Leveraging Machine Learning for Enhanced Financial Inclusion: Revolutionizing Agricultural Banking in Central Africa

Proposed a credit scoring model using alternative data (mobile money transactions, crop yields, weather, demographics) for rural farmers in Central Africa. Compared ML approaches (neural networks, random forests, gradient boosting) against logistic regression baselines; demonstrated significantly higher loan approval rates, lower default risk, and improved operational efficiency. Discussed the critical role of explainable AI in fostering stakeholder trust and regulatory compliance.

Recommended citation: Fokam Ninyim Astride Melvin, Vincess Dongmo. (2025). "Leveraging Machine Learning for Enhanced Financial Inclusion: Revolutionizing Agricultural Banking in Central Africa." OpenReview, Submission #120. CC BY 4.0.
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