An integrated analysis method for bank customer classification

Publication Type:
Conference Proceeding
Citation:
Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006, 2006, pp. 247 - 252
Issue Date:
2006-01-01
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
2006005084.pdf697.38 kB
Adobe PDF
© 2006 by World Scientific Publishing Co. Pte. Ltd. Customer classification is one of the major tasks in customer relationship management. Customers have both static characteristics and dynamic behavioral features. To apply both kinds of data to conduct comprehensive analysis can enhance the reasonability of customer classification. In this paper, customer dynamic data is clustered using a hybrid genetic algorithm and then is combined with customer static data to give reasonable customer segmentation by using neural network technique. A novel classification method which considers both the static and dynamic data of customers is proposed. Applying the proposed method in a bank’s datasets can obviously improve the accuracy of customer classification comparing with traditional methods where only static data is used.
Please use this identifier to cite or link to this item: