Wang, H.; Bellotti, A.; Qu, R.; Bai, R. Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk. Risks2024, 12, 31.
Wang, H.; Bellotti, A.; Qu, R.; Bai, R. Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk. Risks 2024, 12, 31.
Wang, H.; Bellotti, A.; Qu, R.; Bai, R. Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk. Risks2024, 12, 31.
Wang, H.; Bellotti, A.; Qu, R.; Bai, R. Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk. Risks 2024, 12, 31.
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 data set. This novel framework can be adapted by practitioners in the financial industry to improve modelling, estimation and assessment of credit risk.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
