Prudential Reliability of Large Language Models in Reinsurance: Governance, Assurance, and Capital Efficiency

TL;DR

This paper proposes a comprehensive prudential framework for evaluating the reliability of large language models in reinsurance, emphasizing governance, transparency, and accountability to meet regulatory standards.

Contribution

It introduces a five-pillar architecture and the RAIRAB benchmark to systematically assess LLM reliability in reinsurance, aligning AI practices with prudential regulatory expectations.

Findings

Retrieval-grounded models achieved 0.90 grounding accuracy

Reduced hallucination and interpretive drift by roughly 40%

Nearly doubled transparency in LLMs

Abstract

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.