提出动态联合模型，提升信贷违约预测在数据漂移下的稳定性。
Incorporating data drift to perform survival analysis on credit risk
- 融合账户行为特征与离散时间风险模型，结合地标编码与等序校准。
- 三种漂移场景下，模型在区分度与校准性上均优于传统方法。
- 适合关注信贷模型鲁棒性的金融风控从业者使用。
生存分析已成为基于时变协变量建模信贷违约时间的标准方法。然而，现有方法通常隐含假设数据生成过程平稳，而现实中房贷组合会因借款人行为变化、宏观经济波动、政策调整等因素产生多种数据漂移。本文研究数据漂移对基于生存的信贷风险模型的影响，并提出一种动态联合建模框架以增强非平稳环境下的鲁棒性。该模型整合了从余额动态中提取的纵向行为指标与离散时间风险函数，结合地标一热编码和等序校准。在弗里蒙特麦克（Freddie Mac）房贷数据集上，模拟了突发性、渐进性和周期性三类数据漂移。实验表明，所提出的基于地标的联合模型在所有漂移场景下，均在区分度与校准性上显著优于经典生存模型、树基漂移自适应学习器及梯度提升方法，验证了模型设计的优势。
原文摘要 · Abstract (English)
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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