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Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity
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Orientador(es)
Resumo(s)
Every year, criminals launder billions of dollars acquired from serious felonies (e.g.
terrorism, drug smuggling, or human trafficking), harming countless people and
economies. Cryptocurrencies, in particular, have developed as a haven for money
laundering activity. Machine Learning can be used to detect these illicit patterns.
However, labels are so scarce that traditional supervised algorithms are inapplicable.
This research addresses money laundering detection assuming minimal access
to labels. The results show that existing state-of-the-art solutions using unsupervised
anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin
transaction dataset. The proposed active learning solution, however, is capable of
matching the performance of a fully supervised baseline by using just 5% of the labels.
This solution mimics a typical real-life situation in which a limited number of labels
can be acquired through manual annotation by experts.
Descrição
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
Palavras-chave
Anti-money laundering Applied machine learning Supervised learning by classification Anomaly detection Active learning