面向智能体 AI 的 AI 原生保险：定价、承保与端到端自动化
英文标题：AI-Native Insurance for Agentic AI: Pricing, Underwriting, and End-to-End Automation
本文提出面向智能体 AI 部署的 AI 原生保险数学框架，涵盖承保、定价与合同设计。模型将部署抽象为包含自主等级、操作权限和治理成熟度等维度的风险状态，并将其映射到事件概率、损失程度、保费与保单条款。研究在参与、盈利与激励相容约束下优化合同，并揭示了可保性区域、风险暴露增大导致可行性递减以及治理认证阈值等结构性性质。保险被同时视为 AI 运营成本和监管机制，医疗案例验证了自动化理赔流程，为开发者在智能体系统保险设计与合规方面提供指导。
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中文
[于 2026 年 7 月 14 日提交]
面向智能体 AI 的 AI 原生保险：定价、承保与端到端自动化
查看论文《面向智能体 AI 的 AI 原生保险：定价、承保与端到端自动化》的 PDF，作者 Quanyan Zhu
**摘要：**智能体 AI 带来了新的保险挑战，因为自主 AI 系统可以做出决策、调用工具、修改外部环境并与第三方服务交互。本文为智能体 AI 部署开发了一个 AI 原生数学框架，用于承保、定价和合同设计。一个部署由风险状态表示，该状态涵盖自主等级、操作权限、权限暴露、治理成熟度和依赖集中度。该框架将风险状态映射到事件概率、损失严重性、治理成本、保费、免赔额、覆盖分配和保单契约，并制定了一个在参与、盈利和激励相容约束下的保险合同设计优化问题。论文建立了可保性的结构性质，包括可保性区域的刻画、风险暴露增加时可行性的单调衰减以及治理认证阈值。保险进一步被解释为既是 AI 部署的运营成本，也是一种监管机制。医疗案例研究演示了针对智能体 AI 系统的合同优化、敏感性分析和自动化理赔处理。
提交历史
来自: Quanyan Zhu [查看邮箱]
[v1] 2026 年 7 月 14 日星期二 19:46:21 UTC (78 KB)
English / 原文
[Submitted on 14 Jul 2026]
Title:AI-Native Insurance for Agentic AI: Pricing, Underwriting, and End-to-End Automation
View a PDF of the paper titled AI-Native Insurance for Agentic AI: Pricing, Underwriting, and End-to-End Automation, by Quanyan Zhu
Abstract: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.
Submission history
From: Quanyan Zhu [
]
[v1]
Tue, 14 Jul 2026 19:46:21 UTC (78 KB)