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

Yang Lu (), Lian Yang (), Baofeng Shi (), Jiaxiang Li () and Mohammad Zoynul Abedin ()
Additional contact information
Yang Lu: Northwest A & F University
Lian Yang: Northwest A & F University
Baofeng Shi: Northwest A & F University
Jiaxiang Li: Northwest A & F University
Mohammad Zoynul Abedin: Teesside University International Business School, Teesside University

Annals of Operations Research, 2025, vol. 350, issue 2, No 4, 425-452

Abstract: 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.

Keywords: Credit rating; Credit risk; Feature selection; SMEs; Binary opposite whale optimization algorithm; Kolmogorov–Smirnov statistic (search for similar items in EconPapers)
JEL-codes: C53 D81 E51 G21 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-022-04849-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:350:y:2025:i:2:d:10.1007_s10479-022-04849-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-022-04849-3

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().