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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Détails bibliographiques
Auteurs principaux: Seongil Han, Haemin Jung, Paul D. Yoo, Alessandro Provetti, Andrea Cali
Format: Article
Langue:English
Publié: Nature Portfolio 2024-10-01
Collection:Scientific Reports
Sujets:
Conditional Wasserstein generative adversarial networks
Stacked autoencoder
Explainable AI
Imbalanced class
Oversampling
Credit scoring
Accès en ligne:https://doi.org/10.1038/s41598-024-78055-5

Internet

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

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