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

Joana Lorenz, Maria Inês Silva, David Aparício, João Tiago Ascensão, Pedro Bizarro

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)
20 Downloads (Pure)

Abstract

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.

Original languageEnglish
Title of host publicationICAIF 2020 - The First ACM International Conference on AI in Finance
PublisherAssociation for Computing Machinery, Inc
Chapter23
Pages1-8
ISBN (Electronic)9781450375849
DOIs
Publication statusPublished - 15 Oct 2020
Event1st ACM International Conference on AI in Finance, ICAIF 2020 - Virtual, Online, United States
Duration: 15 Oct 202016 Oct 2020

Publication series

NameICAIF 2020 - 1st ACM International Conference on AI in Finance

Conference

Conference1st ACM International Conference on AI in Finance, ICAIF 2020
Country/TerritoryUnited States
CityVirtual, Online
Period15/10/2016/10/20

Keywords

  • Active learning
  • Anomaly detection
  • Anti money laundering
  • Cryptocurrency
  • Supervised classification

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