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Credit default prediction using a support vector machine and a probabilistic neural network

Abedin Abedin, Chi Guotai, Sisira Colombage, Fahmida–E Moula

Journal of Credit Risk, Volume: 14, Issue: 2, Pages: 1 - 27

Swansea University Author: Abedin Abedin

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DOI (Published version): 10.21314/jcr.2017.233

Abstract

The design of consistent classifiers to forecast credit-granting choices is critical for many financial decision-making practices. Although a number of artificial and statistical techniques have been developed to predict customer insolvency, how to provide an inclusive appraisal of prediction models...

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Published in: Journal of Credit Risk
ISSN: 1744-6619 1755-9723
Published: Infopro Digital Services Limited 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa64259
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first_indexed 2023-09-19T09:27:37Z
last_indexed 2023-09-19T09:27:37Z
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spelling v2 64259 2023-08-31 Credit default prediction using a support vector machine and a probabilistic neural network 4ed8c020eae0c9bec4f5d9495d86d415 Abedin Abedin Abedin Abedin true false 2023-08-31 BAF The design of consistent classifiers to forecast credit-granting choices is critical for many financial decision-making practices. Although a number of artificial and statistical techniques have been developed to predict customer insolvency, how to provide an inclusive appraisal of prediction models and recommend adequate classifiers is still an imperative and understudied area in credit default prediction (CDP) modeling. Previous evidence demonstrates that the ranking of classifiers varies for different criteria with measures under different circumstances. In this study, we address this methodological flaw by proposing the simultaneous application of support vector machine and probabilistic neural network (PNN)-based CDP algorithms, together with frequently used high-performance models. We fill the gap by introducing a set of multidimensional evaluation measures combined with some novel metrics that are helpful in discovering unseen features of the model’s performance. For effectiveness and feasibility purposes, six real-world credit data sets have been applied. Our empirical study shows that the PNN model is more robust than its rivals, and traditional performance evaluations are more or less consistent with their original counterparts. With these contributions, therefore, our investigations offer several advantages to practitioners of financial risk management. © Infopro Digital Limited. All rights reserved. Journal Article Journal of Credit Risk 14 2 1 27 Infopro Digital Services Limited 1744-6619 1755-9723 Credit default prediction, CDP, probabilistic neural network, PNN 30 6 2018 2018-06-30 10.21314/jcr.2017.233 http://dx.doi.org/10.21314/jcr.2017.233 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2023-09-20T10:29:57.3706003 2023-08-31T17:58:05.4014276 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Abedin Abedin 1 Chi Guotai 2 Sisira Colombage 3 Fahmida–E Moula 4
title Credit default prediction using a support vector machine and a probabilistic neural network
spellingShingle Credit default prediction using a support vector machine and a probabilistic neural network
Abedin Abedin
title_short Credit default prediction using a support vector machine and a probabilistic neural network
title_full Credit default prediction using a support vector machine and a probabilistic neural network
title_fullStr Credit default prediction using a support vector machine and a probabilistic neural network
title_full_unstemmed Credit default prediction using a support vector machine and a probabilistic neural network
title_sort Credit default prediction using a support vector machine and a probabilistic neural network
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Abedin Abedin
author Abedin Abedin
author2 Abedin Abedin
Chi Guotai
Sisira Colombage
Fahmida–E Moula
format Journal article
container_title Journal of Credit Risk
container_volume 14
container_issue 2
container_start_page 1
publishDate 2018
institution Swansea University
issn 1744-6619
1755-9723
doi_str_mv 10.21314/jcr.2017.233
publisher Infopro Digital Services Limited
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/10.21314/jcr.2017.233
document_store_str 0
active_str 0
description The design of consistent classifiers to forecast credit-granting choices is critical for many financial decision-making practices. Although a number of artificial and statistical techniques have been developed to predict customer insolvency, how to provide an inclusive appraisal of prediction models and recommend adequate classifiers is still an imperative and understudied area in credit default prediction (CDP) modeling. Previous evidence demonstrates that the ranking of classifiers varies for different criteria with measures under different circumstances. In this study, we address this methodological flaw by proposing the simultaneous application of support vector machine and probabilistic neural network (PNN)-based CDP algorithms, together with frequently used high-performance models. We fill the gap by introducing a set of multidimensional evaluation measures combined with some novel metrics that are helpful in discovering unseen features of the model’s performance. For effectiveness and feasibility purposes, six real-world credit data sets have been applied. Our empirical study shows that the PNN model is more robust than its rivals, and traditional performance evaluations are more or less consistent with their original counterparts. With these contributions, therefore, our investigations offer several advantages to practitioners of financial risk management. © Infopro Digital Limited. All rights reserved.
published_date 2018-06-30T10:29:54Z
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score 11.012678

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