基于分类挖掘方法的商业银行个人理财业务客户流失分析

摘要

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 imbal-anced. 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.%针对客户流失分析中实际客户样本数据量大、流失与未流失客户样本分布不平衡的特点,提出一种基于Boosting与代价敏感决策树的集成方法,并将其应用于商业银行个人理财业务的客户流失分析.通过实际商业银行客户数据集测试,并与支持向量机、人工神经网络和Logistic回归等方法进行比较,发现该方法能够有效解决客户流失问题.