Joint models for longitudinal and discrete survival data in credit scoring

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 en-dogenous, 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 mod-els 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 ex-plores 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 discrimi-nation performance compared to survival models. Second, when an autoregressive term is included, this performance can be further improved.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Automated summary

The inclusion of time-varying covariates into survival analysis has improved predictions in behavioural credit scoring models. However, problems arise when these covariates are endogenous, including estimation bias and lack of a prediction framework. This paper explores the application of discrete-time joint models to credit scoring and proposes a novel extension by including autoregressive terms. Empirical analysis shows that discrete joint models can improve discrimination performance, especially when autoregressive terms are included.