From Crisis to Algorithm: Credit Delinquency Prediction in Peru Under Critical External Factors Using Machine Learning

Jomark Noriega (), Luis Rivera, Jorge Castañeda and José Herrera
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Jomark Noriega: Facultad de Ingeniería de Sistemas e Informática, Escuela de Posgrado, Universidad Nacional Mayor de San Marcos, Campus Ciudad Universitaria, Calle Germán Amézaga 375, Lima 15801, Peru
Luis Rivera: Facultad de Ingeniería de Sistemas e Informática, Escuela de Posgrado, Universidad Nacional Mayor de San Marcos, Campus Ciudad Universitaria, Calle Germán Amézaga 375, Lima 15801, Peru
Jorge Castañeda: Facultad de Ingeniería, Escuela de Ingeniería Informática, Universidad San Ignacio de Loyola, Campus 2 La Molina, Av. Fontana 550, La Molina 15026, Peru
José Herrera: Facultad de Ingeniería de Sistemas e Informática, Escuela de Posgrado, Universidad Nacional Mayor de San Marcos, Campus Ciudad Universitaria, Calle Germán Amézaga 375, Lima 15801, Peru

Data, 2025, vol. 10, issue 5, 1-53

Abstract: 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.

Keywords: loan; credit risk; prediction; machine learning; external factors (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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