Comparative Predictive Performance of Logistic Regression, Naive Bayes, and Support Vector Machine Models in Loan Default Classification among Microfinance Institution Clients in Makueni County, Kenya

Abstract

Microfinance institutions (MFIs) play a critical role in improving financial inclusion in Kenya; however, high loan default rates continue to threaten their financial sustainability. This study aimed to develop and compare the performance of Logistic Regression, Naïve Bayes and Support Vector Machine (SVM) models in predicting loan default among clients of MFIs in Makueni County, Kenya. The study adopted a quantitative research design and used secondary data comprising 4,592 borrower records obtained from selected MFIs operating within the county. Data analysis was done using python programming. Borrower socio-economic and financial attributes were extracted from loan records and preprocessed through data cleaning, normalization an. encoding procedures. To ensure robust and unbiased evaluation, Stratified 5-fold cross-validation combined with Grid Search CV hyperparameter tuning was applied across all models. Model performance was assessed using accuracy, precision, recall, F1-score, specificity, and Area Under the Curve (AUC). The results showed that the SVM model achieved the highest predictive performance (accuracy = 0.857, AUC = 0.914), followed by Logistic Regression (accuracy = 0.830, AUC = 0.904), while Naïve Bayes performed least effectively (accuracy = 0.740, AUC = 0.769). The findings demonstrate that SVM provides superior classification ability in capturing complex borrower patterns, while Logistic Regression remains a strong and interpretable baseline model. The study concludes that machine-learning models, particularly SVM, significantly improve credit risk prediction in microfinance environments and can support data-driven lending decisions in rural financial institutions.