Health Insurance Fraud Detection via Multiview Heterogeneous Information Networks With Augmented Graph Structure Learning
Binsheng Hong, Ping Lu, Runze Chen, Kai-Biao Lin, Fan Yang · IEEE Transactions on Computational Social Systems · 2024
With the continuous development of the health insurance system, the problem of health insurance fraud is becoming more and more prominent. Health insurance fraud not only harms health insurance organizations financially but also causes serious disruptions to the normal provision of healthcare services. Current fraud detection methods usually rely on a single data source or specific features, making it difficult to comprehensively capture the complex patterns and intrinsic associations of fraudulent behavior. Therefore, the aim of this study is to efficiently integrate feature information from different perspectives of health insurance data using multiperspective representation learning. Health insurance fraud detection via multiview heterogeneous information networks with augmented graph structure learning (MHINAGSL) is proposed. Initially, multiple heterogeneous graphs are constructed based on health insurance datasets, encompassing diffusion, topology, feature, and semantic graphs. To enhance these foundational perspectives, a confidence fusion optimizer is employed. Additionally, a multichannel semantic graph convolution with shared parameters is introduced to effectively integrate diverse meta-path semantic graphs. Subsequently, an adaptive attention mechanism assigns weights to different views, facilitating the comprehensive fusion of information from multiple perspectives and enabling a more accurate characterization of the richness of health insurance data. Ultimately, the combination of multiple loss functions is employed for end-to-end model training, aiming to fully leverage the complementary advantages of multiview data, thereby enhancing the robustness and generalization ability of fraud detection models. We fully apply the proposed method to real health insurance datasets and provide in-depth validation of its comprehensive utility in real-world scenarios. Extensive experimental results fully confirm that our approach achieves significant results in improving accuracy and recall. Particularly noteworthy is that in the anomaly detection experiments, our method achieved an F1 score as high as 0.92, further proving its excellent performance. This also demonstrates the practical application and research value of the approach based on multiview heterogeneous information network structure learning in the field of health insurance fraud detection.