AI-Driven Regulation and Risk Control Mechanisms for High-Frequency Financial Trading

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Published: 2026-02-22
Keywords:
High-Frequency Trading, Artificial Intelligence, Regulation, Risk Management, Financial Markets, Algorithmic Trading, Market Surveillance

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Alistair Vance
Henley Business School, University of Reading, Reading, UK
Oliver Sterling
Henley Business School, University of Reading, Reading, UK
Simon J. Preece
Surrey Business School, University of Surrey, Guildford, UK

Abstract

This review paper examines the evolving landscape of high-frequency trading (HFT) and the emerging role of artificial intelligence (AI) in its regulation and risk control. HFT, characterized by its speed and complexity, poses unique challenges to market oversight and stability. We explore how AI-driven mechanisms are being developed and deployed to address these challenges, focusing on two core themes: AI for regulatory compliance and AI for risk management. We analyze the potential of AI to enhance surveillance, detect anomalies, and improve the overall resilience of financial markets. Furthermore, we compare existing approaches, highlight current challenges, and discuss future perspectives on the integration of AI in HFT regulation. This review considers the trade-offs between innovation and stability, as well as the ethical implications of AI-driven market oversight. Finally, it provides a direction for researchers and policymakers seeking to navigate the complexities of AI-enhanced financial ecosystems.

Article Details

Issue

Vol. 2 No. 1 (2026)

Section

Articles

How to Cite

AI-Driven Regulation and Risk Control Mechanisms for High-Frequency Financial Trading. (2026). Journal of Sustainability, Policy, and Practice, 2(1), 185-194. https://schoalrx.com/index.php/jspp/article/view/89

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