Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting
Robert Richardson, Josh Meyers, Brian Hartman, David Sandberg
Why It Matters
What makes this one worth your time
Understanding how advanced AI models can enhance actuarial practices is crucial for professionals aiming to leverage technology for efficiency and compliance in decision-making.
This research demonstrates the advantages of agentic AI in improving underwriting processes.
Summary
The paper explores the application of agentic AI and retrieval-augmented models in the context of straight-through underwriting for small commercial Business Owner Policies, comparing different underwriting pipelines to assess performance.
Key contributions
- Development of an agentic AI framework tailored for straight-through underwriting.
- Empirical comparison of single-LLM, naive RAG, and multi-agent RAG systems in underwriting scenarios.
- Identification of performance gains in multi-step and missing-information contexts.
Notable insights
- The agentic AI framework integrates multi-step reasoning and structured retrieval to address missing information, which is often a challenge in underwriting.
- The comparative analysis of different underwriting pipelines provides a clear evaluation of the effectiveness of agentic systems over traditional methods.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2607.07858v1 Announce Type: new 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.