AI-DRIVEN FRAUD DETECTION AND CYBER FRAUD MITIGATION IN NIGERIAN DEPOSIT MONEY BANKS

Abstract

This study examines the role of AI-driven fraud detection systems in mitigating cyber fraud in Nigerian deposit money banks. Specifically, the study investigates the effects of AI fraud detection capability, predictive analytics capability, automated transaction monitoring systems, and FinTech digital infrastructure on cyber fraud mitigation. The study adopted a survey research design and collected data from banking professionals working in fraud risk management, internal control, digital banking operations, and information technology security departments of selected deposit money banks in Nigeria. A total of 123 questionnaires were distributed, out of which 102 were properly completed and used for analysis, representing a response rate of 82.6%. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The model explained 73.2% of the variance in cyber fraud mitigation, indicating strong predictive power. The findings revealed that AI fraud detection capability had a significant positive effect on cyber fraud mitigation. Predictive analytics capability also significantly influenced cyber fraud mitigation. Similarly, automated transaction monitoring systems had a significant positive effect on cyber fraud mitigation. FinTech digital infrastructure recorded the strongest effect on cyber fraud mitigation. The study concludes that AI technologies, predictive analytics, automated monitoring systems, and robust FinTech infrastructure significantly enhance banks’ capacity to detect, prevent, and manage cyber fraud in digital banking environments. The study recommends increased investment in AI-based fraud detection systems, improved FinTech infrastructure, automated transaction monitoring, and stronger cybersecurity frameworks.