Health Insurance Fraud Detection and Characterization

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In Canada, the cost of healthcare is $215 billion annually. Fraud and abuse account for 2% to 10% of this**amount, translating into losses of approximately $2 billion per year. The cost of fraud increases the costs of**prescription drugs and illnesses. Despite the huge cost of fraudulent claims, the fraud detection process**implemented by most insurance companies is very much a manual process, where domain experts might flag**claims that go beyond a certain threshold for further review. These reviews are costly and inefficient. Domain**experts may have spent a lot of time, but often can identify only a small fraction of fraudulent claims. As the**volume of both benign and fraudulent claims continues to grow, there is a pressing need for innovative**solutions that can tackle this important yet challenging problem in the health insurance industry.**Segic is a Canadian-based company that aims at providing innovative software platforms to meet their needs of**the collective insurance industry. This research project is part of Segic's strategic plan to utilize artificial**intelligence (AI) to enhance the capabilities of their offered software services. Specifically, this 6-month**project aims at developing an effective machine learning method to identify and characterize fraudulent claims.**Segic will contribute their expertise of identifying and characterizing fraudulent cases. The McGill team will**contribute their knowledge in the areas of machine learning and cyber forensics. The joint force of the two**teams will result in an effective health insurance fraud detection engine prototype. The research result will**enhance the efficiency and effectiveness of the fraud detection in the health insurance industry, which in turn**contributes to the reduction of healthcare cost in Canada.