Author(s) :

Yashica Mahajan.

Volume/Issue :

Abstract :

Flash crashes, sudden, severe market price dislocations followed by rapid recovery, reflect the growing influence of automation in financial trading. Since the 2010 event that erased nearly one trillion dollars in value within minutes, markets have become increasingly dependent on algorithmic and artificial intelligence (AI)driven systems. This narrative review examines how recent AI advancements, including machine-learning–based prediction and autonomous execution models, influence the frequency and severity of flash crashes. Evidence from empirical studies indicates that AI improves trading speed, liquidity, and efficiency but simultaneously amplifies systemic vulnerabilities through algorithmic clustering, herding effects, and opacity in decision-making. While regulatory mechanisms such as circuit breakers and the SEC’s Limit-Up/Limit-Down Plan mitigate immediate volatility, persistent challenges remain in transparency, governance, and ethical oversight. The review concludes that AI’s ultimate effect on financial stability depends on the balance between innovation and regulation, whether algorithmic intelligence becomes a stabilizing safeguard or a catalyst for new forms of market disruption.