Applied intelligence, 2005, 22,47-60. 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands. A Data Mining Approach for Retailing Bank Customer Attrition Analysis XIAOHUA HU College of Information Science, Drex el Univ ersity , Philadelphia, PA, USA 19104 xiaohua hu@acm.org; thu@cis.drexel.edu Abstract. Deregulation within financial service industries and the widespread acceptance of new technologies is increasing competition in the finance marketplace. Central to business strategy of every financial service company is the ability to retain existing customers and reach new prospective customers. Data mining is adopted to play an important role in these efforts. In this paper, we present a data mining approach for analyzing retailing bank customer attrition. We discuss the challenging issues such as highly skewed data, time series data unrolling, leaker field detection etc, and the procedure of a data mining project for the attrition analysis for retailing bank customers. We use lift as a proper measure for attrition analysis and compare the lift of data mining models of decision tree, boosted naïve Bayesian network, selective Bayesian network, neural network and the ensemble of classifiers of the above methods. Some interesting findings are reported. Our research work demonstrates the effectiveness and efficiency of data mining in attrition analysis for retailing bank. Keywords: data mining, classification method, attrition analysis 1、Introduction Deregulation within financial service industries and the widespread acceptance of new technologies is increasing competition in the finance marketplace. Central to business strategy of every financial service company is the ability to retain existing c...
