A Variational AutoEncoder-Based Relational Model for Cost-Effective Automatic Medical Fraud Detection

This work aims to develop a framework of automatic medical fraud detection (AMFD) which can be deployed in healthcare industry. To address the issue that the medical fraud labels are insufficient in both size and classes for training a good AMFD model, this work proposes a novel Variational AutoEncoder-based Relational Model (VAERM) which can simultaneously exploit Patient-Doctor relational network and one-class fraud labels to improve the fraud detection. Then, the proposed VAERM coupled with active learning strategy can assist healthcare industry experts to conduct cost-effective fraud investigation. Finally, we propose an online model updating method to reduce the computation and memory requirement while preserving the predictive performance. The proposed framework is tested in a real world dataset and it empirically outperforms the state-of-the-art methods in both automatic fraud detection and fraud investigation tasks.