Legal regulation of artificial intelligence application in anti-money laundering: analysis of international experience

Serhii Banakh; Nataliia Holota

doi:10.35774/

Authors

Serhii Banakh West Ukrainian National University https://orcid.org/0000-0002-2300-1220
Nataliia Holota West Ukrainian National University https://orcid.org/0000-0003-4113-7743

DOI:

https://doi.org/10.35774/

Keywords:

artificial intelligence, anti-money laundering, AML systems, corruption, financial monitoring, legal regulation, EU AI Act, machine learning, regulatory supervision, algorithmic accountability

Abstract

The article presents a comprehensive examination of the legal aspects of artificial intelligence (AI) applications in anti-money laundering (AML) and counter-terrorism financing (CTF), drawing on an analysis of international experiences in legal regulation. Special attention is paid to the transformation of public perception of the problem following high-profile information leaks - the Panama Papers (2016) and Paradise Papers (2017), which became catalysts for the transition from relative tolerance of asset concealment schemes to active international counteraction to financial crimes. It has been proven that traditional financial monitoring methods are unable to effectively process millions of daily transactions, which necessitates the implementation of cutting-edge AI technologies, including machine learning algorithms (MLA), deep neural networks, and predictive analytics tools. The legal nature of AI in the financial context and its specific applications in AML monitoring systems are revealed. The evolution from an instrumental understanding of computer programs to the recognition of the special status of autonomous decision-making systems is traced. It is established that AI’s ability to self-learn and form unpredictable conclusions creates new legal challenges, particularly regarding the attribution of responsibility, which are resolved through the distribution of obligations between technology developers and users.

International approaches to AI regulation in the AML/CFT sphere are analyzed. FATF recommendations that establish risk-based approach principles are examined. The European model based on the EU AI Act, which came into force on August 1, 2024, and classified AML systems as high-risk, is studied. The American model, characterized by flexibility through Financial Crimes Enforcement Network (FinCEN) initiatives, as well as regional practices, including initiatives by the Monetary Authority of Singapore and the Financial Conduct Authority of the United Kingdom, is characterized. Two dominant approaches are identified: the European (comprehensive and normatively detailed) and the American (more adaptive and practically oriented). It is concluded that the key problem remains the lack of international harmonization of legal standards, which creates fragmentation of the regulatory environment and complicates operations conducted by transnational financial institutions. The rationale for establishing unified principles at the global level through the activities of international organizations is argued, which will allow ensuring a balance between innovation and legal certainty in the field of AI application. Proposals for improving the legislative regulation of this sphere in Ukraine are substantiated, taking into account European standards and contemporary technological challenges.

References

1. The Panama Papers: The World Reacts. (2016). OCCRP. Retrieved from https://www.occrp.org/en/ [in English].

2. Paradise Papers. (2016). OCCRP. Retrieved from https://www.occrp.org/en/ [in English].

3. Banakh, S. & Holota, N. (2025). Corruption as a Source of Funds for Money Laundering. Legal Mechanisms of Counteraction. Actual Problems of Law, 2, 215‒221 [in English].

4. Teremetskyi, V., Kovalchuk, O., Kolesnikov, A., Udovenko, Z., Shramko, O. & Predmestnikov, O. (2023). Proactive Models for Information Support of the Judicial System. Revista Brasileira de Direito, 19(3), Article e4975 [in English].

5. Yassine, S., Esghir, M. & Ibrihich, O. (2023). Using artificial intelligence tools in the judicial domain and the evaluation of their impact on the prediction of judgments. Procedia Computer Science, 220, 1021-1026 [in English].

6. Orosz, T., Vagi, R., Csanyi, G. M., Nagy, D., Uveges, I., Vadasz, J. P. & Megyeri A. (2022). Evaluating Human versus Machine Learning Performance in a LegalTech Problem. Applied Sciences, 12 (1), Article 297 [in English].

7. Automating injustice: The use of artificial intelligence & automated decision-making systems in criminal justice in Europe. Retrieved from ttps://policehumanrightsresources.org/automating-injustice-the-use-of- artificial-intelligence-automated-decision-making-systems-in-criminal-justice-in-europe [in English].

8. Tokarieva, K. S., Kovalchuk, O. Ya., Kolesnikov, A. P., Dzyurbel, A. D., Bodnar-Petrovska, O. B. & Predmestnikov, O. G. (2024). The Use of AI-Language Models in Judicial Proceedings: Information and Legal Aspects. Revista Juridica Unicuritiba, 2(78), 520-538 [in English].

