Nowadays, with increasingly intense competition in the market, major banks pay more attention on customer relationship management. A real-time and effective credit card holders' churn analysis is important and helpful for bankers to maintain credit card holders. In this research we apply 12 classification algorithms in a real-life credit card holders' behaviors dataset from a major commercial bank in China to construct a predictive churn model. Furthermore, a comparison is made between the predictive performance of classification algorithms based on Multi-Criteria Decision Making techniques such as PROMETHEE II and TOPSIS. The research results show that banks can choose the most appropriate classification algorithm/s for customer churn prediction for noisy credit card holders' behaviors data using MCDM.
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