Discovering cardholders’ payment-patterns based on clustering analysis

doi:10.1016/j.eswa.2011.04.148

淡江大學機構典藏 > 文學院 > 資訊傳播學系暨研究所 > 期刊論文 > Item 987654321/54221

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题名:	Discovering cardholders’ payment-patterns based on clustering analysis
作者:	Shih, Chien-Chou;Chiang, Ding-An;Hu, Yi-Jen;Chen, Chun-Chi
贡献者:	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
关键词:	Credit card;Data mining;Clustering algorithms
日期:	2011-09-15
上传时间:	2011-07-03 00:42:28 (UTC+8)
出版者:	Kidlington: Pergamon
摘要:	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.
關聯:	Expert Systems with Applications 38(10), pp.13284-13290
DOI:	10.1016/j.eswa.2011.04.148
显示于类别:	[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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