In the context of credit scoring, ensemble methods based on decision trees, such as the random forest method, provide better classification performance than standard logistic regression models. However, logistic regression remains the benchmark in the credit risk industry mainly because the lack of interpretability of ensemble methods is incompatible with the requirements of financial regulators. In this paper, we propose a high-performance and interpretable credit scoring method called penalised logistic tree regression (PLTR), which uses information from decision trees to improve the performance of logistic regression. Formally, rules extracted from various short-depth decision trees built with original predictive variables are used as predictors in a penalised logistic regression model. PLTR allows us to capture non-linear effects that can arise in credit scoring data while preserving the intrinsic interpretability of the logistic regression model. Monte Carlo simulations and empirical applications using four real credit default datasets show that PLTR predicts credit risk significantly more accurately than logistic regression and compares competitively to the random forest method.

在信用评分的背景下，基于决策树的集成方法，如随机森林方法，提供比标准逻辑回归模型更好的分类性能。然而，逻辑回归仍然是信用风险行业的基准，主要是因为集成方法缺乏可解释性，不符合金融监管机构的要求。在本文中，我们提出了一种称为惩罚逻辑树回归 (PLTR) 的高性能且可解释的信用评分方法，该方法使用决策树中的信息来提高逻辑回归的性能。正式地，从使用原始预测变量构建的各种短深度决策树中提取的规则被用作惩罚逻辑回归模型中的预测变量。PLTR 使我们能够捕捉信用评分数据中可能出现的非线性效应，同时保留逻辑回归模型的内在可解释性。蒙特卡罗模拟和使用四个真实信用违约数据集的实证应用表明，PLTR 比逻辑回归更准确地预测信用风险，并且与随机森林方法相比具有竞争力。