Community Outlier Based Fraudster Detection

Abstract

Healthcare fraud is causing billions of dollars in loss for public healthcare funds. In existing healthcare fraud cases, the convicted fraudsters are mostly physicians - the healthcare professionals who submit fraudulent bills. Fraudster detection can help us to find suspicious physicians and to combat healthcare fraud in advance. When it comes to the problem of fraudster detection, rule based fraud detection methods are not applicable because fraudsters will try everything to avoid detection rules. Meanwhile, outlier based fraud detection approaches primarily aim to find global outliers and can’t find local outliers accurately. Therefore, we propose Community Outlier Based Fraudster Detection Approach - COBFDA in this paper. The proposed approach divides the physicians into different communities and looks for community outliers in each community. Extensive experiment results show that COBFDA outperforms the comparison approaches in terms of f-measure by over 20%.