• Türkçe
    • English
  • English 
    • Türkçe
    • English
  • Login
View Item 
  •   DSpace@MEF
  • Fakülteler
  • Mühendislik Fakültesi
  • Endüstri Mühendisliği | Industrial Engineering
  • MF, EM, Makale Koleksiyonu
  • View Item
  •   DSpace@MEF
  • Fakülteler
  • Mühendislik Fakültesi
  • Endüstri Mühendisliği | Industrial Engineering
  • MF, EM, Makale Koleksiyonu
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
Advanced Search

Consumer loans' first payment default detection: a predictive model

Thumbnail

View/Open

Yayıncı Sürümü_Makale Dosyası (822.2Kb)

Access

info:eu-repo/semantics/openAccess

Date

2020

Author

Koç, Utku
Sevgili, Türkan

Metadata

Show full item record

Citation

Koç, U., Sevgili, T. ( January 27, 2020). Consumer loans’ first payment default detection: a predictive model. Turkish Journal of Electrical Engineering & Computer Sciences, 28 (1), 167-181. DOI: https://doi.org/10.3906/elk-1809-190

Abstract

A default loan (also called nonperforming loan) occurs when there is a failure to meet bank conditions and repayment cannot be made in accordance with the terms of the loan which has reached its maturity. In this study, we provide a predictive analysis of the consumer behavior concerning a loan’s first payment default (FPD) using a real dataset of consumer loans with approximately 600,000 records from a bank. We use logistic regression, naive Bayes, support vector machine, and random forest on oversampled and undersampled data to build eight different models to predict FPD loans. A two-class random forest using undersampling yielded more than 86% on all performance measures: accuracy, precision, recall, and F1-score. The corresponding scores are even as high as 96% for oversampling. However, when tested on the real and balanced dataset, the performance of oversampling deteriorates as generating synthetic data for an extremely imbalanced dataset harms the training procedure of the algorithms. The study also provides an understanding of the reasons for nonperforming loans and helps to manage credit risks more consciously.

Source

Turkish Journal of Electrical Engineering & Computer Sciences

Volume

28

Issue

1

URI

https://doi.org/10.3906/elk-1809-190
https://hdl.handle.net/20.500.11779/1310

Collections

  • Araştırma Çıktıları, Scopus İndeksli Yayınlar Koleksiyonu [455]
  • Araştırma Çıktıları, TR-Dizin İndeksli Yayınlar Koleksiyonu [106]
  • Araştırma Çıktıları, WOS İndeksli Yayınlar Koleksiyonu [482]
  • MF, EM, Makale Koleksiyonu [34]



DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
Theme by 
@mire NV
 

 




| Instruction | Guide | Contact |

DSpace@MEF

by OpenAIRE

sherpa/romeo

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsInstitution AuthorTitlesORCIDSubjectsTypeLanguageDepartmentCategoryPublisherAccess TypeThis CollectionBy Issue DateAuthorsInstitution AuthorTitlesORCIDSubjectsTypeLanguageDepartmentCategoryPublisherAccess Type

My Account

LoginRegister

Statistics

View Google Analytics Statistics

DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
Theme by 
@mire NV
 

 


|| Guide|| Instruction || Library || MEF University || OAI-PMH ||

MEF University Library, İstanbul, Turkey
If you find any errors in content please report us

Creative Commons License
MEF University Institutional Repository is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License..

DSpace@MEF:


DSpace 6.2

tarafından İdeal DSpace hizmetleri çerçevesinde özelleştirilerek kurulmuştur.