Agentic AI for Commercial Insurance Underwriting with Adversarial Self-Critique

TL;DR

This work tackles the challenge of deploying AI in regulated commercial underwriting by proposing a decision-negative, human-in-the-loop agentic system that uses an adversarial self-critique mechanism to test its conclusions before human review. The system integrates a bounded authority workflow, a retrieval-augmented reasoning process, and a formal failure-mode taxonomy to manage risk in high-stakes decisions. Empirical evaluation on 500 expert-validated cases shows that adversarial critique reduces hallucinations from 11.3% to 3.8% and improves decision accuracy from 92% to 96%, while maintaining human final authority over binding decisions. The approach demonstrates a practical path to safer AI adoption in regulated domains and provides a blueprint for risk management and oversight in high-stakes AI applications.

Abstract

Commercial insurance underwriting is a labor-intensive process that requires manual review of extensive documentation to assess risk and determine policy pricing. While AI offers substantial efficiency improvements, existing solutions lack comprehensive reasoning capabilities and internal mechanisms to ensure reliability within regulated, high-stakes environments. Full automation remains impractical and inadvisable in scenarios where human judgment and accountability are critical. This study presents a decision-negative, human-in-the-loop agentic system that incorporates an adversarial self-critique mechanism as a bounded safety architecture for regulated underwriting workflows. Within this system, a critic agent challenges the primary agent's conclusions prior to submitting recommendations to human reviewers. This internal system of checks and balances addresses a critical gap in AI safety for regulated workflows. Additionally, the research develops a formal taxonomy of failure modes to characterize potential errors by decision-negative agents. This taxonomy provides a structured framework for risk identification and risk management in high-stakes applications. Experimental evaluation using 500 expert-validated underwriting cases demonstrates that the adversarial critique mechanism reduces AI hallucination rates from 11.3% to 3.8% and increases decision accuracy from 92% to 96%. At the same time, the framework enforces strict human authority over all binding decisions by design. These findings indicate that adversarial self-critique supports safer AI deployment in regulated domains and offers a model for responsible integration where human oversight is indispensable.

Figures (8)

• Figure 1: State machine workflow with guard conditions. The self-critique cycle allows one iteration before proceeding to the decision. Human authorization checkpoint ensures no autonomous binding.
• Figure 2: State machine workflow with guard conditions. The self-critique cycle allows one iteration before proceeding to decision. Human authorization checkpoint ensures no autonomous binding. Dashed path shows graceful degradation for uncertain cases.
• Figure 3: Performance comparison across key metrics. The adversarial critique mechanism improved performance across all dimensions, with all improvements statistically significant (p$<$0.05).
• Figure 4: Error type breakdown across 500 test cases. False positives show the largest reduction at 72%. Hallucinations dropped 67% and compliance slips dropped 68%.
• Figure 5: Hallucination severity breakdown. Minor hallucinations are factual errors unlikely to affect decisions. Major hallucinations could materially impact underwriting outcomes. No major hallucinations were observed with the critic in the 500-case evaluation set.