Predicting credit card customer churn in banks using data mining

Authors:
Dudyala Anil Kumar;V. Ravi
Affiliations:
Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad 500 057 (AP), India.;Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad 500 057 (AP), India
Venue:
International Journal of Data Analysis Techniques and Strategies
Year:
2008

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Abstract

In this paper, we solve the customer credit card churnprediction via data mining. We developed an ensemble systemincorporating majority voting and involving Multilayer Perceptron(MLP), Logistic Regression (LR), decision trees (J48), RandomForest (RF), Radial Basis Function (RBF) network and Support VectorMachine (SVM) as the constituents. The dataset was taken from theBusiness Intelligence Cup organised by the University of Chile in2004. Since it is a highly unbalanced dataset with 93% loyal and 7%churned customers, we employed (1) undersampling, (2) oversampling,(3) a combination of undersampling and oversampling and (4) theSynthetic Minority Oversampling Technique (SMOTE) for balancing it.Furthermore, tenfold cross-validation was employed. The resultsindicated that SMOTE achieved good overall accuracy. Also, SMOTEand a combination of undersampling and oversampling improved thesensitivity and overall accuracy in majority voting. In addition,the Classification and Regression Tree (CART) was used for thepurpose of feature selection. The reduced feature set was fed tothe classifiers mentioned above. Thus, this paper outlines the mostimportant predictor variables in solving the credit card churnprediction problem. Moreover, the rules generated by decision treeJ48 act as an early warning expert system.