Agentic AI for Commercial Insurance Underwriting with Adversarial Self-Critique
Joyjit Roy, Samaresh Kumar Singh
Why It Matters
What makes this one worth your time
This research addresses critical safety and reliability concerns in AI applications for regulated industries, making it relevant for both practitioners and researchers focused on responsible AI deployment.
A novel AI system enhances underwriting accuracy through adversarial self-critique.
Summary
The paper presents a human-in-the-loop agentic AI system for commercial insurance underwriting that incorporates an adversarial self-critique mechanism to enhance decision-making reliability and reduce errors.
Key contributions
- Development of a human-in-the-loop agentic system for underwriting.
- Introduction of an adversarial self-critique mechanism to reduce AI hallucination rates.
- Creation of a formal taxonomy for identifying failure modes in decision-negative agents.
Notable insights
- The adversarial self-critique mechanism serves as a unique internal check that enhances decision reliability in high-stakes environments.
- The formal taxonomy of failure modes provides a structured approach to risk management in AI systems.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2602.13213v2 Announce Type: replace 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 and internal mechanisms to ensure reliability in regulated, high-stakes environments. Full automation remains impractical and inadvisable when 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. In 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 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.