ADVANCED FEDERATED LEARNING FOR INSURANCE APPLICATIONS

Traditional insurance risk assessment methods rely on limited data, missing critical insights and leading to incomplete risk profiles. Alternative approaches address this by incorporating diverse data—such as transactional and behavioral information—to deliver more accurate, holistic risk evaluations.

• Confidential Identity Matching Module (CIMM) with functional verification (level of guessing confidence < 0.6).
• Fast Training Strategy Module (FTSM) and speed verification (ratio of speed improvement > 5 times) achieving as much as 17 times comparing to previous Homomorphic Encryption approach.
• Multi-machine-learning module (MMLM) and accuracy verification (ratio of accuracy improvement > 12%) achieving as much as 18% improvement comparing to conventional Logistic Regression.

• Ensures secure, privacy-preserving identity verification with <0.6 guessing confidence, minimizing unauthorized access risks. Enhances trust in sensitive systems (e.g., finance, healthcare) by enabling accurate, confidential matching while reducing probabilistic exploitation of identity data.
• Delivers 5–17x speed improvements in homomorphic encryption training, drastically cutting computational latency. Accelerates real-time applications (e.g., encrypted analytics, federated learning) by optimizing model training efficiency without compromising security, making large-scale encrypted datasets more actionable.
• Boosts prediction accuracy by 12–18% over conventional logistic regression through hybrid ML frameworks. Enables more precise insights in complex scenarios (e.g., fraud detection, risk assessment) by integrating diverse algorithms, ensuring robust performance across datasets and outperforming single-model approaches.

• Comprehensive risk analysis, sensitive information matching, authentication applications: minimize exposure to sensitive data
• Accelerate the training speed of federated learning systems, realize federated learning, and enable the application and deployment of large-scale encrypted datasets
• Improve the prediction ability of the federated learning system and provide more accurate reference in complex scenarios