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...

وصف كامل

التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Seongil Han, Haemin Jung, Paul D. Yoo, Alessandro Provetti, Andrea Cali
التنسيق: مقال
اللغة:English
منشور في: Nature Portfolio 2024-10-01
سلاسل:Scientific Reports
الموضوعات:
Conditional Wasserstein generative adversarial networks
Stacked autoencoder
Explainable AI
Imbalanced class
Oversampling
Credit scoring
الوصول للمادة أونلاين:https://doi.org/10.1038/s41598-024-78055-5

الانترنت

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

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