Alternative Data and Credit Risk Assessment in Emerging African Economies: A Quantitative and Regulatory Framework

Formal credit penetration in sub-Saharan Africa remains structurally constrained. Traditional scoring systems inadequately evaluate consumers and Small and Mediumsized Enterprises (SMEs) due to their reliance on verifiable financial histories and formal banking data, resulting in notable market inefficiency. This paper assesses the utility of alternative data-specifically behavioral and mobile-financial indicators-as a supplementary mechanism for improving borrower risk assessment under conditions of limited formal data. We introduce the AfriScore Alternative Credit Framework to systematize predictive variable taxonomy and present a prototype empirical analysis demonstrating the discriminatory capability of ensemble machine learning models (AUC = 0.954) over baseline linear models on synthetic alternative datasets. Concurrently, the paper identifies critical limitations inherent to alternative data, including data sparsity and selection bias. Furthermore, we analyze regional regulatory architectures across the BEAC, CBN, and OHADA jurisdictions, proposing a Three-Layer Regulatory Framework focusing on data governance, algorithmic auditing, and consumer explainability. The integration of alternative data requires localized regulatory models to balance credit expansion with systemic stability.