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Dynamic churn prediction framework with more effective use of rare event data: The case of private banking
Affiliation:1. College of Administrative Sciences and Economics, Business Administration, Koç University, Sarıyer, Istanbul, Turkey;2. Department of Industrial Engineering, Koç University, Sarıyer, Istanbul, Turkey;1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China;2. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China;3. Department of Computing, The Hong Kong Polytechnic University, Hong Kong;1. Gradiant Research Centre, Vigo, Spain;2. AtlantTIC Research Center for Information and Communication Technologies, Department of Telematics Engineering, University of Vigo, Spain;1. College of Computer Science, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China;2. Provident Technology Pte. Ltd., 7030 Ang Mo Kio Ave 5, #03-25 Northstar, Singapore 569880, Singapore;3. State Key Laboratory of Software Engineering, Computer School, Wuhan University, 299 Bayi Road, Wuhan 430072, China;1. University of the Basque Country UPV/EHU, San Sebastian, Spain;2. IKERBASQUE, Basque Foundation for Science, Bilbao, Spain;3. IRTES-SET, UTBM, 90010 Belfort, France;1. Department of Industrial Engineering and Management, National Kaohsiung University of Applied Sciences, Taiwan;2. Graduate Institute of Mechanical and Precision Engineering, National Kaohsiung University of Applied Sciences, Taiwan;3. Lac Hong University, Bien Hoa, Dong Nai, Viet Nam;4. Department of Industrial Engineering and Management, Cheng Shiu University, Taiwan
Abstract:
Keywords:Dynamic churn prediction  Data mining  Customer retention  Private banking  Customer relationship management  Rare event  Sampling  Training data generation
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