Credit card customer churn prediction based on the RST and LS-SVM

Abstract

The credit card business in the bank possesses high risk and high profit. How to control the customer churn of credit card has already become the problem to solve in the urgent need. In order to support the bank to reduce churn rate, we need to predict which customers are high risk of churn and optimize their marketing intervention resource to prevent as many customers as possible from churning. Considering the shortcomings of conventional prediction methods, Rough Set Theory (RST) and Least Squares Support Vector Machine (LS-SVM) is adopted to establish the prediction model of credit card customer churn, which could predict the customer churn efficiently and effectively. Predicting the tendency of customer churn according to LS-SVM will provide a scientific guide for the credit card customer marketing of the bank.

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