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.

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.

This research aims to introduce a model, SectraBank, designed to mitigate cyber fraud attacks, specifically targeting SIM Swapping and Fake App schemes in mobile banking users. A quantitative methodology with an experimental design was meticulously employed to examine the rise of cyber fraud within mobile banking. The study involved a sample of 100 professionals with advanced knowledge in Information and Communication Technologies (ICTs). Surveys were utilized to measure awareness of threats and perceptions of security before and after the implementation of the proposed model. SectraBank leverages unique user identifiers such as fingerprint authentication and the International Mobile Equipment Identity (IMEI) number, in addition to geolocation and One Time Passwords (OTP) for validating electronic transactions in mobile banking. The effectiveness of the proposed model was notable, achieving a 96.1% efficacy rate in mitigating Fake App attacks and a 90.2% success rate in countering SIM Swapping attacks. These findings underscore the potential of SectraBank as a robust solution to enhance cybersecurity measures in the realm of mobile banking.

Landslides are among the most recurrent and destructive natural hazards worldwide, generating severe impacts on human life, infrastructure, and ecosystems. Recent advances in Internet of Things (IoT) technologies, smart sensing, and data-driven architectures have significantly enhanced the capabilities of Early Warning Systems (EWS) for landslide risk mitigation. However, current implementations remain fragmented across technological layers and lack standardized validation and socio-technical integration. This study presents a systematic literature review of IoT-based landslide EWS published between 2019 and 2025, following the PRISMA protocol. A total of 33 primary studies were selected from Scopus, Web of Science, and IEEE Xplore using predefined inclusion and exclusion criteria. The review analyzes key system dimensions, including sensing technologies, communication infrastructures, data processing architectures, validation approaches, and human-centered evaluation. The results reveal the predominance of Micro-Electro-Mechanical Systems (MEMS) and optical sensing technologies such as Fiber Bragg Grating (FBG) and Distributed Acoustic/Temperature Sensing (DAS/DTS), along with communication protocols such as Long Range (LoRa) and 5G. In addition, hybrid Edge–Cloud architectures are widely adopted to enable real-time monitoring and predictive analytics. Despite these advances, critical gaps persist in data validation standardization, system interoperability, and the integration of human-centered and participatory evaluation approaches. This study contributes by proposing an integrated socio-technical framework that unifies sensing, communication, data processing, validation, and human-centered dimensions in IoT-based EWS. The findings highlight the need for standardized validation protocols, AI-driven predictive integration, and user-centered design strategies to enhance the scalability, reliability, and social effectiveness of next-generation early warning systems.

The integration of external risk factors (EF) has become increasingly vital for robust credit risk assessment, particularly in emerging economies subject to compounded disruptions. This paper presents a comprehensive, scenario-based methodology for incorporating EF—including pandemic variables (COVID-19 mortality and positive cases), climate anomalies (temperature, road blockages), and social unrest—into machine learning (ML) models. We employ an adapted CRISP-DM workflow, testing stationarity (via Dickey-Fuller), causality (via Granger), and model performance (via AUC and ACC) under scenarios with and without government-backed guarantees. Post-hoc explainability techniques (SHAP, LIME) further reveal which features drive delinquency predictions in EF-enriched contexts. Empirical results, derived from over 8.2 million Peruvian credit records (January 2020–September 2023), confirm that EF integration significantly improves predictive accuracy. Time-lagged mortality (COVID MOV) emerges as the strongest single predictor, while combined crises (pandemic, climate, social unrest) amplify delinquency risk. Models such as XGB, CNN, and XNN exhibit the highest adaptability, consistently attaining statistical significance across different partitions of economic activities. Beyond enhancing model accuracy, EF inclusion highlights key drivers—borrower income stability, debt management, EF severity—thereby supporting more transparent, proactive strategies for financial institutions aiming to mitigate credit defaults in volatile, multi-factor environments.