MAÜ GCRIS Standart veritabanının içerik oluşturulması ve kurulumu Research Ecosystems (https://www.researchecosystems.com) tarafından devam etmektedir. Bu süreçte gördüğünüz verilerde eksikler olabilir.
 

CONSUMER CREDITS AND MACHINE LEARNING

dc.authorid 0000-0001-9577-7274
dc.authorscopusid 58548283900
dc.authorwosid JTS-3767-2023
dc.contributor.author Aksu, Mervan
dc.contributor.other Department of Management / İşletme Bölümü
dc.date.accessioned 2023-12-28T12:15:02Z
dc.date.available 2023-12-28T12:15:02Z
dc.date.issued 2023
dc.department MAÜ, Fakülteler, İktisadi ve İdari Bilimler Fakültesi, İşletme Bölümü en_US
dc.description.abstract The fusion of consumer credit analysis with machine learning algorithms has revolutionized credit risk assessment, providing banks with more efficient and reliable methods for evaluating creditworthiness. By leveraging these advanced techniques, banks can enhance their risk management practices, reduce the ratio of non-performing loans, and ultimately contribute to their profitability and stability in the financial market. As machine learning continues to evolve, it is likely to play an increasingly critical role in the financial sector and beyond. en_US
dc.identifier.endpage 34 en_US
dc.identifier.isbn 9786256408639
dc.identifier.startpage 23 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12514/5322
dc.identifier.volume 16 en_US
dc.institutionauthor Aksu, Mervan
dc.language.iso en en_US
dc.publisher Eğitim yayınevi en_US
dc.relation.ispartof CONSUMER CREDITS AND MACHINE LEARNING en_US
dc.relation.publicationcategory Kitap Bölümü - Uluslararası en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Consumer Credits en_US
dc.subject Machine Learning en_US
dc.title CONSUMER CREDITS AND MACHINE LEARNING en_US
dc.title.alternative International Research in Social, Human and Administrative Sciences XVI en_US
dc.type Book Part en_US
dspace.entity.type Publication
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relation.isAuthorOfPublication.latestForDiscovery ed33f371-c30a-48e0-8e32-e291d327e182
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relation.isOrgUnitOfPublication.latestForDiscovery 6734bfde-68ad-4d0b-9652-37a492c2eff0

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