Catching Credit Card Fraudsters without Knowing One: A Micro-Cluster-Based Detection Approach

This paper showcases the application of a clustering method for credit card fraud detection. Unlike supervised approaches, we micro-cluster data into small homogeneous subsegments related to fraud or non-fraud instances. We then sample each cluster. For each sampled record, the true target label is acquired. Through this approach, we significantly reduce the cost of detecting fraudulent cases. We regard clusters with fraud rates significantly higher than the estimated basic fraud rate as high-risk clusters and proceed to investigate the rest of the cases in those clusters. By targeting the high-risk clusters, we achieved a 500 -fold improvement over baseline precision for a credit card fraud detection application. In addition, by targeting only high-risk clusters, the proposed approach achieves significant cost reduction and is especially useful when fraud cases are not known beforehand.