Role of artificial intelligence (AI) in supervision of national microfinance banks in Nigeria

This study investigates the role of artificial intelligence (AI) in enhancing the supervision of National Microfinance Banks (MFBs) in Nigeria. It examines how AI–driven tools - specifically data analytics, real-time monitoring, anomaly-detection algorithms, and automated reporting - affect key supervisory outcomes: fraud-detection accuracy, compliance timeliness, and non-performing loan (NPL) forecasting precision. Employing a cross-sectional explanatory survey design, 150 supervisory stakeholders (senior supervisors, compliance managers, risk-control officers, and fintech specialists) from 59 licensed National MFBs provided primary data via a validated 32-item questionnaire. Secondary performance indicators were sourced from annual reports of the Central Bank of Nigeria and the Nigeria Deposit Insurance Corporation. Exploratory and confirmatory factor analyses confirmed the reliability and validity of measurement constructs, while structural equation modeling revealed significant positive paths from AI deployment to fraud-detection accuracy (β = .45, p < .001), compliance timeliness (β = .39, p < .001), and NPL forecasting precision (β = .32, p = .0004). Multi-group analyses indicated that organizational readiness moderates these relationships, with high-readiness institutions realizing greater benefits. The findings highlight the critical need for phased suptech roadmaps, capacity-building initiatives, robust data-governance frameworks, and ethical oversight mechanisms. These recommendations aim to guide policymakers, regulators, and MFB practitioners in leveraging AI to strengthen financial inclusion, mitigate supervisory risks, and improve sectoral resilience.