AI Powered Enhancements in Healthcare Insurance Fraud Detection and Prevention

Abstract

The integrity of healthcare systems is threatened by healthcare insurance fraud, which continues to be a major problem and cause large financial losses. Conventional fraud detection techniques frequently don't have the precision and effectiveness needed to successfully stop complex fraudulent activity. This study examines AI-powered improvements created especially for the identification and prevention of health insurance fraud in 2023. The goal of this research is to create a thorough and reliable framework that can detect and stop fraud in real time by combining deep learning methods, blockchain technology, and sophisticated machine learning algorithms. This discovery is important because it has the potential to significantly increase the accuracy and speed of fraud detection systems, protecting financial resources and guaranteeing the quality of healthcare services.This paper's original contributions include the creation of a novel AI-driven framework that uses deep learning models to improve fraud detection accuracy, improves data security and transparency through blockchain integration, and provides a scalable solution that can be adapted to different healthcare insurance systems. A comprehensive literature study emphasizes the shortcomings of current techniques and the demand for novel strategies. By offering a more effective and efficient mechanism for fraud detection and prevention, the suggested framework is anticipated to close these gaps. In-depth discussions of this framework's architecture, methodology, and possible effects offer practitioners and researchers in the field insightful information. A summary of the main conclusions and recommendations for further research are included at the end of this paper.

Keywords: Healthcare Insurance, Fraud Detection, Artificial Intelligence, Machine Learning, Blockchain Technology, Deep Learning, Data Security, Real-Time Detection