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.
@article{zhou2023fraudauditor,title={FraudAuditor: A Visual Analytics Approach for Collusive Fraud in Health Insurance},author={Zhou, Jiehui and Wang, Xumeng and Wang, Jie and Ye, Hui and Wang, Huanliang and Zhou, Zihan and Han, Dongming and Ying, Haochao and Wu, Jian and Chen, Wei},journal={IEEE Transactions on Visualization and Computer Graphics},year={2023},publisher={IEEE},video={https://www.youtube.com/watch?v=PlCQhtpYylQ},}
2022
DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy
Jiehui Zhou, Xumeng Wang, Jason K Wong, and
8 more authors
IEEE Transactions on Visualization and Computer Graphics, 2022
Data privacy is an essential issue in publishing data visualizations. However, it is challenging to represent multiple data patterns in privacy-preserving visualizations. The prior approaches target specific chart types or perform an anonymization model uniformly without considering the importance of data patterns in visualizations. In this paper, we propose a visual analytics approach that facilitates data custodians to generate multiple private charts while maintaining user-preferred patterns. To this end, we introduce pattern constraints to model users’ preferences over data patterns in the dataset and incorporate them into the proposed Bayesian network-based Differential Privacy (DP) model PriVis . A prototype system, DPVisCreator , is developed to assist data custodians in implementing our approach. The effectiveness of our approach is demonstrated with quantitative evaluation of pattern utility under the different levels of privacy protection, case studies, and semi-structured expert interviews.
@article{zhou2022dpviscreator,title={DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy},author={Zhou, Jiehui and Wang, Xumeng and Wong, Jason K and Wang, Huanliang and Wang, Zhongwei and Yang, Xiaoyu and Yan, Xiaoran and Feng, Haozhe and Qu, Huamin and Ying, Haochao and Chen, Wei},journal={IEEE Transactions on Visualization and Computer Graphics},volume={29},number={1},pages={809--819},year={2022},publisher={IEEE},video={https://www.youtube.com/watch?v=LYdxLA3hD3c},}
2021
MedicareVis: a Joint Visual Analytics Approach for Anti-Fraud in Medical Insurance
Jiehui Zhou, Rongchen Zhu, Wei Zhang, and
4 more authors
@article{18981,title={MedicareVis: a Joint Visual Analytics Approach for Anti-Fraud in Medical Insurance},author={Zhou, Jiehui and Zhu, Rongchen and Zhang, Wei and Lu, Junhua and Ying, Haochao and Wu, Jian and Chen, Wei},journal={Journal of CAD & CG},volume={33},number={18981},pages={1311},year={2021},issn={1003-9775},keywords={可视分析, 医疗保险, 欺诈检测},url={https://www.jcad.cn/article/doi/10.3724/SP.J.1089.2021.18981}}
