Employing transaction aggregation strategy to detect credit card fraud

Authors:
Sanjeev Jha;Montserrat Guillen;J. Christopher Westland
Affiliations:
Department of Decision Sciences, Whittemore School of Business and Economics, University of New Hampshire, McConnell Hall, Durham, New Hampshire 03824-3593, USA;Department of Econometrics, Riskcenter-IREA, University of Barcelona, Diagonal, 690, 08034 Barcelona, Spain;Department of Information & Decision Sciences (MC 294), Room 2400, University Hall, University of Illinois, Chicago, 601 S. Morgan Street, Chicago, IL 60607-7124, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Credit card fraud costs consumers and the financial industry billions of dollars annually. However, there is a dearth of published literature on credit card fraud detection. In this study we employed transaction aggregation strategy to detect credit card fraud. We aggregated transactions to capture consumer buying behavior prior to each transaction and used these aggregations for model estimation to identify fraudulent transactions. We use real-life data of credit card transactions from an international credit card operation for transaction aggregation and model estimation.