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...

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Detalles Bibliográficos
Main Authors: Seongil Han, Haemin Jung, Paul D. Yoo, Alessandro Provetti, Andrea Cali
Formato: Artigo
Idioma:English
Publicado: Nature Portfolio 2024-10-01
Series:Scientific Reports
Subjects:
Conditional Wasserstein generative adversarial networks
Stacked autoencoder
Explainable AI
Imbalanced class
Oversampling
Credit scoring
Acceso en liña:https://doi.org/10.1038/s41598-024-78055-5

Internet

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

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