(untitled) (Journal of Business, Universidad del Pacífico (Lima, Peru), 2026)
Abstract. This study proposes a comprehensive approach for the early detection of default risk in microfinance portfolios, combining machine learning techniques with historical analysis of clients’ payment behavior. A database of more than 50,000 microcredits granted in Peru by a microfinance institution in Huancayo (2019–2021) was used, constructing a risk indicator based on the proportion of days in arrears relative to the agreed payment frequency, with a critical threshold of 25% of the installment period. This criterion differentiates clients with a higher propensity to default without penalizing minor delays, improving analytical accuracy. The study focuses on microenterprises and informal entrepreneurs, traditionally excluded from formal banking. It provides predictive tools adapted to segments with limited credit history, fostering financial inclusion and strengthening risk management in microfinance institutions. Four predictive models were evaluated, representing the main families of supervised learning: Gradient Boosting Machine (GBM) for Boosting, Bayesian Additive Regression Trees (BART) for Bayesian ensembles, Random Forest (RF) for Bagging, and Support Vector Machines (SVM) as optimal margin classifiers. This selection allows contrasting methodologies and identifying the most suitable approach for the microfinance context. The use of supervised learning is justified because the problem has historical labels of default and non-default, enabling predictions directly applicable to credit decision-making. Performance was assessed using metrics such as Cohen’s Kappa, Geometric Mean, and F1-score. Results show that GBM delivers the most consistent performance, BART achieves the best F1-score, and SVM excels in geometric precision. These findings validate the effectiveness of supervised learning in segmenting credit risk, optimizing operational management, and laying the foundation for incorporating artificial intelligence agents to monitor payments in real time and reduce losses from default. Keywords: Microfinance, credit risk, payment default, small businesses, machine learning, predictive modeling, artificial intelligence agents.