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Graph-Based Fraud Detection with the Free Energy Distance

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Complex Networks and Their Applications VIII (COMPLEX NETWORKS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 882))

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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.

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Notes

  1. 1.

    Expected total cost of the paths plus scaled relative entropy of the probability distribution of following these paths (see [20] for details).

  2. 2.

    Notice that the usual free energy distance (not directed) is defined by symmetrization of \(\phi _{ij}\) (Eq. 2, so that the resulting distance is symmetric [14, 20]), but this quantity will not be used in this work.

  3. 3.

    So that a score near 0 represents a genuine transaction and a high value represents a fraud. The higher the score, the higher the risk.

  4. 4.

    Numerical values differ from [21] because the dataset was further curated: some obvious fraud cases were removed.

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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.

Author information

Authors and Affiliations

  1. LOURIM, Université catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium

    Sylvain Courtain, Bertrand Lebichot & Marco Saerens

  2. ICTEAM, Université catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium

    Marco Saerens

  3. MLG, Université Libre de Bruxelles, Brussels, Belgium

    Bertrand Lebichot

  4. Department of Computer Science, Aalto University, Espoo, Finland

    Ilkka Kivimäki

Authors
  1. Sylvain Courtain
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  2. Bertrand Lebichot
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  3. Ilkka Kivimäki
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  4. Marco Saerens
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Corresponding author

Correspondence to Sylvain Courtain .

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Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito

  3. University of Aveiro, Aveiro, Portugal

    José Fernendo Mendes

  4. Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

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Courtain, S., Lebichot, B., Kivimäki, I., Saerens, M. (2020). Graph-Based Fraud Detection with the Free Energy Distance. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_4

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