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Articles
Published: 2025-05-28

Who benefits from AI in money laundering in Europe: The organised criminals or the aml services?

Silesian University of Technology; Sumy State University
Technical University of Denmark
University of Kalisz
John von Neumann University; WSB University, Dabrowa Górnicza; University of Johannesburg
artificial intelligence anti-money laundering Basel AML Index AI Vibrancy Score AI Vibrancy subindexes

Abstract

Artificial Intelligence (AI) has been exerting a growing influence on financial security, particularly in the area of anti-money laundering (AML). This study examines the relationship between AI adoption and AML effectiveness across selected European countries between 2017 and 2023. Employing a panel data econometric model, the analysis incorporates AI Vibrancy Scores, governance indicators, and economic variables to assess the multifaceted impact of AI integration. The findings reveal that greater AI adoption is generally associated with improved AML performance, as reflected by a statistically significant negative relationship between the AI Vibrancy Score and the Basel AML Index. However, the incorporation of a quadratic term indicates an inverted U-shaped relationship, suggesting that while moderate levels of AI adoption enhance AML outcomes, excessive integration may introduce systemic vulnerabilities exploitable by financial criminals. Governance variables – most notably the Rule of Law and Control of Corruption – emerge as key enablers of effective AI-driven AML strategies. Furthermore, factors such as public perception of AI and the presence of responsible AI governance frameworks significantly influence the success of AI applications in AML contexts. These results underscore the necessity of balanced AI policy development, robust institutional frameworks, and international regulatory coordination to harness AI’s potential while mitigating its associated risks.

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References

  1. Androniceanu, A. (2024). Generative artificial Intelligence, present and perspectives in public administration. Administratie si Management Public, 43(43), 105–119. https://doi.org/10.24818/amp/2024.43-06 DOI: https://doi.org/10.24818/amp/2024.43-06
  2. Awe, O. O., & Dias, R. (2022). Comparative Analysis of ARIMA and Artificial Neural Network Techniques for Forecasting Non-Stationary Agricultural Output Time Series. Agris on-line Papers in Economics and Informatics, 14(4), 3–9. https://doi.org/10.7160/aol.2022.140401 DOI: https://doi.org/10.7160/aol.2022.140401
  3. Balcerzak, A. P., & Valaskova, K. (2024). Artificial Intelligence: Financial management under pressure of transformative technology. Equilibrium. Quarterly Journal of Economics and Economic Policy, 19(4), 1127-1137. https://doi.org/10.24136/eq.3394 DOI: https://doi.org/10.24136/eq.3394
  4. Barbu, L., Horobeț, A., Belașcu, L., & Ilie, A. G. (2024). Approaches to tax evasion: a bibliometric and mapping analysis of Web of Science indexed studies. Journal of Business Economics and Management, 25(1), 1–20. https://doi.org/10.3846/jbem.2024.20691 DOI: https://doi.org/10.3846/jbem.2024.20691
  5. Basel Institute on Governance. (n.d.). Basel AML Index - Assessing money laundering risks around the world. Basel AML Index. https://index.baselgovernance.org/
