Aplicación de Modelos de Machine Learning para la Predicción de Quiebra en el Mercado Financiero Peruano Durante el Periodo 2014-2024 - Leon Campos, Piero Martin (2025)
The early prediction of corporate bankruptcy is a key challenge for economic stability and risk management in emerging markets. This study explores the application of various Machine Learning techniques to anticipate the probability of insolvency in companies operating in the Peruvian market. The main objective is to compare the predictive capacity of both classical and modern models, including multiple discriminant analysis, logistic regression, Support Vector Machines, Random Forest, Bagging, Boosting, and neural networks, using accounting and financial information. The methodology involved building a database of financial series from Peruvian firms, normalizing ratios, splitting the data into training and testing sets, and evaluating performance through widely used classification metrics such as accuracy, recall, F1 score, Type I and II errors, and AUC. The results show that ensemble-based models, particularly Boosting, Support Vector Machines, and Random Forest, outperform traditional techniques, highlighting the usefulness of machine learning approaches in strengthening financial risk management in local contexts. These findings provide practical implications for both financial institutions and regulators.