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

Stella C. Dong

TL;DR

This paper addresses the prudential integration of large language models (LLMs) into reinsurance by proposing a Five-Pillar Governance framework and the RAIRAB benchmark, which translate Solvency II, SR 11-7, and global AI guidance into measurable lifecycle controls. It demonstrates that governance-embedded configurations—especially retrieval-grounded generation with structured logging and human-in-the-loop oversight—achieve high grounding accuracy ($$GA ≈ 0.9$$), substantially reduced hallucinations ($$HR ≈ 40 ext{%}$$), and enhanced transparency ($$TI$$ near 0.86) across six task families. The empirical results show that governance design, not model scale, drives prudential readiness, with interpretive drift reduced and compliance artifacts increased, thereby lowering supervisory frictions and implying potential capital-efficiency gains. The work integrates technical controls with insurance economics to position prudential AI as an auditable, regulator-aligned capability that strengthens solvency resilience and market efficiency.

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.

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

TL;DR

This paper addresses the prudential integration of large language models (LLMs) into reinsurance by proposing a Five-Pillar Governance framework and the RAIRAB benchmark, which translate Solvency II, SR 11-7, and global AI guidance into measurable lifecycle controls. It demonstrates that governance-embedded configurations—especially retrieval-grounded generation with structured logging and human-in-the-loop oversight—achieve high grounding accuracy (), substantially reduced hallucinations (), and enhanced transparency ( near 0.86) across six task families. The empirical results show that governance design, not model scale, drives prudential readiness, with interpretive drift reduced and compliance artifacts increased, thereby lowering supervisory frictions and implying potential capital-efficiency gains. The work integrates technical controls with insurance economics to position prudential AI as an auditable, regulator-aligned capability that strengthens solvency resilience and market efficiency.

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.

Paper Structure

This paper contains 75 sections, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background: AI Assurance and Supervisory Governance in Reinsurance
  3. From Predictive Models to Generative Reasoning Systems
  4. Regulatory Convergence on AI Governance
  5. Information Frictions and Prudential Efficiency
  6. Information Frictions and Prudential Efficiency
  7. Motivation for a Prudential Benchmark
  8. The Five-Pillar Prudential Framework
  9. Pillar 1: Governance and Oversight
  10. Pillar 2: Data Lineage and Integrity
  11. Pillar 3: Assurance and Transparency
  12. Pillar 4: Operational Resilience
  13. Pillar 5: Ethical and Regulatory Alignment
  14. Cross-Pillar Interactions and Measurement Linkage
  15. LLM-Enabled Workflows Across the Reinsurance Value Chain
  16. ...and 60 more sections

Figures (3)

  • Figure 1: Linkage between the Five-Pillar Prudential Framework and RAIRAB metrics. This mapping operationalizes supervisory doctrine (Solvency II / SR 11-7 / EIOPA / IAIS) as measurable indicators for reinsurance workflows.
  • Figure 2: Alignment of LLM-enabled workflows with prudential functions in reinsurance.
  • Figure A1: Operational data-control flow for Pillar 2 (Data Lineage, Integrity, and Protection). Source artifacts are captured with provenance metadata, validated for quality, and accessed only through governed LLM/RAG services. All interactions are logged and exportable for supervisory review.