Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting

Short summary

Researchers compare three AI approaches for automated insurance underwriting: single LLM, naive RAG, and multi-agent agentic RAG. The agentic system outperforms on complex scenarios requiring multi-step reasoning and incomplete-data handling. Framework prioritizes transparency and auditability—critical in regulated finance.

• •Multi-agent agentic system outperformed single-LLM and naive RAG baselines in insurance underwriting tasks
• •Agentic approach excels at multi-step reasoning, missing-data scenarios, and explicit rule evaluation
• •Framework designed for regulated domains with emphasis on transparency, auditability, and human oversight

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