Data mining for credit card fraud: A comparative study

Data mining for credit card fraud: A comparative study

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Article ID: iaor20112092
Volume: 50
Issue: 3
Start Page Number: 602
End Page Number: 613
Publication Date: Feb 2011
Journal: Decision Support Systems
Authors: , , ,
Keywords: datamining, statistics: regression
Abstract:

Credit card fraud is a serious and growing problem. While predictive models for credit card fraud detection are in active use in practice, reported studies on the use of data mining approaches for credit card fraud detection are relatively few, possibly due to the lack of available data for research. This paper evaluates two advanced data mining approaches, support vector machines and random forests, together with the well‐known logistic regression, as part of an attempt to better detect (and thus control and prosecute) credit card fraud. The study is based on real‐life data of transactions from an international credit card operation.

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