Data mining for credit card fraud: A comparative study

Authors:
Siddhartha Bhattacharyya;Sanjeev Jha;Kurian Tharakunnel;J. Christopher Westland
Affiliations:
Department of Information and Decision Sciences (MC 294), College of Business Administration, University of Illinois, Chicago, 601 South Morgan Street, Chicago, Illinois 60607-7124, USA;Department of Decision Sciences, Whittemore School of Business and Economics, University of New Hampshire, McConnell Hall, Durham, New Hampshire 03824-3593, USA;Tabor School of Business, Millikin University, 1184 West Main Street, Decatur, IL 62522, USA;Department of Information & Decision Sciences (MC 294), College of Business Administration, University of Illinois, Chicago, 601 S. Morgan Street, Chicago, IL 60607-7124, USA
Venue:
Decision Support Systems
Year:
2011

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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.