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dc.contributor.authorAlam, Talha M.
dc.contributor.authorShaukat, Kamran
dc.contributor.authorHameed, Ibrahim A.
dc.contributor.authorLuo, Suhuai
dc.contributor.authorSarwar, Muhammad U.
dc.contributor.authorShabir, Shakir
dc.contributor.authorLi, Jiaming
dc.contributor.authorKhushi, Matloob
dc.identifier.citationIEEE Access. 2020, .en_US
dc.description.abstractFinancial threats are displaying a trend about the credit risk of commercial banks as the incredible improvement in the financial industry has arisen. In this way, one of the biggest threats faces by commercial banks is the risk prediction of credit clients. Recent studies mostly focus on enhancing the classifier performance for credit card default prediction rather than an interpretable model. In classification problems, an imbalanced dataset is also crucial to improve the performance of the model because most of the cases lied in one class, and only a few examples are in other categories. Traditional statistical approaches are not suitable to deal with imbalanced data. In this study, a model is developed for credit default prediction by employing various credit-related datasets. There is often a significant difference between the minimum and maximum values in different features, so Min-Max normalization is used to scale the features within one range. Data level resampling techniques are employed to overcome the problem of the data imbalance. Various undersampling and oversampling methods are used to resolve the issue of class imbalance. Different machine learning models are also employed to obtain efficient results. We developed the hypothesis of whether developed models using different machine learning techniques are significantly the same or different and whether resampling techniques significantly improves the performance of the proposed models. One-way Analysis of Variance is a hypothesis-testing technique, used to test the significance of the results. The split method is utilized to validate the results in which data has split into training and test sets. The results on imbalanced datasets show the accuracy of 66.9% on Taiwan clients credit dataset, 70.7% on South German clients credit dataset, and 65% on Belgium clients credit dataset. Conversely, the results using our proposed methods significantly improve the accuracy of 89% on Taiwan clients credit datase...en_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleAn Investigation of Credit Card Default Prediction in the Imbalanced Datasetsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.source.journalIEEE Accessen_US
dc.description.localcodeThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal