Abstract
Nowadays, customer attrition is increasingly serious in commercial banks, particularly with respect tomiddle- and high-valued customers in retail banking. To combat this attrition it is incumbent for banks to develop a prediction mechanism so as to identify 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 this paper we identify a set of features, for customer “attrition vs. non-attrition” classification, based on the RFM (Recency, Frequency and Monetary) model. The reported evaluation indicates that proposed set of features produces a much more effective classifier than that generated using previously suggested features.
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Wang, Y.J., Di, G., Yu, J., Lei, J., Coenen, F. (2013). Feature Representation for Customer Attrition Risk Prediction in Retail Banking. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2013. Lecture Notes in Computer Science(), vol 7987. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39736-3_18
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DOI: https://doi.org/10.1007/978-3-642-39736-3_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39735-6
Online ISBN: 978-3-642-39736-3
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