Credit scoring, which is typically transformed into a classification problem, is a powerful tool to manage credit risk since it forecasts the probability of default (PD) of a loan application. However, there is a growing trend of integrating survival analysis into credit scoring to provide a dynamic prediction on PD over time and a clear explanation on censoring. A novel dynamic credit scoring model (i.e., SurvXGBoost) is proposed based on survival gradient boosting decision tree (GBDT) approach. Our proposal, which combines survival analysis and GBDT approach, is expected to enhance predictability relative to statistical survival models. The proposed method is compared with several common benchmark models on a real-world consumer loan dataset. The results of out-of-sample and out-of-time validation indicate that SurvXGBoost outperform the benchmarks in terms of predictability and misclassification cost. The incorporation of macroeconomic variables can further enhance performance of survival models. The proposed SurvXGBoost meanwhile maintains some interpretability since it provides information on feature importance.

中文翻译：

---

基于生存梯度提升决策树方法的动态信用评分模型

信用评分通常会转化为分类问题，是管理信用风险的强大工具，因为它可以预测贷款申请的违约概率 (PD)。然而，越来越多的趋势是将生存分析整合到信用评分中，以随着时间的推移提供对 PD 的动态预测以及对审查的清晰解释。基于生存梯度提升决策树（GBDT）方法提出了一种新颖的动态信用评分模型（即SurvXGBoost）。我们的提议结合了生存分析和 GBDT 方法，有望提高相对于统计生存模型的可预测性。将所提出的方法与现实世界消费贷款数据集上的几种常见基准模型进行了比较。样本外和超时验证的结果表明，SurvXGBoost 在可预测性和误分类成本方面优于基准。宏观经济变量的结合可以进一步提高生存模型的性能。提议的 SurvXGBoost 同时保持了一些可解释性，因为它提供了关于特征重要性的信息。