Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics. (arXiv:2206.04803v3 [cs.CR] UPDATED)

In shaping the Internet of Money, the application of blockchain and
distributed ledger technologies (DLTs) to the financial sector triggered
regulatory concerns. Notably, while the user anonymity enabled in this field
may safeguard privacy and data protection, the lack of identifiability hinders
accountability and challenges the fight against money laundering and the
financing of terrorism and proliferation (AML/CFT). As law enforcement agencies
and the private sector apply forensics to track crypto transfers across
ecosystems that are socio-technical in nature, this paper focuses on the
growing relevance of these techniques in a domain where their deployment
impacts the traits and evolution of the sphere. In particular, this work offers
contextualized insights into the application of methods of machine learning and
transaction graph analysis. Namely, it analyzes a real-world dataset of Bitcoin
transactions represented as a directed graph network through various
techniques. The modeling of blockchain transactions as a complex network
suggests that the use of graph-based data analysis methods can help classify
transactions and identify illicit ones. Indeed, this work shows that the neural
network types known as Graph Convolutional Networks (GCN) and Graph Attention
Networks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCN
outperform other classic approaches and GAT are applied for the first time to
detect anomalies in Bitcoin. Ultimately, the paper upholds the value of
public-private synergies to devise forensic strategies conscious of the spirit
of explainability and data openness.