AI-enabled trading platforms like Robinhood's Agentic Accounts and eToro's AI portfolios don't require entirely new regulatory frameworks; instead, they should be governed through existing financial services obligations focused on governance, accountability, and operational risk management. The key distinction lies not in whether a product uses artificial intelligence, but in how well firms can supervise and audit it from the client's initial instruction through market execution.

Why Existing Financial Rules Already Cover AI Trading?

AI-powered trading is less revolutionary than it appears on the surface. Financial markets have permitted automated execution for decades through Expert Advisors on MetaTrader, algorithms connected via FIX APIs (Financial Information eXchange, a standard protocol for trading systems), and sophisticated quantitative models that execute trades automatically. The fundamental principle remains unchanged: the client determines the investment strategy, while the broker provides market access. AI simply changes how those instructions are expressed and formed.

Instead of writing code, clients now write in plain English. Instead of programming a strategy, they describe it conversationally. The AI translates those instructions into executable orders. Assuming the investment decision continues to originate with the client, this should not be viewed as an entirely new category of regulated activity requiring fresh rulebooks.

What Governance Questions Should Regulators Actually Ask?

Rather than debating whether a product uses artificial intelligence, compliance officers and regulators should focus on understanding a firm's governance and operational risk framework. Before approving any AI-powered trading product, stakeholders need clarity on the entire lifecycle of a trade, from the client's initial prompt to the execution of the order in the market.

Key governance considerations include:

• Authentication and Permissions: How is the AI authenticated before accessing a client's account, what permissions does it receive, and can those permissions be appropriately constrained to prevent unauthorized actions?
• Audit Trails: Can the firm reconstruct the client's original instruction, the AI's interpretation, and the order ultimately executed if a dispute arises months later?
• Model Availability and Updates: How does the product behave if the underlying AI model becomes unavailable, and how are model updates governed if identical prompts begin producing different outputs?
• Latency and Market Stress: What latency exists between instruction and execution during periods of market stress, and have these scenarios been tested and incorporated into the firm's broader operational resilience program?

These questions are not new. Firms have always been expected to supervise new products, new technologies, and new channels of distribution. AI should be no different.

How to Apply Existing Regulatory Frameworks to AI Trading

• UK Approach: Leverage existing obligations such as Consumer Duty, the Senior Managers and Certification Regime, operational resilience requirements, and outsourcing expectations to govern AI trading products without creating AI-specific rules.
• US Approach: Firms should look first to existing supervisory obligations, governance arrangements like business continuity, disaster recovery, and operational controls before searching for AI-specific regulation.
• Risk-Based Differentiation: Distinguish between AI that executes a client's own instructions and AI that begins recommending investments or making discretionary decisions; the further a product moves toward advice, the more complex the regulatory analysis becomes.

The products currently entering the market are taking different approaches. Some remain execution tools, while others are beginning to explore autonomous portfolio management. Those differences matter, but they do not change the fundamental starting point: regulators are unlikely to ask whether a product uses artificial intelligence. They are far more likely to ask whether the firm's governance framework was capable of supervising it.

This outcome-focused approach to AI governance in financial services reflects a broader principle: technology evolves faster than regulation ever will. Good regulation should therefore focus on outcomes, governance, and accountability, not on the technology itself. By applying this philosophy to AI-powered trading, regulators can ensure consumer protection and market integrity without stifling innovation through premature or overly prescriptive AI-specific rules.