Assessing Artificial Intelligence Driven Algorithmic Trading Implications on Market Liquidity Risk and Financial Systemic Vulnerabilities

Abstract

The rapid integration of Artificial Intelligence (AI) into algorithmic trading systems has transformed financial markets, enabling faster, data-driven decision-making and the automation of complex trading strategies. While AI-driven algorithmic trading enhances market efficiency and execution speed, it also introduces new dimensions of market liquidity risk and systemic vulnerabilities. This review paper critically examines the implications of AI in algorithmic trading on market liquidity, highlighting scenarios where algorithmic behavior exacerbates flash crashes, herding effects, and liquidity dry-ups. Additionally, the paper explores the systemic risks posed by AI models, including model opacity, correlated strategies, and the amplification of shocks across interconnected financial systems. Through an interdisciplinary synthesis of current literature and empirical case studies, the review identifies regulatory gaps, the limitations of existing risk assessment frameworks, and proposes strategic recommendations for policymakers and financial institutions. The findings underscore the urgent need for transparent, interpretable AI models, robust monitoring mechanisms, and adaptive regulation to ensure financial market stability in the age of autonomous trading systems.