Modeling automobile credit scoring using machine learning models

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dc.authorid0000-0002-5919-1986
dc.contributor.authorYiğit, Pakize
dc.date.accessioned2024-06-10T06:58:16Z
dc.date.available2024-06-10T06:58:16Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Temel Tıp Bilimleri Bölümü, Biyoistatistik ve Tıp Bilişimi Ana Bilim Dalı
dc.description.abstractThis study aimed to apply ML models to determine variables related to automobile credit scoring in a financial institute. We used four machine learning methods, including logistic regression (L.R), artificial neural network (ANN), random forest (R.F.), and Extreme Gradient Boosting (Xgboost) with 10-fold cross-validation repeated 10 times. The RF algorithm achieved the best performance in all performance metrics. It performed 96.2% under the receiver operating characteristic (AUROC) score. AUROC scores for other models were 91.7% for LR, 91.6% for Xgboost, and 91.5% for ANN. SHAP (SHapley Additive exPlanations) values were also calculated to better explain the indicators’ importance. The most relevant features of the model were debt to income ratio score, documented wealth score, and down payment rate score. To sum up, this study might help automobile credit providers and applicants for their credit evaluation process.
dc.identifier.citationYiğit, P. (2024). Modeling automobile credit scoring using machine learning models. International Conference on Emerging Trends and Applications in Artificial Intelligence, ICETAI 2023 içinde 960, (424-436. ss.). İstanbul, September 8-9, 2023. http://dx.doi.org/10.1007/978-3-031-56728-5_36
dc.identifier.doi10.1007/978-3-031-56728-5_36
dc.identifier.endpage436
dc.identifier.isbn9783031567278
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-85193592055
dc.identifier.scopusqualityQ4
dc.identifier.startpage424
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-031-56728-5_36
dc.identifier.urihttps://hdl.handle.net/20.500.12511/12599
dc.identifier.volume960
dc.indekslendigikaynakScopus
dc.institutionauthorYiğit, Pakize
dc.language.isoen
dc.relation.ispartofInternational Conference on Emerging Trends and Applications in Artificial Intelligence, ICETAI 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCredit Scoring
dc.subjectMachine Learning
dc.subjectRandom Forest
dc.subjectXAI Methods
dc.titleModeling automobile credit scoring using machine learning models
dc.typeConference Object

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