On July 8, 2026, researchers Robert Richardson, Josh Meyers, Brian Hartman, and David Sandberg published a paper exploring the impact of agentic AI and retrieval-augmented models on underwriting processes, particularly for small commercial Business Owner Policies (BOPs). The study highlights the potential of these technologies to enhance actuarial practices by improving transparency and decision-making.
Understanding Agentic AI in Underwriting
Agentic AI refers to systems capable of planning, retrieving information, and executing tasks autonomously. This paper delves into how such systems can streamline straight-through underwriting, a process that allows insurance applications to be processed without human intervention. The authors argue that integrating agentic systems can significantly improve efficiency and accuracy in underwriting.
The study presents a comparative analysis of three different underwriting pipelines: (i) a traditional single large language model (LLM) baseline, (ii) a naive retrieval-augmented generation (RAG) system, and (iii) a sophisticated multi-agent Agentic RAG pipeline. The latter combines various data retrieval methods and rule evaluations to optimize decision-making.
Benefits of Retrieval-Augmented Models
Retrieval-augmented models enhance the ability of AI systems to handle unstructured data, which is critical in actuarial practices. The research indicates that these models can effectively manage heterogeneous data sources and maintain compliance with regulatory requirements. The agentic system demonstrated superior performance, especially in scenarios where multi-step reasoning and handling missing information were essential.
