Customer Churn Analysis in Personal Financial Services of Commercial Bank Based on Classification Mining Method

In customer churn analysis of personal financial services in commercial bank, there are large number of customer samples, and the number of churn samples and that of nonchurn samples are imbalanced. Thus, it is a challenging problem to do the churn analysis. To solve this problem, an integrated method that combines boosting algorithm with costsensitive decision tree is presented. To show the effectiveness of the proposed method, it is applied to a case study. Comparison shows that the proposed method outperforms the other existing ones, such as support vector machine, artificial neural network, and logistic regression.