Centro de Documentação da PJ
Analítico de Periódico
CD 341
Investigating the determinants of money laundering risk [Recurso eletrónico] / Yaseen Ghulam, Blandina Szalay
Journal of Money Laundering Control, Vol. 27, n. 1 (2024), p. 139-157
Ficheiro de 330 KB em formato PDF.
BRANQUEAMENTO DE CAPITAIS, CRIME ECONÓMICO, MERCADO FINANCEIRO, ESTUDO DE CASOS, SUÍÇA
Purpose – With the growing interconnectedness of global markets brought about by globalization and technological innovation, there is a heightened worldwide risk of money laundering, posing a considerable negative impact on economies and social equality. Therefore, the primary purpose of this research is to examine factors that underpin the pervasiveness of money laundering risk. Design/methodology/approach – By using a cross-section sample of 84 countries, the study uses ordered logit and multinomial logit regression to test and explain the role of main and varied determinants of money laundering risk covering countries’ economic, social, regulatory and corporate environment. Findings – The authors conclude that, overall, the macroeconomic indicators are less relevant in influencing money laundering risk than the other factors adopted from the Basel report. Nonetheless, the volume of exports and the exchange rate were robust in both the ordered and multinomial regression analyses alongside financial secrecy, auditing standards and corporate transparency. While more financial secrecy and a higher volume of exports were found to increase this risk, the other variables showed a negative relationship. The authors further conclude that it is mostly less secrecy, more transparency and better auditing that could gradually transform a high-risk country into medium risk. Practical implications – This study recommends the implementation of publicly accessible ownership registries to address the issues around secrecy, transparency and auditing misconducts. Additionally, the general strengthening of laws and policies in these three domains is also necessary alongside the application of current technologies, such as machine learning, for the detection of money laundering. Originality/value – The authors believe this study uses advanced econometric techniques rarely used in the literature on money laundering. Separating the impact of economic and social/regulatory is also valuable.
