Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF

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dc.contributor.authorKayode Omotosho Alabi, Sulaiman Olaniyi Abdulsalam, Roseline Oluwaseun Ogundokun, and Micheal Olaolu Arowolo
dc.date.accessioned2023-07-18T12:57:54Z
dc.date.available2023-07-18T12:57:54Z
dc.date.issued2020-11-24
dc.description.abstractFinancial credit risk analysis management has been a foremost influence with a lot of challenges, especially for banks reducing their principal loss. AQ1 In this study, the machine learning technique is a promising area used for credit scoring for analyzing risks in banks. It has become critical to extract beneficial knowledge for a great number of complex datasets. In this study, a machine learning approach using Chi-Square with SVM-RBF classifier was analyzed for Taiwan bank credit data. The model provides important information with enhanced accuracy that will help in predicting loan status. The experiment achieves 93% accuracy compared to the state-of-the-art. Keywords: Credit risk · Chi-square · SVM · Bank · Machine learning
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/433
dc.language.isoen
dc.publisherSpringer Nature Switzerland
dc.titleCredit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF
dc.typeArticle
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