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

This paper presents a new methodology for real-time detection of malicious business models in financial markets using Generative Adversarial Networks (GANs). The proposed system combines deep learning techniques with special physical systems for real-time analysis of complex business processes while maintaining business-critical low-latency requirements. print many times. The framework uses a number of design elements that process business data flows at multiple time horizons, including business microstructure features and order book quality. A comprehensive analysis of data across 24 months of business data from multiple markets shows the foundation's performance, achieving 94.7% detection accuracy with sub-3ms latency. The system handles up to 150,000 transactions per second while maintaining stable performance across transactions. The framework's adaptive thresholding mechanism and hierarchical feature fusion approach reduce the vulnerability during high traffic times. Comparative analysis shows a 15.5% improvement in identification accuracy over traditional methods. This implementation includes a robust data pre-processing pipeline and an efficient computational model, enabling deployment to the manufacturing environment. These studies contribute to the advancement of business intelligence technology by introducing new applications of GANs in detecting real-world anomalies while solving critical problems. in processing business documents frequently.