References
- ‘Anti-money laundering’ (no date) Wikipedia. Available at: https://en.wikipedia.org/wiki/Anti%E2%80%93money_laundering (Accessed: 18 January 2025).
- Bi, W., Trinh, T.K. and Fan, S. (2024) ‘Machine learning-based pattern recognition for anti-money laundering in banking systems’, Journal of Advanced Computing Systems, 4(11), pp. 30–41. Available at: https://doi.org/10.69987/JACS.2024.41103
- Boersma, M. (2024a) ‘PyTorch geometric: Elliptic(++) dataset’ (2024) Medium, 3 April. Available at: https://medium.com/@marcelboersma/elliptic-fbc7e008db2b (Accessed: 27 January 2025).
- Boersma, M. (2024b) ‘Unveiling Bitcoin network secrets with the Elliptic++ dataset: from transactions to graph neural networks’, Medium, 29 March. Available at: https://medium.com/@marcelboersma/unveiling-bitcoin-network-secrets-with-the-elliptic-dataset-from-transactions-to-graph-neural-384edd61ec85 (Accessed: 27 January 2025).
- Charaia, V., Chochia, A. and Lashkhi, M. (2020) ‘The impact of FDI on economic development: the case of Georgia’, TalTech Journal of European Studies, 10(2), pp. 96–116. Available at: https://doi.org/10.1515/bjes-2020-0017
- Charaia, V., Chochia, A. and Lashkhi, M. (2021) ‘Promoting fintech financing for SME in S. Caucasian and Baltic States during the COVID-19 global pandemic’, Journal of Business Management and Economics Engineering, 19(2), pp. 358–372. Available at: https://doi.org/10.3846/bmee.2021.14755
- Chochia, A., Kerikmäe, T. and Skvarciany, V. (2023) ‘Global economic challenges for sustainable entrepreneurs’, TalTech Journal of European Studies, 13(1), pp. 3–7. Available at: https://doi.org/10.2478/bjes-2023-0001
- Elliptic (2024) ‘Our new research: enhancing blockchain analytics through AI’, 1 May. Available at: https://www.elliptic.co/blog/our-new-research-enhancing-blockchain-analytics-through-ai (Accessed: 1 February 2025).
- ‘Elliptic++ dataset: a graph network of Bitcoin blockchain transactions and wallet addresses’ (2025) Github. Available at: https://github.com/git-disl/EllipticPlusPlus (Accessed: 27 January 2025).
- ‘FinTorch’ (2025) Github. Available at: https://github.com/AI4FinTech/FinTorch/ (Accessed: 27 January 2025).
- ForkLog (2024) ‘Elliptic navchyla shi vyiavliaty vidmyvannia hroshei cherez Bitcoin’, 2 May. Available at: https://forklog.com.ua/news/elliptic-navchyla-shivyyavlyaty-vidmyvannya-groshej-cherez-bitkoyin (Accessed: 23 January 2025).
- Gandhi, H., Tandon, K., Gite, S., Pradhan, B. and Alamri, A. (2024) ‘Navigating the complexity of money laundering: anti–money laundering advancements with AI/ML insights’, International Journal on Smart Sensing and Intelligent Systems, 17(1), pp. 1–29. Available at: https://doi.org/10.2478/ijssis-2024-0024
- Göksal, Ş.İ., Solarte-Vásquez, M.C. and Chochia, A. (2025) ‘The EU AI Act’s alignment within the European Union’s regulatory framework on artificial intelligence’, International and Comparative Law Review, 24(2), pp. 25–53. Available at: https://doi.org/10.2478/iclr-2024-0017
- ‘Graph neural network’ (no date) Wikipedia. Available at: https://en.wikipedia.org/wiki/Graph_neural_network (Accessed: 19 January 2025).
- Hovorushchenko, T., Boyarchuk, A. and Pavlova, O. (2019) ‘Ontology-based intelligent agent for semantic parsing the natural language specifications of software requirements’, International Journal on Information Technologies & Security, 11(2), pp. 59–71.
