In this systematic review of the literature on using Machine Learning (ML) for credit risk prediction, we raise the need for financial institutions to use Artificial Intelligence (AI) and ML to assess credit risk, analyzing large volumes of information. We posed research questions about algorithms, metrics, results, datasets, variables, and related limitations in predicting credit risk. In addition, we searched renowned databases responding to them and identified 52 relevant studies within the credit industry of microfinance. Challenges and approaches in credit risk prediction using ML models were identified; we had difficulties with the implemented models such as the black box model, the need for explanatory artificial intelligence, the importance of selecting relevant features, addressing multicollinearity, and the problem of the imbalance in the input data. By answering the inquiries, we identified that the Boosted Category is the most researched family of ML models; the most commonly used metrics for evaluation are Area Under Curve (AUC), Accuracy (ACC), Recall, precision measure F1 (F1), and Precision. Research mainly uses public datasets to compare models, and private ones to generate new knowledge when applied to the real world. The most significant limitation identified is the representativeness of reality, and the variables primarily used in the microcredit industry are data related to the Demographic, Operation, and Payment behavior. This study aims to guide developers of credit risk management tools and software towards the existing ability of ML methods, metrics, and techniques used to forecast it, thereby minimizing possible losses due to default and guiding risk appetite.

In this systematic review of the literature on using Machine Learning (ML) for credit risk prediction, we raise the need for financial institutions to use AI and ML to assess credit risk, analyzing large volumes of information. We posed research questions about algorithms, metrics, results, data sets, variables, and related limitations in predicting credit risk. We searched renowned databases to answer them and identified 52 relevant studies with the credit industry microfinance. Challenges and approaches in credit risk prediction using ML models we identified, difficulties with the implemented models such as the black box model, the need for explanatory artificial intelligence, the importance of selecting relevant features, addressing multicollinearity, and the problem of the imbalance in the input data. By answering the questions, we identified that the Boosted Category is the most researched family of ML models; the most commonly used metrics for evaluation are Area Under Curve (AUC), Accuracy (ACC), Recall, precision measure F1 (F1), and Precision; Research mainly uses public data sets to compare models, and private ones to generate new knowledge when applied to the real world. The most significant limitation identified is the representativeness of reality, and the variables primarily used in the microcredit industry are related data to the demographic, the operation, and payment behavior. This study aims to guide the developers of credit risk management tools and software towards the existing offer of ML methods, metrics, and techniques used to forecast it, thereby minimizing possible losses due to default and guiding risk appetite.

The teaching–learning process, at each educational level, is often an open problem for educators and researchers related to the stated topic. Researchers combine emerging technologies to formulate learning tools in order to understand the abstract contents of the subjects; however, the problem still persists. A technological learning tool would be effective when projected into an educational model that looks at motivation, usability, engagement, and technological acceptability. Some of these aspects could be attributed through the use of augmented reality and games. The aim of this work is to analyze, in the literature, the trends of learning models based on computer technologies for an effective and enjoyable learning activity. The analysis of the literature in that context—emphasizing acceptability, categories, entertainment, educational models—shows that it is still not well explored.

Robust credit risk prediction in emerging economies increasingly demands the integration of external factors (EFs) beyond borrowers’ control. This study introduces a scenario-based methodology to incorporate EF—namely COVID-19 severity (mortality and confirmed cases), climate anomalies (temperature deviations, weather-induced road blockages), and social unrest—into machine learning (ML) models for credit delinquency prediction. The approach is grounded in a CRISP-DM framework, combining stationarity testing (Dickey–Fuller), causality analysis (Granger), and post hoc explainability (SHAP, LIME), along with performance evaluation via AUC, ACC, KS, and F1 metrics. The empirical analysis uses nearly 8.2 million records compiled from multiple sources, including 367,000 credit operations granted to individuals and microbusiness owners by a regulated Peruvian financial institution (FMOD) between January 2020 and September 2023. These data also include time series of delinquency by economic activity, external factor indicators (e.g., mortality, climate disruptions, and protest events), and their dynamic interactions assessed through Granger causality to evaluate both the intensity and propagation of external shocks. The results confirm that EF inclusion significantly enhances model performance and robustness. Time-lagged mortality (COVID MOV) emerges as the most powerful single predictor of delinquency, while compound crises (climate and unrest) further intensify default risk—particularly in portfolios without public support. Among the evaluated models, CNN and XGB consistently demonstrate superior adaptability, defined as their ability to maintain strong predictive performance across diverse stress scenarios—including pandemic, climate, and unrest contexts—and to dynamically adjust to varying input distributions and portfolio conditions. Post hoc analyses reveal that EF effects dynamically interact with borrower income, indebtedness, and behavioral traits. This study provides a scalable, explainable framework for integrating systemic shocks into credit risk modeling. The findings contribute to more informed, adaptive, and transparent lending decisions in volatile economic contexts, relevant to financial institutions, regulators, and risk practitioners in emerging markets.