Code, Compliance and Crashes: A Risk-Based Framework for AI Trading Systems

The use of AI in securities markets has transformed the design of securities markets by introducing adaptive machine-learning and reinforcement-learning algorithms in order generation, routing and execution. This chapter focuses on the re-definition of market, operational and conduct risks by such systems and suggests a risk-based system of internal governance of institutions that use AI trading strategies. The analysis involves a combination of the analysis of the doctrines of regulatory regimes of key financial centres and syntheses of academic literature, case facts of flash crashes and near-misses, and conceptual maps of risk channels throughout the data-model-execution lifecycle. The results indicate a closed risk universe where market integrity issues, model and data risk, technology failures, cyber threats and data-protection concerns are intertwined. The current algorithmic trading regulations, which are mainly based on deterministic code, partially cover these dynamics. The chapter advances a governance framework based on human-in-the-loop supervision, proportional clarification, explicit duty all the way along the three lines of defence, and graded oversight that is aligned with the risk of strategy. Tools such as concrete tools, improved testing regimes, dynamic pre-trade limits, kill-switches, ongoing monitoring with explainability analytics, and detailed model inventories and audit trails are all concrete tools. The discussion admits shortcomings of a conceptual and legal-analysis method: there is yet to be empirically calibrated proposed controls, particularly in realistic multi-agent AI market settings. However, the framework has definite implications on regulators and companies. The regulators are urged to improve standards of algorithmic and AI trading, increase system wide stress testing and facilitate cross-border coordination. The companies are urged to incorporate AI-specific risk policies, ethics codes and psychosocial support to supervisors facing opaque and high-speed systems. Besides, ethical guidelines of fairness, non-manipulation, transparency and inclusiveness are also applied to design decisions concerning data selection, validation practises, client communication and internal ethics management. The difference is in the fact that microstructural risk analysis, comparative regulation, internal governance design and human-factor considerations are combined within one, coherent structure of AI trading. The chapter therefore adds to the current discussions on reliable financial AI by describing how trading technology innovation can be balanced with systemic stability and investor protection.