- Hovorushchenko, T., Moskalenko, A. and Osyadlyi, V. (2023) ‘Methods of medical data management based on blockchain technologies’, Journal of Reliable Intelligent Environments, 9(1), pp. 5–16. Available at: https://doi.org/10.1007/s40860-022-00178-1
- Javaid, H.A. (2024) ‘Revolutionizing AML: how AI is leading the charge in detection and prevention’, Journal of Innovative Technologies, 7(1), pp. 1–16.
- Kitov, A.V. (2024) Development and research of the use of neural networks in the tasks of detecting fraudulent transactions of the Ethereum network: explanatory note to the qualification work of a higher education applicant at the second (master’s) level, Specialty 122 Computer Science. Kharkiv: Ministry of Education and Science of Ukraine, National University of Radio Electronics.
- Koduru, L. (2025) ‘Driving business success through AI-driven fraud detection innovations in AML and risk monitoring systems’, in S. Kulkarni, M. Valeri and P. William (eds) Driving business success through eco-friendly strategies. IGI Global Scientific Publishing, pp. 115–130. Available at: https://doi.org/10.4018/979-8-3693-9750-3.ch006
- Konstantinidis, G. and Gegov, A. (2024) ‘Deep neural networks for anti-money laundering using explainable artificial intelligence’, in 2024 IEEE 12th International Conference on Intelligent Systems (IS), pp. 1–6. Available at: https://doi.org/10.1109/IS61756.2024.10705194
- Kotagiri, A. (2024) ‘AML detection and reporting with intelligent automation and machine learning’, International Machine Learning Journal and Computer Engineering, 7(7), pp. 1–17.
- Künnapas, K., Maslionkina, P., Hinno, R. and Liberts, R. (2025) ‘Current AI applications by Estonian tax authorities and use case scenarios’, TalTech Journal of European Studies, 15(1), pp. 179–208. Available at: https://doi.org/10.2478/bjes-2025-0010
- Lashkhi, M., Charaia, V., Boyarchuk, A. and Ebralidze, L. (2022) ‘The impact of fintech on financial institutions: the case of Georgia’, TalTech Journal of European Studies, 12(1), pp. 20–42. Available at: https://doi.org/10.2478/bjes-2022-0010
- Lyeonov, S., Draskovic, V., Kubaščikova, Z. and Fenyves, V. (2024) ‘Artificial intelligence and machine learning in combating illegal financial operations: bibliometric analysis’, Human Technology, 20(2), pp. 325–360. Available at: https://doi.org/10.14254/1795-6889.2024.20-2.5
- Oyedokun, O., Ewim, S.E. and Oyeyemi, O.P. (2024) ‘A comprehensive review of machine learning applications in AML transaction monitoring’, International Journal of Engineering Research and Development, 20(11), pp. 730–743.
- Pazos, J.F.M., González, J.G., Lorenzo, D.B., Morales, J.A.R. and Álvarez, M.M.R. (2024) ‘Fraud transaction detection for anti-money laundering systems based on deep learning’, Journal of Emerging Computer Technologies, 3(1), pp. 29–34. Available at: https://doi.org/10.57020/ject.1428146
- Raj, M., Khan, H., Kathuria, S., Chanti, Y. and Sahu, M. (2024) ‘The use of artificial intelligence in anti-money laundering (AML)’, in 2024 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL), pp. 272–277. Available at: https://doi.org/10.1109/ICSADL61749.2024.00050
- Weber, M., Domeniconi, G., Chen, J., Weidele, D.K.I., Bellei, C., Robinson, T. and Leiserson, C.E. (2019) ‘Anti-money laundering in Bitcoin: experimenting with graph convolutional networks for financial forensics’, ArXiv. Available at: https://doi.org/10.48550/arXiv.1908.02591
- Wu, R., Ma, B, Jin, H., Zhao, W., Wang, W. and Zhang, T. (2023) ‘GRANDE: a neural model over directed multigraphs with application to anti-money laundering’, in 2022 IEEE International Conference on Data Mining (ICDM), pp. 558–567. Available at: https://doi.org/10.48550/arXiv.2302.02101
- Zolkin, V., Chochia, A. and Hoffmann, T. (2023) ‘Automated decision-making in the EU Member State’s public administration: the compliance of automated decisions of the Estonian Unemployment Insurance Fund with Estonian administrative procedure law’, European Studies, 10(2), pp. 178–202. Available at: https://doi.org/10.2478/eustu-2023-0017