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.
Expert Systems with Applications 38(10), pp.13284-13290