Fraud Detection of Medical Insurance Employing Outlier Analysis

Fraud detection is an important issue in the area of data science, and it has a lot of practical applications in related fields, such as business, health, and environment. Most traditional methods detect fraud based on rulemaking. Unfortunately, it is not always useful in the medical field since the boundary of fraud detection is vague. As a result, outlier detection is a promising method. This paper develops an outlier detection method of analyzing the correlation of patients to detect fraud. We construct a heterogeneous information network which bridges the medicines used and diseases of patients. In light of the network, we calculate the correlation score of different patients and design a discriminant rule. Through the discriminating rule, fraudulent patients represented by the abnormal nodes can be found. Our experiments use real medical insurance data sets and the results confirm that our method is accurate and effective.