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Graph-Based Fraud Detection with the Free Energy Distance

2019; Springer Nature; Linguagem: Inglês

10.1007/978-3-030-36683-4_4

1860-9503

Sylvain Courtain, Bertrand Lebichot, Ilkka Kivimäki, Marco Saerens,

Data Stream Mining Techniques

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.