The Inter-Role of Cybersecurity, AI and Blockchain in Preventing Money Laundering and Terrorism Financing

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.