Using Graph Attention Networks in Healthcare Provider Fraud Detection

Abstract

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.