Authors : Abayomi Oluwaseun Japinye

Volume/Issue : Volume 10 - 2025, Issue 10 - October

Google Scholar : https://tinyurl.com/4pp4tep5

Scribd : https://tinyurl.com/ycydtvnv

DOI : https://doi.org/10.38124/ijisrt/25oct127

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Abstract : This study investigates the integration of cybersecurity, artificial intelligence (AI), and blockchain technologies in mitigating money laundering and terrorism financing risks. An online survey was conducted via Google Forms, targeting 400 LinkedIn users with certifications or job roles in cybersecurity, compliance, anti-money laundering (AML), or counter- terrorist financing (CTF). Convenience sampling was employed to select participants, striking a balance between statistical power and practicality, with a sample size sufficient to capture diverse perspectives within the target population. The survey assessed integration levels and perceived effectiveness of cybersecurity, AI, and blockchain technologies, using a multiple- choice Likert scale to ensure uniform responses. Pearson's correlation coefficient was used to assess relationships between integration levels and perceived effectiveness for each technology. Multivariate regression analysis explored interactions between these technologies and their impact on integration levels. The Pearson correlation analysis showed weak but statistically significant relationships between integration levels and perceived effectiveness for cybersecurity (-0.068), AI (0.032), and blockchain (0.041). Regression analysis indicated that perceived effectiveness of cybersecurity and blockchain significantly predicts integration levels, while AI does not. This study highlights the complexities and stakeholder expectations involved in integrating these technologies, suggesting areas for improvement and future research to enhance their effectiveness in combating financial crimes. Ethical considerations, including informed consent and anonymity, were strictly adhered to throughout the research process.

Keywords : Cybersecurity, Artificial Intelligence, Blockchain, Integration, Perceived Effectiveness, Money Laundering, Terrorism Financing.

References :

