AI as a Systemic Risk Amplifier in High-Frequency Trading: Presenting a Conceptual Regulatory Framework

Artificial Intelligence (AI) has changed how the financial market operates, particularly in High-Frequency Trading (HFT) and autonomous execution. While AI enhances liquidity, speed, and price discovery, it also causes new systemic vulnerabilities. This paper investigates how AI operates as a risk amplifier in financial markets and suggests a novel conceptual framework that integrates transmission risks with governance principles. The paper connects AI-driven mechanisms with systemic outcomes. Drawing on literature, institutional reports, and historical incidents such as the Flash Crash on 6th May 2010, the study underscores important transmission risks such as feedback loops, model opacity, flash crash, algorithmic herding, and regulatory gaps. To mitigate these risks and to reap the benefits of technology, the study proposes a framework that prioritises explainability, accountability, and transparency, model diversity, layered monitoring, stress testing, flexible macroprudential supervision, and international harmonisation. By aligning with previous studies and regulatory warnings, this research contributes to academic discourse and provides practical insights for policymakers aiming to strike a balance between AI-driven efficiency and systemic stability.