AI Underwriting: Governing Large Language Models in Reinsurance

by

Author: Denis Avetisyan

A new framework for ensuring the reliability, transparency, and regulatory compliance of AI models used in critical reinsurance functions.

This paper introduces the RAIRAB benchmark—a five-pillar approach to model risk management, data governance, and capital efficiency when deploying large language models in reinsurance, addressing requirements like Solvency II.

Despite increasing reliance on artificial intelligence for complex financial modeling, robust frameworks for ensuring the prudential oversight of large language models (LLMs) remain underdeveloped. This paper, ‘Prudential Reliability of Large Language Models in Reinsurance: Governance, Assurance, and Capital Efficiency’, addresses this gap by proposing a five-pillar architecture and the Reinsurance AI Reliability and Assurance Benchmark (RAIRAB) to evaluate LLM reliability across key risk management functions. Results demonstrate that governance-embedded, retrieval-grounded LLMs significantly improve data integrity, transparency, and accountability—reducing informational frictions and supporting efficient capital allocation. Can these findings establish a precedent for broader regulatory acceptance of AI within highly regulated financial sectors?

Regulation Follows Innovation

The insurance sector increasingly adopts Artificial Intelligence (AI) for efficiency and improved customer service. However, existing regulatory frameworks weren’t designed for the complexities and risks inherent in these advanced models. Traditional model risk management (MRM) proves inadequate for Large Language Models (LLMs) due to their opacity, scale, and evolving nature. Adapting existing frameworks or developing new approaches is crucial. Global bodies like the FSB, BIS, and IAIS are actively exploring applying existing principles, creating uncertainty for firms seeking compliance. True innovation endures through refined, responsible practice.

Five Pillars of AI Governance

Effective AI governance requires a multi-faceted structure encompassing governance, data integrity, assurance, resilience, and regulatory alignment. Data lineage is fundamental for validating AI outputs and ensuring accountability; tracking data origin and transformation identifies biases. Transparency, objectively measured, correlates positively with the adoption of Retrieval-Augmented Generation (RAG), detailed logging, and Human-in-the-Loop (HITL) configurations – enhancing interpretability and auditability.

Benchmarking LLM Reliability

The Reinsurance AI Reliability and Assurance Benchmark (RAIRAB) provides a standardized methodology for evaluating Large Language Model (LLM) performance within the reinsurance industry. Addressing the need for consistent assessment as LLMs integrate into vital functions, RAIRAB prioritizes Human-in-the-Loop (HITL) oversight. HITL facilitates error detection, validation, and expert judgment, particularly where financial or regulatory stakes are high. A key focus is ‘Interpretive Drift’ – variability in model outputs. Improvements in semantic stability are demonstrated through robust governance controls and regular retraining, critical for reliable reinsurance processes.

Functional Equivalence in an AI World

Current regulatory approaches emphasize ‘Functional Equivalence’ – AI systems should face the same control and oversight as traditional models performing similar functions. The technology underpinning a model shouldn’t dictate governance stringency, but rather the risk it poses. Regulatory bodies like EIOPA and NAIC are adapting frameworks like Solvency II and SR 11-7 to address AI’s unique challenges, focusing on model risk management, data quality, and explainability. Research shows governance-embedded LLMs, utilizing RAG, logging, and HITL, can achieve approximately 0.9 Grounding Accuracy (GA) and reduce Hallucination Rates by 40–45%, with inter-rater reliability reaching 0.87 (κ). Principles endure, abstractions fade.

The pursuit of robust governance for large language models, as detailed in the proposed RAIRAB framework, echoes a fundamental principle: simplification through rigorous reduction. The article champions a system built on data lineage, transparency, and accountability – elements designed to remove uncertainty and potential for error. This aligns with G.H. Hardy’s assertion: “A mathematician, like a painter or a poet, is a maker of patterns.” The patterns here aren’t aesthetic, but logical; a meticulously constructed system where each component’s function is clear, and extraneous complexity is discarded. The focus on model risk management isn’t about adding layers of security, but rather stripping away potential vulnerabilities until only essential reliability remains.

What Remains?

The proposition of a benchmark – RAIRAB – for large language models in reinsurance addresses a symptom, not the disease. The underlying complexity of these models, and the opacity of their reasoning, will not be legislated away by five pillars, however elegantly constructed. Future effort must relentlessly pursue simplification, not merely assurance. The field will not advance through increasingly elaborate validation procedures, but through models demanding fewer of them.

Current discourse centers on data lineage, a necessary but insufficient condition. Traceability is valuable only if the data itself is meaningful, and the transformations applied to it are demonstrably logical. The focus should shift from proving what a model has learned, to understanding how it learns, and, crucially, why it makes its decisions. If a model’s output cannot be explained in a single, coherent sentence, its integration into a prudential framework remains a speculative exercise.

Ultimately, the question is not whether these models can be governed, but whether they need to exist. Capital efficiency, regulatory compliance – these are means, not ends. A simpler, more transparent system, even if less ‘intelligent’, may prove more resilient, and therefore, more valuable. The pursuit of complexity, for its own sake, is a fool’s errand.

Original article: https://arxiv.org/pdf/2511.08082.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2025-11-12 15:14