Effect of AI-Powered Credit Assessment on Loan Performance and Small Business Growth Stability in Nigeria

Abstract

SMEs consist of 96% of Nigerian businesses, account for 48% of GDP, and provide 84% of employment, and concurrently, present a ₦24.2 trillion (≈USD 50 billion) credit gap. The credit models that rely on proper collateral placement, formal credit histories, and manual credit assessment and approval are ill-advised to the Nigerian economy and its environment. Within the Nigerian FinTech space, the AI-enabled credit assessment models that utilize alternative data sources, such as mobile money, utility bills, and digital footprints, are on the rise. While the enthusiasm for these range of technologies presents a positive outlook for the use of AI FinTech to both curb non-performing loans and improve credit possibilities for SMEs, the actual on the ground data that measures the impact on SME business growth, as well as the impact on the NPLs, are non-existent. This study seeks to address this gap.The study, grounded in Information Asymmetry Theory and the Technology Acceptance Model, used a survey of 150 OPay credit staff in Lagos. Simple linear regression tested four hypotheses. All were rejected (p < 0.001). AI‑powered credit assessment explained 41.2% of loan performance variance, 34.5% of NPL reduction, 49.1% of access to finance, and 42.9% of SME growth stability. Access to finance showed the strongest association; NPL reduction the weakest. AI thus enables lending to viable SMEs that traditional systems exclude. The study recommends that the Central Bank of Nigeria establish a transparent, consumer‑protective regulatory framework for AI lending, and that SMEs formalise digital records and use multiple AI platforms to build credit history.