[2604.03976] Quantifying Trust: Financial Risk Management for Trustworthy AI Agents

Computer Science > Artificial Intelligence arXiv:2604.03976 (cs) [Submitted on 5 Apr 2026] Title:Quantifying Trust: Financial Risk Management for Trustworthy AI Agents Authors:Wenyue Hua, Tianyi Peng, Chi Wang, Ian Kaufman, Bryan Lim, Chandler Fang View a PDF of the paper titled Quantifying Trust: Financial Risk Management for Trustworthy AI Agents, by Wenyue Hua and 5 other authors View PDF HTML (experimental) Abstract:Prior work on trustworthy AI emphasizes model-internal properties such as bias mitigation, adversarial robustness, and interpretability. As AI systems evolve into autonomous agents deployed in open environments and increasingly connected to payments or assets, the operational meaning of trust shifts to end-to-end outcomes: whether an agent completes tasks, follows user intent, and avoids failures that cause material or psychological harm. These risks are fundamentally product-level and cannot be eliminated by technical safeguards alone because agent behavior is inherently stochastic. To address this gap between model-level reliability and user-facing assurance, we propose a complementary framework based on risk management. Drawing inspiration from financial underwriting, we introduce the \textbf{Agentic Risk Standard (ARS)}, a payment settlement standard for AI-mediated transactions. ARS integrates risk assessment, underwriting, and compensation into a single transaction framework that protects users when interacting with agents. Under ARS, users receive pr...