Anti-Money Laundering in Cryptocurrencies Through Graph Neural Networks: A Comparative Study

Abstract

Money laundering in cryptocurrencies is a significant concern, as it facilitates and conceals crime and can distort markets and the broader financial system. To combat this issue, researchers have turned to techniques to develop effective Anti-Money Laundering (AML) frameworks. The findings contribute to the ongoing efforts to promote social good by reducing the impact of criminal activities on society. By preventing money laundering, we can also help to combat other criminal activities such as drug trafficking, corruption, and terrorism. This paper focuses on the use of Graph Neural Networks (GNNs) to classify cryptocurrencies transactions. Specifically, the study employs Graph Convolutional Networks (GCNs), Graph Attention Networks (GAT), the Chebyshev spatial convolutional neural network (ChebNet), and GraphSAGE network to classify Bitcoin transactions. The study finds that ChebNet, GraphSAGE and a variant of GAT outperform other methods and improve upon the state of the art in terms of recall and F1 scores, thus suggesting that they can be more reliable in identifying illicit transactions.