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

arXiv: https://arxiv.org/abs/2512.11933 · HTML: https://mlsanity.com/paper/2512.11933/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 4 of its 69 references that are in the map.
- First posted on arXiv 12 Dec 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 4 of 4)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Hidden Trigger Backdoor Attacks](https://mlsanity.com/paper/1910.00033/) (2019)
- [Ethics-Based Auditing to Develop Trustworthy AI](https://mlsanity.com/paper/2105.00002/) (2021)
- [Learning Not to Spoof](https://mlsanity.com/paper/2306.06087/) (2023)

## Built on by (most cited first, 3 of 3)

- [Agentic Problem Frames: A Systematic Approach to Engineering Reliable Domain Agents](https://mlsanity.com/paper/2602.19065/) (2026)
- [Auditing Asset-Specific Preferences in Financial Large Language Models: Evidence from Bitcoin Representations and Portfolio Allocation](https://mlsanity.com/paper/2606.02528/) (2026)
- [Mechanical Enforcement for LLM Governance:Evidence of Governance-Task Decoupling in Financial Decision Systems](https://mlsanity.com/paper/2605.14744/) (2026)

## Abstract (arXiv)

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.