Nasiri Sharifi, R., Rastgarpour, M. (2017). The Application of Combined Fuzzy Clustering Model and Neural Networks to Measure Valuably of Bank Customers. International Journal of Information, Security and Systems Management, 6(1), 631-636.
Raheleh Nasiri Sharifi; Maryam Rastgarpour. "The Application of Combined Fuzzy Clustering Model and Neural Networks to Measure Valuably of Bank Customers". International Journal of Information, Security and Systems Management, 6, 1, 2017, 631-636.
Nasiri Sharifi, R., Rastgarpour, M. (2017). 'The Application of Combined Fuzzy Clustering Model and Neural Networks to Measure Valuably of Bank Customers', International Journal of Information, Security and Systems Management, 6(1), pp. 631-636.
Nasiri Sharifi, R., Rastgarpour, M. The Application of Combined Fuzzy Clustering Model and Neural Networks to Measure Valuably of Bank Customers. International Journal of Information, Security and Systems Management, 2017; 6(1): 631-636.
The Application of Combined Fuzzy Clustering Model and Neural Networks to Measure Valuably of Bank Customers
1Student of Master’s Degree in the Field of Computer Software Technology Engineering; Technical & Engineering Faculty, Islamic Azad University, Electronic Branch, Tehran; Iran
2Assistant Professor; Technical & Engineering Faculty, Department of Computer, Islamic Azad University; Saveh Branch, Saveh, Iran
Abstract
Currently, acquisition of resources in banks is subject to attraction of the resources of banking customers. Meanwhile, the Bank’s valuable customers are one of the best resources to make profit for banks. Several different models are introduced for evaluation of profitability of the customers; but most of them are classical models and they are unable to evaluate the customers in complete and optimized manner. In this way, the conditions are ready to enter artificial intelligence to this scope. In this study, we tried to design an intellectual system for identification of valuable customers of Bank. Such a model is capable of reasoning and explaining the expert system together with adaptation and learning capability of neural network at the same time. Expertise is added to it through data mining. As a result, evaluating the proposed model is done through transaction data of the customer, and accuracy of 95.46 percent is gained.
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