Machine Learning in Money Laundering Detection Over Blockchain Technology
Layering through cryptocurrency transactions represents a sophisticated mechanism for laundering money within cybercrime circles. This process methodically merges illegal funds into the legitimate financial system. Blockchain technology plays a crucial role in this integration by facilitating the qu...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10658980/ |
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| author | Algimantas Venckauskas Sarunas Grigaliunas Linas Pocius Rasa Bruzgiene Andrejs Romanovs |
| author_facet | Algimantas Venckauskas Sarunas Grigaliunas Linas Pocius Rasa Bruzgiene Andrejs Romanovs |
| author_sort | Algimantas Venckauskas |
| collection | DOAJ |
| description | Layering through cryptocurrency transactions represents a sophisticated mechanism for laundering money within cybercrime circles. This process methodically merges illegal funds into the legitimate financial system. Blockchain technology plays a crucial role in this integration by facilitating the quick and automated dispersal of assets across various digital wallets and exchanges. Machine learning emerges as a powerful tool for analyzing and identifying illicit transactions within Blockchain networks; however, a significant challenge remains in the form of a gap in advanced pattern recognition algorithms. This paper introduces a novel machine learning-based approach called Value-driven-Transactional tracking Analytics for Crypto compliance (VTAC) for the detection of illegal crypto transactions via Blockchain. The approach combines machine learning algorithms with a pre-training process, normalization, model training, and a de-anonymization process to analyze and identify illicit transactions effectively. Experimental evaluations show VTAC’s capability to detect illegal transactions with a 97.5% accuracy using the XG Boost model, outperforming existing methods with an accuracy of up to 95.9%. Key performance metrics, including precision, recall, and F1-score, consistently exceeded 95%, highlighting VTAC’s enhanced precision and reliability. The proposed solution will serve as an advisory framework to help financial crime investigators enhance the detection and reporting of suspicious cryptocurrency transactions in cyberspace. |
| format | Article |
| id | doaj-art-ebed62f706bd46c091f7ca143d25d874 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ebed62f706bd46c091f7ca143d25d8742025-01-15T00:03:06ZengIEEEIEEE Access2169-35362025-01-01137555757310.1109/ACCESS.2024.345200310658980Machine Learning in Money Laundering Detection Over Blockchain TechnologyAlgimantas Venckauskas0https://orcid.org/0000-0002-4567-5023Sarunas Grigaliunas1https://orcid.org/0000-0001-9268-9244Linas Pocius2Rasa Bruzgiene3https://orcid.org/0000-0002-0816-8700Andrejs Romanovs4https://orcid.org/0000-0003-1645-2741Department of Computer Sciences, Kaunas University of Technology, Kaunas, LithuaniaDepartment of Computer Sciences, Kaunas University of Technology, Kaunas, LithuaniaDepartment of Computer Sciences, Kaunas University of Technology, Kaunas, LithuaniaDepartment of Computer Sciences, Kaunas University of Technology, Kaunas, LithuaniaInformation Technology Institute, Riga Technical University, Riga, LatviaLayering through cryptocurrency transactions represents a sophisticated mechanism for laundering money within cybercrime circles. This process methodically merges illegal funds into the legitimate financial system. Blockchain technology plays a crucial role in this integration by facilitating the quick and automated dispersal of assets across various digital wallets and exchanges. Machine learning emerges as a powerful tool for analyzing and identifying illicit transactions within Blockchain networks; however, a significant challenge remains in the form of a gap in advanced pattern recognition algorithms. This paper introduces a novel machine learning-based approach called Value-driven-Transactional tracking Analytics for Crypto compliance (VTAC) for the detection of illegal crypto transactions via Blockchain. The approach combines machine learning algorithms with a pre-training process, normalization, model training, and a de-anonymization process to analyze and identify illicit transactions effectively. Experimental evaluations show VTAC’s capability to detect illegal transactions with a 97.5% accuracy using the XG Boost model, outperforming existing methods with an accuracy of up to 95.9%. Key performance metrics, including precision, recall, and F1-score, consistently exceeded 95%, highlighting VTAC’s enhanced precision and reliability. The proposed solution will serve as an advisory framework to help financial crime investigators enhance the detection and reporting of suspicious cryptocurrency transactions in cyberspace.https://ieeexplore.ieee.org/document/10658980/Machine learningblockchaincybercrimecryptocurrencymoney laundering |
| spellingShingle | Algimantas Venckauskas Sarunas Grigaliunas Linas Pocius Rasa Bruzgiene Andrejs Romanovs Machine Learning in Money Laundering Detection Over Blockchain Technology IEEE Access Machine learning blockchain cybercrime cryptocurrency money laundering |
| title | Machine Learning in Money Laundering Detection Over Blockchain Technology |
| title_full | Machine Learning in Money Laundering Detection Over Blockchain Technology |
| title_fullStr | Machine Learning in Money Laundering Detection Over Blockchain Technology |
| title_full_unstemmed | Machine Learning in Money Laundering Detection Over Blockchain Technology |
| title_short | Machine Learning in Money Laundering Detection Over Blockchain Technology |
| title_sort | machine learning in money laundering detection over blockchain technology |
| topic | Machine learning blockchain cybercrime cryptocurrency money laundering |
| url | https://ieeexplore.ieee.org/document/10658980/ |
| work_keys_str_mv | AT algimantasvenckauskas machinelearninginmoneylaunderingdetectionoverblockchaintechnology AT sarunasgrigaliunas machinelearninginmoneylaunderingdetectionoverblockchaintechnology AT linaspocius machinelearninginmoneylaunderingdetectionoverblockchaintechnology AT rasabruzgiene machinelearninginmoneylaunderingdetectionoverblockchaintechnology AT andrejsromanovs machinelearninginmoneylaunderingdetectionoverblockchaintechnology |