Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting

2026-07-08

AI Summary

This study addresses the challenges of unstructured documents, heterogeneous data, and automated compliance-aware decision-making in insurance underwriting by proposing an “Agentic RAG” multi-agent framework for straight-through underwriting of small commercial policies. The approach integrates multi-agent planning, reflection mechanisms, and retrieval-augmented generation (RAG) to enable transparent, auditable, and human-in-the-loop decision-making through structured retrieval, third-party data validation, and explicit multi-step rule evaluation. Experimental results demonstrate that, compared to single large language models and naive RAG baselines, the proposed framework significantly enhances decision reliability and regulatory compliance—particularly in scenarios involving missing information or complex reasoning—while effectively preventing unjustified straight-through underwriting approvals.

Abstract

Artificial intelligence (AI) is beginning to reshape actuarial practice, particularly in domains that require reasoning over unstructured documents, heterogeneous data sources, and regulated decision workflows. Actuaries now face a design space that ranges from traditional rule-based automation to large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent ``agentic'' systems that plan, retrieve, call tools, and reflect. This paper examines how these emerging architectures can support actuarial priorities such as transparency, auditability, and human-in-the-loop governance, with a focus on straight-through decision processes. To make these ideas concrete, we develop and analyze an agentic AI framework for straight-through underwriting of small commercial Business Owner Policies (BOPs). We construct a synthetic but realistic experimental environment and compare three underwriting pipelines: (i) a single-LLM baseline, (ii) a naive RAG system, and (iii) a multi-agent ``Agentic RAG'' pipeline that combines targeted retrieval, third-party data checks, and explicit multi-step rule evaluation. The agentic system performs best overall, with the largest gains in multi-step and missing-information scenarios, where structured retrieval and reflection help the model avoid unsupported straight-through decisions.

Problem

• Agentic AI
• Retrieval-Augmented Generation
• Straight-Through Underwriting
• Actuarial Transparency
• Human-in-the-Loop Governance

Innovation

• Agentic AI
• Retrieval-Augmented Generation
• Straight-Through Underwriting
• Multi-Agent Systems
• Actuarial Automation