Biclusteringsim: A Novel Method to Identify Abnormal Cooccurrence Medical Visit Behaviors Abnormal cooccurrence medical visit behavior refers to the fraudulent behavior of fraudsters who hold multiple medical insurance cards and frequently purchase medicines within the same time period at the same location to defraud insurance medical insurance fund. Identifying abnormal cooccurrence medical visit behaviors plays a critical role in automated medical insurance fraud detection. However, the conventional methods mainly focus on the mining of frequent patterns, which may lead to misjudgement of normal patients who regularly seek medical treatment for a long time. In order to address this problem, we propose a novel biclustering algorithm, which finds suspicious patient groups who frequent purchased medicines within the same time period at the same location, and filters out normal patients from suspicious patients. Experimental results show that Bicluteringsim outperforms the competitor in detecting medical insurance fraud.

Health Insurance Anomaly Detection Based on Dynamic Heterogeneous Information Network With the development of health insurance, the consequent problem of health insurance fraud has become increasingly serious. Outlier detection is a common method for health insurance anomaly detection. Researchers use some prior knowledge to assume patterns and indicators of interest. However, fraud pattern is concealed and changeable. The method of mining anomalies from fixed patterns is difficult to meet the current needs. In order to overcome this limitation, this paper combines the rich expression ability of heterogeneous information networks to model the complex relationships between entities, and establish a health insurance business representation model. The paper explores all possible business patterns, interrelated business portfolio patterns and related indicators in the field of health insurance. Considering the dynamic nature of a network, anomaly mining is carried out from both horizontal and vertical perspectives. Among them, the horizontal comparison adopts a fixed time period. The vertical comparison dynamically adjusts the time period according to the frequency of occurrence of the health insurance pattern instance, and then performs indicator calculation and outlier detection. Finally, the experimental results on the real data set show that our approach can narrow the scope of professional review and find more records of possible fraud than traditional methods.