Dual Sequential Variational Autoencoders for Fraud Detection

International audienc

Graph-based fraud detection with the free energy distance

This paper investigates a real-world application of the free energy distance between nodes of a graph 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