Introduction to Autonomous Trading and Its Risks
The line between “autonomy” and “automation” in modern markets is becoming increasingly blurred. With the advent of AI agents capable of placing orders, negotiating fees, reading filings, and rebalancing company portfolios, the efficiency of financial transactions has reached new heights. However, this increased efficiency also introduces a new class of risks that must be addressed. As AI agents operate outside their test environments and directly with client funds, the potential for systemic risks and liability gaps grows.
Current State of AI Governance and Controls
Current AI governance and controls are outdated and inadequate for the challenges posed by autonomous trading. Regulatory bodies such as the Financial Stability Board (FSB), the International Organization of Securities Commissions (IOSCO), and central banks have warned that the opaque behavior, clustering, and shared dependencies of AI agents could lead to market instability. The industry’s reliance on disclaimers to separate intent and liability is no longer sufficient, as software with the ability to move funds or publish prices requires a higher level of accountability.
Risks and Challenges of Autonomous Trading
The use of autonomous trading agents increases the risk of feedback loops, where the actions of multiple agents interact and amplify each other, leading to rapid and unpredictable market movements. The FSB has identified clustering, opaque behavior, and reliance on third-party models as key risks that can destabilize the market. The Bank of England has also warned that the wider adoption of AI without appropriate safeguards can pose significant risks, particularly in times of market stress.
Need for Verifiable Controls
To mitigate these risks, it is essential to establish verifiable controls that ensure the safe and responsible operation of autonomous trading agents. This includes the use of provable identity, verified data entries, immutable audit trails, and coded ethical constraints that make accountability predictable and compliance verifiable. The International Organization of Securities Commissions (IOSCO) has called for controls that can be verified end-to-end, including understanding provider concentration, untested behaviors under stress, and the limits of explainability.
Implementing Ethics in Autonomous Trading
Implementing ethics in autonomous trading requires a multi-faceted approach that includes scoped identity, input permissibility, and sealing decisions. Each agent should work behind a named, verifiable account with clear, role-based boundaries that define what they can access, change, or execute. Permissions should be expressly granted and monitored, and any changes to these boundaries should require approval from multiple parties and leave a cryptographic trail that can be independently verified.
Conclusion
In conclusion, autonomous trading demands verifiable controls to ensure the safe and responsible operation of AI agents. The use of provable identity, verified data entries, immutable audit trails, and coded ethical constraints can help mitigate the risks associated with autonomous trading and ensure accountability. As the industry continues to evolve, it is essential to prioritize the development of verifiable controls and ethics in autonomous trading. For more information, visit https://crypto.news/autonomous-trading-demands-verifiable-controls-opinion/
Selwyn Zhou (Joe) is co-founder of DeAgentAI and brings a strong combination of experience as an AI PhD student, former SAP data scientist, and top venture investor. Prior to founding his web3 company, he was an investor at leading VCs and an early-stage investor in several AI unicorns, leading investments in companies such as Shein (valued at $60 billion), Pingpong (a $4 billion AI payfi company), publicly traded Black Sesame Technology (HKG: 2533), and Enflame (a $4 billion AI chip company).
