Prescription fraud detection through statistic modeling

The emergence of prescription fraud will reduce the effectiveness of health insurance investment. This paper will propose a new model to identify potentially fraudulent prescriptions and apply it to real prescription data to test its performance. Because of the low efficiency and high cost of prescription fraud through artificial experts, and because of the limitations of human knowledge, artificial detection is slow and insensitive to new fraud. We used the statistical characteristics of prescription data and other features related to the prescription to measure the risk level of the prescription, and found a prescription with high risk. The potential of this model can be used not only for off-line and online analysis and prediction of prescription fraud, but also for automatic updating of new fraud prescriptions. We test the model on real prescription data sets and compared to other approaches. The experimental results show that our model is promising for discovering the prescription fraud from the real health care data sets.