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Credit default prediction modeling: an application of support vector machine

Fahmida E. Moula, Chi Guotai, Abedin Abedin

Risk Management, Volume: 19, Issue: 2, Pages: 158 - 187

Swansea University Author: Abedin Abedin

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DOI (Published version): 10.1057/s41283-017-0016-x

Abstract

Credit default prediction (CDP) modeling is a fundamental and critical issue for financial institutions. However, the previous studies indicate that the classifier’s performances in CDP analysis differ using different performance criterions on different databases under different circumstances. The p...

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Published in: Risk Management
ISSN: 1460-3799 1743-4637
Published: Springer Science and Business Media LLC 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa64269
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first_indexed 2023-09-18T14:00:43Z
last_indexed 2023-09-18T14:00:43Z
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spelling v2 64269 2023-08-31 Credit default prediction modeling: an application of support vector machine 4ed8c020eae0c9bec4f5d9495d86d415 Abedin Abedin Abedin Abedin true false 2023-08-31 BAF Credit default prediction (CDP) modeling is a fundamental and critical issue for financial institutions. However, the previous studies indicate that the classifier’s performances in CDP analysis differ using different performance criterions on different databases under different circumstances. The performance assessment exercise under a set of criteria remains understudied in nature, on the one hand, and the real–scenario is not taken into account in that a single/very limited number of measure only are used, on the other hand. These problems affect the ability to make a consistent conclusion. Therefore, the aim of this study is to address this methodological issue by applying support vector machine (SVM)-based CDP algorithm by means of a set of representative performance criterions, with enclosing some novel performance measures, its performance compare with the results gained by statistical and intelligent approaches using six different types of databases from the credit prediction domains. Experimental results show that SVM model is marginally superior to CART with DA, being more robust than its other counterparts. In consequence, this study recommends that the supremacy of a classifier is linked to the way in which evaluations are measured. Journal Article Risk Management 19 2 158 187 Springer Science and Business Media LLC 1460-3799 1743-4637 Credit default prediction, Support vector machine, Performance measures 31 5 2017 2017-05-31 10.1057/s41283-017-0016-x http://dx.doi.org/10.1057/s41283-017-0016-x COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2023-09-19T16:16:35.0772662 2023-08-31T19:03:07.8899874 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Fahmida E. Moula 1 Chi Guotai 2 Abedin Abedin 3
title Credit default prediction modeling: an application of support vector machine
spellingShingle Credit default prediction modeling: an application of support vector machine
Abedin Abedin
title_short Credit default prediction modeling: an application of support vector machine
title_full Credit default prediction modeling: an application of support vector machine
title_fullStr Credit default prediction modeling: an application of support vector machine
title_full_unstemmed Credit default prediction modeling: an application of support vector machine
title_sort Credit default prediction modeling: an application of support vector machine
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Abedin Abedin
author Abedin Abedin
author2 Fahmida E. Moula
Chi Guotai
Abedin Abedin
format Journal article
container_title Risk Management
container_volume 19
container_issue 2
container_start_page 158
publishDate 2017
institution Swansea University
issn 1460-3799
1743-4637
doi_str_mv 10.1057/s41283-017-0016-x
publisher Springer Science and Business Media LLC
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://dx.doi.org/10.1057/s41283-017-0016-x
document_store_str 0
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description Credit default prediction (CDP) modeling is a fundamental and critical issue for financial institutions. However, the previous studies indicate that the classifier’s performances in CDP analysis differ using different performance criterions on different databases under different circumstances. The performance assessment exercise under a set of criteria remains understudied in nature, on the one hand, and the real–scenario is not taken into account in that a single/very limited number of measure only are used, on the other hand. These problems affect the ability to make a consistent conclusion. Therefore, the aim of this study is to address this methodological issue by applying support vector machine (SVM)-based CDP algorithm by means of a set of representative performance criterions, with enclosing some novel performance measures, its performance compare with the results gained by statistical and intelligent approaches using six different types of databases from the credit prediction domains. Experimental results show that SVM model is marginally superior to CART with DA, being more robust than its other counterparts. In consequence, this study recommends that the supremacy of a classifier is linked to the way in which evaluations are measured.
published_date 2017-05-31T16:16:38Z
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score 11.012678

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