Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification
Cryptocurrency money laundering is a pressing issue, as it not only facilitates and hides criminal activities but also disrupts markets and the overall financial system. To respond this challenge, researchers are trying to develop robust Anti-Money Laundering (AML) frameworks. These efforts play a c...
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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/10930500/ |
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| author | Stefano Ferretti Gabriele D'Angelo Vittorio Ghini |
| author_facet | Stefano Ferretti Gabriele D'Angelo Vittorio Ghini |
| author_sort | Stefano Ferretti |
| collection | DOAJ |
| description | Cryptocurrency money laundering is a pressing issue, as it not only facilitates and hides criminal activities but also disrupts markets and the overall financial system. To respond this challenge, researchers are trying to develop robust Anti-Money Laundering (AML) frameworks. These efforts play a crucial role in promoting societal welfare by mitigating the impact of criminal activities. This paper explores the application of Graph Neural Networks (GNNs) for classifying Bitcoin transactions. The research specifically employs Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), the Chebyshev spatial convolutional neural networks, and GraphSAGE networks. Based on the dataset analysis, we experiment with different subsets of features. Our findings suggest that the use of Graph Neural Network convolutions, combined with a final linear layer and skip connections, allow for an improvement in the state-of-the-art results, especially when Chebyshev and GATv2 convolutions are used. |
| format | Article |
| id | doaj-art-e35ffc49f252435d91a0b88f6a3a295e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e35ffc49f252435d91a0b88f6a3a295e2025-08-20T03:40:52ZengIEEEIEEE Access2169-35362025-01-0113502015021510.1109/ACCESS.2025.355224010930500Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction ClassificationStefano Ferretti0https://orcid.org/0000-0002-1911-4708Gabriele D'Angelo1https://orcid.org/0000-0002-3690-6651Vittorio Ghini2Department of Computer Science and Engineering (DISI), University of Bologna, Bologna, ItalyDepartment of Computer Science and Engineering (DISI), University of Bologna, Bologna, ItalyDepartment of Computer Science and Engineering (DISI), University of Bologna, Bologna, ItalyCryptocurrency money laundering is a pressing issue, as it not only facilitates and hides criminal activities but also disrupts markets and the overall financial system. To respond this challenge, researchers are trying to develop robust Anti-Money Laundering (AML) frameworks. These efforts play a crucial role in promoting societal welfare by mitigating the impact of criminal activities. This paper explores the application of Graph Neural Networks (GNNs) for classifying Bitcoin transactions. The research specifically employs Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), the Chebyshev spatial convolutional neural networks, and GraphSAGE networks. Based on the dataset analysis, we experiment with different subsets of features. Our findings suggest that the use of Graph Neural Network convolutions, combined with a final linear layer and skip connections, allow for an improvement in the state-of-the-art results, especially when Chebyshev and GATv2 convolutions are used.https://ieeexplore.ieee.org/document/10930500/Anti-money launderingdeep learninggraph neural networksclassification |
| spellingShingle | Stefano Ferretti Gabriele D'Angelo Vittorio Ghini Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification IEEE Access Anti-money laundering deep learning graph neural networks classification |
| title | Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification |
| title_full | Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification |
| title_fullStr | Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification |
| title_full_unstemmed | Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification |
| title_short | Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification |
| title_sort | enhancing anti money laundering frameworks an application of graph neural networks in cryptocurrency transaction classification |
| topic | Anti-money laundering deep learning graph neural networks classification |
| url | https://ieeexplore.ieee.org/document/10930500/ |
| work_keys_str_mv | AT stefanoferretti enhancingantimoneylaunderingframeworksanapplicationofgraphneuralnetworksincryptocurrencytransactionclassification AT gabrieledangelo enhancingantimoneylaunderingframeworksanapplicationofgraphneuralnetworksincryptocurrencytransactionclassification AT vittorioghini enhancingantimoneylaunderingframeworksanapplicationofgraphneuralnetworksincryptocurrencytransactionclassification |