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 http://www.worldline.com).
|Title of host publication||Advances in Artificial Intelligence: From Theory to Practice|
|Subtitle of host publication||30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017|
|Publication status||Published - 3 Jun 2017|
|Series||Lecture Notes in Computer Science|