Supporting the Age-Period-Cohort model of default rate prediction with interpretable machine learning

Maciej Paweł Kwiatkowski

doi:https://doi.org/10.59139/ps.2023.01.3

Maciej Paweł Kwiatkowski Freelance researcher, Poland ORCID:https://orcid.org/0000-0001-6564-7786 Przegląd Statystyczny. Statistical Review, vol. 70, 2023, 1, pages: 54-78 Published online: 30 August 2023 DOI https://doi.org/10.59139/ps.2023.01.3 Citation: Kwiatkowski, M. P. (2023). Supporting the Age-Period-Cohort model of default rate prediction with interpretable machine learning. Przegląd Statystyczny. Statistical Review, 70(1), 54–78. https://doi.org/10.59139/ps.2023.01.3.

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ARTICLE

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ABSTRACT

Regular short-term forecasting of defaults is a basic activity of a retail portfolio risk manager. From a business perspective, not only the quality of the forecast is significant, but also the understanding of the trends and their driving factors. The vintage analysis and a more advanced Age-Period-Cohort approach are popular tools used for the purpose. The aim of this article is to demonstrate that interpretable machine learning can support the Age-Period- Cohort approach, facilitating forecasting beyond the time range of training data, eliminating the model identification problem and attributing cohort quality to the specific characteristics of loans approved in a given month. The study is based on real consumer finance portfolios from the Polish market.

KEYWORDS

credit risk, macroeconomic impact, age-period-cohort, machine learning, XGBoost, SHAP

JEL

C41, C53, C55, C58, G20, G21

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