Machine Learning for Cross-Market Anomaly Detection in Coordinated Manipulation Using Synthetic Market Simulation and Temporal Graph Neural Networks
DOI:
https://doi.org/10.63665/IJAICE.0201.10Keywords:
Machine Learning, Cross-Market Anomaly Detection, Coordinated Manipulation, Synthetic Market Simulation, Temporal Graph Neural Network, Explainable AI, Financial Market SurveillanceAbstract
Coordinated manipulation throughout interconnected financial markets exhibits crucial issues for the conventional anomaly detection strategies. The research recommends a framework of machine learning incorporating cross-market feature engineering, synthetic market simulation, as well as “Temporal Graph Neural Networks (TGNN)” for the detection of coordinated manipulation activities. A synthetic data is developed to cross-market, model pump-and-dump, as well as coordinated trading contexts. Representations of the Temporal graph grasp dynamic connections within the financial entities, while the “Explainable Artificial Intelligence (XAI)” recognizes significant detection components. Experimental outcomes exhibit adequate anomaly recognition through comparative evaluation with conventional machine learning approaches. The respective framework improves relational, temporal, as well as interpretable financial surveillance abilities.
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