Predicting Credit Card Holder Churn in Banks of China Using Data Mining and MCDM

Abstract

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.