Bridging Data Divides: AI as a New Paradigm for Unstructured Data

The rise of large language models (LLMs) represents a transformative opportunity for the actuarial profession. While public discourse often focuses on applications like chatbots and content generation, these AI tools hold immense potential for addressing long-standing challenges in insurance and risk assessment. Specifically, LLMs excel at unifying and synthesizing vast amounts of fragmented and unstructured data, enabling actuaries to incorporate a broader range of factors into their standard pricing models (like GLMs), thereby improving the accuracy of risk evaluations.However, the stochastic and opaque nature of LLMs contrasts with the transparency traditionally valued in actuarial science, posing unique challenges. To address this issue, robust evaluation frameworks have emerged, enabling teams to systematically assess and refine LLM outputs using both human reviewers and AI-based scoring. Crucially, retrieval-augmented generation (RAG) pipelines help ground model responses in verifiable facts, curbing the risk of “hallucinations” and facilitating regulatory compliance. This paper presents a case study analyzing EPA environmental reports to illustrate the practical application of AI techniques. By leveraging embeddings, structured information retrieval, and RAG, actuaries can extract actionable insights from dense technical documents, demonstrating how AI can augment traditional methods in data-intensive scenarios.