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...

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Principais autores:	Seongil Han, Haemin Jung, Paul D. Yoo, Alessandro Provetti, Andrea Cali
Format:	Artigo
Jezik:	Inglês
Izdano:	Nature Portfolio 2024-10-01
Serija:	Scientific Reports
Teme:	Conditional Wasserstein generative adversarial networks Stacked autoencoder Explainable AI Imbalanced class Oversampling Credit scoring
Online dostop:	https://doi.org/10.1038/s41598-024-78055-5
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