A carregar...
Publicação
Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 661.58 KB | Adobe PDF |
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. 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.
Descrição
Lorenz, J., Silva, M. I., Aparício, D., Ascensão, J. T., & Bizarro, P. (2020). Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity. In ICAIF 2020 - 1st ACM International Conference on AI in Finance (pp. 1-8). [3422549] (ICAIF 2020 - 1st ACM International Conference on AI in Finance). Association for Computing Machinery, Inc. https://doi.org/10.1145/3383455.3422549
Palavras-chave
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
Contexto Educativo
Citação
Editora
ACM - Association for Computing Machinery