Improving Card Fraud Detection Through Suspicious Pattern Discovery

  • Fabian Braun
  • Olivier Caelen
  • Evgueni N. Smirnov
  • Steven Kelk
  • Bertrand Lebichot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10351)

Abstract

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).

Keywords

Credit card fraud detection Supervised learning Feature engineering Frequent pattern mining Bicliques Graph analysis 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fabian Braun
    • 1
  • Olivier Caelen
    • 2
  • Evgueni N. Smirnov
    • 3
  • Steven Kelk
    • 3
  • Bertrand Lebichot
    • 4
  1. 1.R&D, Worldline GmbHAachenGermany
  2. 2.R&D, Worldline SABrusselsBelgium
  3. 3.Department of Data Science and Knowledge EngineeringMaastricht UniversityMaastrichtThe Netherlands
  4. 4.Université catholique de LouvainLouvain-la-neuveBelgium

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