9. Teremetskyi, V. I. & Kovalchuk, O. Ya. (2024). Artificial Intelligence as a Factor in the Digital Transformation of the Justice System. Forum Prava, 78(1), 106-115 [in English].

10. EU AI Act: first regulation on artificial intelligence (2023). European Parliament. Retrieved from https:// www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on- artificial-intelligence [in English].

11. Regulators’ strategic approaches to AI. (2024). Go UK. Retrieved from https://www.gov.uk/government/ publications/regulators-strategic-approaches-to-ai [in English].

12. Poon, C.-H., Kwok, J., Chow, C. & Choi, J.-H. (2025). LineMVGNN: Anti-Money Laundering with Line- Graph-Assisted Multi-View Graph Neural Networks. AI, 6(4), Article 69 [in English].

13. Masunda, M. & Barot, H. (2025). Disruption in Southern Africa’s Money Laundering Activity by Artificial Intelligence Technologies. Journal of Risk and Financial Management, 18(8), Article 441 [in English].

14. Ding, K., Gerhard, D. & Fan, L. (2025). AML-Based Multi-Dimensional Co-Evolution Approach Supported by Blockchain: Architecture Design and Case Study on Intelligent Production Lines for Industry 4.0. Information, 16(3), Article 243 [in English].

15. Kovalchuk, O., Kolesnikov, A., Proskurniak, I., Halunko, V. & Коvach, Y. (2024). Implementation of inno- vative information technologies in judicial proceedings: organizational and legal aspect. Amazonia Investiga, 13 (76), 53-62 [in English].

16. Ramos, G. (2021). Ethics of Artificial Intelligence. UNESCO. Retrieved from https://www.unesco.org/en/ artificial-intelligence/recommendation-ethics [in English].

17. Shirvanporzour, A. (2025, March). Artificial intelligence in banking risk management and anti-money laun-

dering: A comprehensive review. Retrieved from https://doi.org/10.13140/RG.2.2.27192.25602 [in English].

18. Fox, S. & Victores, J. G. (2024). Safety of Human–Artificial Intelligence Systems: Applying Safety Science to Analyze Loopholes in Interactions between Human Organizations, Artificial Intelligence, and Individual People. Informatics, 11(2), Article 36 [in English].

19. Bila, V. R. & Bondarchuk, V. V. (2025). Instytutsiina reforma systemy zapobihannia ta protydiyi legalizatsii (vidmyvanniu) dokhodiv, oderzhanikh zlochynnym shliakhom, finansuvanniu terozyzmu v Yevropeyskomu Soiuzi: vyklyky ta znachennia dlia Ukrainy [Institutional reform of the system for prevention and counteraction of money laundering and terrorist financing in the EU: challenges and significance for Ukraine]. Yurydychnyi naukovyi elektronychnyi zhurnal - Legal Scientific Electronic Journal, 4, 241-243 [in Ukrainian].

20. Utkina, M. S. (2023). Vprovadzhennia ryzyk-orientovanoho pidkhodu v systemi finansovoho monitoryngu Ukrainy [Implementation of a risk-based approach in the financial monitoring system of Ukraine]. Aktualni problemy vitchyznianoi yurysprudentsii - Current issues in domestic jurisprudence, 2, 127-133 [in Ukrainian].

21. Ponomarenko, V. S., Kyrkach, S. M., Pleskun, I. V. & Lozinska, O. I. (2020). Ryzyk-orientovanyi pidkhid u pervynnomu finansovomu monitoryngu bankiv [Risk-based approach in primary financial monitoring of banks]. Financial and Credit Activity: Problems of Theory and Practice - Current Issues of Domestic Jurisprudence, 3(34) 42-52 [in Ukrainian].

22. Sidor, M. I. (2023). Vplyv didzhytalizatsii na zdijsnennia finansovoho kontroliu [The impact of digitalization on the implementation of financial control]. Yurydychnyi naukovyi elektronychnyi zhurnal - Legal Scientific Electronic Journal, 2, 379-393 [in Ukrainian].

23. Shyshkina, O. V. (2023). Tsyfrovi tekhnolohii finansovykh ustanov: ryzyky i perspektyvy vykorystannia [Digital technologies of financial institutions: risks and prospects of use]. The Actual Problems of Regional Economy Development, 2(19), 130-143 [in Ukrainian].

24. Honcharenko, I. (2023). Kiberzahrozy finansovoho sektora v umovakh viiny [Cyber threats to the financial sector in wartime]. Ekonomika ta suspilstvo - Economy and Society, 50 [in Ukrainian].