  6. Basu, D., & Tetteh, G. K. (2024). Using automation and AI to combat money laundering. University of Strathclyde. https://www.strath.ac.uk/media/departments/accountingfinance/fril/whitepapers/Using_Automation_and_AI_to_Combat_Money_Laundering.pdf
  7. Bian, X., & Wang, B. (2024). Exploring the Influence of Artificial Intelligence Usage on Ethical Decision Making Among Public Sector Employees: Insights into Moral Identity and Service Motivation. Business Ethics and Leadership, 8(3), 133-150. https://doi.org/10.61093/bel.8(3).133-150.2024 DOI: https://doi.org/10.61093/bel.8(3).133-150.2024
  8. Botoc, F. C., Khaled, M. D., Milos, L. R., & Bilti, R. S. (2023). The role of big data in the fintech industry: a bibliometric analysis. Transformations in Business & Economics, 22(3A (60A)), 832–852. http://www.transformations.knf.vu.lt/60a/article/therl
  9. Burrell, D. N. (2024). Exploring the Cyberpsychology of Social Media Addiction and Public Health Risks among Black American Women in the USA. Health Economics and Management Review, 5(2), 14-31. https://doi.org/10.61093/hem.2024.2-02 DOI: https://doi.org/10.61093/hem.2024.2-02
  10. Clyde & Co. (2024). Singapore’s AI Model Risk Management Paper: Key Insights for Financial Institutions : Clyde & Co. Global Law Firm | Clyde & Co : Clyde & Co. https://www.clydeco.com/en/insights/2024/12/singapore-s-ai-model-risk-management-paper-key-ins
  11. Dale, B. (2024). Dark web AI defeats human verification on crypto exchanges. https://www.axios.com/2024/10/09/crypto-ai-dark-web-exchanges-human-verification
  12. Dearden, L. (2024). AI increasingly used for sextortion, scams and child abuse, says senior UK police chief. the Guardian. https://www.theguardian.com/technology/2024/nov/24/ai-increasingly-used-for-sextortion-scams-and-child-abuse-says-senior-uk-police-chief
  13. Dobrovolska, O., & Rozhkova, M. (2024a). Development of the Country’s Sustainable Cyberspace Strategy to Ensure the Country’s National Security. SocioEconomic Challenges, 8(2), 197-214. https://doi.org/10.61093/sec.8(2).197-214.2024 DOI: https://doi.org/10.61093/sec.8(2).197-214.2024
  14. Dobrovolska, O., & Rozhkova, M. (2024b). The Impact of Digital Transformation on the Anti-Corruption and Cyber-Fraud System. Business Ethics and Leadership, 8(3), 231-252. https://doi.org/10.61093/bel.8(3).231-252.2024 DOI: https://doi.org/10.61093/bel.8(3).231-252.2024
  15. European Parliamentary Research Service (EPRS). (2020). Artificial Intelligence: From ethics to policy. https://www.europarl.europa.eu/RegData/etudes/STUD/2020/641507/EPRS_STU(2020)641507_EN.pdf doi: 10.2861/247
  16. Financial Action Task Force (FATF) (2021), Opportunities and Challenges of New Technologies for AML/CFT, FATF, Paris, France, https://www.fatf-gafi.org/publications/ fatfrecommendations/documents/opportunities-challenges-newtechnologies-aml-cft.html
  17. Financial Crime Academy (FCA). (2024). AI: The Game Changer In Anti-Money Laundering. Financial Crime Academy. https://financialcrimeacademy.org/ai-in-anti-money-laundering/
  18. Financial Market Supervisory Authority (FINMA). (2024). FINMA guidance on governance and risk management when using artificial Intelligence. Eidgenössische Finanzmarktaufsicht FINMA. https://www.finma.ch/en/news/2024/12/20241218-mm-finma-am-08-24/
  19. Garškaitė-Milvydienė, K., Maknickienė, N., Tvaronavičienė M., et al. (2023). Insights into attitudes towards financial innovations by its users. Polish Journal of Management Studies, 27(2), 106-119. https://doi.org/10.17512/pjms.2023.27.2.07 DOI: https://doi.org/10.17512/pjms.2023.27.2.07
