Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting
06:00 · July 11, 2026 · arXiv cs.AI RSS
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.
Summary
Artificial intelligence is reshaping actuarial workflows that must reason over unstructured documents and heterogeneous data under regulatory constraints. In insurance, straight-through underwriting of small commercial policies now serves as a test case for comparing architectures that range from single large language models to retrieval-augmented generation and multi-agent systems. The latter combine targeted retrieval, external data checks, and explicit rule evaluation within a stateful loop that plans subtasks, invokes tools, and reflects on intermediate results before terminating or escalating.
The authors constructed a synthetic but realistic Business Owner Policy dataset to evaluate three pipelines side by side. A baseline single-LLM approach relies solely on parametric knowledge. A naive RAG variant adds one-shot retrieval from policy documents. The agentic pipeline layers structured retrieval, third-party verification steps, and multi-step rule checks, routing uncertain cases for human review. Across decision accuracy and the quality of generated rationales, the agentic system showed the clearest gains precisely where information was missing or required sequential reasoning.
These outcomes underscore the continued importance of transparency and auditability. Even when statistical planning heuristics improve performance, the underlying models remain dependent on external grounding and human oversight rather than genuine autonomy. The work therefore positions agentic designs not as replacements for actuarial judgment but as mechanisms that surface facts, flag gaps, and produce traceable drafts within established governance frameworks.
Why it matters
The article is highly relevant for Dutch AI researchers and InsurTech practitioners as it provides a concrete, reproducible framework for deploying multi-agent LLM systems in highly regulated domains. Its strong emphasis on auditability, transparency, and human-in-the-loop governance aligns perfectly with the EU AI Act and the Netherlands' strategic focus on ethical AI.
