Abstract
Credit card is one of the most popular e-payment approaches in current online e-commerce. To consolidate valuable customers, card issuers invest a lot of money to maintain good relationship with their customers. Although several efforts have been done in studying card usage motivation, few researches emphasize on credit card usage behavior analysis when time periods change from t to t+1. To address this issue, an integrated data mining approach is proposed in this paper. First, the customer profile and their transaction data at time period t are retrieved from databases. Second, a LabelSOM neural network groups customers into segments and identify critical characteristics for each group. Third, a fuzzy decision tree algorithm is used to construct usage behavior rules of interesting customer groups. Finally, these rules are used to analysis the behavior changes between time periods t and t+1. An implementation case using a practical credit card database provided by a commercial bank in Taiwan is illustrated to show the benefits of the proposed framework.
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Tsai, CY. (2008). A Hybrid Data Mining Approach for Credit Card Usage Behavior Analysis. In: Filipe, J., Obaidat, M.S. (eds) E-business and Telecommunications. ICETE 2007. Communications in Computer and Information Science, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88653-2_6
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DOI: https://doi.org/10.1007/978-3-540-88653-2_6
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