Modeling drivers of fintech adoption in Sub-Saharan Africa using machine learning

This study explores the socio-demographic, financial, and technological determinants of FinTech adoption across 36 Sub-Saharan African countries utilizing micro-level data from the 2021 World Bank Global Findex survey. We employ a hybrid methodological framework integrating traditional econometric models (Logit and Probit) with supervised machine learning algorithms (Random Forest, Gradient Boosting, and XGBoost) to enhance predictive accuracy. Our findings show that formal account ownership and access to technology, particularly mobile phone ownership and internet connectivity, are the strongest drivers of FinTech adoption. Digital transactions are closely linked to formal financial infrastructure, while mobile money extends financial access to unbanked and rural populations. Income, education, employment, and regional disparities significantly influence adoption patterns. These results highlight the complementary role of formal banking and digital finance. The study recommends targeted policies focusing on rural digital infrastructure, integrated digital-banking strategies, and digital literacy programs. This research provides an empirical foundation for evidence-based policymaking to accelerate financial inclusion through FinTech in Sub-Saharan Africa.