Behavioral and AI-Driven Predictive Analytics for Proactive Fraud Prevention in U.S. Healthcare Cyber security Biometrics

Mukasa, Alex Lwembawo and Makandah, Esther A and Christopher, Haruna Atabo and Dickson, Dako Apaleokhai (2025) Behavioral and AI-Driven Predictive Analytics for Proactive Fraud Prevention in U.S. Healthcare Cyber security Biometrics. World Journal of Advanced Research and Reviews, 27 (2). pp. 1652-1661. ISSN 2581-9615

The challenges in healthcare cybersecurity are growing due to the increase in fraudulent activities, such as identity theft, insurance scams, and unauthorized entry to electronic health records (EHRs). Conventional authentication methods like passwords and two-factor authentication have shown to be insufficient in countering advanced cyber threats. This research examines the combination of behavioral biometrics and AI-based predictive analytics for proactive fraud prevention in cybersecurity within U.S. healthcare. Behavioral biometrics, such as keystroke dynamics, mouse movement patterns, and gait analysis, provide an ongoing authentication method that improves security while maintaining user workflow continuity. AI-powered predictive analytics, utilizing both supervised and unsupervised machine learning models, facilitate immediate fraud detection by recognizing unusual user activities within healthcare processes. Even with its benefits, implementing behavioral biometrics and AI models poses various technical hurdles, such as accuracy constraints, false positives, and biases in algorithms. Additionally, AI systems need extensive, high-quality datasets to detect fraud effectively, which brings about concerns regarding privacy and ethical implications. To tackle these issues, additional investigation into adaptive biometric systems, privacy-preserving AI methods, and regulatory structures is essential for harmonizing security with compliance obligations. This research suggests that future studies focus on hybrid biometric authentication systems, reducing bias in AI-enabled fraud detection, and utilizing privacy-enhancing technologies like federated learning and homomorphic encryption. By implementing AI-based cybersecurity systems, healthcare organizations can improve fraud detection strategies, safeguard patient information, and maintain compliance with regulations. The results highlight the necessity for teamwork among healthcare professionals, cybersecurity specialists, and policymakers to develop strong, ethical, and efficient security measures.