  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.
