NOTE: non-parametric oversampling technique for explainable credit scoring

NOTE: non-parametric oversampling technique for explainable credit scoring

Abstract Credit scoring models are critical for financial institutions to assess borrower risk and maintain profitability. Although machine learning models have improved credit scoring accuracy, imbalanced class distributions remain a major challenge. The widely used Synthetic Minority Oversampling...

Полное описание

Библиографические подробности
Главные авторы: Seongil Han, Haemin Jung, Paul D. Yoo, Alessandro Provetti, Andrea Cali
Формат: Статья
Язык:English
Опубликовано: Nature Portfolio 2024-10-01
Серии:Scientific Reports
Предметы:
Conditional Wasserstein generative adversarial networks
Stacked autoencoder
Explainable AI
Imbalanced class
Oversampling
Credit scoring
Online-ссылка:https://doi.org/10.1038/s41598-024-78055-5

Internet

https://doi.org/10.1038/s41598-024-78055-5

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