  20. Gikay, A. A. (2024). Risks, innovation, and adaptability in the UK’s incrementalism versus the European Union’s comprehensive artificial intelligence regulation. International Journal of Law and Information Technology, 32(1). https://doi.org/10.1093/ijlit/eaae013 DOI: https://doi.org/10.1093/ijlit/eaae013
  21. Höller, S., Dilger, T., Spiess, T., Ploder, C., & Bernsteiner, R. (2023). Awareness of Unethical Artificial Intelligence and its Mitigation Measures. European Journal of Interdisciplinary Studies, 15(2), 67–89. https://doi.org/10.24818/ejis.2023.17 DOI: https://doi.org/10.24818/ejis.2023.17
  22. Ioan-Franc, V., & Gaf-Deac, I. I. (2024). Participation of artificial Intelligence in economic growth in Romania. Amfiteatru Economic, 26(67), 713–726. https://doi.org/10.24818/ea/2024/67/944 DOI: https://doi.org/10.24818/EA/2024/67/944
  23. Iskakova, A., Kuchukova, N., Akhpanov, A., Sidorova, N., Kussainova, L., & Omarova, A. (2025). Innovative Approaches to Financial Sustainability and Ensuring Access to Justice for the Population Using Artificial Intelligence Tools. Montenegrin Journal of Economics, 21(1). https://doi.org/10.14254/1800-5845/2025.21-1.20 DOI: https://doi.org/10.14254/1800-5845/2025.21-1.20
  24. Ishwardat, S., van Steenbergen, E., Coffeng, T., & Ellemers, N. (2024). Stimulating Regulatory Compliance and Ethical Behavior of Organisations: A Review. Business Ethics and Leadership, 8(3), 151-172. https://doi.org/10.61093/bel.8(3).151-172.2024 DOI: https://doi.org/10.61093/bel.8(3).151-172.2024
  25. Kabachenko, D., Churikanova, O., Oneshko, S., Avhustyn, R., & Slatvinska, V. (2022). Application of information technologies for management decision making in the conditions of the instability of the external economic space. International Journal for Quality Research, 16(4), 1121–1132. https://doi.org/10.24874/ijqr16.04-11 DOI: https://doi.org/10.24874/IJQR16.04-11
  26. Košovská, I., Hallová, M., Váryová, I., Šilerová, E., Hennyeyová, K., & Cihelka, P. (2022). The Digital Economy in the Context of Digital Transformation and Their Impact on the Electronification of Accounting Processes in the Slovak Republic. Agris on-line Papers in Economics and Informatics, 14(4), 53–65. https://doi.org/10.7160/aol.2022.140405 DOI: https://doi.org/10.7160/aol.2022.140405
  27. Kuzior, A., Tiutiunyk, I., Zielińska, A., & Kelemen, R. (2024). Cybersecurity and cybercrime: Current trends and threats. Journal of International Studies, 17(2), 220- 239. doi:10.14254/2071-8330.2024/17-2/12 DOI: https://doi.org/10.14254/2071-8330.2024/17-2/12
  28. Kuzior, A., Vasylieva, T., Kuzmenko, O., Koibichuk, V., & Brożek, P. (2022). Global Digital Convergence: Impact of Cybersecurity, Business Transparency, Economic Transformation, and AML Efficiency. Journal of Open Innovation: Technology, Market, and Complexity, 8(4), 195. https://doi.org/10.3390/joitmc8040195 DOI: https://doi.org/10.3390/joitmc8040195
  29. Larson, K. E. (2024). FinCEN alert: Fraud schemes using generative artificial Intelligence to circumvent financial institution's identity verification, authentication, and due diligence controls. Money Laundering Watch. https://www.moneylaunderingnews.com/2024/11/fincen-alert-fraud-schemes-using-generative-artificial-intelligence-to-circumvent-financial-institutions-identity-verification-authentication-and-due-diligence-controls
