The Agentic Regulator: Risks for AI in Finance and a Proposed Agent-based Framework for Governance
This addresses the need for adaptive governance in financial systems to prevent risks from AI technologies, offering a practical solution for regulators and firms, though it is incremental as it builds on existing model-risk rules.
The paper tackles the problem of AI governance in finance by proposing a modular, agent-based framework to manage risks from generative and agentic AI, demonstrating its effectiveness in a case study on emergent spoofing in multi-agent trading with real-time control.
Generative and agentic artificial intelligence is entering financial markets faster than existing governance can adapt. Current model-risk frameworks assume static, well-specified algorithms and one-time validations; large language models and multi-agent trading systems violate those assumptions by learning continuously, exchanging latent signals, and exhibiting emergent behavior. Drawing on complex adaptive systems theory, we model these technologies as decentralized ensembles whose risks propagate along multiple time-scales. We then propose a modular governance architecture. The framework decomposes oversight into four layers of "regulatory blocks": (i) self-regulation modules embedded beside each model, (ii) firm-level governance blocks that aggregate local telemetry and enforce policy, (iii) regulator-hosted agents that monitor sector-wide indicators for collusive or destabilizing patterns, and (iv) independent audit blocks that supply third-party assurance. Eight design strategies enable the blocks to evolve as fast as the models they police. A case study on emergent spoofing in multi-agent trading shows how the layered controls quarantine harmful behavior in real time while preserving innovation. The architecture remains compatible with today's model-risk rules yet closes critical observability and control gaps, providing a practical path toward resilient, adaptive AI governance in financial systems.