Correlating Medi-Claim Service by Deep Learning Neural Networks
This addresses financial losses for insured individuals and health insurance companies, but appears incremental as it applies existing deep learning methods to a specific domain.
The paper tackled medical insurance fraud detection by using Convolutional Neural Networks and regression models to correlate claims, achieving detection of fraudulent activities and money laundering across providers.
Medical insurance claims are of organized crimes related to patients, physicians, diagnostic centers, and insurance providers, forming a chain reaction that must be monitored constantly. These kinds of frauds affect the financial growth of both insured people and health insurance companies. The Convolution Neural Network architecture is used to detect fraudulent claims through a correlation study of regression models, which helps to detect money laundering on different claims given by different providers. Supervised and unsupervised classifiers are used to detect fraud and non-fraud claims.