# FraudAuditor: A Visual Analytics Approach for Collusive Fraud in Health Insurance

arXiv: https://arxiv.org/abs/2303.13491 · HTML: https://mlsanity.com/paper/2303.13491/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2023.

## Built on by (most cited first, 4 of 4)

- [Aiding Humans in Financial Fraud Decision Making: Toward an XAI-Visualization Framework](https://mlsanity.com/paper/2408.14552/) (2024)
- ["Show Me What's Wrong!": Combining Charts and Text to Guide Data Analysis](https://mlsanity.com/paper/2410.00727/) (2024)
- [Collusion Detection with Graph Neural Networks](https://mlsanity.com/paper/2410.07091/) (2024)
- [From Prediction to Explainable Provider Behavior Profiles for Fraud, Waste, and Abuse Review](https://mlsanity.com/paper/2609.28477/) (2026)

## Abstract (arXiv)

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.