Research from Xiamen University of Technology Yields New Study Findings on Science and Technology (Health insurance fraud detection based on multi-channel heterogeneous graph structure learning): Science – Science and Technology

Health Policy and Law Daily

2024 MAY 30 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- Research findings on science and technology are discussed in a new report. According to news originating from Xiamen University of Technology by NewsRx correspondents, research stated, “Health insurance fraud is becoming more common and impacting the fairness and sustainability of the health insurance system. Traditional health insurance fraud detection primarily relies on recognizing established data patterns.”

Our news reporters obtained a quote from the research from Xiamen University of Technology: “However, with the ever-expanding and complex nature of health insurance data, it is difficult for these traditional methods to effectively capture evolving fraudulent activity and tactics and keep pace with the constant improvements and innovations of fraudsters. As a result, there is an urgent need for more accurate and flexible analytics to detect potential fraud. To address this, the Multi-channel Heterogeneous Graph Structured Learning-based health insurance fraud detection method (MHGSL) was proposed. MHGSL constructs a graph of health insurance data from various entities, such as patients, departments, and medicines, and employs graph structure learning to extract topological structure, features, and semantic information to construct multiple graphs that reflect the diversity and complexity of the data. We utilize deep learning methods such as heterogeneous graph neural networks and graph convolutional neural networks to combine multi-channel information transfer and feature fusion to detect anomalies in health insurance data. The results of extensive experiments on real health insurance data demonstrate that MHGSL achieves a high level of accuracy in detecting potential fraud, which is better than existing methods, and is able to quickly and accurately identify patients with fraudulent behaviors to avoid loss of health insurance funds. Experiments have shown that multi-channel heterogeneous graph structure learning in MHGSL can be very helpful for health insurance fraud detection.”

According to the news editors, the research concluded: “It provides a promising solution for detecting health insurance fraud and improving the fairness and sustainability of the health insurance system. Subsequent research on fraud detection methods can consider semantic information between patients and different types of entities.”

For more information on this research see: Health insurance fraud detection based on multi-channel heterogeneous graph structure learning. Heliyon, 2024,10(9):e30045. (Heliyon - http://www.heliyon.com). The publisher for Heliyon is Elsevier.

A free version of this journal article is available at https://doi.org/10.1016/j.heliyon.2024.e30045.

Our news journalists report that more information may be obtained by contacting Binsheng Hong, School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, 361024, Fujian Province, People’s Republic of China. Additional authors for this research include Ping Lu, Hang Xu, Jiangtao Lu, Kaibiao Lin, Fan Yang.