One of the most important objectives of any modern organization is to gain competitive advantage of customers' data. In order to find hidden patterns or models from data, application of modern and steady methodologies is a necessity. Banking industry is not exceptional from this trend and they may often wish to make more profit by providing appropriate services to potential customers. Analyzing databases to manage customer behaviors seems difficult since databases are multi-dimensional, comprised of monthly account records and daily transactional records. Therefore, to analyze databases, we propose a methodology by considering human factors and building an integrated data utilization system. Moreover, self-organizing neural network map is used to identify groups of customers based on repayment behavior, recency, frequency, and monetary behavioral scoring predicators. We also perform more analysis using Apriori association rule to make marketing strategies for services used by banks.

Taghv, M., Bamakan, S & Toufani, T. (2011). A data mining method for service marketing: A case study of banking industry.Management Science Letters, 1(3), 253-262.

Aggelis, V., & Christodoulakis, D. (2003). Association Rules and Predictive Models for e-Banking Services. In 1stBalkan Conference on Informatics.Aggelis,V., & Christodoulakis, D. (2005). RFM analysis for decision support in e-banking area.WSEAS Transactions on Computers Journal. ISSN 1109-2750.Aggelis, V. (2004). RFM analysis with data mining. scientific yearbook, Technological Education Institute of Piraeus.

Agarwal, R., Imielinski, T., & Swami, A. (1993). A mining association rules between sets of items in large databases. ACM SIGMOD International Conference on Management of Data. Washington DC, USA.

Bose, I., & Mahapatra, R. K. (2001). Business data mining-a machine learning perspective. Information Management, 39(3), 211-225.

Bhattacharya, S., Jha, S., Tharakunnel, K., Westland, J. C. (2010). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3), 602-613.

Cohen, M. D. (2004). Exploiting response models-optimizing cross-sell and up-sell opportunities in banking. Information Systems, 29, 327–341.

Chiang, W. Y. (2011). To mine association rules of customer values via a data mining procedure with improved model: An empirical case study. Expert Systems with Applications, 38(3), 1716-1722.

Desai, V. S., Crook, J. N., & Overstreet, G. A. (1996). A comparison of neural networks and linear scoring models in the creditunion environment. European Journal of Operational Research, 95, 24-37.

Dillon, W. R. & Goldstein, M. (1984). Multivariate analysis. John Wiley & Sons Inc.Dyche, J., & Dych, J. (2001). The CRM handbook: a business guide to customer relationship management. Reading, MA: Addison-Wesley.

Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. (2001). Knowledge discovery in databases: an overview. AI Magazine, 13(3), 57–70.

Hsieh, N. C. (2004). An integrated data mining and behavioral scoring model for analyzing bank customer. Expert Systems with Applications, 27, 623–633.

Hung C., Tsai, C. F. (2008). Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand. Expert Systems with Applications, 34, 780–787.

Jain, A. K., Murthy, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM Computing Surveys. 31(3), 264–323.

Javaheri, H. (2008). Response modeling in direct marketing, A data mining based approach for target selection. Master Thesis, Department Industrial Engineering, Tarbiat Modares University.Kim, Y. S., & Sohn, S. Y. (2004). Managing loan customers using misclassification patterns of credit scoring model. Expert Systems with Applications, 26, 567–573.

Kohonen, T. (2001). Self-organizing maps (3rded.). Berlin, Heidelberg,New York: Springer-Verlag.

Kotler, Ph., & Keller K. (2008). Marketing Management (13th Edition). Prentice Hall.McNicholas, P.D., Murphy B., & O’Regan M. (2008). Standardising the lift of an association rule. Computational Statistics and Data Analysis, 52, 4712–4721.

Min, S. H. & Han, I. (2005). Detection of the customer time-variant pattern for improving recommender systems. Expert Systems with Applications, 28, 189–199.

Minaee, B., & Asghari, F. (2008). An integrated data mining and behavioral scoring model for analyzing bank customers, 2ndIran Data Mining Conference (IDMC2008), 11- 12 NOV, Amir Kabir University.

Nadeali, A., & Khanbabaees, M. (2008). Implementing of decision tree and genetic algorithm for crediting banks’ customers in decision support system. 2nd Iran Data Mining Conference (IDMC2008), 11- 12 NOV, Amir Kabir University.Ngai E.W.T., Hu, Y., Wong Y. H., Chen Y., & Sun X. (2010). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559-569.

Pal, N. R., Bezdek, J. C., & Tsao, E. C. K. (1993). Generalized clustering networks and Kohonen’s self-organizing scheme. IEEE Transaction on Neural Networks, 4(4), 549–557.

Setiono, R., Thong, J. Y. L., & Yap, C. S. (1998). Symbolic rule extraction from neural networks—an application to identifying organizations adopting IT. Information and Management, 34(2), 91–101.

Turban, E., Aronson, G. E., Liang, T. P., & Sharda, R. (2007). Decision support and business intelligence systems (Eighth ed.). Pearson Education.Wu, S., & Chow, T. W. S.(2003). Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density. Pattern Recognition, 37, 175 – 188.

Zhu, D., Premkumar, G., Zhang, X., & Chu, C.Fall. (2001). Data mining for network intrusion detection: A comparison of alternative methods. Decision Sciences, 32(4), 635–660.