Credit risk evaluation has a relevant role to financial institutions, since lending may result in real and immediate losses. In particular, default prediction is one of the most challenging activities for managing credit risk. This study analyzes the adequacy of borrower’s classification models using a Brazilian bank’s loan database, and exploring machine learning techniques. We develop Support Vector Machine, Decision Trees, Bagging, AdaBoost and Random Forest models, and compare their predictive accuracy with a benchmark based on a Logistic Regression model. Comparisons are analyzed based on usual classification performance metrics. Our results show that Random Forest and Adaboost perform better when compared to other models. Moreover, Support Vector Machine models show poor performance using both linear and nonlinear kernels. Our findings suggest that there are value creating opportunities for banks to improve default prediction models by exploring machine learning techniques.

信用风险评估对金融机构具有重要作用，因为贷款可能会导致实际和直接损失。特别是，违约预测是管理信用风险最具挑战性的活动之一。这项研究使用巴西银行的贷款数据库分析了借款人分类模型的充分性，并探索了机器学习技术。我们开发了支持向量机，决策树，装袋，AdaBoost和随机森林模型，并将它们的预测准确性与基于Logistic回归模型的基准进行比较。根据常规分类性能指标分析比较。我们的结果表明，与其他模型相比，Random Forest和Adaboost的性能更好。此外，支持向量机模型使用线性和非线性内核均显示出较差的性能。