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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Dettagli Bibliografici
Autori principali: Seongil Han, Haemin Jung, Paul D. Yoo, Alessandro Provetti, Andrea Cali
Natura: Articolo
Lingua:English
Pubblicazione: Nature Portfolio 2024-10-01
Serie:Scientific Reports
Soggetti:
Conditional Wasserstein generative adversarial networks
Stacked autoencoder
Explainable AI
Imbalanced class
Oversampling
Credit scoring
Accesso online:https://doi.org/10.1038/s41598-024-78055-5

Accesso online

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

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