AI trading poses a legal challenge to compliance with existing financial regulations. Many regulatory frameworks were designed with human decision-makers in mind and may not adequately address AI systems’ unique characteristics. Regulators around the world are working to adapt and create updated guidelines for AI trading. Key regulatory considerations include:

1. Transparency – Regulators increasingly demand financial institutions explain AI trading systems’ decision-making processes. This requirement poses challenges for complex machine learning models, particularly deep learning systems, which are opaque in their operations. Firms employing AI trading must develop methods to make these systems more transparent and interpretable to satisfy regulatory scrutiny.
2. Market manipulation – AI trading systems must be designed and operated to prevent market manipulation. This includes avoiding practices such as spoofing, layering, or creating false markets. It is crucial that AI algorithms, which identify and exploit market inefficiencies in novel ways, do not cross the line into manipulative behaviour.
3. Highest execution – Financial institutions have a legal obligation to seek the most efficient execution for their clients’ trades. With AI trading, questions arise about how to define and demonstrate the best execution when decisions are made by algorithms at high speeds. Firms must ensure their AI systems are programmed to prioritize client interests and provide evidence of this in their trading patterns.
4. Audit trails and record-keeping – Regulatory bodies require detailed records of trading activities for monitoring and investigation purposes. AI trading systems must be designed to maintain comprehensive and accessible audit trails, capturing not only the trades executed but also the decision-making processes leading to those trades.
5. Cross-border compliance – immediate 1a pro air trading often operates across multiple jurisdictions, each with unique requirements. Financial institutions must navigate this complex landscape, ensuring their AI systems comply with varying and sometimes conflicting regulations in different countries.

Liability and accountability

The use of AI in trading raises significant questions about liability and accountability when things get wrong. Unlike human traders, AI systems cannot be held personally responsible for their actions. This shift in agency creates legal challenges in determining who is accountable for errors, losses, or regulatory violations resulting from AI trading activities. Key considerations include:

• Allocation of responsibility – Courts and regulators must determine how to allocate responsibility among various parties involved in AI trading, including software developers, data providers, system operators, and the financial institutions deploying these systems.
• Duty of care – Financial institutions and investment advisors have a legal duty of care to their clients. AI trading systems raise questions about how this duty applies when investment decisions are largely or entirely automated.
• System malfunctions – In cases where AI trading systems malfunction, causing losses or market disruptions, legal frameworks must establish clear guidelines for determining liability and compensation.
• Algorithmic errors – When AI trading algorithms make errors or produce unexpected results, questions arise about who is legally responsible – the developers, the operators, or the institutions employing these systems.
• Director and officer liability – Corporate leaders may face increased liability risks related to AI trading systems oversight and governance within their organizations.

An ongoing dialogue between these parties will likely shape AI trading to balance AI’s transformative potential with legal and ethical safeguards.