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[2602.13213] Agentic AI for Commercial Insurance Underwriting with Adversarial Self-Critique
Summary
This paper presents an agentic AI system for commercial insurance underwriting that incorporates adversarial self-critique to enhance decision-making accuracy and safety in regulated environments.
Why It Matters
The study addresses the critical need for reliable AI systems in high-stakes industries like insurance, where human judgment is essential. By introducing a safety architecture that includes adversarial critique, it aims to reduce errors and improve decision-making, thus fostering trust in AI applications.
Key Takeaways
- The proposed AI system reduces hallucination rates from 11.3% to 3.8%.
- Decision accuracy improves from 92% to 96% with the adversarial critique mechanism.
- The framework maintains human oversight over all binding decisions.
- A formal taxonomy of failure modes aids in risk management for AI applications.
- The findings support safer AI deployment in regulated domains.
Computer Science > Artificial Intelligence arXiv:2602.13213 (cs) [Submitted on 21 Jan 2026] Title:Agentic AI for Commercial Insurance Underwriting with Adversarial Self-Critique Authors:Joyjit Roy, Samaresh Kumar Singh View a PDF of the paper titled Agentic AI for Commercial Insurance Underwriting with Adversarial Self-Critique, by Joyjit Roy and Samaresh Kumar Singh View PDF HTML (experimental) 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 st...
