-By Joana Lorenz , Maria Inês Silva, David Aparício, João Tiago Ascensão, Pedro Bizarro Feedzai Paper link Git hub link 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. Image courtesy: BitCoin Magazine In the financial sector, Anti-Money Laundering (AML) efforts often rely on rule-based systems. However, vulnerabilities