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...

תיאור מלא

מידע ביבליוגרפי
Main Authors: Seongil Han, Haemin Jung, Paul D. Yoo, Alessandro Provetti, Andrea Cali
פורמט: Article
שפה:English
יצא לאור: Nature Portfolio 2024-10-01
סדרה:Scientific Reports
נושאים:
Conditional Wasserstein generative adversarial networks
Stacked autoencoder
Explainable AI
Imbalanced class
Oversampling
Credit scoring
גישה מקוונת:https://doi.org/10.1038/s41598-024-78055-5

אינטרנט

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

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