Corporate Resilience against Financial Crimes: Integrating Anti-Money Laundering Systems into Risk Governance

This paper discusses the ways in which the current Anti-Money Laundering (AML) systems can be efficiently incorporated in the corporate risk governance to enhance the overall resiliency of an organization against the financial offenses. The study combines scholarly and institutional knowledge on governance, leadership, and technology through a qualitative systematic literature review with PRISMA. The results indicate that the AML-related strategic integration into governance systems increases the coherence of compliance, efficiency, and regulatory alignment. Proactive compliance behavior is highly dependent on the leadership commitment and ethical corporate culture. Furthermore, AML monitoring and detection processes have been transformed by digital transformation that has introduced AI, machine learning, and analytics in processes. Nevertheless, such problems as the privacy of data, the transparency of algorithms, and moral responsibility still exist. The paper provides a conclusion that corporate resilience and financial integrity are guaranteed by a balanced strategy of integrity in governance, ethical leadership and technological innovation.

Anti-Money Laundering, Corporate Governance, Risk Management, Financial Crimes, Ethical Leadership, Compliance Culture, Artificial Intelligence, Organizational Resilience

1. Agorbia-Atta, C., & Atalor, I. (2024). Enhancing anti-money laundering capabilities: The strategic use of AI and cloud technologies in financial crime prevention. World Journal of Advanced Research and Reviews. https://doi.org/10.30574/wjarr.2024.23.2.2508. [Google Scholar] [Crossref]

2. Aidoo, S., & AML, I. D. (2025). Regulatory Frameworks for Combating Financial Crime in Emerging Markets. Available at: [Google Scholar] [Crossref]

3. https://www.researchgate.net/publication/391632497_Regulatory_Frameworks_for_Combating_Financial_Crime_in_Emerging_Markets [Google Scholar] [Crossref]

4. Akartuna, E. A., Johnson, S. D., & Thornton, A. (2022). Preventing the money laundering and terrorist financing risks of emerging technologies: An international policy Delphi study. Technological [Google Scholar] [Crossref]

5. Forecasting and Social Change, 179, 121632. [Google Scholar] [Crossref]

6. Al Qudah, A., Mazhar Al Zoubi, N., & ALhaddad, L. (2024). Mitigating Financial Crimes: How Anti-Money Laundering Mechanisms Shape Bank Outcomes. Advances in Decision Sciences, 28 (3) . Available at SSRN: https://ssrn.com/abstract=5318862 [Google Scholar] [Crossref]

7. Al-Ababneh, M. M. (2020). Linking ontology, epistemology and research methodology. Science and Philosophy, 8(1), 75-91. [Google Scholar] [Crossref]

8. Alexandre, C., & Balsa, J. (2023). Incorporating machine learning and a risk-based strategy in an anti-money laundering multiagent system. Expert Syst. Appl., 217, 119500. [Google Scholar] [Crossref]

9. https://doi.org/10.1016/j.eswa.2023.119500. [Google Scholar] [Crossref]

10. Amoako, E., Boateng, V., Ajay, O., Adukpo, T., & Mensah, N. (2025). Exploring the role of Machine Learning and Deep Learning in Anti-Money Laundering (AML) strategies within U.S. Financial Industry: A systematic review of implementation, effectiveness, and challenges. Finance & Accounting Research Journal. https://doi.org/10.51594/farj.v7i1.1808. [Google Scholar] [Crossref]

11. BK, M., & Ramasubramanian, V. H. (2025). Anti money laundering system in detecting and preventing money laundering activities: a systematic review. Journal of Money Laundering Control, 28(2), 385-407. https://doi.org/10.1108/JMLC-07-2024-0108 [Google Scholar] [Crossref]

12. Bociga, D., Lord, N., & Bellotti, E. (2024). Dare to share: information and intelligence sharing within the UK’s anti-money laundering regime. Policing and Society. [Google Scholar] [Crossref]

13. https://doi.org/10.1080/10439463.2024.2428735. [Google Scholar] [Crossref]

14. Carney, W. J., & Sharfman, K. (2023). The Exit Theory of Judicial Appraisal. Fordham J. Corp. & Fin. L., 28, 1.. https://ir.lawnet.fordham.edu/jcfl/vol28/iss1/1 [Google Scholar] [Crossref]

15. Dzwigol, H. (2022). Research methodology in management science: Triangulation. Virtual Economics, 5(1), 78-93. [Google Scholar] [Crossref]

