AI-Powered Credit Scoring Models for Inclusive Finance: Evaluating the Role of AI in Bridging Nigeria’s Credit Gap

In spite of advancements in financial technology across many developing economies, a large segment of Nigeria's population, particularly low-income earners and informal sector participants, remains excluded from formal credit systems. However, the evolution of Artificial Intelligence (AI) presents new opportunities to bridge these gaps through data-driven credit assessment and inclusive financial innovation. This study, therefore, examines the role of AI in expanding credit access and promoting financial inclusion in Nigeria. The study adopts a cross-sectional survey research design, using primary data collected through structured questionnaires using a Likert scale. Based on a survey of 312 unbanked and low-income respondents across Nigeria, descriptive statistics, correlation analysis and Ordinary Least Squares (OLS) regression were employed to evaluate the relationships among the variables. The results revealed that the use of alternative data (β = 0.312, p < 0.05), trust and transparency (β = 0.284, p < 0.05), and awareness and usability (β = 0.261, p < 0.05) have positive and significant effects on financial inclusion, while perceived risks (β = -0.194, p < 0.05) exert a negative influence. The model explains 71.8% of the variations (R² = 0.718) in financial inclusion, suggesting a strong explanatory power of the independent variables. The study concludes that AI-driven credit systems serve as transformative mechanisms for inclusive finance effectively reducing information asymmetry and improving access for unbanked individuals in Nigeria. The study recommends the implementation of ethical AI governance frameworks, capacity-building initiatives in digital literacy, and regulatory policies that promote equitable and responsible AI deployment to achieve sustainable financial inclusion.