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

By Giacomo Domeniconi, Jie Chen, Daniel Karl I. Weidele, Claudio Bellei, Tom Robinson, Charles E. Leiserson. arXiv.org, 2019

480 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/68140108c35dea33e6d00efb3639e5df7bdc6073

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Citations in the corpus

Adversarial Attack and Defense on Graph Data: A Survey (2018, unknown): “Others Node Scale-free [4] [108], [109], [128] Node NELL [131] 65,755 266,144 5,414 210 [40], [31] Graph Bitcoin [110] [122], [141] Graph/Node AIDS [85] [97], [122], [49] Graph/Node DHFR [94] [97], [49]”
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters (2020, background): “Though previous works have proposed other fraud datasets like Epinions [18] and Bitcoin [40], they only contain graph structures and compacted features, with which we cannot build meaningful multi-relation graphs.”
Streaming Graph Neural Networks via Continual Learning (2020, methodology): “• Elliptic [29] is a bitcoin transaction network and we extract a sub-network with 31448 nodes, 34230 edges, 166 features, 2 labels and 9 time steps.”