为自主AI系统设计保险框架，实现风险定价与自动化合约。
AI-Native Insurance for Agentic AI: Pricing, Underwriting, and End-to-End Automation
- 构建基于风险状态的AI保险数学模型，涵盖自治、权限等维度。
- 明确保险可行性边界，揭示权限暴露与风险上升的单调关系。
- 案例验证合同优化与自动理赔，适合监管与AI部署者参考。
自主AI因能自主决策、调用工具、修改外部环境并对接第三方服务，带来新的保险挑战。本文提出一种面向AI原生的数学框架，用于自主AI部署的承保、定价与合约设计。部署风险以包含自治水平、操作权限、权限暴露、治理成熟度和依赖集中度的风险状态表征。该框架将风险状态映射为事件概率、损失严重性、治理成本、保费、免赔额、保障分配及保单契约，并在参与、盈利性和激励相容约束下构建保险合约设计优化问题。论文揭示了可保性的结构性特征，包括可保区域刻画、暴露程度增加导致可行性单调下降，以及治理认证阈值。保险被进一步视为运营成本与监管机制。通过医疗领域案例研究，展示了合约优化、敏感性分析与自主AI系统的自动化理赔流程。
原文摘要 · Abstract (English)
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.
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