Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11779/2331
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dc.contributor.authorGebizlioğlu Özvural, Özden-
dc.contributor.authorÇakar, Tuna-
dc.contributor.authorFiliz, Gözde-
dc.contributor.authorNicat, Şahin-
dc.date.accessioned2024-09-08T16:52:57Z-
dc.date.available2024-09-08T16:52:57Z-
dc.date.issued2024-
dc.identifier.isbn9798350388961-
dc.identifier.urihttps://hdl.handle.net/20.500.11779/2331-
dc.identifier.urihttps://doi.org/10.1109/SIU61531.2024.10600848-
dc.description.abstractThis study investigates the effectiveness and reliability of using psychometric tests in the credit decision-making processes within the finance sector. Psychometric tests, by measuring individuals' cognitive and psychological traits, hold the potential to broaden access to credit and identify high credit risk. However, after the literature review, it was seen that there was a need for more studies on the reliability and validity of these tests in finance. This study is designed to measure the test-retest reliability of a machine learning model and its inputs that utilize psychometric test results. Within the scope of the research, 115 participants were re-subjected to the same psychometric tests after an average of 6 months. Findings showed that psychometric tests and the machine learning model were generally consistent over time. This work has the potential to fill the gaps in the literature regarding the use of psychometric tests in the finance sector and lays a foundation for future research. © 2024 IEEE.en_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings -- 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 -- 15 May 2024 through 18 May 2024 -- Mersin -- 201235en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCredit scoringen_US
dc.subjectPsychometric dataen_US
dc.subjectAlternative data sourcesen_US
dc.titleReliability Study of Psychometric Tests in a Credit Scoring Model;en_US
dc.title.alternativeKredi Skorlama Modelinde Psikometrik Testlerin Güvenirlik Çalışmasıen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/SIU61531.2024.10600848-
dc.identifier.scopus2-s2.0-85200921274en_US
dc.authorscopusid58634304800-
dc.authorscopusid58634073400-
dc.authorscopusid59254920400-
dc.authorscopusid56329345400-
dc.description.PublishedMonthMayısen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.departmentMühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.institutionauthorÇakar, Tuna-
dc.institutionauthorFiliz, Gözde-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.languageiso639-1tr-
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.02. Department of Computer Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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