National Bank of Ukraine Econometric Model for the Assessment of Banks’ Credit Risk and Support Vector Machine Alternative

Dmytro Pokidin
Additional contact information
Dmytro Pokidin: National Bank of Ukraine

Visnyk of the National Bank of Ukraine, 2015, issue 234, 52-72

Abstract: Econometric models of credit scoring started with the introduction of Altman’s simple z-model in 1968, but since then these models have become more and more sophisticated, some even use Artificial Neural Networks (ANN) and Support Vector Machine (SVM) techniques. This paper focuses on the use of SVM as a model for default prediction. I start with an introduction to SVM as well as to some of its widespread alternatives. Then, these different techniques are used to model NBU data on banks’ clients, which allows us to compare the accuracy of SVM to the accuracy of other models. While SVM is generally more accurate, I discuss some of the features of SVM that make its practical implementation controversial. I then discuss some ways for overcoming those features. I also present the results of the Logistic Regression (Logit) model which will be used by the NBU.

Keywords: machine learning; svm; credit risk; scoring model (search for similar items in EconPapers)
JEL-codes: C45 C51 C52 C53 (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journal.bank.gov.ua/en/article/2015/234/03 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ukb:journl:y:2015:i:234:p:52-72

DOI: 10.26531/vnbu2015.234.052

Access Statistics for this article

More articles in Visnyk of the National Bank of Ukraine from National Bank of Ukraine Contact information at EDIRC.
Bibliographic data for series maintained by Research Unit ().