Studies from Zhejiang University Describe New Findings in Insurance (Fraudauditor: a Visual Analytics Approach for Collusive Fraud In Health Insurance): Insurance

2023 JUN 21 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- Data detailed on Insurance have been presented. According to news reporting originating from Zhejiang, People’s Republic of China, by NewsRx correspondents, research stated, “Collusive fraud, in which multiple fraudsters collude to defraud health insurance funds, threatens the operation of the healthcare system. However, existing statistical and machine learning-based methods have limited ability to detect fraud in the scenario of health insurance due to the high similarity of fraudulent behaviors to normal medical visits and the lack of labeled data.”

Financial support for this research came from National Natural Science Foundation of China (NSFC).

Our news editors obtained a quote from the research from Zhejiang University, “To ensure the accuracy of the detection results, expert knowledge needs to be integrated with the fraud detection process. By working closely with health insurance audit experts, we propose FraudAuditor, a three-stage visual analytics approach to collusive fraud detection in health insurance. Specifically, we first allow users to interactively construct a co-visit network to holistically model the visit relationships of different patients. Second, an improved community detection algorithm that considers the strength of fraud likelihood is designed to detect suspicious fraudulent groups. Finally, through our visual interface, users can compare, investigate, and verify suspicious patient behavior with tailored visualizations that support different time scales. We conducted case studies in a real-world healthcare scenario, i.e., to help locate the actual fraud group and exclude the false positive group.”

According to the news editors, the research concluded: “The results and expert feedback proved the effectiveness and usability of the approach.”

This research has been peer-reviewed.

For more information on this research see: Fraudauditor: a Visual Analytics Approach for Collusive Fraud In Health Insurance. IEEE Transactions on Visualization and Computer Graphics, 2023;29(6):2849-2861. IEEE Transactions on Visualization and Computer Graphics can be contacted at: Ieee Computer Soc, 10662 Los Vaqueros Circle, PO Box 3014, Los Alamitos, CA 90720-1314, USA. (Institute of Electrical and Electronics Engineers - http://www.ieee.org/; IEEE Transactions on Visualization and Computer Graphics - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=2945)

The news editors report that additional information may be obtained by contacting Haochao Ying, Zhejiang University, State Key Lab Cad&Cg, Hangzhou 310027, Zhejiang, People’s Republic of China. Additional authors for this research include Jiehui Zhou, Zihan Zhou, Dongming Han, Wei Chen, Xumeng Wang, Jie Wang, Hui Ye and Jian Wu.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/TVCG.2023.3261910. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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