16. Eceiza, J., Kristensen, I., Krivin, D., Samandari, H., & White, O. (2020). The future of operational-risk management in financial services. Preuzeto, 17, [Google Scholar] [Crossref]

17. 2022.https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/Risk/Our%20Insights/The%20future%20of%20operational%20risk%20management%20in%20financial%20services/The-future-of-operational-risk-management-in-financial-services-vF.pdf [Google Scholar] [Crossref]

18. Eghaghe, V., Osundare, O., Ewim, C., & Okeke, I. (2024a). Advancing AML tactical approaches with data analytics: Transformative strategies for improving regulatory compliance in banks. Finance & Accounting Research Journal. https://doi.org/10.51594/farj.v6i10.1644. [Google Scholar] [Crossref]

19. Eghaghe, V., Osundare, O., Ewim, C., & Okeke, I. (2024b). Navigating the ethical and governance challenges of ai deployment in AML practices within the financial industry. International Journal of Scholarly Research and Reviews. https://doi.org/10.56781/ijsrr.2024.5.2.0047. [Google Scholar] [Crossref]

20. EL HARRAS, M., & SALAHDDINE, M. A. Combating Money Laundering and Terrorism Financing: Adaptive Strategies in a Globalized Financial Environment. Available at: [Google Scholar] [Crossref]

21. https://ibimapublishing.com/p-articles/44ECO/2024/4447924/ [Google Scholar] [Crossref]

22. Elaev, M. (2024). PECULIARITIES OF IMPLEMENTATION OF COMPLIANCE PRACTICES IN THE USA. EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA. [Google Scholar] [Crossref]

23. https://doi.org/10.36871/ek.up.p.r.2025.01.10.008. [Google Scholar] [Crossref]

24. Gelle, V. (2025). Enhancing financial security: AI-driven anti-money laundering (AML) and compliance monitoring in the banking sector. World Journal of Advanced Research and Reviews. https://doi.org/10.30574/wjarr.2025.25.1.0365. [Google Scholar] [Crossref]

25. Gilmour, P. M. (2023). Reexamining the anti-money-laundering framework: a legal critique and new approach to combating money laundering. Journal of Financial Crime, 30(1), 35-47. [Google Scholar] [Crossref]

26. https://doi.org/10.1108/JFC-02-2022-0041 [Google Scholar] [Crossref]

27. Greening, N. (2019). Phenomenological research methodology. Scientific Research Journal, 7(5), 88-92. [Google Scholar] [Crossref]

28. Gupta, A., and Gupta, N. (2022). Research methodology. SBPD Publications. [Google Scholar] [Crossref]

29. Henning, J., & Hauman, M. (2017). Fortifying a risk-based approach in the South African AML/CFT process. Journal of Financial Crime, 24, 520-528. https://doi.org/10.1108/jfc-01-2017-0007. [Google Scholar] [Crossref]

30. Hoffman, B., Okeniyi, J., & Samuel, S. (2024). Antecedents of Compliance with Anti-Money Laundering Regulations in the Banking Sector of Ghana. Journal of Risk and Financial Management. https://doi.org/10.3390/jrfm17080373. [Google Scholar] [Crossref]

31. Iheonunekwu, P. (2024). The trifecta against financial crimes: A collaborative analysis of the roles of banks, consulting firms and governments. World Journal of Advanced Research and Reviews, 23(03), 1445-1468. Available at: https://doi.org/10.30574/wjarr.2024.23.3.2739 [Google Scholar] [Crossref]

32. Jamil, A. H., Mohd Sanusi, Z., Yaacob, N. M., Mat Isa, Y., & Tarjo, T. (2022). The Covid-19 impact on financial crime and regulatory compliance in Malaysia. Journal of Financial Crime, 29(2), 491-505. [Google Scholar] [Crossref]

33. Kothandapani, H. (2024). Automating financial compliance with AI: A New Era in regulatory technology (RegTech). International Journal of Science and Research Archive. [Google Scholar] [Crossref]

34. https://doi.org/10.30574/ijsra.2024.11.1.0040. [Google Scholar] [Crossref]

35. Liu, J. (2025). The Roles of Technology in Anti-Money Laundering. Science Insights. https://doi.org/10.15354/si.25.re1175. [Google Scholar] [Crossref]

