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

Zhou, Jiehui; Wang, Xumeng; Wang, Jie; Ye, Hui; Wang, Huanliang; Zhou, Zihan; Han, Dongming; Ying, Haochao; Wu, Jian; Chen, Wei

doi:10.1109/tvcg.2023.3261910

Cited by 5 publications

(1 citation statement)

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“…It significantly improves detection performance in real-life scenarios using network embedding and pseudo-labeling techniques. In [17], the research addresses collusive fraud in health insurance, a complex problem due to the higher relationship between normal and fraudulent medical visits and the scarcity of labeled information samples. To improve detection accuracy, the authors propose a three-stage visual analytics approach and FraudAuditor.…”

Section: Detailed Study Of Fraud Detection Methodsmentioning

confidence: 99%

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

L. K. Vishwamitra

2024

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An in-depth familiarity with ML and DL models for fraud detection is essential due to the growing frequency and complexity of fraudulent activity across many domains. Despite the abundance of research on the subject, empirical analyses of these models, especially in their real-time implementations, are typically lacking. This study fills that need by meticulously reviewing and analysing ML and DL models developed for fraud detection. We draw attention to the shortcomings of existing approaches, which are crucial in the ever-changing field of fraud detection and include problems with recall, scalability, complexity, precision, and accuracy. By evaluating various ML and DL models using these measures, our evaluation method is based on a rigorous empirical approach. Provided that insights into the practical consequences as well as flexibility of each model in real-time circumstances, they thoroughly assess their performance. There are many ways in which this work will be useful; for example, it will help professionals choose the best models for their fraud detection needs, improve academic knowledge of these models in practice, and open the door to more studies that will concentrate on developing better fraud detection tools. This comprehensive research gives academics and companies a foundation for better, more effective and more scalable fraud detection systems in this period of essential digital security.

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Section: Detailed Study Of Fraud Detection Methodsmentioning

confidence: 99%