to customer groups. The findings underline the importance of the stochastic model for calculating customer lifetime value based on customer behavior. The deduced findings are illustrated with supplementary context from an out-standing case study. The study follows a stochastic dynamic programming model that is based on the Markov chain. The proposed framework calcu-lates individual customer’s lifetime value dynamically. This study provides a stochastic dynamic programming model with a Markov chain that explicitly focuses on the customer as well as a new model for valuing the customers in the banking industry. The results of this survey indicate that the potential of the given structure to recognize the rate of trust in Internet bank user’s behavior might be at reasonable level for experts in this area. In this research, qualitative data was gathered from interviews with banking experts, analyzed by Expert Choice to identify the most important variables of customer behavior analysis, and to analyze customer behavior and customer bank Internet transaction data for a period of one year by MATLAB and Clementine. The hybrid data-mining and knowledge based structure has been adapted in this algorithm according to fuzzy systems. In addition to identifying behavioral models of customers, this algorithm compares the behaviors of any customer with this model and finally computes the rate of trust in customer’s behavior. A new algorithm has been provided in this article to improve security and to specify the limits of giving special services to Internet banking users in order to pave appropriate ground for virtual banking. As a result of changes in approach from traditional to virtual banking system, security in data exchange has become more important thus, it seems essentially necessary to present a pattern based on smart models in order to reduce fraud in this field.
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