Model Selection Strategy for Customer Attrition Risk Prediction in Retail Banking

Provided by: Australian Computer Society
Topic: Data Management
Format: PDF
Now-a-days, 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.

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