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Every year, criminals launder billions of dollars acquired from seriousfelonies (e.g., terrorism, drug smuggling, or human trafficking) harmingcountless people and economies. Cryptocurrencies, in particular, have developedas a haven for money laundering activity. Machine Learning can be used todetect these illicit patterns. However, labels are so scarce that traditionalsupervised algorithms are inapplicable. Here, we address money launderingdetection assuming minimal access to labels. First, we show that existingstate-of-the-art solutions using unsupervised anomaly detection methods areinadequate to detect the illicit patterns in a real Bitcoin transactiondataset. Then, we show that our proposed active learning solution is capable ofmatching the performance of a fully supervised baseline by using just 5\% ofthe labels. This solution mimics a typical real-life situation in which alimited number of labels can be acquired through manual annotation by experts.

Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity