AI-Powered Fraud Detection in Healthcare Systems: A Data-Driven Approach

Abstract

This paper discusses and analyzes the transformative role of artificial intelligence in fraud detection within healthcare systems, emphasizing a data-driven approach. The integration of AI in fraud detection not only enhances accuracy but also minimizes financial losses and operational inefficiencies, which are critical in modern healthcare. The increasing volume of electronic health records (EHRs) and insurance claims has heightened the risk of fraudulent activities, making traditional detection methods inadequate. AI-driven fraud detection leverages machine learning, anomaly detection, and predictive analytics to identify suspicious patterns, unauthorized claims, and billing discrepancies in real time. These technologies enhance fraud prevention by processing vast amounts of healthcare data and detecting irregularities that might go unnoticed by conventional systems. Case studies across various healthcare sectors illustrate the effectiveness of AI-powered fraud detection in mitigating risks and ensuring data integrity. While AI significantly strengthens fraud prevention, challenges such as high implementation costs and ethical concerns remain. This study underscores that AI is not a flawless solution but a vital component of modern fraud detection frameworks, reinforcing healthcare security and trust.