AI-enabled hybrid ensemble learning for imbalanced credit risk prediction: A human-in-the-loop decision support framework

Abstract

Microfinance institutions (MFIs) are pivotal to financial inclusion in emerging economies, yet they face heightened credit risk due to borrower informality, data scarcity, and severe class imbalance. Motivated by the microfinance context, this study proposes a human-centered hybrid machine learning framework that integrates ensemble learning with Synthetic Minority Over-sampling Technique (SMOTE) to enhance default detection while supporting transparent and responsible decision-making. Using a large-scale public credit application dataset as an empirical benchmark, we compare logistic regression, Random Forest, AdaBoost and Naïve Bayes models under imbalanced and rebalanced conditions. The results indicate that class rebalancing substantially improves minority-class detection, with the Random Forest + SMOTE configuration achieving the best performance (F1 = 0.73; AUC = 0.97). Beyond predictive accuracy, the findings highlight the importance of human oversight and explainability to mitigate exclusionary risks. The study offers practical guidance for MFIs seeking to leverage artificial intelligence while preserving financial sustainability and social inclusion objectives.