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Improving Card Fraud Detection Through Suspicious Pattern Discovery

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Braun, Fabian [R&D, Worldline GmbH, Aachen, Germany] Caelen, Olivier [R&D, Worldline SA, Brussels, Belgium] Smirnov, Evgueni N. [Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands] Kelk, Steven [Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands] Lebichot, Bertrand [UCL]

We propose a new approach to detect credit card fraud based on suspicious payment patterns. According to our hypothesis fraudsters use stolen credit card data at specific, recurring sets of shops. We exploit this behavior to identify fraudulent transactions. In a first step we show how suspicious patterns can be identified from known compromised cards. The transactions between cards and shops can be represented as a bipartite graph. We are interested in finding fully connected subgraphs containing mostly compromised cards, because such bicliques reveal suspicious payment patterns. Then we define new attributes which capture the suspiciousness of a transaction indicated by known suspicious patterns. Eventually a non-linear classifier is used to assess the predictive power gained through those new features. The new attributes lead to a significant performance improvement compared to state-of-the-art aggregated transaction features. Our results are verified on real transaction data provided by our industrial partner (Worldline http://www.worldline.com).

Document type Communication à un colloque (Conference Paper) – Présentation orale avec comité de sélection
Access type Accès interdit
Publication date 2017
Language Anglais
Conference "The 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems", Arras (du 27/06/2017 au 30/06/2017)
Journal information "Advances in Artificial Intelligence: From Theory to Practice (IEA/AIE 2017)" - Vol. 10351, p. 181-190 (2017)
Peer reviewed yes
Publisher Benferhat S., Tabia K., Ali M. (eds)
Publication status Publié
Affiliations UCL - SST/ICTM - Institute of Information and Communication Technologies, Electronics and Applied Mathematics
UCL - SSH/LouRIM - Louvain Research Institute in Management and Organizations
Keywords Credit card fraud detection ; Supervised learning ; Feature engineering ; Frequent pattern mining ; Bicliques ; Graph analysis
Links
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Bibliographic reference Braun, Fabian ; Caelen, Olivier ; Smirnov, Evgueni N. ; Kelk, Steven ; Lebichot, Bertrand. Improving Card Fraud Detection Through Suspicious Pattern Discovery.The 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (Arras, du 27/06/2017 au 30/06/2017). In: Advances in Artificial Intelligence: From Theory to Practice (IEA/AIE 2017), Vol. 10351, p. 181-190 (2017)
Permanent URL http://hdl.handle.net/2078.1/192933