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In shaping the Internet of Money, the application of blockchain anddistributed ledger technologies (DLTs) to the financial sector triggeredregulatory concerns. Notably, while the user anonymity enabled in this fieldmay safeguard privacy and data protection, the lack of identifiability hindersaccountability and challenges the fight against money laundering and thefinancing of terrorism and proliferation (AML/CFT). As law enforcement agenciesand the private sector apply forensics to track crypto transfers acrossecosystems that are socio-technical in nature, this paper focuses on thegrowing relevance of these techniques in a domain where their deploymentimpacts the traits and evolution of the sphere. In particular, this work offerscontextualized insights into the application of methods of machine learning andtransaction graph analysis. Namely, it analyzes a real-world dataset of Bitcointransactions represented as a directed graph network through varioustechniques. The modeling of blockchain transactions as a complex networksuggests that the use of graph-based data analysis methods can help classifytransactions and identify illicit ones. Indeed, this work shows that the neuralnetwork types known as Graph Convolutional Networks (GCN) and Graph AttentionNetworks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCNoutperform other classic approaches and GAT are applied for the first time todetect anomalies in Bitcoin. Ultimately, the paper upholds the value ofpublic-private synergies to devise forensic strategies conscious of the spiritof explainability and data openness.

Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics