Model selection strategy for customer attrition risk prediction in retail banking
Fan Li, Juan Lei, Yingjie Tian, Sakuna Punyapatthanakul, Yanbo J. Wang · Australasian Data Mining Conference · 2011
Nowadays customer attrition is increasingly serious in commercial banks, particularly, high-valued customers in retail banking. Hence, it is encouraged to develop a prediction mechanism and identify such customers who might be at risk of attrition. This prediction mechanism can be considered to be a classifier. In particular, the problem of predicting risk of customer attrition can be prototyped as a binary classification task in data mining. In previous studies, a number of techniques have been introduced in (binary) classification study, i. e. artificial-based model, Bayesian-based model, case-based model, tree-based model, regression-based model, rule-based model, etc. With regards to a particular application --- predicting customer attrition risk for retail banking, this paper presents four principles in (classification) model selection. To support this model selection study, a set of experiments were run, based on a collection of real customer data in retail banking. These results and consequent recommendations are given in this paper.