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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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Resumo: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. Here, we address money laundering detection assuming minimal access to labels. First, we 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. Then, we show that our proposed active learning solution 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.
Autores principais:Lorenz, Joana
Outros Autores:Silva, Maria Inês; Aparício, David; Ascensão, João Tiago; Bizarro, Pedro
Assunto:Active learning Anomaly detection Anti money laundering Cryptocurrency Supervised classification Artificial Intelligence Finance SDG 5 - Gender Equality SDG 8 - Decent Work and Economic Growth SDG 16 - Peace, Justice and Strong Institutions
Ano:2020
País:Portugal
Tipo de documento:documento de conferência
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

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