Abstract:
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. 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. 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.
摘要:
医疗保险欺诈越来越普遍,影响了医疗保险制度的公平性和可持续性。传统的健康保险欺诈检测主要依赖于识别已建立的数据模式。然而,随着健康保险数据的不断扩大和复杂性,这些传统方法很难有效地捕捉不断发展的欺诈活动和策略,并跟上欺诈者的不断改进和创新。因此,迫切需要更准确和灵活的分析来检测潜在的欺诈行为。为了解决这个问题,提出了基于多通道异构图结构化学习的医疗保险欺诈检测方法(MHGSL)。MHGSL构造了来自各种实体的健康保险数据图,如患者,部门,和药物,并采用图结构学习来提取拓扑结构,特点,和语义信息,构造多个图,反映数据的多样性和复杂性。我们利用异构图神经网络和图卷积神经网络等深度学习方法,将多通道信息传递和特征融合相结合,以检测健康保险数据中的异常。对真实健康保险数据的大量实验结果表明,MHGSL在检测潜在欺诈方面具有很高的准确性,比现有的方法更好,并且能够快速准确地识别患者的欺诈行为,避免医保基金的损失。实验表明,MHGSL中的多通道异构图结构学习对医疗保险欺诈检测非常有帮助。它为检测健康保险欺诈和提高健康保险制度的公平性和可持续性提供了一个有前途的解决方案。随后对欺诈检测方法的研究可以考虑患者和不同类型实体之间的语义信息。