将数据漂移纳入信用风险中的生存分析
Incorporating data drift to perform survival analysis on credit risk
AI总结：
本文提出一种动态联合建模框架，通过整合纵向行为标记与离散时间危险公式，提升信用风险模型在数据漂移下的鲁棒性和准确性。
摘要：
生存分析已成为建模信用风险中时间到违约时间的常用方法，通过时间变化的协变量。与大多数现有方法隐式假设数据生成过程是稳定的不同，在实践中，抵押贷款组合会受到借款人行为、宏观经济状况、政策制度等变化导致的数据漂移的影响。本研究探讨了数据漂移对基于生存的信用风险模型的影响，并提出了一种动态联合建模框架，以提高非稳定环境下的鲁棒性。所提出的模型整合了从平衡动态中衍生出的纵向行为标记与离散时间危险公式，结合地标一热编码和等比校准。三种类型的数据漂移（突然、渐进和重复）在弗里茨麦克抵押贷款数据集上进行了模拟和分析。实验和相应证据表明，所提出的基于地标的联合模型在所有漂移场景中均在判别和校准方面优于经典生存模型、基于树的漂移适应学习者和梯度提升方法，这证实了我们模型设计的优越性。
英文摘要：
Survival analysis has become a standard approach for modelling time to default by time-varying covariates in credit risk. Unlike most existing methods that implicitly assume a stationary data-generating process, in practise, mortgage portfolios are exposed to various forms of data drift caused by changing borrower behaviour, macroeconomic conditions, policy regimes and so on. This study investigates the impact of data drift on survival-based credit risk models and proposes a dynamic joint modelling framework to improve robustness under non-stationary environments. The proposed model integrates a longitudinal behavioural marker derived from balance dynamics with a discrete-time hazard formulation, combined with landmark one-hot encoding and isotonic calibration. Three types of data drift (sudden, incremental and recurring) are simulated and analysed on mortgage loan datasets from Freddie Mac. Experiments and corresponding evidence show that the proposed landmark-based joint model consistently outperforms classical survival models, tree-based drift-adaptive learners and gradient boosting methods in terms of discrimination and calibration across all drift scenarios, which confirms the superiority of our model design.
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