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...

Бүрэн тодорхойлолт

Номзүйн дэлгэрэнгүй
Үндсэн зохиолчид: Seongil Han, Haemin Jung, Paul D. Yoo, Alessandro Provetti, Andrea Cali
Формат: Өгүүллэг
Хэл сонгох:English
Хэвлэсэн: Nature Portfolio 2024-10-01
Цуврал:Scientific Reports
Нөхцлүүд:
Conditional Wasserstein generative adversarial networks
Stacked autoencoder
Explainable AI
Imbalanced class
Oversampling
Credit scoring
Онлайн хандалт:https://doi.org/10.1038/s41598-024-78055-5

Интернэт

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

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