1. Adebayo, T. A. (2025a). The relationship between financial development and economic growth in Nigeria. Review of Business and Economics Studies, 13(1), 24–42. https://doi.org/10.26794/2308-944x-2025-13-1-24-42
2. Akartuna, E.A., Johnson, S. & Thornton, A. (2022). Preventing the Money Laundering and Terrorist Financing Risks of Emerging technologies: an International Policy Delphi Study. Technological Forecasting and Social Change, 179, p.121632. Doi: https://doi.org/10.1016/j.techfore.2022.121632.
3. Antunes, S. & Cabral, S. (2019). The Impact of Blockchain Technology on Anti-Money Laundering and Counter-Terrorism Financing Management by Financial Institutions. [online] Available at: https://repositorio.iscte-iul.pt/bitstream/10071/19391/4/master_sofia_silva_cabral.pdf.
4. Cheng, H.K., Hu, D., Puschmann, T. & Zhao, J.L. (2021). The Landscape of Blockchain Research: Impacts and Opportunities. Information Systems and e-Business Management. Doi:https://doi.org/10.1007/s10257-021-00544-1.
5. Coffey, S.M. & Elliott, M.R. (2023). Optimising Data Collection Interventions to Balance Cost and Quality in a Sequential Multimode Survey. Journal of survey statistics and methodology. doi:https://doi.org/10.1093/jssam/smad007.
6. Drescher, D. (2017). Blockchain Basics. Berkeley, CA: Apress. doi:https://doi.org/10.1007/978-1-4842-2604-9.
7. Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H. & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences, 13(12), pp.1216–1216. doi:https://doi.org/10.3390/educsci13121216.
8. Granadeiro, C. (2023). Decoding the Future of Artistic Creations: the Legal Challenges and Possible Solutions for the Regulation of Non-Fungible Tokens (NFTs). Business Law Review, [online] 44(6). Available at: https://kluwerlawonline.com/journalarticle/Business+Law+Review/44.6/BULA2023025 [Accessed 3 Mar. 2024].
9. Gundur, R.V., Levi, M., Topalli, V., Ouellet, M., Stolyarova, M., Chang, L.-C. & Domínguez Mejía, D. (2021). Evaluating Criminal Transactional Methods in Cyberspace as Understood in an International Context. [online] Research @ Flinders. CrimRxiv. Available at: https://researchnow.flinders.edu.au/en/publications/evaluating-criminal-transactional-methods-in-cyberspace-as-unders.
10. Kalam, A.A.E. & Mssassi, S. (2024). Leveraging Blockchain for Enhanced Traceability and Transparency in Sustainable Development. Lecture Notes in Networks and Systems, pp.162–177. doi:https://doi.org/10.1007/978-3-031-54318-0_14.
11. Kshetri, N. (2018). Blockchain’s Roles in Meeting Key Supply Chain Management Objectives. International Journal of Information Management, [online] 39(39), pp.80–89. doi: https://doi.org/10.1016/j.ijinfomgt.2017.12.005.
12. Kumar, P., Murphy, A., Werner, S. & Christophe Rougeaux (2022). The fight against money laundering: Machine learning is a game changer. [online] McKinsey & Company. Available at: https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/the-fight-against-money-laundering-machine-learning-is-a-game-changer [Accessed 3 Jun. 2024].
13. Li, J., Maiti, A. & Fei, J. (2023). Features and Scope of Regulatory Technologies: Challenges and Opportunities with Industrial Internet of Things. Future Internet, [online] 15(8), p.256. doi: https://doi.org/10.3390/fi15080256.
14. Maboe, R. (2018). An Overview of Blockchain Technology in the South African Financial Industry. [online] Available at: https://wiredspace.wits.ac.za/server/api/core/bitstreams/afbd275a-494a-4018-b43a-510f86d7db7b/content.
15. Memon, M.A., Ting, H., Cheah, J.-H., Thurasamy, R., Chuah, F. & Cham, T.H. (2020). Sample Size for Survey Research: Review and Recommendations. Journal of Applied Structural Equation Modeling, [online] 4(2). doi: https://doi.org/10.47263/jasem.4(2)01.
16. Pavlidis, G. (2023). Deploying Artificial Intelligence for Anti-Money Laundering and Asset Recovery: The Dawn of a New Era. Deploying Artificial Intelligence for Anti-Money Laundering and Asset Recovery: The Dawn of a New Era, 26(7), pp.155–166. doi: https://doi.org/10.1108/jmlc-03-2023-0050.
17. Renaud, K. & Coles-Kemp, L. (2022). Accessible and Inclusive Cyber Security: A Nuanced and Complex Challenge. SN Computer Science, 3(5). doi: https://doi.org/10.1007/s42979-022-01239-1.
18. Rijanto, A. (2024). Blockchain Technology Roles to Overcome accounting, Accountability and Assurance Barriers in Supply Chain Finance. Asian Review of Accounting. doi: https://doi.org/10.1108/ara-03-2023-0090.
19. Rocha-Salazar, J.-J., Segovia-Vargas, M.-J. and Camacho-Miñano, M.-M. (2021). Money Laundering and Terrorism Financing Detection Using Neural Networks and an Abnormality Indicator. Expert Systems with Applications, [online] 169, p.114470. doi: https://doi.org/10.1016/j.eswa.2020.114470.
20. Rose-Ackerman, S. & Palifka, B.J. (2018). Corruption, Organized Crime, and Money Laundering. Institutions, Governance and the Control of Corruption, [online] pp.75–111. doi: https://doi.org/10.1007/978-3-319-65684-7_4.
21. Rueda, M. del M., Martínez-Puertas, S. & Castro-Martín, L. (2022). Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles. Mathematics, [online] 10(24), p.4726. doi: https://doi.org/10.3390/math10244726.
22. Sartori, M., Seher, I. & Prasad, C. (2023). The Illicit Use of Cryptocurrency on the Darknet by Cyber Criminals to Evade Authorities. Lecture notes in electrical engineering, pp.449–459. doi: https://doi.org/10.1007/978-3-031-29078-7_39.
23. Saunders, M., Lewis, P. & Thornhill, A. (2019). Research Methods for Business Students. 8th ed. United Kingdom: Pearson.
24. Schwarz, N., Chen, M.K., Poh, M.K., Jackson, M.G., Kao, K., Fernando, M.F. & Markevych, M. (2021). Virtual Assets and Anti-Money Laundering and Combating the Financing of Terrorism (1): Some Legal and Practical Considerations. [online] Google Books. International Monetary Fund. Available at: https://books.google.com.ng/books?hl=en&lr=&id=WxVNEAAAQBAJ&oi=fnd&pg=PP6&dq=However [Accessed 3 Mar. 2024].
25. Shaikh, F.A. & Siponen, M. (2023). Organisational Learning from Cybersecurity Performance: Effects on Cybersecurity Investment Decisions. Inf Syst Front, 26. doi: https://doi.org/10.1007/s10796-023-10404-7.
26. Sharif, M.R. (2023). Exploring the Potential: Smart Contracts and the Fight against Trade-Based Money Laundering in International Trade. Social Science Research Network. [online] doi: https://doi.org/10.2139/ssrn.4616946.
27. Sobolewski, M. and Allessie, D. (2021). Blockchain Applications in the Public Sector: Investigating Seven Real-Life Blockchain Deployments and Their Benefits. Public Administration and Information Technology, pp.97–126. doi: https://doi.org/10.1007/978-3-030-55746-1_5.
28. Toufaily, E., Zalan, T. & Dhaou, S.B. (2021). A Framework of Blockchain Technology Adoption: an Investigation of Challenges and Expected Value. Information & Management, 58(3), p.103444. doi: https://doi.org/10.1016/j.im.2021.103444.
29. Upadhyay, N. (2020). Demystifying Blockchain: A Critical Analysis of Challenges, Applications, and Opportunities. International Journal of Information Management, 54. doi: https://doi.org/10.1016/j.ijinfomgt.2020.102120.
30. Yampolskiy, R.V. (2021). AI Risk Scepticism. [online] arXiv.org. doi: https://doi.org/10.48550/arXiv.2105.02704.
31. Yang, X. and Chan, J. (2021). Blockchain and Identity Management. Springer eBooks, pp.192–204. doi: https://doi.org/10.1007/978-3-030-93179-7_15.
32. Yıldırım, U. (2023). Exploring the Trends, Challenges, and Opportunities of Regulatory Technology (RegTech) in the Financial Industry: A Systematic Literature Review. Cankaya.edu.tr. [online] doi: http://hdl.handle.net/20.500.12416/7165.