Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional\n Networks for Financial Forensics

Abstract

Anti-money laundering (AML) regulations play a critical role in safeguarding\nfinancial systems, but bear high costs for institutions and drive financial\nexclusion for those on the socioeconomic and international margins. The advent\nof cryptocurrency has introduced an intriguing paradox: pseudonymity allows\ncriminals to hide in plain sight, but open data gives more power to\ninvestigators and enables the crowdsourcing of forensic analysis. Meanwhile\nadvances in learning algorithms show great promise for the AML toolkit. In this\nworkshop tutorial, we motivate the opportunity to reconcile the cause of safety\nwith that of financial inclusion. We contribute the Elliptic Data Set, a time\nseries graph of over 200K Bitcoin transactions (nodes), 234K directed payment\nflows (edges), and 166 node features, including ones based on non-public data;\nto our knowledge, this is the largest labelled transaction data set publicly\navailable in any cryptocurrency. We share results from a binary classification\ntask predicting illicit transactions using variations of Logistic Regression\n(LR), Random Forest (RF), Multilayer Perceptrons (MLP), and Graph Convolutional\nNetworks (GCN), with GCN being of special interest as an emergent new method\nfor capturing relational information. The results show the superiority of\nRandom Forest (RF), but also invite algorithmic work to combine the respective\npowers of RF and graph methods. Lastly, we consider visualization for analysis\nand explainability, which is difficult given the size and dynamism of\nreal-world transaction graphs, and we offer a simple prototype capable of\nnavigating the graph and observing model performance on illicit activity over\ntime. With this tutorial and data set, we hope to a) invite feedback in support\nof our ongoing inquiry, and b) inspire others to work on this societally\nimportant challenge.\n