Prudential Reliability of Large Language Models in Reinsurance: Governance, Assurance, and Capital Efficiency
This addresses the need for reliable AI in reinsurance to reduce informational frictions in risk transfer and capital allocation, though it is incremental as it adapts existing prudential doctrines to AI governance.
The paper tackles the problem of ensuring the reliability of large language models (LLMs) in reinsurance by developing a prudential framework with a five-pillar architecture, resulting in retrieval-grounded configurations achieving higher grounding accuracy (0.90), reducing hallucination and interpretive drift by roughly 40%, and nearly doubling transparency across six task families.
This paper develops a prudential framework for assessing the reliability of large language models (LLMs) in reinsurance. A five-pillar architecture--governance, data lineage, assurance, resilience, and regulatory alignment--translates supervisory expectations from Solvency II, SR 11-7, and guidance from EIOPA (2025), NAIC (2023), and IAIS (2024) into measurable lifecycle controls. The framework is implemented through the Reinsurance AI Reliability and Assurance Benchmark (RAIRAB), which evaluates whether governance-embedded LLMs meet prudential standards for grounding, transparency, and accountability. Across six task families, retrieval-grounded configurations achieved higher grounding accuracy (0.90), reduced hallucination and interpretive drift by roughly 40%, and nearly doubled transparency. These mechanisms lower informational frictions in risk transfer and capital allocation, showing that existing prudential doctrines already accommodate reliable AI when governance is explicit, data are traceable, and assurance is verifiable.