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Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk

Author

Listed:
  • Hao Wang

    (School of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, China)

  • Anthony Bellotti

    (School of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, China)

  • Rong Qu

    (School of Computer Science, University of Nottingham, Nottingham NG7 2RD, UK)

  • Ruibin Bai

    (School of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, China)

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.

Suggested Citation

  • Hao Wang & Anthony Bellotti & Rong Qu & Ruibin Bai, 2024. "Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk," Risks, MDPI, vol. 12(2), pages 1-26, February.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:2:p:31-:d:1332628
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    References listed on IDEAS

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    1. Gabriel Blumenstock & Stefan Lessmann & Hsin-Vonn Seow, 2022. "Deep learning for survival and competing risk modelling," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(1), pages 26-38, January.
    2. Daniele De Leonardis & Roberto Rocci, 2008. "Assessing the default risk by means of a discrete‐time survival analysis approach," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(4), pages 291-306, July.
    3. Gerardo Alfonso Perez & Raquel Castillo, 2023. "Nonlinear Techniques and Ridge Regression as a Combined Approach: Carcinoma Identification Case Study," Mathematics, MDPI, vol. 11(8), pages 1-12, April.
    4. C. Gourieroux & A. Monfort & V. Polimenis, 2006. "Affine Models for Credit Risk Analysis," Journal of Financial Econometrics, Oxford University Press, vol. 4(3), pages 494-530.
    5. Lore Dirick & Gerda Claeskens & Bart Baesens, 2017. "Time to default in credit scoring using survival analysis: a benchmark study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 652-665, June.
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