Money Laundering

Money laundering, the process of disguising the origins of illegally obtained funds, poses a significant global challenge. Current research focuses on leveraging network analytics and machine learning, particularly graph neural networks (GNNs) and other advanced algorithms, to detect complex laundering patterns within massive transaction datasets. These efforts utilize both supervised and self-supervised learning approaches, often incorporating dimensionality reduction techniques to improve model efficiency and accuracy. The development of more effective anti-money laundering (AML) systems has significant implications for financial security and the integrity of global economic systems.