Correlating Medi-Claim Service by Deep Learning Neural Networks

TL;DR

This paper presents a deep learning approach using CNNs and both supervised and unsupervised classifiers to detect fraudulent medical insurance claims, aiming to combat organized healthcare frauds and money laundering.

Contribution

It introduces a novel application of CNNs and correlation analysis for fraud detection in medical insurance claims, combining multiple classifier types.

Findings

CNN-based models effectively identify fraudulent claims

Correlation study improves detection accuracy

Supervised and unsupervised classifiers complement each other

Abstract

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.