Improving Card Fraud Detection Through Suspicious Pattern Discovery

Fabian Braun*, Olivier Caelen, Evgueni N. Smirnov, Steven Kelk, Bertrand Lebichot

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic


We propose a new approach to detect credit card fraud based on suspicious payment patterns. According to our hypothesis fraudsters use stolen credit card data at specific, recurring sets of shops. We exploit this behavior to identify fraudulent transactions. In a first step we show how suspicious patterns can be identified from known compromised cards. The transactions between cards and shops can be represented as a bipartite graph. We are interested in finding fully connected subgraphs containing mostly compromised cards, because such bicliques reveal suspicious payment patterns. Then we define new attributes which capture the suspiciousness of a transaction indicated by known suspicious patterns. Eventually a non-linear classifier is used to assess the predictive power gained through those new features. The new attributes lead to a significant performance improvement compared to state-of-the-art aggregated transaction features. Our results are verified on real transaction data provided by our industrial partner (worldline
Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence: From Theory to Practice
Subtitle of host publication30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017
PublisherSpringer, Cham
ISBN (Electronic)978-3-319-60045-1
ISBN (Print)978-3-319-60044-4
Publication statusPublished - 3 Jun 2017

Publication series

SeriesLecture Notes in Computer Science


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