面向智能体人工智能的原生人工智能保险：定价、承保与端到端自动化
AI-Native Insurance for Agentic AI: Pricing, Underwriting, and End-to-End Automation
- NYU Tandon School of Engineering, New York University（纽约大学坦登工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对智能体人工智能带来的保险挑战，开发原生人工智能数学框架，通过风险状态映射相关要素并制定优化问题，建立可保性结构属性，经医疗案例展示了合同优化等内容。
AI中文摘要：
智能体人工智能带来了新的保险挑战，因为自主人工智能系统可进行决策、调用工具、修改外部环境并与第三方服务交互。本文为智能体人工智能部署开发了一个用于承保、定价和合同设计的原生人工智能数学框架。用风险状态表示部署，该状态涵盖自主性水平、操作权限、许可暴露、治理成熟度和依赖集中度。框架将风险状态映射到事件概率、损失严重程度、治理成本、保费、免赔额、保险范围分配和政策契约，并在参与、盈利性和激励兼容性约束下为保险合同设计制定优化问题。还建立了可保性的结构属性，包括可保性区域的特征、随着暴露增加可行性的单调恶化以及治理认证阈值。保险还被解释为人工智能部署的运营成本和监管机制。一个医疗案例研究展示了智能体人工智能系统的合同优化、敏感性分析和自动理赔处理。
英文摘要：
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.
