Using Graph Attention Networks in Healthcare Provider Fraud Detection

作     者：Mardani, Shahla Moradi, Hadi 

作者机构：Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran 111554563 Iran SKKU Intelligent Syst Res Inst Suwon 16419 South Korea 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2024年第12卷

页      面：132786-132800页

核心收录：

主　　题：Medical services Fraud Feature extraction Vectors Support vector machines Logistics Detection algorithms Insurance Graphical models Fraud detection graph attention network graph embedding healthcare 

摘      要：Healthcare fraud increases healthcare expenses for insurers, premiums for policyholders, and dissatisfaction of legitimate patients and causes severe damage to the health system. Therefore, it is critically important to address healthcare fraud detection. Most fraud detection models consider only claims data for analysis. Since committing healthcare fraud can include more than one party, i.e., healthcare providers, physicians, and patients, it is crucial to consider the relationship among them. In this paper, we propose a healthcare provider fraud detection model that applies the effect of the parties interdependencies on the claims data. It leverages a graph attention network for embedding the relationships and classifies samples using a feed-forward neural network. Our explicit contribution is using the latent information of the interdependency of claims parties to detect healthcare provider fraud more accurately. The information, along with other features, can identify complex patterns of fraud. We tested our approach on the healthcare provider fraud detection dataset and reached 0.56 recall compared to best available approaches such as GTN with 0.5 and XGBoost with 0.46 recall.