Using Hybrid Approaches for Credit Application Scoring
| dc.authorscopusid | 58641292900 | |
| dc.authorscopusid | 7801329641 | |
| dc.contributor.author | Mirza,F.K. | |
| dc.contributor.author | Ogrenci,A.S. | |
| dc.date.accessioned | 2024-06-23T21:38:35Z | |
| dc.date.available | 2024-06-23T21:38:35Z | |
| dc.date.issued | 2023 | |
| dc.department | Kadir Has University | en_US |
| dc.department-temp | Mirza F.K., Kadir Has University, Dept. of Electrical-Electronics Engineering, Istanbul, Turkey; Ogrenci A.S., Kadir Has University, Dept. of Electrical-Electronics Engineering, Istanbul, Turkey | en_US |
| dc.description.abstract | Using various methods of computational intelligence, scores for credit applications are predicted for making a decision to accept or to reject. Past data about credits accepted in a financial institution are used to develop hybrid models implementing gradient boosting and attention supported neural networks. The performance (Gini scores above 0.7), limitations and further research directions are discussed. © 2023 IEEE. | en_US |
| dc.description.sponsorship | Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK, (1505-5220055) | en_US |
| dc.identifier.citationcount | 0 | |
| dc.identifier.doi | 10.1109/CINTI59972.2023.10382025 | |
| dc.identifier.endpage | 116 | en_US |
| dc.identifier.isbn | 979-835034294-9 | |
| dc.identifier.scopus | 2-s2.0-85184121699 | |
| dc.identifier.startpage | 111 | en_US |
| dc.identifier.uri | https://doi.org/10.1109/CINTI59972.2023.10382025 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12469/5813 | |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | IEEE 23rd International Symposium on Computational Intelligence and Informatics, CINTI 2023 - Proceedings -- 23rd IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2023 -- 20 November 2023 through 22 November 2023 -- Budapest -- 196335 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.scopus.citedbyCount | 3 | |
| dc.subject | attention mechanism | en_US |
| dc.subject | credit scoring | en_US |
| dc.subject | gradient boosting | en_US |
| dc.subject | neural networks | en_US |
| dc.subject | unbalanced data | en_US |
| dc.title | Using Hybrid Approaches for Credit Application Scoring | en_US |
| dc.type | Conference Object | en_US |
| dspace.entity.type | Publication |