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

• DOI: 10.48550/arxiv.1908.02591
• Publication Date: 2019-01-01
• Authors: Weber, Mark; Domeniconi, Giacomo; Chen, Jie; Weidele, Daniel K...; Bellei, Claudio; Robinson, Tom; Leiserson, Charle...
• Keywords: Social and Information Networks (cs.SI); FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Social and Information Networks; 02 engineering and technology; 16. Peace & justice; Machine Learning (cs.LG); FOS: Economics and business; Computer Science - Computers and Society; Computers and Society (cs.CY); 0202 electrical engineering, electronic engineering, information engineering; Quantitative Finance - General Finance; General Finance (q-fin.GN)

Anti-money laundering (AML) regulations play a critical role in safeguarding financial systems, but bear high costs for institutions and drive financial exclusion for those on the socioeconomic and international margins. The advent of cryptocurrency has introduced an intriguing paradox: pseudonymity allows criminals to hide in plain sight, but open data gives more power to investigators and enables the crowdsourcing of forensic analysis. Meanwhile advances in learning algorithms show great promise for the AML toolkit. In this workshop tutorial, we motivate the opportunity to reconcile the cause of safety with that of financial inclusion. We contribute the Elliptic Data Set, a time series graph of over 200K Bitcoin transactions (nodes), 234K directed payment flows (edges), and 166 node features, including ones based on non-public data; to our knowledge, this is the largest labelled transaction data set publicly available in any cryptocurrency. We share results from a binary classification task predicting illicit transactions using variations of Logistic Regression (LR), Random Forest (RF), Multilayer Perceptrons (MLP), and Graph Convolutional Networks (GCN), with GCN being of special interest as an emergent new method for capturing relational information. The results show the superiority of Random Forest (RF), but also invite algorithmic work to combine the respective powers of RF and graph methods. Lastly, we consider visualization for analysis and explainability, which is difficult given the size and dynamism of real-world transaction graphs, and we offer a simple prototype capable of navigating the graph and observing model performance on illicit activity over time. With this tutorial and data set, we hope to a) invite feedback in support of our ongoing inquiry, and b) inspire others to work on this societally important challenge.<br/>7 pages, Tutorial in the Anomaly Detection in Finance Workshop at the 25th SIGKDD Conference on Knowledge Discovery and Data Mining<br/>