  30. Letkovsky, S., Jencova, S., Vasanicova, P., Gavura, S., & Bacik, R. (2023). Predicting bankruptcy using artificial Intelligence: The case of the engineering industry. Economics and Sociology, 16(4), 178-190. doi:10.14254/2071- 789X.2023/16-4/8 DOI: https://doi.org/10.14254/2071-789X.2023/16-4/8
  31. Liu, S., Zhou, L., & Yang, J. (2023). Exploring the formation mechanism of technology standard competitiveness in artificial intelligence industry: a fuzzy-set qualitative comparative analysis. Journal of Business Economics and Management, 24(4), 653–675. https://doi.org/10.3846/jbem.2023.18845 DOI: https://doi.org/10.3846/jbem.2023.18845
  32. Longoni, C., Fradkin, A., Cian, L., & Pennycook, G. (2022). News from Generative Artificial Intelligence is believed less. 2022 ACM Conference on Fairness, Accountability, and Transparency, 97–106. https://doi.org/10.1145/3531146.3533077 DOI: https://doi.org/10.1145/3531146.3533077
  33. Máté, D., Raza, H., Ahmad, I., & Kovács, S. (2024). Next step for bitcoin: Confluence of technical indicators and machine learning. Journal of International Studies, 17(3), 68-94. doi:10.14254/2071-8330.2023/17-3/4 DOI: https://doi.org/10.14254/2071-8330.2023/17-3/4
  34. Memarian, B., & Doleck, T. (2024). A review of caveats and future considerations of research on responsible AI across disciplines. Human Technology, 20(3), 488–524. https://doi.org/10.14254/1795-6889.2024.20-3.4 DOI: https://doi.org/10.14254/1795-6889.2024.20-3.4
  35. Merkle Science. (2022). Mixers and Tumblers: Regulatory Overview and Use in Illicit Activities | Merkle Science. The Predictive Crypto Risk & Intelligence Platform. https://www.merklescience.com/blog/mixers-and-tumblers-regulatory-overview-and-use-in-illicit-activities
  36. Moravec, V., Hynek, N., Gavurova, B., & Kubak, M. (2024). Everyday artificial Intelligence unveiled: Societal awareness of technological transformation. Oeconomia Copernicana, 15(2), 367-406. https://doi.org/10.24136/oc.2961 DOI: https://doi.org/10.24136/oc.2961
  37. Murko, E., Babšek, M., & Aristovnik, A. (2024). Artificial intelligence and public governance models in socioeconomic welfare: some insights from Slovenia. Administratie si Management Public, 43(43), 41–60. https://doi.org/10.24818/amp/2024.43-03 DOI: https://doi.org/10.24818/amp/2024.43-03
  38. Neacsu, M.-C., Eregep, E.-Y., & Diaconescu, M. (2025). Artificial Intelligence as a geopolitical tool. Amfiteatru Economic, 27(68), 253–268. https://doi.org/10.24818/ea/2025/68/253 DOI: https://doi.org/10.24818/EA/2025/68/253
  39. Okulich-Kazarin, V., Artyukhov, A., Skowron, Ł., Artyukhova, N., Dluhopolskyi, O., & Cwynar, W. (2023). Sustainability of Higher Education: Study of Student Opinions about the Possibility of Replacing Teachers with AI Technologies. Sustainability, 16(1), 55. https://doi.org/10.3390/su16010055 DOI: https://doi.org/10.3390/su16010055
  40. Okulich-Kazarin, V., Artyukhov, A., Skowron, Ł., Artyukhova, N., & Wołowiec, T. (2024). When Artificial Intelligence Tools Meet “Non-Violent” Learning Environments (SDG 4.3): Crossroads with Smart Education. Sustainability, 16(17), 7695. https://doi.org/10.3390/su16177695 DOI: https://doi.org/10.3390/su16177695
  41. Pattara, M. (2023). Regulating AI in AML under different jurisdictions: AI good practices and regulations for compliance frameworks worldwide. NAPIER AI. https://www.napier.ai/post/ai-compliance-regional-regulations
  42. Piotrowski, D., & Orzeszko, W. (2023). Artificial Intelligence and customers' intention to use robo-advisory in banking services. Equilibrium. Quarterly Journal of Economics and Economic Policy, 18(4), 967-1007. https://doi.org/10.24136/eq.2023.031 DOI: https://doi.org/10.24136/eq.2023.031
