Graph-Based Fraud Detection with the Free Energy Distance

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationComplex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019
EditorsHocine Cherifi, Sabrina Gaito, José Fernendo Mendes, Esteban Moro, Luis Mateus Rocha
Pages40-52
Number of pages13
DOIs
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Complex Networks and their Applications - Lisbon, Portugal
Duration: 10 Dec 201912 Dec 2019
Conference number: 8
https://www.complexnetworks.org/

Publication series

NameStudies in Computational Intelligence
Volume882 SCI
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

ConferenceInternational Conference on Complex Networks and their Applications
Abbreviated titleComplex Networks
CountryPortugal
CityLisbon
Period10/12/201912/12/2019
Internet address

Keywords

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

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