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A novel framework of credit risk feature selection for SMEs during industry 4.0

Yang Lu, Lian Yang, Baofeng Shi Orcid Logo, 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...

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Published in: Annals of Operations Research
ISSN: 0254-5330 1572-9338
Published: Springer Science and Business Media LLC
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URI: https://cronfa.swan.ac.uk/Record/cronfa64230
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spelling v2 64230 2023-08-31 A novel framework of credit risk feature selection for SMEs during industry 4.0 4ed8c020eae0c9bec4f5d9495d86d415 Abedin Abedin Abedin Abedin true false 2023-08-31 BAF 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. Journal Article Annals of Operations Research Springer Science and Business Media LLC 0254-5330 1572-9338 Credit rating, Credit risk, Feature selection, SMEs, Binary opposite whale optimization algorithm, Kolmogorov–Smirnov statistic 0 0 0 0001-01-01 10.1007/s10479-022-04849-3 http://dx.doi.org/10.1007/s10479-022-04849-3 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2023-09-25T16:57:53.8978051 2023-08-31T17:32:07.0210875 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yang Lu 1 Lian Yang 2 Baofeng Shi 0000-0003-1244-5886 3 Jiaxiang Li 4 Abedin Abedin 5
title A novel framework of credit risk feature selection for SMEs during industry 4.0
spellingShingle A novel framework of credit risk feature selection for SMEs during industry 4.0
Abedin Abedin
title_short A novel framework of credit risk feature selection for SMEs during industry 4.0
title_full A novel framework of credit risk feature selection for SMEs during industry 4.0
title_fullStr A novel framework of credit risk feature selection for SMEs during industry 4.0
title_full_unstemmed A novel framework of credit risk feature selection for SMEs during industry 4.0
title_sort A novel framework of credit risk feature selection for SMEs during industry 4.0
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Abedin Abedin
author Abedin Abedin
author2 Yang Lu
Lian Yang
Baofeng Shi
Jiaxiang Li
Abedin Abedin
format Journal article
container_title Annals of Operations Research
institution Swansea University
issn 0254-5330
1572-9338
doi_str_mv 10.1007/s10479-022-04849-3
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/10.1007/s10479-022-04849-3
document_store_str 0
active_str 0
description 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.
published_date 0001-01-01T16:57:54Z
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score 11.013171

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