Prudential Reliability of Large Language Models in Reinsurance: Governance, Assurance, and Capital Efficiency
Published 11 Nov 2025 · arXiv · Stella C. Dong
Overview
Stella C. Dong's paper presents a prudential framework for assessing the reliability of large language models (LLMs) in reinsurance. The framework focuses on governance, data lineage, assurance, resilience, and regulatory alignment, translating supervisory expectations into measurable controls.
Key Insights
- Framework Implementation: The Reinsurance AI Reliability and Assurance Benchmark (RAIRAB) evaluates LLMs against prudential standards.
- Performance Metrics: Retrieval-grounded configurations achieved a grounding accuracy of 0.90, reduced hallucination and interpretive drift by 40%, and nearly doubled transparency.
- Regulatory Alignment: The framework aligns with Solvency II, SR 11-7, and guidance from EIOPA, NAIC, and IAIS.
BFSI Relevance
- Why Relevant: The framework enhances the reliability and transparency of AI in reinsurance, crucial for risk management and capital allocation.
- Primary Sector: Insurance
- Subsectors: Reinsurance
- Actionable Implications: Reinsurance firms should adopt the framework to ensure AI models meet prudential standards, improving governance and reducing risk.
researcher peer-reviewed-paper insurance-reinsurance regulatory-and-standards global