Monitoring of Trading Processes and Automation of Control in the Commodity Exchange Based on Artificial Intelligence

This paper explores the application of Artificial Intelligence (AI) technologies in monitoring trading processes and automating control mechanisms within commodity exchanges. As the volume and velocity of high-frequency trading data continue to grow, traditional manual oversight becomes increasingly inefficient. This research analyzes the effectiveness of machine learning algorithms and neural networks in detecting market anomalies, predicting price volatility, and preventing fraudulent manipulations. Furthermore, the study proposes a framework for real-time automated control systems designed to minimize human error and enhance the transparency of exchange operations. The findings demonstrate that AI-driven monitoring systems significantly improve risk management precision and operational efficiency in modern digital commodity markets.