Credit card churn forecasting by logistic regression and decision tree

Authors:
Guangli Nie;Wei Rowe;Lingling Zhang;Yingjie Tian;Yong Shi
Affiliations:
Research Center on Fictitious Economy and Data Science, CAS, Beijing 100190, China and Postdoctoral Programme of Agricultural Bank of China, Beijing 100005, China;Department of Finance, Banking & Law, University of Nebraska at Omaha, NE 68182-0048, USA;Research Center on Fictitious Economy and Data Science, CAS, Beijing 100190, China and Management School, Graduate University of Chinese Academy of Sciences, Beijing 100190, China;Research Center on Fictitious Economy and Data Science, CAS, Beijing 100190, China;Research Center on Fictitious Economy and Data Science, CAS, Beijing 100190, China and College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

In this paper, two data mining algorithms are applied to build a churn prediction model using credit card data collected from a real Chinese bank. The contribution of four variable categories: customer information, card information, risk information, and transaction activity information are examined. The paper analyzes a process of dealing with variables when data is obtained from a database instead of a survey. Instead of considering the all 135 variables into the model directly, it selects the certain variables from the perspective of not only correlation but also economic sense. In addition to the accuracy of analytic results, the paper designs a misclassification cost measurement by taking the two types error and the economic sense into account, which is more suitable to evaluate the credit card churn prediction model. The algorithms used in this study include logistic regression and decision tree which are proven mature and powerful classification algorithms. The test result shows that regression performs a little better than decision tree.