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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
