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. 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. The results and expert feedback proved the effectiveness and usability of the approach.

翻译：合谋欺诈（多个欺诈者相互勾结骗取医疗保险资金）威胁着医疗体系的正常运行。然而，由于欺诈行为与正常就诊行为高度相似且缺乏标记数据，现有的统计和机器学习方法在医疗保险场景下检测能力有限。为确保检测结果的准确性，需要将专家知识与欺诈检测过程相结合。通过密切配合医疗保险审计专家，我们提出了FraudAuditor——一种面向医疗保险合谋欺诈检测的三阶段可视化分析方法。具体而言：首先使用户能够交互式构建共诊网络，以整体建模不同患者的就诊关系；其次设计考虑欺诈可能性强度的改进社区检测算法，用于识别可疑欺诈群体；最后通过可视化界面，用户可借助支持多时间尺度定制的可视化图表，对可疑患者行为进行比较、调查与验证。我们在真实医疗场景中开展了案例研究（即协助定位实际欺诈群体并排除误报群体），结果表明该方法兼具有效性与实用性，专家反馈亦验证了其可用性。