The Agentic Regulator: Risks for AI in Finance and a Proposed Agent-based Framework for Governance

TL;DR

This paper introduces an agent-based governance framework for AI in finance, addressing risks from emergent behaviors of adaptive models through layered oversight and real-time controls.

Contribution

It proposes a novel modular governance architecture inspired by complex systems theory, enabling adaptive oversight of continuously learning AI models in financial markets.

Findings

Layered controls effectively quarantine harmful behaviors in real time.

The framework closes critical observability gaps in current AI governance.

Case study demonstrates improved risk management in multi-agent trading.

Abstract

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.