Using support vector machine for the prediction of unpaid credit card debts
| dc.contributor.author | Yontar, Meltem | |
| dc.contributor.author | Dağ, Özge Hüsniye Namlı | |
| dc.contributor.author | Yanık, Seda | |
| dc.date.accessioned | 2021-01-08T21:51:31Z | |
| dc.date.available | 2021-01-08T21:51:31Z | |
| dc.date.issued | 2020 | |
| dc.department | TAÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü | en_US |
| dc.description | International Conference on Intelligent and Fuzzy Systems, INFUS 2019, 23 July 2019 through 25 July 2019, , 228529 | en_US |
| dc.description.abstract | Customer 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 with high accuracy and necessary actions can be taken in time. For forecasting the customers’ payment status of next months, we use support vector machine which is one of the traditional artificial intelligent algorithms. Our dataset includes 30000 customer’s records obtained from a large bank in Taiwan. These records consist of customer information such as 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 method to divide our dataset into two parts as training and test sets. Then, we evaluate prediction accuracy of the algorithm using performance metrics. The evaluation results show that support vector machine provides high accuracy (more than 80%) to forecast the customers’ payment status for next month. © 2020, Springer Nature Switzerland AG. | |
| dc.identifier.doi | 10.1007/978-3-030-23756-1_47 | |
| dc.identifier.endpage | 385 | en_US |
| dc.identifier.isbn | 9783030237554 | |
| dc.identifier.issn | 2194-5357 | |
| dc.identifier.scopus | 2-s2.0-85069480754 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.startpage | 377 | en_US |
| dc.identifier.uri | https://doi.org/10.1007/978-3-030-23756-1_47 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12846/323 | |
| dc.identifier.volume | 1029 | en_US |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Dağ, Özge Hüsniye Namlı | |
| dc.language.iso | en | |
| dc.publisher | Springer Verlag | |
| dc.relation.ispartof | Advances in Intelligent Systems and Computing | |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.subject | Classification | en_US |
| dc.subject | Credit card | en_US |
| dc.subject | Support vector machine | en_US |
| dc.title | Using support vector machine for the prediction of unpaid credit card debts | |
| dc.type | Conference Object |
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