Identifying Money Laundering Subgraphs on the Blockchain

Anti-Money Laundering (AML) involves the identification of money laundering crimes in financial activities, such as cryptocurrency transactions. Recent studies advanced AML through the lens of graph-based machine learning, modeling the web of financial transactions as a graph and developing graph methods to identify suspicious activities. For instance, a recent effort on opensourcing datasets and benchmarks, \emph{Elliptic2}, treats a set of Bitcoin addresses, considered to be controlled by the same entity, as a graph node and transactions among entities as graph edges. This modeling reveals the ``shape'' of a money laundering scheme---a subgraph on the blockchain, such as a peeling chain or a nested service. Despite the attractive subgraph classification results benchmarked by the paper, competitive methods remain expensive to apply due to the massive size of the graph; moreover, existing methods require candidate subgraphs as inputs which may not be available in practice.

In this work, we introduce \emph{RevTrack}, a graph-based framework that enables large-scale AML analysis with a lower cost and a higher accuracy. The key idea is to track the initial senders and the final receivers of funds; these entities offer a strong indication of the nature (licit vs. suspicious) of their respective subgraph. Based on this framework, we propose \emph{RevClassify}, which is a neural network model for subgraph classification. Additionally, we address the practical problem where subgraph candidates are not given, by proposing \emph{RevFilter}. This method identifies new suspicious subgraphs by iteratively filtering licit transactions, using \emph{RevClassify}. Benchmarking these methods on \emph{Elliptic2}, a new standard for AML, we show that \emph{RevClassify} outperforms state-of-the-art subgraph classification techniques in both cost and accuracy. Furthermore, we demonstrate the effectiveness of \emph{RevFilter} in discovering new suspicious subgraphs, confirming its utility for practical AML.