Using machine learning techniques to develop prediction models for detecting unpaid credit card customers

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dc.contributor.authorYontar, Meltem
dc.contributor.authorDağ, Özge Hüsniye Namlı
dc.contributor.authorYanık, Seda
dc.date.accessioned2021-01-08T21:51:31Z
dc.date.available2021-01-08T21:51:31Z
dc.date.issued2020
dc.departmentTAÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractCustomer behavior prediction is gaining more importance in the banking sector like in any other sector recently. This study aims to propose a model to predict whether credit card users will pay their debts or not. Using the proposed model, potential unpaid risks can be predicted and necessary actions can be taken in time. For the prediction of customers' payment status of next months, we use Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification and Regression Tree (CART) and C4.5, which are widely used artificial intelligence and decision tree algorithms. Our dataset includes 10713 customer's records obtained from a well-known bank in Taiwan. These records consist of customer information such as the amount of credit, gender, education level, marital status, age, past payment records, invoice amount and amount of credit card payments. We apply cross validation and hold-out methods to divide our dataset into two parts as training and test sets. Then we evaluate the algorithms with the proposed performance metrics. We also optimize the parameters of the algorithms to improve the performance of prediction. The results show that the model built with the CART algorithm, one of the decision tree algorithm, provides high accuracy (about 86%) to predict the customers' payment status for next month. When the algorithm parameters are optimized, classification accuracy and performance are increased. © 2020 - IOS Press and the authors. All rights reserved.
dc.identifier.doi10.3233/JIFS-189080
dc.identifier.endpage6087en_US
dc.identifier.issn1064-1246
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85096956071
dc.identifier.scopusqualityQ2
dc.identifier.startpage6073en_US
dc.identifier.urihttps://doi.org/10.3233/JIFS-189080
dc.identifier.urihttps://hdl.handle.net/20.500.12846/330
dc.identifier.volume39en_US
dc.identifier.wosWOS:000595520600010
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorDağ, Özge Hüsniye Namlı
dc.language.isoen
dc.publisherIOS Press BV
dc.relation.ispartofJournal of Intelligent and Fuzzy Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectANNen_US
dc.subjectCARTen_US
dc.subjectclassificationen_US
dc.subjectCredit carden_US
dc.subjectmachine learningen_US
dc.subjectparameter optimizationen_US
dc.subjectSVMen_US
dc.titleUsing machine learning techniques to develop prediction models for detecting unpaid credit card customers
dc.typeArticle

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