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Anti-Cash Laundering in Bitcoin: Experiments with Graph Convolutional Networks


Graph deep studying may very well be a strong instrument in anti-money laundering as a result of it will probably seize hidden info in advanced networks. On this presentation on the KDD Anomaly Detection in Finance Workshop, Mark Weber presents a tutorial paper, “Anti-Cash Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Monetary Forensics.”

Anti-money laundering (AML) laws play a crucial position in safeguarding monetary programs, however bear excessive prices for establishments and drive monetary exclusion for these on the socioeconomic and worldwide margins. The appearance of cryptocurrency has launched an intriguing paradox: pseudonymity permits criminals to cover in plain sight, however open knowledge provides extra energy to investigators and allows the crowdsourcing of forensic evaluation. In the meantime advances in studying algorithms present nice promise for the AML toolkit. On this workshop tutorial, we encourage the chance to reconcile the reason for security with that of economic inclusion. We contribute the Elliptic Information Set, a time sequence graph of over 200K Bitcoin transactions (nodes), 234K directed cost flows (edges), and 166 node options, together with ones based mostly on personal knowledge; to our information, that is the most important labelled transaction knowledge set publicly accessible in any cryptocurrency. We share outcomes from a binary classification process predicting illicit transactions utilizing variations of Logistic Regression (LR), Random Forest (RF), Multilayer Perceptrons (MLP), and Graph Convolutional Networks (GCN), with GCN being of particular curiosity as an emergent new methodology for capturing relational info. The outcomes present the prevalence of Random Forest (RF), but additionally invite algorithmic work to mix the respective powers of RF and graph strategies. Lastly, we contemplate visualization for evaluation and explainability, which is troublesome given the dimensions and dynamism of real-world transaction graphs, and we provide a easy prototype able to navigating the graph and observing mannequin efficiency on illicit exercise over time. With this tutorial and knowledge set, we hope to a) invite suggestions in help of our ongoing inquiry, and b) encourage others to work on this societally vital problem.

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