Generative AI-Driven Fraud Detection in Health Care Enhancing Data Loss Prevention and Cybersecurity Analytics for Real-Time Protection of Patient Records

Abstract

The health care industry faces persistent challenges related to fraud, significantly impacting financial stability and patient safety. Traditional fraud detection methods, such as rule-based systems and manual audits, often fail to keep pace with sophisticated cyber-attacks, exposing critical vulnerabilities. This review paper explores the integration of generative AI-driven models, including Generative Adversarial Networks (GANs), into health care fraud detection systems to enhance data loss prevention and cybersecurity analytics. The paper delves into the limitations of current fraud detection strategies, highlighting the transformative potential of generative AI technologies in identifying complex patterns and anomalies. Methodologies for incorporating generative AI into cybersecurity frameworks are discussed, focusing on data collection techniques, algorithm selection, and evaluation metrics for assessing effectiveness. Case studies illustrate the advantages of real-time fraud prevention facilitated by AI integration. The discussion also addresses the ethical and data privacy concerns associated with deploying AI in health care, offering strategic recommendations for enhancing cybersecurity protocols. This review concludes with insights into the future of AI-driven fraud detection and its critical role in ensuring the protection of patient records and the resilience of health care systems.