AI-Native Insurance for Agentic AI: Pricing, Underwriting, and End-to-End Automation
Quick Answer
This paper presents an AI-native framework for underwriting and pricing insurance for autonomous AI systems, addressing challenges in risk assessment and governance.
Quick Take
It formulates an optimization problem for contract design, considering factors like autonomy level and operational authority, and illustrates its application through a healthcare case study.
Key Points
- Develops a mathematical framework for insurance of agentic AI deployments.
- Maps risk states to event probabilities, loss severities, and governance costs.
- Establishes insurability properties like exposure deterioration and certification thresholds.
- Interprets insurance as both operational cost and regulatory mechanism.
- Includes a case study on healthcare for contract optimization and claims processing.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Agentic AI introduces new insurance challenges because autonomous AI systems can make decisions, invoke tools, modify external environments, and interact with third-party services. This paper develops an AI-native mathematical framework for underwriting, pricing, and contract design for agentic AI deployments. A deployment is represented by a risk state that captures autonomy level, operational authority, permission exposure, governance maturity, and dependency concentration. The framework maps the risk state to event probabilities, loss severities, governance costs, premiums, deductibles, coverage allocation, and policy covenants, and formulates an optimization problem for insurance contract design under participation, profitability, and incentive compatibility constraints. The paper establishes structural properties of insurability, including characterization of an insurability region, monotone deterioration of feasibility with increasing exposure, and governance certification thresholds. Insurance is further interpreted as both an operational cost and a regulatory mechanism for AI deployment. A healthcare case study illustrates contract optimization, sensitivity analysis, and automated claims processing for agentic AI systems.
| Subjects: | Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computers and Society (cs.CY) |
| MSC classes: | 91B30, 91B70, 91G40, 90B50, 68T07, 68M25 |
| ACM classes: | K.6.5; J.1; I.2.11 |
| Cite as: | arXiv:2607.13230 [cs.AI] |
| (or arXiv:2607.13230v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.13230 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Quanyan Zhu [view email]
[v1]
Tue, 14 Jul 2026 19:46:21 UTC (78 KB)
— Originally published at arxiv.org
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