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

Battle Count: 68

The paper is primarily about AI governance and regulation rather than trading strategy development. However, it is relevant to quantitative trading in several ways: (1) it addresses risks of agentic AI in trading systems including spoofing, market manipulation, and collusion; (2) the case study directly involves multi-agent trading simulations (ABIDES, Market-Sim); (3) the framework could constrain or enable AI-driven trading strategies; (4) it discusses real-time monitoring and circuit breakers relevant to algorithmic trading risk management. The relevance is indirect (governance of trading AI) rather than direct (improving trading performance).

Extremely high complexity. The framework requires: (1) designing and deploying four distinct governance layers with different hosting entities (firms, regulators, auditors); (2) building standardized regulatory block libraries covering multiple jurisdictions and regulations; (3) implementing real-time telemetry aggregation across thousands of models; (4) developing regulator-hosted AI agents with cross-institutional data access; (5) establishing cryptographic attestation and privacy-preserving computation infrastructure; (6) coordinating between multiple regulatory agencies (SEC, OCC, FINRA, CFTC, Federal Reserve, FDIC); (7) creating interoperable APIs (REST, gRPC, message queues) across all layers; (8) managing adaptive agency levels dynamically. This is a multi-year, multi-stakeholder institutional engineering challenge.

This is a purely conceptual and theoretical governance framework proposal. No code, no experiments, no datasets, and no quantitative evaluations are provided. The case study on spoofing references prior literature (Market-Sim, ABIDES simulations) but does not present new empirical results. Reproduction would require implementing the entire proposed architecture from scratch.