- Ian Molloy15,
- Suresh Chari15,
- Ulrich Finkler15,
- Mark Wiggerman16,
- Coen Jonker16,
- Ted Habeck15,
- Youngja Park15,
- Frank Jordens16 &
- …
- Ron van Schaik16
Part of the book series:Lecture Notes in Computer Science ((LNSC,volume 9603))
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Abstract
We present a new approach to cross channel fraud detection: build graphs representing transactions from all channels and use analytics on features extracted from these graphs. Our underlying hypothesis iscommunity based fraud detection: an account (holder) performs normal or trusted transactions within a community that is “local” to the account. We explore several notions of community based on graph properties. Our results show that properties such asshortest distance between transaction endpoints, whether they are in the samestrongly connected component, whether the destination has highpage rank, etc., provide excellent discriminators of fraudulent and normal transactions whereas traditional social network analysis yields poor results. Evaluation on a large dataset from a European bank shows that such methods can substantially reducefalse positives in traditional fraud scoring. We show that classifiers built purely out of graph properties are very promising, with high AUC, and can complement existing fraud detection approaches.
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Authors and Affiliations
IBM Thomas J. Watson Research Center, Yorktown Heights, USA
Ian Molloy, Suresh Chari, Ulrich Finkler, Ted Habeck & Youngja Park
ABN AMRO Bank N.V., Amsterdam, The Netherlands
Mark Wiggerman, Coen Jonker, Frank Jordens & Ron van Schaik
- Ian Molloy
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- Suresh Chari
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Technical University Munich, Garching, Germany
Jens Grossklags
Department of Electrical Engineering-ESAT, KU Leuven, Leuven, Belgium
Bart Preneel
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Molloy, I.et al. (2017). Graph Analytics for Real-Time Scoring of Cross-Channel Transactional Fraud. In: Grossklags, J., Preneel, B. (eds) Financial Cryptography and Data Security. FC 2016. Lecture Notes in Computer Science(), vol 9603. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54970-4_2
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