An anti-fraud framework for medical insurance based on deep learning

Given rising medical costs, medical expense control has become an important task in the healthcare domain. To solve the shortage of medical reimbursement mechanisms based on medical service items, single-disease payment models have been extensively studied. However, the approach of payment via a single-disease model is also flawed, and fraud may occur. Herein, we present an anti-fraud framework for medical insurance based on deep learning to automatically identify suspicious medical records, ensure the effective implementation of single-disease charges, and reduce the workload of medical insurance auditors. The framework first predicts the probabilities of diseases according to patients’ chief complaints and then evaluates whether the disease codes written in medical records are reasonable via the predicted probabilities; finally, medical records with unreasonable disease codes are selected as abnormal cases for manual auditing. We conduct experiments on a real-world dataset from a large hospital and demonstrate that our model can play an effective role in anti-fraud for medical insurance.