AI-Native Insurance for Agentic AI: Pricing, Underwriting, and End-to-End Automation

• URL: http://arxiv.org/abs/2607.13230v1
• Date: Tue, 14 Jul 2026 19:46:21 GMT
• Title: AI-Native Insurance for Agentic AI: Pricing, Underwriting, and End-to-End Automation
• Abstract summary: This paper develops an AI-native mathematical framework for underwriting, pricing, and contract design for agentic AI deployments.<n>A healthcare case study illustrates contract optimization, sensitivity analysis, and automated claims processing for agentic AI systems.
• Score: 20.036142577511455
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Underwriting the Agent Economy: The Blueprint for an AI Insurance Stack [21.273902447867464]
  The emerging AI agent economy, projected to handle trillions of dollars in transactions by 2030, looks to be the next such development.<n>Yet insurers' exposure to AI agent risk currently sits largely unpriced across existing insurance lines.<n>Insurability is trending the wrong way; AI agent capabilities appear to be outpacing reliability.<n>This report argues that affirmative AI coverage with limits in the billions is achievable by 2030, but only with industry-wide coordination.
  arXiv  Detail & Related papers  (2026-07-13T17:48:25Z)
• AgentCanary: A Security Evaluation Framework for Autonomous AI Agents in Real Executable Environments [32.982317538632806]
  We propose AgentCanary, a comprehensive security evaluation framework for autonomous AI agents.<n>AgentCanary provides a systematic solution along three contributions.<n>We evaluate a broad set of frontier models on AgentCanary against multiple established adversarial attack methods.
  arXiv  Detail & Related papers  (2026-06-09T06:55:37Z)
• Insurance of Agentic AI [20.036142577511455]
  Agentic artificial intelligence (AI) systems are transforming the risk landscape by extending beyond information generation to autonomous planning.<n>These capabilities introduce novel exposures that do not fit neatly within traditional insurance categories such as cyber, professional liability, product liability, or directors and officers coverage.<n>This paper examines the emerging insurance market for agentic AI and develops a framework for understanding its underwriting, pricing, reinsurance, and product-design implications.
  arXiv  Detail & Related papers  (2026-06-03T21:10:38Z)
• Incentive-Aware AI Safety via Strategic Resource Allocation: A Stackelberg Security Games Perspective [31.55000083809067]
  We show how game-theoretic deterrence can make AI oversight proactive, risk-aware, and resilient to manipulation.<n>We illustrate how this framework can inform (1) training-time auditing against data/feedback poisoning, (2) pre-deployment evaluation under constrained reviewer resources, and (3) robust multi-model deployment in adversarial environments.
  arXiv  Detail & Related papers  (2026-02-06T23:20:26Z)
• The Role of Risk Modeling in Advanced AI Risk Management [33.357295564462284]
  Rapidly advancing artificial intelligence (AI) systems introduce novel, uncertain, and potentially catastrophic risks.<n>Managing these risks requires a mature risk-management infrastructure whose cornerstone is rigorous risk modeling.<n>We argue that advanced-AI governance should adopt a similar dual approach and that verifiable, provably-safe AI architectures are urgently needed.
  arXiv  Detail & Related papers  (2025-12-09T15:37:33Z)
• AURA: An Agent Autonomy Risk Assessment Framework [0.0]
  AURA (Agent aUtonomy Risk Assessment) is a unified framework designed to detect, quantify, and mitigate risks arising from agentic AI.<n>AURA provides an interactive process to score, evaluate and mitigate the risks of running one or multiple AI Agents, synchronously or asynchronously.<n>AURA supports a responsible and transparent adoption of agentic AI and provides robust risk detection and mitigation while balancing computational resources.
  arXiv  Detail & Related papers  (2025-10-17T15:30:29Z)
• The 2025 OpenAI Preparedness Framework does not guarantee any AI risk mitigation practices: a proof-of-concept for affordance analyses of AI safety policies [35.43144920451646]
  We analyse the OpenAI 'Preparedness Framework Version 2' (April 2025) using the Mechanisms & Conditions model of affordances and the MIT AI Risk Repository.<n>We find that this safety policy requests evaluation of a small minority of AI risks, encourages deployment of systems with 'Medium' capabilities for unintentionally enabling'severe harm'<n>These findings suggest that effective mitigation of AI risks requires more robust governance interventions beyond current industry self-regulation.
  arXiv  Detail & Related papers  (2025-09-29T07:42:10Z)
• Embodied AI: Emerging Risks and Opportunities for Policy Action [46.48780452120922]
  Embodied AI (EAI) systems can exist in, learn from, reason about, and act in the physical world.<n>EAI systems pose significant risks, including physical harm from malicious use, mass surveillance, as well as economic and societal disruption.
  arXiv  Detail & Related papers  (2025-08-28T17:59:07Z)
• Reinsuring AI: Energy, Agriculture, Finance & Medicine as Precedents for Scalable Governance of Frontier Artificial Intelligence [0.0]
  This paper proposes a novel framework for governing such high-stakes models through a three-tiered insurance architecture.<n>It shows how the federal government can stabilize private AI insurance markets without resorting to brittle regulation or predictive licensing regimes.
  arXiv  Detail & Related papers  (2025-04-02T21:02:19Z)
• Real AI Agents with Fake Memories: Fatal Context Manipulation Attacks on Web3 Agents [36.49717045080722]
  This paper investigates the vulnerabilities of AI agents within blockchain-based financial ecosystems when exposed to adversarial threats in real-world scenarios.<n>We introduce the concept of context manipulation -- a comprehensive attack vector that exploits unprotected context surfaces.<n>Using ElizaOS, we showcase that malicious injections into prompts or historical records can trigger unauthorized asset transfers and protocol violations.
  arXiv  Detail & Related papers  (2025-03-20T15:44:31Z)
• Agentic AI: Autonomy, Accountability, and the Algorithmic Society [0.2209921757303168]
  Agentic Artificial Intelligence (AI) can autonomously pursue long-term goals, make decisions, and execute complex, multi-turn.<n>This transition from advisory roles to proactive execution challenges established legal, economic, and creative frameworks.<n>We explore challenges in three interrelated domains: creativity and intellectual property, legal and ethical considerations, and competitive effects.
  arXiv  Detail & Related papers  (2025-02-01T03:14:59Z)
• Computing Power and the Governance of Artificial Intelligence [51.967584623262674]
  Governments and companies have started to leverage compute as a means to govern AI. compute-based policies and technologies have the potential to assist in these areas, but there is significant variation in their readiness for implementation. naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power.
  arXiv  Detail & Related papers  (2024-02-13T21:10:21Z)
• AI Liability Insurance With an Example in AI-Powered E-diagnosis System [22.102728605081534]
  We use an AI-powered E-diagnosis system as an example to study AI liability insurance. We show that AI liability insurance can act as a regulatory mechanism to incentivize compliant behaviors and serve as a certificate of high-quality AI systems.
  arXiv  Detail & Related papers  (2023-06-01T21:03:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.