Hao Wang
Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk
Wang, Hao; Bellotti, Anthony; Qu, Rong; Bai, Ruibin
Authors
Anthony Bellotti
Professor RONG QU rong.qu@nottingham.ac.uk
PROFESSOR OF COMPUTER SCIENCE
Ruibin Bai
Abstract
Survival models have become popular for credit risk estimation. Most current credit risk survival models use an underlying linear model. This is beneficial in terms of interpretability but is restrictive for real-life applications since it cannot discover hidden nonlinearities and interactions within the data. This study uses discrete-time survival models with embedded neural networks as estimators of time to default. This provides flexibility to express nonlinearities and interactions between variables and hence allows for models with better overall model fit. Additionally, the neural networks are used to estimate age–period–cohort (APC) models so that default risk can be decomposed into time components for loan age (maturity), origination (vintage), and environment (e.g., economic, operational, and social effects). These can be built as general models or as local APC models for specific customer segments. The local APC models reveal special conditions for different customer groups. The corresponding APC identification problem is solved by a combination of regularization and fitting the decomposed environment time risk component to macroeconomic data since the environmental risk is expected to have a strong relationship with macroeconomic conditions. Our approach is shown to be effective when tested on a large publicly available US mortgage dataset. This novel framework can be adapted by practitioners in the financial industry to improve modeling, estimation, and assessment of credit risk.
Citation
Wang, H., Bellotti, A., Qu, R., & Bai, R. (2024). Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk. Risks, 12(2), Article 31. https://doi.org/10.3390/risks12020031
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 18, 2024 |
Online Publication Date | Feb 3, 2024 |
Publication Date | 2024-02 |
Deposit Date | Apr 2, 2024 |
Publicly Available Date | Apr 3, 2024 |
Journal | Risks |
Electronic ISSN | 2227-9091 |
Publisher | MDPI |
Peer Reviewed | Peer Reviewed |
Volume | 12 |
Issue | 2 |
Article Number | 31 |
DOI | https://doi.org/10.3390/risks12020031 |
Keywords | credit risk; survival model; neural network; age–period–cohort |
Public URL | https://nottingham-repository.worktribe.com/output/31603713 |
Publisher URL | https://www.mdpi.com/2227-9091/12/2/31 |
Files
Risks2024
(6.4 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
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