Machine Learning in Money Laundering Detection over Blockchain Technology
IEEE Access 2025
Algimantas Venčkauskas, Šarūnas Grigaliūnas, Linas Pocius, Rasa Bruzgiene, Andrejs Romānovs

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.


Keywords
Machine learning , Blockchain , cybercrime , cryptocurrency , money laundering
DOI
10.1109/ACCESS.2024.3452003
Hyperlink
https://ieeexplore.ieee.org/document/10658980

Venčkauskas, A., Grigaliūnas, Š., Pocius, L., Bruzgiene, R., Romānovs, A. Machine Learning in Money Laundering Detection over Blockchain Technology. IEEE Access, 2024, Vol. 4, No. 2024, pp.1-20. e-ISSN 2169-3536. Available from: doi:10.1109/ACCESS.2024.3452003

Publication language
English (en)
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