Abstract
This disclosure presents an AI-powered federated learning system designed to enhance health insurance underwriting and fraud detection while maintaining strict privacy standards. The proposed system enables healthcare providers, insurers, and regulatory bodies to collaboratively train machine learning models without sharing sensitive patient data. The federated architecture ensures secure data processing, bias mitigation, and real-time fraud detection. Key innovations include distributed AI training, differential privacy, blockchain-based claim tracking, reinforcement learning for policy adjustments, and NLP-driven prior authorization automation. This system optimizes risk assessment, reduces fraudulent claims, and ensures transparency in health insurance operations.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Methuku, Vijayalaxmi, "AI-Enabled Federated Learning System for Privacy-Preserving Health Insurance Underwriting and Fraud Detection", Technical Disclosure Commons, (March 03, 2025)
https://www.tdcommons.org/dpubs_series/7864