AI-Driven Credit Assessment and SME Access to Finance in Africa: Opportunities, Risks and Regulatory Responses.

Small and medium-sized enterprises (SMEs) contribute substantially to employment, innovation and economic diversification in Africa, yet many remain excluded from formal finance because they lack collateral, audited accounts and established credit histories. This study examined the opportunities, risks and regulatory responses associated with AI-driven credit assessment and SME finance in Africa through an integrative systematic review of peer-reviewed studies, regulatory documents and institutional reports published mainly between 2021 and 2026. A PRISMA-informed selection process and thematic analysis organised the evidence around inclusion, alternative data, efficiency, bias, privacy, cybersecurity and consumer protection. The findings indicate that machine learning can reduce information asymmetry, underwriting costs and processing time for thin-file SMEs. Outcomes nevertheless differ by country. Kenya combines mature mobile finance with direct digital-credit rules; Nigeria has market scale but fragmented oversight; South Africa has stronger financial and data-protection institutions; Ghana is formalising digital-credit supervision; and Rwanda has an explicit national AI policy but a smaller credit market. Current models remain vulnerable in low-data environments because sparse, unrepresentative and unstable records weaken calibration, fairness and transferability. The study proposes an integrated framework linking AI capability, data quality, financing outcomes, technological risks, institutional conditions and regulatory safeguards. Responsible adoption requires locally validated models, measurable subgroup fairness tests, understandable adverse-decision explanations, meaningful human review, cybersecurity controls and coordinated cross-border supervision.

Artificial Intelligence; Credit Assessment; SME Finance; Alternative Credit Data

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