  43. Polishchuk, Y. (2023). Fintech future trends. The European Digital Economy, 204–220. https://doi.org/10.4324/9781003450160-15 DOI: https://doi.org/10.4324/9781003450160-15
  44. Pulungan, A. H., Setiawan, S., & Windiarti, F. (2024). The Risk of Corruption and Money Laundering: An Analysis of Personal Predespositions and Socioeconomic Challenges. SocioEconomic Challenges, 8(4), 121-136. https://doi.org/10.61093/sec.8(4).121-136.2024 DOI: https://doi.org/10.61093/sec.8(4).121-136.2024
  45. Reshetnikova, M. S., & Mikhaylov, I. A. (2023). Artificial Intelligence Development: Implications for China. Montenegrin Journal of Economics, 19(1). https://doi.org/10.14254/1800-5845/2023.19-1.12 DOI: https://doi.org/10.14254/1800-5845/2023.19-1.12
  46. Roba, M., & Moulay, O. K. (2024). Risk Management in Using Artificial Neural Networks. SocioEconomic Challenges, 8(2), 302-313. https://doi.org/10.61093/sec.8(2).302-313.2024 DOI: https://doi.org/10.61093/sec.8(2).302-313.2024
  47. Sampat, B., Mogaji, E. and Nguyen, N.P. (2024), "The dark side of FinTech in financial services: a qualitative enquiry into FinTech developers’ perspective", International Journal of Bank Marketing, Vol. 42 No. 1, pp. 38-65. https://doi.org/10.1108/IJBM-07-2022-0328 DOI: https://doi.org/10.1108/IJBM-07-2022-0328
  48. Sheikh, U. M., & Garellek, M. (2024). Canada: Amplified risks for financial institutions from AI, OSFI and FCAC Report. Global Compliance News. https://www.globalcompliancenews.com/2024/12/12/https-insightplus-bakermckenzie-com-bm-financial-institutions_1-canada-amplified-risks-for-financial-institutions-from-ai-osfi-and-fcac-report_11282024
  49. Sheliemina, N. (2024). The Use of Artificial Intelligence in Medical Diagnostics: Opportunities, Prospects and Risks. Health Economics and Management Review, 5(2), 104-124. https://doi.org/10.61093/hem.2024.2-07 DOI: https://doi.org/10.61093/hem.2024.2-07
  50. Siddiqui, Z., & Rivera, C. (2024). Mapping fintech landscape in Latvia: taxonomy-based classification and economic impact analysis. Polish Journal of Management Studies, 30(1), 304-319. https://doi.org/10.17512/pjms.2024.30.1.18 DOI: https://doi.org/10.17512/pjms.2024.30.1.18
  51. Stanford University. (n.d.). Global AI vibrancy tool - AI index. Stanford University Human-Centered Artificial Intelligence. https://aiindex.stanford.edu/vibrancy
  52. The Law Society. (2024). High-risk third countries for AML purposes. Home | The Law Society. https://www.lawsociety.org.uk/topics/anti-money-laundering/high-risk-third-countries-for-aml-purposes
  53. The Swiss Startup News channel. (2024). Banks and insurers launch solutions powered by Swiss Fintechs. Startupticker.ch News. https://www.startupticker.ch/en/news/banks-and-insurers-launch-solutions-powered-by-swiss-fintech
  54. Thrale, E. (2024). Global economies can save $3.13 trillion annually using AI to detect and prevent money laundering and terrorist financing, finds inaugural Napier AI / AML Index report - GlobalData. GlobalData. https://www.globaldata.com/media/banking/global-economies-can-save-3-13-trillion-annually-using-ai-detect-prevent-money-laundering-terrorist-financing-finds-inaugural-napier-ai-aml-index-report/
  55. Tiutiunyk, I., Drabek, J., Antoniuk, N., Navickas, V., & Rubanov, P. (2021). The impact of digital transformation on macroeconomic stability: Evidence from EU countries. Journal of International Studies, 14(3), 220–234. https://doi.org/10.14254/2071-8330.2021/14-3/14 DOI: https://doi.org/10.14254/2071-8330.2021/14-3/14
