@orionent

Machine learning enhances money laundering detection by identifying complex patterns in blockchain transactions. Supervised models trained on labeled illicit data classify suspicious activities, while unsupervised techniques detect anomalies in large datasets. Graph neural networks analyze transaction networks to uncover hidden relationships. However, adversarial attacks, where launderers manipulate data to evade detection, pose challenges. Continuous model retraining and collaboration with law enforcement improve accuracy. Balancing privacy concerns with regulatory compliance remains critical for effective implementation.

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