A medical insurance fraud detection model with knowledge graph and machine learning

In recent years, cases of medical insurance fraud emerge in endlessly. We urgently need to develop an effective way to detect fraud. However, efficiently mining the heterogeneous medical text data is a complicated and tough assignment in fraud detection. Therefore, a medical insurance fraud detection model with knowledge graph and machine learning is proposed in this paper. Firstly, a knowledge graph with 53,164 nodes and 1,209,847 edges is built based on the medical insurance text data of 20,000 insured members. Secondly, representation learning and improved label propagation algorithm (LPA) are used for feature engineering based on the knowledge graph. On this basis, combined with the expense data, the medical insurance fraud detection model is constructed by using easy ensemble and XGBoost. The experimental results show that the model proposed in this paper greatly improves the effect of medical insurance fraud detection. In addition, it is proved that text data plays an important role in medical insurance fraud detection.