工业工程

工业工程 ›› 2011, Vol. 14 ›› Issue (6): 126-132.

• 实践与应用 • 上一篇    下一篇

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

  

  1. 重庆大学 经济与工商管理学院，重庆  400044
  • 出版日期:2011-12-31 发布日期:2011-12-23

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

  1. School of Economics and Business Administration, Chongqing University, Chongqing 400044, China
  • Online:2011-12-31 Published:2011-12-23

摘要： 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。

关键词: 客户流失, 数据挖掘, 决策树, Boosting算法, 代价敏感学习, 商业银行个人理财业务

Abstract: 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.  

Key words: customer churn, data mining, decision tree, boosting algorithm, cost-sensitive learning, personal financial services in commercial bank