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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Seongil Han, Haemin Jung, Paul D. Yoo, Alessandro Provetti, Andrea Cali
Μορφή: Άρθρο
Γλώσσα:English
Έκδοση: Nature Portfolio 2024-10-01
Σειρά:Scientific Reports
Θέματα:
Conditional Wasserstein generative adversarial networks
Stacked autoencoder
Explainable AI
Imbalanced class
Oversampling
Credit scoring
Διαθέσιμο Online:https://doi.org/10.1038/s41598-024-78055-5

Διαδίκτυο

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

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