Journal article 346 views
A novel framework of credit risk feature selection for SMEs during industry 4.0
Yang Lu,
Lian Yang,
Baofeng Shi 
,
Jiaxiang Li,
Abedin Abedin
,
Jiaxiang Li,
Abedin Abedin
Annals of Operations Research
Swansea University Author: Abedin Abedin
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DOI (Published version): 10.1007/s10479-022-04849-3
Abstract
With the development of industry 4.0, the credit data of SMEs are characterized by a large volume, high speed, diversity and low-value density. How to select the key features that affect the credit risk from the high-dimensional data has become the critical point to accurately measure the credit ris...
| Published in: | Annals of Operations Research |
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| ISSN: | 0254-5330 1572-9338 |
| Published: |
Springer Science and Business Media LLC
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa64230 |
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| Abstract: |
With the development of industry 4.0, the credit data of SMEs are characterized by a large volume, high speed, diversity and low-value density. How to select the key features that affect the credit risk from the high-dimensional data has become the critical point to accurately measure the credit risk of SMEs and alleviate their financing constraints. In doing so, this paper proposes a credit risk feature selection approach that integrates the binary opposite whale optimization algorithm (BOWOA) and the Kolmogorov–Smirnov (KS) statistic. Furthermore, we use seven machine learning classifiers and three discriminant methods to verify the robustness of the proposed model by using three actual bank data from SMEs. The empirical results show that although no one artificial intelligence credit evaluation method is universal for different SMEs’ credit data, the performance of the BOWOA-KS model proposed in this paper is better than other methods if the number of indicators in the optimal subset of indicators and the prediction performance of the classifier are considered simultaneously. By providing a high-dimensional data feature selection method and improving the predictive performance of credit risk, it could help SMEs focus on the factors that will allow them to improve their creditworthiness and more easily access loans from financial institutions. Moreover, it will also help government agencies and policymakers develop policies to help SMEs reduce their credit risks. |
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| Keywords: |
Credit rating, Credit risk, Feature selection, SMEs, Binary opposite whale optimization algorithm, Kolmogorov–Smirnov statistic |
| College: |
Faculty of Humanities and Social Sciences |