The FinTech-AI Nexus: A Paradigm Shift in Anti-Money Laundering Surveillance and Enforcement (Potchefstroom Electronic Law Journal/Potchefstroomse Elektroniese Regsblad)

Summary

• Data collection conducted between February and June 2025 yielded 500 initial survey responses, resulting in a final sample of 328 valid responses after screening [p.9].p.9
• The model demonstrates high predictive power, explaining an R² of 0.707 of the variance in AML surveillance efficiency [p.17].p.17
• The conceptual framework explains an R² of 0.508 of the variance in AML enforcement outcomes [p.17].p.17
• Normative pressures are the strongest predictor of AML surveillance efficiency with a path coefficient of β = 0.725 [p.17].p.17
• Regulatory support shows a strong direct effect on AML enforcement outcomes with β = 0.571 [p.17].p.17

Key insights

1. FinTech capabilities and AI adoption act as strategic resources that significantly enhance anti-money laundering surveillance efficiency [p.6, p.7].p.6
2. Normative pressures from industry standards and international expectations drive institutions to improve surveillance systems [p.7].p.7
3. Regulatory support directly strengthens enforcement success and positively moderates the link between surveillance efficiency and enforcement [p.17].p.17
4. Importance-Performance Map Analysis reveals that regulatory support has the highest total effect (0.571) and performance (70.397) on enforcement outcomes [p.19].p.19
5. Institutional forces outweigh purely technological drivers in determining overall AML enforcement success [p.21].p.21