Publication

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

Published at KDD 2020 - Machine Learning in Finance workshop

Machine Learning

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.

Materials
PDF arXiv

Page printed in 26 Nov 2022. Plase see https://research.feedzai.com/publication/machine-learning-methods-to-detect-money-laundering-in-the-bitcoin-blockchain-in-the-presence-of-label-scarcity for the latest version.