25. Kiberbezpeka ta innovatsiynist finansovykh instrumentiv na birzhovomu rynku: materialy krugloho stolu (m. Kyiv, 30 lystopada 2023 r.): elektronn. nauk. vyd. [Cybersecurity and innovativeness of financial instruments in the stock market: round table materials (Kyiv, November 30, 2023): electronic scientific publication] / Za nauk. red. O. V. Bezpalenko. Kyiv: VNZ «Kyivskyi universytet rynkovykh vidnosyn», 2023 [in Ukrainian].

26. Kovalchuk, O., Karpinski, M., Banakh, S., Kasianchuk, M., Shevchuk, R. & Zagorodna, N. (2023). Prediction Machine Learning Models on Propensity Convicts to Criminal Recidivism. Information, 14(3), Article 161 [in English].

27. Lendvai, G. F. & Gosztonyi, G. (2025). Algorithmic Bias as a Core Legal Dilemma in the Age of Artificial Intel- ligence: Conceptual Basis and the Current State of Regulation. Laws, 14(3), Article 41 [in English].

28. Digital Transformation of AML/CFT. (2021). FATF. Retrieved from https://www.fatf-gafi.org/en [in English].

29. Directive (EU) 2024/1640 of the European Parliament and of the Council of 31 May 2024 on the mechanisms to be put in place by Member States for the prevention of the use of the financial system for the purposes of money laundering or terrorist financing, amending Directive (EU) 2019/1937, and amending and repealing Directive (EU) 2015/849. EUR-Lex. Retrieved from https://eur-lex.europa.eu/eli/dir/2024/1640/oj/eng [in English].

30. Regulation (EU) 2024/1620 of the European Parliament and of the Council of 31 May 2024 establishing the Authority for Anti-Money Laundering and Countering the Financing of Terrorism and amending Regulations (EU) No 1093/2010, (EU) No 1094/2010 and (EU) No 1095/2010. EUR-Lex. Retrieved from https://eur-lex.europa.eu/eli/reg [in English].

31. Proposal for a directive on the mechanisms to be put in place by the Member States for the prevention of the use of the financial system for the purposes of money laundering or terrorist financing and repealing Directive (EU) 2015/849 (2021). European Parliament. Retrieved from https://www.europarl.europa.eu [in English].

32. Directive (EU) 2015/2366 of the European Parliament and of the Council of 25 November 2015 on payment services in the internal market, amending Directives 2002/65/EC, 2009/110/EC and 2013/36/EU and Regulation (EU) No 1093/2010, and repealing Directive 2007/64/EC. EUR-Lex. Retrieved from https://eur-lex.europa.eu/ eli/dir/2015/2366/oj/eng [in English].

33. AML in 2025: How are AI, real-time monitoring, and global regulation are transforming compliance? (2025).

Moody’s. Retrieved from https://www.moodys.com [in English].

34. Twelve-Month Pilot Program of Anti-Money Laundering and Combating the Financing of Terrorism (AML/ CFT) Assessments-Joint Report on the Review of the Pilot Program (2025). IMF. Retrieved from https://www. elibrary.imf.org [in English].

35. FinCEN Alert on Fraud Schemes Involving Deepfake Media Targeting Financial Institutions (2024). FinCEN. Retrieved from https://www.fincen.gov [in English].

36. A Framework for OFAC Compliance Commitments (2019). OFAC. Retrieved from https://ofac.treasury.gov [in English].

37. Calvery, J. Harnessing the power of AI to fight financial crime (2024). HSBC. Retrieved from https://www.hsbc. com [in English].

38. Johnson, B. M. (May, 2025). Integrating Artificial Intelligence and Blockchain for Enhanced AML Compliance: A Cross-Jurisdictional Perspective. Retrieved from https://www.researchgate.net/ publication/391678501_Integrating_Artificial_Intelligence_and_Blockchain_for_Enhanced_ AML_Compliance_A_Cross-Jurisdictional_Perspective [in English].

39. Top 10 Anti Money Laundering Software Companies Strengthening Fraud Detection Capabilities of Organiza- tions Worldwide (2023). Emergen. Retrieved from https://www.emergenresearch.com [in English].

40. Guardian (2025). Monetary Authority of Singapore. Retrieved from https://www.mas.gov.sg [in English].

41. Financial Inclusion TechSprint (2024). FCA. Retrieved from https://www.fca.org.uk [in English].

42. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natu- ral persons with regard to the processing of personal data and on the free movement of such data, and repealing Direc- tive 95/46/EC (General Data Protection Regulation). EUR-Lex. Retrieved from https://eur-lex.europa.eu [in English]