The supervision of inclusive financial institutions in emerging economies represents a critical challenge for financial stability. While cooperatives and mutualists are pillars of financial inclusion, there is a lack of explainable early warning models adapted to their structural risk profiles. This study evaluates an explainable machine learning framework for early supervision, utilizing a longitudinal dataset covering 584 unique financial institutions (cooperatives and mutualists) with 24,232 monthly observations across the 2018–2024 predictor observation window (yielding 17,799 healthy transition candidate observations and 74 evaluated distress episodes: 68 in the training set and 6 in the out-of-time validation set). Adhering to ISO/IEC 25012 data quality standards and a six-month forecast horizon (T + 6), the performance of an XGBoost-based model was evaluated against traditional logistic regression benchmarks. Given the extreme class imbalance (0.1556% event rate in the out-of-time test set), model utility was assessed primarily through the precision–recall AUC (PR-AUC). The results demonstrate that XGBoost achieved a higher PR-AUC point estimate (0.2466) than the logistic regression benchmark (0.0931) in the 2024 out-of-time validation sample. The use of SHAP explainability revealed distinct risk drivers for cooperatives and mutualists, providing exploratory support for segmented supervisory monitoring. This study proposes an interpretable decision support framework that enables proactive, risk-based prioritization in a dollarized emerging economy.