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...

詳細記述

書誌詳細
主要な著者: Seongil Han, Haemin Jung, Paul D. Yoo, Alessandro Provetti, Andrea Cali
フォーマット: 論文
言語:English
出版事項: Nature Portfolio 2024-10-01
シリーズ:Scientific Reports
主題:
Conditional Wasserstein generative adversarial networks
Stacked autoencoder
Explainable AI
Imbalanced class
Oversampling
Credit scoring
オンライン･アクセス:https://doi.org/10.1038/s41598-024-78055-5

インターネット

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

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