Agentic AI for Automated Stress Testing and Financial Risk Scenario Analysis

Abstract

Agentic artificial intelligence is emerging as a transformative layer for automated stress testing and financial risk scenario analysis. Unlike conventional models that produce forecasts or classifications within narrowly specified tasks, agentic systems can plan, coordinate tools, retrieve evidence, simulate counterfactuals, and adapt their behavior across changing market regimes. This paper examines the system-level implications of deploying such systems in banks, regulators, insurers, asset managers, and market infrastructures. It argues that the value of agentic AI in stress testing depends less on isolated predictive accuracy than on the architecture that connects scenario generation, model orchestration, data provenance, human oversight, and regulatory accountability. The paper develops a conceptual framework for agentic stress testing as a socio-technical infrastructure, emphasizing structural trade-offs among autonomy, auditability, robustness, fairness, and sustainability. It discusses how multi-agent designs can improve coverage of tail events, climate transition pathways, cyber shocks, liquidity spirals, and sovereign-bank feedback loops, while also introducing risks of correlated behavior, model monoculture, opaque reasoning, and governance drift. The analysis highlights the need for trust calibration, adversarial validation, invariant learning, model risk controls, and continuous auditing. It further considers deployment constraints, computational cost, energy intensity, and institutional capacity. The conclusion proposes that agentic stress testing should be governed as critical financial infrastructure, with clear escalation paths, contestability mechanisms, and policy safeguards that preserve human responsibility under uncertainty.