Correlating Medi-Claim Service by Deep Learning Neural Networks

Organized crime is a continuous issue, and predicting it is always under research. Medical insurance claims are one of the 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 the insured people and the 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. By using different attributes of patient case studies, diagnostic reports, and service provider reimbursement claim codes as control variables and attributes of the target class to detect performance metrics, this paper highlights the top reason for organized crime through the public dataset. The claims are filed by the provider, so the fraud can be organized crime. The performance metrics of accuracy, sensitivity, specificity, recall, precision, AUC, and f1-scores are calculated.