对比5种机器学习模型，提升贷款违约概率预测精度
A comparative analysis of machine learning algorithms for predicting probabilities of default
- 用随机森林、XGBoost等5种算法比对逻辑回归
- 在信用数据集上，梯度提升类模型表现更优
- 适合金融风控从业者参考模型选型
预测潜在贷款的违约概率（PD）是金融机构的核心目标。近年来，机器学习（ML）算法在各类预测任务中取得显著成果，但在信用风险分析领域仍应用有限。本文通过对比五种预测模型——随机森林、决策树、XGBoost、梯度提升和AdaBoost——与主流的逻辑回归，在Scheule等人（《信用风险分析：R伴侣》）提供的基准数据集上的表现，揭示了各方法的优劣，为贷款组合中的违约概率预测提供了有效的机器学习算法选择依据。
原文摘要 · Abstract (English)
Predicting the probability of default (PD) of prospective loans is a critical objective for financial institutions. In recent years, machine learning (ML) algorithms have achieved remarkable success across a wide variety of prediction tasks; yet, they remain relatively underutilised in credit risk analysis. This paper highlights the opportunities that ML algorithms offer to this field by comparing the performance of five predictive models-Random Forests, Decision Trees, XGBoost, Gradient Boosting and AdaBoost-to the predominantly used logistic regression, over a benchmark dataset from Scheule et al. (Credit Risk Analytics: The R Companion). Our findings underscore the strengths and weaknesses of each method, providing valuable insights into the most effective ML algorithms for PD prediction in the context of loan portfolios.
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