Nicat, SahinFiliz, GozdeOzvural, Ozden GebizliogluÇakar, Tuna2024-09-082024-09-082024979835038897897983503889612165-0608https://doi.org/10.1109/SIU61531.2024.10600848https://hdl.handle.net/20.500.11779/2331This 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.trinfo:eu-repo/semantics/closedAccessCredit ScoringAlternative Data SourcesPsychometric DataConference Object10.1109/SIU61531.2024.106008482-s2.0-85200921274