Authors : Mazimpaka Richard; Dr. Nizeyimana Pacifique; Dr. Kundan Kumar; Mukwende Placide; Nshimiyimana Jerome

Volume/Issue : Volume 10 - 2025, Issue 8 - August

Google Scholar : https://tinyurl.com/39udnf8m

Scribd : https://tinyurl.com/4kaddz6k

DOI : https://doi.org/10.38124/ijisrt/25aug218

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Abstract : Umurenge SACCOs are instrumental in fostering financial inclusion in Rwanda, yet they face significant challenges with high loan default rates that threaten their long-term sustainability. This study develops a predictive model using machine learning techniques to assess loan default risk among SACCO borrowers. Using a real, anonymized dataset of 2,000 loan applications from the Rwanda Cooperative Agency (RCA), we compare six machine learning algorithms: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, AdaBoost, and XGBoost. The study addresses class imbalance through balanced training approaches and evaluates models using accuracy, precision, recall, and F1-score metrics. XGBoost achieved the highest performance with 89.5% accuracy, while Logistic Regression demonstrated optimal balance between performance (86.5% accuracy, 85.2% F1-score) and interpretability, making it suitable for real-world deployment in SACCO environments. Key predictors identified include credit score, past loan repayment behavior, and monthly income. These findings provide a scalable, data-driven approach for SACCOs to transition from intuition-based to evidence-based credit risk assessment, supporting Rwanda's digital transformation goals in financial services.

Keywords : Credit Risk Assessment, Logistic Regression, SACCOs, Machine Learning, Loan Default Prediction, Financial Inclusion.

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