Incorporating data drift to perform survival analysis on credit risk

TL;DR

This paper introduces a dynamic joint modelling framework that incorporates data drift to enhance the robustness of survival analysis models in credit risk, particularly under non-stationary conditions caused by changing borrower and macroeconomic factors.

Contribution

It proposes a novel model integrating behavioral markers with hazard models, addressing data drift explicitly, and demonstrates its effectiveness through simulations on mortgage datasets.

Findings

The proposed model outperforms classical survival models in discrimination.

It maintains calibration better under various data drift scenarios.

The approach is robust across sudden, incremental, and recurring data drifts.

Abstract

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.