PREDICTING FRAUDS IN INSURANCE CLAIMS USING MACHINE LEARNING

Patients depend on health insurance provided by the government systems, private systems, or both to utilize the high-priced healthcare expenses. This dependency on health insurance draws some healthcare service providers to commit insurance frauds. Although the number of such service providers is small, it is reported that the insurance providers lose billions of dollars every year due to frauds. In this paper, I formulate the fraud detection problem over a minimal, definitive claim data consisting of medical diagnosis and procedure codes. I present a solution to the fraudulent claim detection problem using a novel representation learning approach, which translates diagnosis and procedure codes into Mixtures of Clinical Codes (MCC). I also investigate extensions of MCC using Long Short Term Memory networks and Robust Principal Component Analysis. My experimental results demonstrate promising outcomes in identifying fraudulent records.