Unsupervised Anomaly Detection of Healthcare Providers Using Generative Adversarial Networks

Abstract

Healthcare fraud is considered a challenge for many societies. Health care funding that could be spent on medicine, care for the elderly or emergency room visits are instead lost to fraudulent activities by materialistic practitioners or patients. With rising healthcare costs, healthcare fraud is a major contributor to these increasing healthcare costs. This study evaluates previous anomaly detection machine learning models and proposes an unsupervised framework to identify anomalies using a Generative Adversarial Network (GANs) model. The GANs anomaly detection (GAN-AD) model was applied on two different healthcare provider data sets. The anomalous healthcare providers were further analysed through the application of classification models with the logistic regression and extreme gradient boosting models showing good performance. Results from the SHapley Additive exPlanation (SHAP) also signifies that the predictors used explain the anomalous healthcare providers.