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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/54221


    Title: Discovering cardholders’ payment-patterns based on clustering analysis
    Authors: Shih, Chien-Chou;Chiang, Ding-An;Hu, Yi-Jen;Chen, Chun-Chi
    Contributors: 淡江大學資訊傳播學系;淡江大學資訊工程學系;淡江大學保險學系
    Keywords: Credit card;Data mining;Clustering algorithms
    Date: 2011-09-15
    Issue Date: 2011-07-03 00:42:28 (UTC+8)
    Publisher: Kidlington: Pergamon
    Abstract: This paper sampled approximately 9.3 million entries of data, concerning payments from 300,000 credit card customers over the past two years of Bank A in Taiwan. By applying data mining techniques to decipher customers’ behavior and perform risk analysis, the clustering algorithms divides card users into 9 groups of different levels of contributions and risk profiles, according to their consumption patterns. We generalize a set of clustering rules to identify high risk customer groups in advance. Therefore, the proposed suggestions could tell who was a bad risk and either deny their application or, for those who were already cardholders, start shrinking their available credit and increasing minimum payments to squeeze out as much cash as possible before they defaulted. On the other hand, banks are advised to adjust credit limits in a timely manner for the customer groups whose risks are low and contributions are high, in addition to the provision of value added services, in order to enhance earnings.
    Relation: Expert Systems with Applications 38(10), pp.13284-13290
    DOI: 10.1016/j.eswa.2011.04.148
    Appears in Collections:[風險管理與保險學系] 期刊論文
    [資訊工程學系暨研究所] 期刊論文
    [資訊傳播學系暨研究所] 期刊論文

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