Journal article 314 views
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...
Published in: | Journal of Credit Risk |
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ISSN: | 1744-6619 1755-9723 |
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Infopro Digital Services Limited
2018
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URI: | https://cronfa.swan.ac.uk/Record/cronfa64259 |
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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 |
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Journal article |
container_title |
Journal of Credit Risk |
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14 |
container_issue |
2 |
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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 |
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facultyofhumanitiesandsocialsciences |
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Faculty of Humanities and Social Sciences |
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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 |
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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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1777548336435298304 |
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11.017731 |