Abstract

The article summarises the arguments and counterarguments within the scientific debate on the issue of improving the system of combating money laundering. The research’s primary goal is to evaluate the system’s effectiveness in combating money laundering. The study of the issue of evaluating the system’s effectiveness for combating money laundering is carried out in the article in the following logical sequence: informative base forming; determination of terminal events as criteria for the system’s effectiveness for combating money laundering; survival tables construction, which provide for the probability of a court verdict on financial monitoring issues; evaluation of the system’s effectiveness of institutional changes in combating money laundering. Survival analysis methods or survival tables, the Kaplan-Meier method, were used to conduct the research. The developed scientific-methodical approach to evaluating the system’s effectiveness for combating money laundering was approved based on financial monitoring data in Ukraine; the study period was 2009-2022. The time intervals and established probabilities of avoiding punishment for the crime of money laundering were defined based on the analysis. The authors of the article empirically determined that with an increase in the time between the time of the commission of the crime and the time of the court’s conviction, the probability that the court will not be convicted decreases. If three years and seven months pass after the crime, the probability of a guilty verdict will be 50.9%. Based on the obtained calculations, the changes in the organisational and functional composition of the combating money laundering implemented in recent years could have improved the quality of combating money laundering. Further research should be directed to a detailed analysis of the structural elements in the institutional part of the system of combating money laundering to identify the weaknesses of each stage: financial monitoring, investigation and the judicial system.

Keywords: financial fraud, effectiveness, money laundering, regulation, survival analysis.

JEL Classification: C25, G18, O17.

Cite as: Juhaszova, Z., Boyko, A., Bozhenko, V., Mynenko, S., Buriak, A., & Vynnychenko, N. (2023). An Innovative Approach to Evaluate the Effectiveness of Combating Money Laundering. Marketing and Management of Innovations, 14(2), 227-235. https://doi.org/10.21272/mmi.2023.2-21

This work is licensed under a Creative Commons Attribution 4.0 International License

References

1. Aliyeva, Z. (2022). Innovation in healthcare management: drug decriminalization for reducing the health damage from crime. Marketing i menedžment innovacij. [Google Scholar] [CrossRef]
2. Baio, G. (2020). survHE: survival analysis for health economic evaluation and cost-effectiveness modeling. Journal of Statistical Software, 95, 1-47. [Google Scholar] [CrossRef]
3. Bracco, E., & Onnis, L. (2022). Immigration, amnesties, and the shadow economy. Bulletin of Economic Research, 74(4), 1135-1162. [Google Scholar] [CrossRef]
4. Canhoto, A. I. (2021). Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective. Journal of business research, 131, 441-452. [Google Scholar] [CrossRef]
5. Dmytrov, S., & Medvid, T. (2017). An approach to the use of indices-based analysis subject to money laundering and terrorist financing national risk assessment. [Google Scholar] [CrossRef]
6. Jiroudkova, A., Anna, L., Strielkowski, W., & Slosarcík, I. (2015). EU accession, transition and further integration for the countries of Central and Eastern Europe. Economics & Sociology, 8(2), 11. [Google Scholar] [CrossRef]
7. Kuznietsova, A. Y., Kuznietsova, K. A. Y., Tiutiunyk, I. V., Panimash, Y., Zsolt, Z., & Zsolt, P. (2022). Management of Innovations in Public Administration: Strategies to Prevent the Participation of Financial Intermediaries in Shadow Operations. [Google Scholar] [CrossRef]
8. Lu, J., Ren, L., Qiao, J., Yao, S., Strielkowski, W., & Streimikis, J. (2019). Corporate social responsibility and corruption: Implications for the sustainable energy sector. Sustainability, 11(15), 4128. [Google Scholar] [CrossRef]
9. Lyeonov, S., Kuzmenko, O., Yarovenko, H., & Dotsenko, T. (2019). The innovative approach to increasing cybersecurity of transactions through counteraction to money laundering. Marketing & Management of Innovations, (3). [Google Scholar] [CrossRef]
10. Mazurenko, O., Tiutiunyk, I., & Derkach, L. (2021). The Impact of Tax Morality on Tax Evasion: Evidence of EU Countries. [Google Scholar] [CrossRef]
11. Njoku, K., Barr, C. E., Hotchkies, L., Quille, N., Wan, Y. L., & Crosbie, E. J. (2021). Impact of socio‐economic deprivation on endometrial cancer survival in the North West of England: a prospective database analysis. BJOG: An International Journal of Obstetrics & Gynaecology, 128(7), 1215-1224. [Google Scholar] [CrossRef]
12. Salehi A., Ghazanfari M., Fathian M. Data mining techniques for anti-money laundering. International Journal of Applied Engineering Research, 2017, 12(20). Р. 10084–10094. [CrossRef]
13. State Financial Monitoring Service of Ukraine (2022). Information on the results of the State Financial Monitoring Service for 2022. Retrieved from [Link]
14. Stojanov, R., Strielkowski, W., & Drbohlav, D. (2011). Labour migration and remittances: current trends in times of economic recession. Geografie, 116(4), 375-400. [Google Scholar] [CrossRef]
15. Strielkowski, W., & Höschle, F. (2016). Evidence for economic convergence in the EU: The analysis of past EU enlargements. Technological and Economic Development of Economy, 22(4), 617-630.[Google Scholar] [CrossRef]
16. The Unified State Register of Court Decisions. Retrieved from [Link]
17. Tiutiunyk, I. & Kozhushko, I. (2022). Modelling the Impact of Shadow Financial Transactions on the Country’s Financial Potential. Financial Markets, Institutions and Risks, 6(4), 134-143. [Google Scholar] [CrossRef]
18. Wang, P., Li, Y., & Reddy, C. K. (2019). Machine learning for survival analysis: A survey. ACM Computing Surveys (CSUR), 51(6), 1-36. [Google Scholar] [CrossRef]
19. Yarovenko, H., & Rogkova, M. (2022). Dynamic and bibliometric analysis of terms identifying the combating financial and cyber fraud system. Financial Markets, Institutions and Risks (FMIR), 6(3), 93-104. [Google Scholar] [CrossRef]