A graph-based, semi-supervised, credit card fraud detection system
(2017) 5th International Workshop on Complex Networks and their Applications — Location: Milan, Italy (30.November.2016)
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- Abstract
- Global card fraud losses amounted to 16.31 Billion US dollars in 2014 [18]. To recover this huge amount, automated Fraud Detection Systems (FDS) are used to deny a transaction before it is granted. In this paper, we start from a graph-based FDS named APATE [28]: this algorithm uses a collective inference algorithm to spread fraudulent influence through a network by using a limited set of confirmed fraudulent transactions. We propose several improvements from the network data analysis literature [16] and semi-supervised learning [9] to this approach. Furthermore, we redesigned APATE to fit to e-commerce field reality. Those improvements have a high impact on performance, multiplying Precision@100 by three, both on fraudulent card and transaction prediction. This new method is assessed on a three-months real-life e-commerce credit card transactions data set obtained from a large credit card issuer.
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Citations
Lebichot, B., Braun, F., Caelen, O., & Saerens, M. (2017). A graph-based, semi-supervised, credit card fraud detection system. In Hocine Cherifi, Sabrina Gaito, Walter Quattrociocchi, Alessandra Sala (ed.), Complex Networks & Their Applications V, Proceedings of the 5th International Workshop on Complex Networks and their Applications (p. p. 721-733). Springer International Publishing. https://doi.org/10.1007/978-3-319-50901-3