Journal article 431 views
Credit default prediction using a support vector machine and a probabilistic neural network
Mohammad Abedin 
,
Chi Guotai,
Sisira Colombage,
Fahmida–E Moula
,
Chi Guotai,
Sisira Colombage,
Fahmida–E Moula
Journal of Credit Risk, Volume: 14, Issue: 2, Pages: 1 - 27
Swansea University Author:
Mohammad Abedin
Full text not available from this repository: check for access using links below.
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 |
|---|---|
| ISSN: | 1744-6619 1755-9723 |
| Published: |
Infopro Digital Services Limited
2018
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa64259 |
| 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 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. |
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| Keywords: |
Credit default prediction, CDP, probabilistic neural network, PNN |
| College: |
Faculty of Humanities and Social Sciences |
| Issue: |
2 |
| Start Page: |
1 |
| End Page: |
27 |