Impact of Cybersecurity, Artificial Intelligence, and Corruption on Money Laundering Risk: A Cross-National Quantitative Analysis of Financial Action Task Force Member States

Abstract

This study examines the impact of national-level cybersecurity capacity, AI readiness, and corruption on money laundering risk across member countries of the FATF. The study relies on secondary data from well-known sources: the Basel AML Index (for money laundering risk), the National Cyber Security Index (for cybersecurity), the Government AI Readiness Index (for AI readiness), and the Corruption Perceptions Index (for corruption). In total, 36 member states were analyzed. An ordinary least squares (OLS) multiple linear regression was used to test hypotheses. The analysis showed that higher cybersecurity capacity and lower perceived corruption, indicated by higher CPI scores, are significantly linked to lower money laundering risk. However, AI readiness did not show a statistically significant relationship, which goes against theoretical expectations. Together, the model accounted for 55.8% of the variation in money laundering risk among the sampled countries. The study relies on index-based proxy measures, which may not fully reflect the real-world conditions of AI use in AML practices. In addition, the cross-sectional design limits causal inference and temporal analysis. The findings highlight the important roles of strong cybersecurity infrastructure and anti-corruption efforts in reducing money laundering threats. While AI readiness seems statistically insignificant, its practical effect might still matter and calls for more focused metrics and longitudinal research. These insights can help guide FATF policymaking and capacity building by UNODC and inform cross-national AML strategies, especially in improving technological and governance frameworks.