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Graph-Based Fraud Detection with the Free Energy Distance

par Courtain, Sylvain;Lebichot, Bertrand ;Kivimäki, Ilkka;Saerens, Marco
Référence Studies in Computational Intelligence, 882 SCI, page (40-52)
Publication Publié, 2020-10-01

Article révisé par les pairs

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Résumé :

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.