{"slug": "agentic-ai-and-retrieval-augmented-models-in-straight-through-underwriting", "title": "Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting", "summary": "Researchers developed an agentic AI framework for straight-through underwriting of small commercial Business Owner Policies, comparing single-LLM, naive RAG, and multi-agent pipelines. The agentic system outperformed others, especially in multi-step and missing-information scenarios, by combining targeted retrieval, third-party data checks, and explicit rule evaluation.", "body_md": "arXiv:2607.07858v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/agentic-ai-and-retrieval-augmented-models-in-straight-through-underwriting", "canonical_source": "https://arxiv.org/abs/2607.07858", "published_at": "2026-07-10 04:00:00+00:00", "updated_at": "2026-07-10 04:10:25.575356+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-research"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/agentic-ai-and-retrieval-augmented-models-in-straight-through-underwriting", "markdown": "https://wpnews.pro/news/agentic-ai-and-retrieval-augmented-models-in-straight-through-underwriting.md", "text": "https://wpnews.pro/news/agentic-ai-and-retrieval-augmented-models-in-straight-through-underwriting.txt", "jsonld": "https://wpnews.pro/news/agentic-ai-and-retrieval-augmented-models-in-straight-through-underwriting.jsonld"}}