Real-Time Detection of Anomalous Trading Patterns in Financial Markets Using Generative Adversarial Networks

This paper presents a novel framework for real-time detection of anomalous trading patterns in financial markets using Generative Adversarial Networks (GANs). The proposed system integrates advanced deep learning techniques with specialised temporal attention mechanisms to identify complex market manipulation schemes while maintaining low latency requirements essential for high-frequency trading environments. The framework implements a multi-scale architecture that processes market data streams at multiple time horizons, incorporating market microstructure features and order book dynamics. Experimental evaluation on a comprehensive dataset spanning 24 months of trading data from various markets demonstrates the framework's superior performance, achieving 94.7% detection accuracy with sub-3ms latency. The system processes up to 150,000 transactions per second while maintaining stable performance across market conditions. The framework's adaptive threshold mechanism and hierarchical feature fusion approach significantly reduce false positives during periods of high market volatility. Comparative analysis shows a 15.5% improvement in detection accuracy over traditional methods. The implementation incorporates robust data preprocessing pipelines and efficient computational architectures, enabling practical deployment in production environments. The research contributes to the advancement of financial market surveillance technology by introducing innovative applications of GANs in real-time anomaly detection while addressing critical challenges in processing high-frequency trading data.