Anomaly Detection in Trading Data Using Machine Learning Techniques

This study applies machine learning models, including Isolation Forest, DBSCAN, and Random Forest, to detect anomalies in trading data. By comparing supervised and unsupervised approaches, the research identifies effective methods for real-time detection of unusual trading activities, aiding in fraud preven-tion and market manipulation detection.