Artificial Intelligence (AI) is reshaping actuarial practice, particularly in areas 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 can 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, focusing 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 results show that 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.
Blogger's Review: This paper provides an in-depth analysis of the potential of agentic AI systems in the actuarial field, particularly in complex decision-making scenarios. By comparing different models, the study highlights the advantages of agentic technology in enhancing decision transparency and efficiency, making it worthy of attention and exploration in the industry.