Joint models for longitudinal and discrete survival data in credit scoring

Victor Medina Olivares*, Raffaella Calabrese, Jonathan Crook, Finn Lindgren

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

The inclusion of time-varying covariates into survival analysis has led to better predictions of the time to default in behavioural credit scoring models. However, when these time-varying covariates are endogenous, there are two major problems: estimation bias of the survival model and lack of a prediction framework for future values of both the event and the endogenous time-varying covariates. Joint models for longitudinal and survival data is an appropriate framework to model the mutual evolution of the survival time and the endogenous time-varying covariates. To the best of our knowledge,this paper explores for the first time the application of discrete-time joint models to credit scoring. Moreover, we propose a novel extension to the joint model literature by including autoregressive terms in modelling the endogenous time-varying covariates. We present the method via simulations and by applying it to US mortgage loans. The empirical analysis shows, first, that discrete joint models can increase the discrimination performance compared to survival models. Second, when an autoregressive term is included, this performance can be further improved.
Original languageEnglish
Pages (from-to)1457-1473
Number of pages17
JournalEuropean Journal of Operational Research
Issue number3
Early online date20 Oct 2022
Publication statusPublished - 16 Jun 2023

Keywords / Materials (for Non-textual outputs)

  • OR in banking
  • Bayesian joint models
  • discrete time
  • autoregressive process


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