Credit unions in Latin America play an important role in the financial system, making a significant contribution to the achievement of the Sustainable Development Goals (SDGs) through their focus on financial inclusion, sustainability, and economic resilience. Assessing the social responsibility of these cooperatives ensures ethical, sustainable operations that benefit the population. Unlike traditional financial institutions, cooperatives are based on principles focused on mutual benefit, democratic participation, and responsibility toward their members and the community. This critical literature review, conducted through scientific databases, synthesizes findings on social responsibility in credit unions. The financial system is relevant for global economic stability and growth, comprising institutions like credit unions that facilitate capital flow. It operates through financial instruments, intermediaries, and markets, ensuring efficient resource allocation and risk management. Effective financial management involves planning, organizing, directing, and controlling resources to achieve stability and growth, integrating social responsibility. Credit unions in Latin America highlight cooperative principles, emphasizing member service, community development, and sustainable practices over profit maximization, thereby fostering economic inclusion and ethical business practices. In conclusion, credit unions provide affordable financial services while promoting values of solidarity and equity. However, as entities directly linked to communities, it is essential for them to monitor their actions in terms of social responsibility. This is important to measure and ensure their impact on society and its context. Finally, future research should focus on balancing economic viability with social responsibility, exploring innovative models, governance frameworks, and technological impacts.

This study introduces AgroTec 4.0, which is a smart farming system designed to revolutionize strawberry cultivation in greenhouses through the integration of edge computing technology in the Andean region of Ecuador. The primary objective has been to enhance cultivation efficiency by comparing results from strawberry crops with and without the system, under identical greenhouse conditions. Given the low educational and economic status of local farmers, AgroTec 4.0 was engineered to be user-friendly, easy to operate, and cost-effective, empowering producers with data-driven decision-making capabilities. Key findings underscore the potential of AgroTec 4.0 and agricultural data, including a 15% increase in strawberry yield, from 5.0 kg/m² in the control greenhouse to 5.75 kg/m² with AgroTec 4.0, highlighting the system’s ability to maximize productivity. There has also been a significant 20% reduction in water usage, decreasing from 80 L/m² in the control greenhouse to 64 L/m² with the system, showcasing AgroTec 4.0’s efficiency in resource management. Furthermore, there were significant improvements in fruit quality, with an 11.8% increase in the Brix index (from 8.5 to 9.5) and a 16.7% increase in average fruit weight (from 30 to 35 g), demonstrating the system’s capacity to enhance product quality. Finally, there has been an impressive 103.03% return on investment (ROI) with AgroTec 4.0, compared to no change in ROI in the control greenhouse, emphasizing the economic value of implementing this technology. These results underscore the transformative potential of AgroTec 4.0 in precision agriculture, offering a scalable and sustainable approach for small-scale producers in Ecuador. The system’s modularity and real-time data analysis capabilities allow for flexible adaptation to various needs, providing farmers with an intuitive interface for managing crops and optimizing resource use. This study demonstrates the feasibility of leveraging agricultural data and edge computing to improve cultivation practices and enhance productivity, contributing efficiently to the sustainability of agriculture in challenging environments.

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.