Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting
Quick Answer
This paper explores the use of agentic AI and retrieval-augmented models in straight-through underwriting for Business Owner Policies (BOPs).
Quick Take
The 'Agentic ' pipeline outperforms traditional models, particularly in complex scenarios, enhancing transparency and governance in actuarial practices.
Key Points
- Agentic AI framework supports transparency and human-in-the-loop governance in underwriting.
- Three underwriting pipelines compared: single-, naive RAG, and Agentic RAG.
- Agentic RAG shows best performance in multi-step and missing-information scenarios.
- Structured retrieval and reflection help avoid unsupported decisions in underwriting.
- Emerging AI architectures reshape actuarial practices with unstructured data handling.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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)
— Originally published at arxiv.org
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