Publicação
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity
| dc.contributor.advisor | Pinheiro, Flávio Luís Portas | |
| dc.contributor.advisor | Silva, Maria Inês | |
| dc.contributor.advisor | Aparício, David | |
| dc.contributor.author | Lorenz, Joana Susan | |
| dc.date.accessioned | 2021-04-01T14:58:31Z | |
| dc.date.available | 2021-04-01T14:58:31Z | |
| dc.date.issued | 2021-03-18 | |
| dc.description | Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics | pt_PT |
| dc.description.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. This research addresses money laundering detection assuming minimal access to labels. The results 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. The proposed active learning solution, however, 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. | pt_PT |
| dc.identifier.tid | 202690644 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/114825 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Anti-money laundering | pt_PT |
| dc.subject | Applied machine learning | pt_PT |
| dc.subject | Supervised learning by classification | pt_PT |
| dc.subject | Anomaly detection | pt_PT |
| dc.subject | Active learning | pt_PT |
| dc.title | Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Métodos Analíticos Avançados | pt_PT |
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