  56. Utkina, M. (2023). Leveraging Blockchain Technology for Enhancing Financial Monitoring: Main Challenges and Opportunities. European Journal of Interdisciplinary Studies, 15(2), 134–151. https://doi.org/10.24818/ejis.2023.21 DOI: https://doi.org/10.24818/ejis.2023.21
  57. Vasilyeva, T., Ziółko, A., Kuzmenko, O., Kapinos, A., & Humenna, Y. (2021). Impact of digitalisation and the COVID-19 pandemic on the AML scenario: Data mining analysis for good governance. Economics & Sociology, 14(4), 326–354. https://doi.org/10.14254/2071-789x.2021/14-4/19 DOI: https://doi.org/10.14254/2071-789X.2021/14-4/19
  58. Wang, Y. & Shan, R. (2024). The intrinsic logic and analytical framework of Artificial Intelligence's impact on enterprise management decisions. Transformations in Business and Economics, 23, 3A(63A), 682 – 699. http://www.transformations.knf.vu.lt/63a/article/thei
  59. World Bank. (n.d.). World Bank Open Data. World Bank Open Data. https://data.worldbank.org/indicator
  60. Wright, J. (2023). Healthcare cybersecurity and cybercrime supply chain risk management. Health Economics and Management Review, 4(4), 17-27. https://doi.org/10.61093/hem.2023.4-02 DOI: https://doi.org/10.61093/hem.2023.4-02
  61. Yarovenko, H., Lopatka, A., Vasilyeva, T., & Vida, I. (2023). Socioeconomic profiles of countries - cybercrime victims. Economics and Sociology, 16(2), 167-194. doi:10.14254/2071-789X.2023/16-2/11 DOI: https://doi.org/10.14254/2071-789X.2023/16-2/11
  62. Yarovenko, H., Kuzior, A., Norek, T., & Lopatka, A. (2024a). The future of artificial Intelligence: Fear, hope or indifference?. Human Technology, 20(3), 611–639. https://doi.org/10.14254/1795-6889.2024.20-3.10 DOI: https://doi.org/10.14254/1795-6889.2024.20-3.10
  63. Yarovenko, H., Pozovna, I., & Bylbas, R. (2024b). The Risk of Escalating Cyberattacks and Financial Fraud During Wartime: The Maturity of The County’s Judicial System in Combating Cyber and Financial Crimes. Financial Markets, Institutions and Risks, 8(4), 126-147. https://doi.org/10.61093/fmir.8(4).126-147.2024 DOI: https://doi.org/10.61093/fmir.8(4).126-147.2024
  64. Yarovenko, H. and Rogkova, M. (2022). Dynamic and bibliometric analysis of terms identifying the combating financial and cyber fraud system. Financial Markets, Institutions and Risks, 6(3), 93-104. https://doi.org/10.21272/fmir.6(3).93-104.2022 DOI: https://doi.org/10.21272/fmir.6(3).93-104.2022
  65. Zada, M., Khan , S., Mehmood, S., & Contreras-Barraza, N. (2024). Generative Artificial Intelligence in FinTech: Applications, environmental, social, and governance considerations, and organisational performance: The moderating role of ethical dilemmas. Oeconomia Copernicana, 15(4), 1303-1347. https://doi.org/10.24136/oc.3323 DOI: https://doi.org/10.24136/oc.3323
  66. Zámek, D. & Zakharkina, Z. (2024). Research Trends in the Impact of Digitization and Transparency on National Security: Bibliometric Analysis. Financial Markets, Institutions and Risks, 8(1), 173-188. https://doi.org/10.61093/fmir.8(1).173-188.2024 DOI: https://doi.org/10.61093/fmir.8(1).173-188.2024

How to Cite

Lyeonov, S., Hrytsenko, L., Trojanek, R., & Popp, J. (2025). Who benefits from AI in money laundering in Europe: The organised criminals or the aml services?. Human Technology, 21(1), 222–245. https://doi.org/10.14254/1795-6889.2025.21-1.11