An Improved Ensemble Method With Data Resampling for Credit Risk Prediction

The increasing complexity and dynamic nature of financial data present significant challenges in accurately predicting credit risk, a critical task in the banking and finance sector. The application of machine learning (ML) in credit risk prediction has been hindered by the imbalanced nature of cred...

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Veröffentlicht in:	IEEE access Jg. 13; S. 71275 - 71287
Hauptverfasser:	Aruleba, Idowu, Sun, Yanxia
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Artificial neural networks Banking CNN Convolutional neural networks Credit risk Data models Datasets Employment Ensemble learning Loans Logistic regression Machine learning MLP Multilayer perceptrons Predictive models random forest Random forests Regression analysis Resampling Risk analysis Risk assessment Sensitivity and specificity
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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