Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting
This paper examines how AI architectures, from single LLMs to multi-agent agentic systems, can support straight-through underwriting by comparing three pipelines in a synthetic small commercial Business Owner Policies (BOPs) environment. The agentic RAG pipeline, combining retrieval, third-party data checks, and multi-step rule evaluation, outperforms others, especially in complex scenarios requiring multi-step reasoning and handling missing information.
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[Submitted on 8 Jul 2026]
Title:Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting
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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.
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2607.07858 [cs.AI]
(or arXiv:2607.07858v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.07858
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Robert Richardson [view email] [v1] Wed, 8 Jul 2026 18:43:34 UTC (3,586 KB)
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