- Tytuł:
- Supporting the Age-Period-Cohort model of default rate prediction with interpretable machine learning
- Autorzy:
- Kwiatkowski, Maciej Paweł
- Powiązania:
- https://bibliotekanauki.pl/articles/11542306.pdf
- Data publikacji:
- 2023-08-30
- Wydawca:
- Główny Urząd Statystyczny
- Tematy:
-
credit risk
macroeconomic impact
age-period-cohort
machine learning
XGBoost
SHAP - Opis:
- 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-PeriodCohort 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.
- Źródło:
-
Przegląd Statystyczny; 2023, 70, 1; 54-78
0033-2372 - Pojawia się w:
- Przegląd Statystyczny
- Dostawca treści:
- Biblioteka Nauki
Artykuł