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Graph-Based Fraud Detection with the Free Energy Distance

  • Sylvain CourtainEmail author
  • Bertrand Lebichot
  • Ilkka Kivimäki
  • Marco Saerens
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 882)

Abstract

This paper investigates a real-world application of the free energy distance between nodes of a graph [14, 20] by proposing an improved extension of the existing Fraud Detection System named APATE [36]. It relies on a new way of computing the free energy distance based on paths of increasing length, and scaling on large, sparse, graphs. This new approach is assessed on a real-world large-scale e-commerce payment transactions dataset obtained from a major Belgian credit card issuer. Our results show that the free-energy based approach reduces the computation time by one half while maintaining state-of-the art performance in term of Precision@100 on fraudulent card prediction.

Keywords

Credit card fraud detection Network science Network data analysis Free energy distance Semi-supervised learning 

Notes

Acknowledgements

This work was partially supported by the Immediate funded by Wallon Region project and by the Defeatfrauds project funded by Innoviris. We thank these institutions for giving us the opportunity to conduct both fundamental and applied research. We also thank Worldline SA/NV, R&D, for providing us the data and expertise.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sylvain Courtain
    • 1
    Email author
  • Bertrand Lebichot
    • 1
    • 3
  • Ilkka Kivimäki
    • 4
  • Marco Saerens
    • 1
    • 2
  1. 1.LOURIM, Université catholique de LouvainOttignies-Louvain-la-NeuveBelgium
  2. 2.ICTEAM, Université catholique de LouvainOttignies-Louvain-la-NeuveBelgium
  3. 3.MLG, Université Libre de BruxellesBrusselsBelgium
  4. 4.Department of Computer ScienceAalto UniversityEspooFinland

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