36. Menz, M. (2025). Reorienting the lens–towards a new research agenda on the lived experiences of money laundering prevention officers. Journal of Money Laundering Control, 28(1), 64-79. Available at: https://doi.org/10.1108/JMLC-04-2024-0062 [Google Scholar] [Crossref]

37. Mikkelsen, D., Rajdev, S., & Stergiou, V. (2022). Managing financial crime risk in digital payments. McKinsey & Company. [Google Scholar] [Crossref]

38. https://www.mckinsey.com/~/media/mckinsey/business%20functions/risk/our%20insights/managing%20financial%20crime%20risk%20in%20digital%20payments/managing-financial-crime-risk-in-digital-payments.pdf [Google Scholar] [Crossref]

39. Mishra, S. B., and Alok, S. (2022). Handbook of research methodology. [Google Scholar] [Crossref]

40. Mong, D., & Thanh, H. (2025). Relationship between internal legal compliance culture, leadership style, organizational trust, and employee engagement in Vietnam’s banking sector. Banks and Bank Systems. https://doi.org/10.21511/bbs.20(1).2025.13. [Google Scholar] [Crossref]

41. Muntean, N., Tocelovska, N. and Muntean, I. (2025), "The dark side of business: economic and financial crime as a catalyst for failure in Europe", Journal of Risk Finance, Vol. 26 No. 3, pp. 469-484. [Google Scholar] [Crossref]

42. Available at: https://doi.org/10.1108/JRF-10-2024-0298 [Google Scholar] [Crossref]

43. Odetunde, A., Adekunle, B., & Ogeawuchi, J. (2025). A Unified Compliance Operations Framework Integrating AML, ESG, and Transaction Monitoring Standards. International Journal of Multidisciplinary Research and Growth Evaluation. https://doi.org/10.54660/.ijmrge.2022.3.2.639-649. [Google Scholar] [Crossref]

44. Olawale, H., Ogedengbe, A., Ameyaw, M., Jejeniwa, T., & Friday, S. (2024). Standardizing Compliance Practices across AML, ESG, and Transaction Monitoring for Financial Institutions. Journal of Frontiers in Multidisciplinary Research. [Google Scholar] [Crossref]

45. https://doi.org/10.54660/.jfmr.2024.5.2.75-93. [Google Scholar] [Crossref]

46. Oluwaferanmi, A. (2025). Bridging Innovation and Regulation: Legal and Technological Responses to Cryptocurrency-Driven Financial Crime. Available at: [Google Scholar] [Crossref]

47. https://www.researchgate.net/publication/392325168_Bridging_Innovation_and_Regulation_Legal_and_Technological_Responses_to_Cryptocurrency-Driven_Financial_Crime [Google Scholar] [Crossref]

48. Oyedokun, O., Ewim, S., & Oyeyemi, O. (2024). Developing a conceptual framework for the integration of natural language processing(NLP) to automate and optimize AML compliance processes, highlighting potential efficiency gains and challenges. Computer Science & IT Research Journal. https://doi.org/10.51594/csitrj.v5i10.1675. [Google Scholar] [Crossref]

49. Raghavan, K. (2007). A survey of corporate governance and overlapping regulations in banking. International Journal of Disclosure and Governance, 4, 181-194. [Google Scholar] [Crossref]

50. https://doi.org/10.1057/palgrave.jdg.2050058. [Google Scholar] [Crossref]

51. Reis, O., Aderoju, A., Shitu, K., Ononiwu, M., Igwe, A., Ofodile, O., & Ewim, C. (2024). Geospatial data in AML risk management: A review of applications and best practices. Open Access Research Journal of Science and Technology. https://doi.org/10.53022/oarjst.2024.12.1.0126. [Google Scholar] [Crossref]

52. Simonova, A. (2011). The risk‐based approach to anti‐money laundering: problems and solutions. Journal of Money Laundering Control, 14(4), 346-358. [Google Scholar] [Crossref]

53. https://doi.org/10.1108/13685201111173820 [Google Scholar] [Crossref]

54. Tsingou, E. (2018). New governors on the block: the rise of anti-money laundering professionals. Crime, Law and Social Change, 69, 191-205. https://doi.org/10.1007/s10611-017-9751-x. [Google Scholar] [Crossref]

55. Zavoli, I., & King, C. (2021). The challenges of implementing anti‐money laundering regulation: an empirical analysis. The Modern Law Review, 84(4), 740-771. https://doi.org/10.1111/1468-2230.12628 [Google Scholar] [Crossref]