AI-Based Quote Stuffing Detection in HFT Quote stuffing is a market manipulation technique in high-frequency trading (HFT) where traders rapidly send and cancel large volumes of orders to create artificial market congestion. This deceptive practice can mislead competitors, slow down execution, and manipulate stock prices. AI-driven surveillance systems play a crucial role in detecting and preventing quote stuffing in real time. Machine learning models analyze vast amounts of trading data to identify abnormal spikes in order placements and cancellations. AI-driven pattern recognition detects inconsistencies in order flow, flagging suspicious trading activities. Deep learning algorithms can differentiate between legitimate high-frequency trading and manipulative behavior by analyzing order execution patterns, latency anomalies, and market depth fluctuations. Natural language processing (NLP) enhances detection by integrating regulatory reports and market news to refine risk assessments. AI-powered anomaly detection systems continuously adapt to evolving manipulation tactics, ensuring regulatory compliance. By leveraging AI, exchanges and regulators can enforce fair trading practices, protect market integrity, and prevent disruptions caused by malicious HFT strategies.