# Incorporating data drift to perform survival analysis on credit risk

arXiv: https://arxiv.org/abs/2601.20533 · HTML: https://mlsanity.com/paper/2601.20533/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Where are we with calibration under dataset shift in image classification?](https://mlsanity.com/paper/2507.07780/) (2025)
- [Combining Dynamic Predictions from Joint Models for Longitudinal and Time-to-Event Data using Bayesian Model Averaging](https://mlsanity.com/paper/1303.2797/) (2013)
- [pencal: an R Package for the Dynamic Prediction of Survival with Many Longitudinal Predictors](https://mlsanity.com/paper/2309.15600/) (2023)
- [Dynamic prediction of an event using multiple longitudinal markers: a model averaging approach](https://mlsanity.com/paper/2412.08857/) (2024)
- [CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems](https://mlsanity.com/paper/2508.19281/) (2025)

## Abstract (arXiv)

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.