IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v350y2025i2d10.1007_s10479-024-06054-w.html
   My bibliography  Save this article

A big data analytics method for assessing creditworthiness of SMEs: fuzzy equifinality relationships analysis

Author

Listed:
  • Baofeng Shi

    (Northwest A&F University)

  • Chunguang Bai

    (University of Electronic Science and Technology of China)

  • Yizhe Dong

    (University of Edinburgh)

Abstract

Nowadays, many financial institutions are beginning to use Big Data Analytics (BDA) to help them make better credit underwriting decisions, especially for small and medium-sized enterprises (SMEs) with limited financial histories and other information. The various complexities and the equifinality problem of Big Data make it difficult to apply traditional statistical techniques to creditworthiness evaluation, or credit scoring. In this study, we extend the existing research in the field of creditworthiness assessment and propose a novel approach based on neighborhood rough sets (NRSs), to evaluate and investigate the complexities and fuzzy equifinality relationships in the presence of Big Data. We utilize a real SME loan dataset from a Chinese commercial bank to generate interval number rules that provide insight into the fuzzy equifinality relationships between borrowers’ demographic information, company financial ratios, loan characteristics, other non-financial information, local macroeconomic indicators and rated creditworthiness level. In addition, the interval number rules are used to predict creditworthiness levels based on test data and the accuracy of the prediction is found to be 75.44%. One of the major advantages of using the proposed BDA approach is that it helps us to reduce complexity and identify equivalence relationships when using Big Data to assess the creditworthiness of SMEs. This study also provides important implications for practices in financial institutions and SMEs.

Suggested Citation

  • Baofeng Shi & Chunguang Bai & Yizhe Dong, 2025. "A big data analytics method for assessing creditworthiness of SMEs: fuzzy equifinality relationships analysis," Annals of Operations Research, Springer, vol. 350(2), pages 879-909, July.
    • Handle: RePEc:spr:annopr:v:350:y:2025:i:2:d:10.1007_s10479-024-06054-w
    DOI: 10.1007/s10479-024-06054-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-024-06054-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-024-06054-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. R. Rajesh, 2022. "Sustainability performance predictions in supply chains: grey and rough set theoretical approaches," Annals of Operations Research, Springer, vol. 310(1), pages 171-200, March.
    2. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
    3. Mar Molinero, C. & Apellaniz Gomez, P. & Serrano Cinca, C., 1996. "A multivariate study of spanish bond ratings," Omega, Elsevier, vol. 24(4), pages 451-462, August.
    4. Stevenson, Matthew & Mues, Christophe & Bravo, Cristián, 2021. "The value of text for small business default prediction: A Deep Learning approach," European Journal of Operational Research, Elsevier, vol. 295(2), pages 758-771.
    5. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2017. "Enhancing two-stage modelling methodology for loss given default with support vector machines," European Journal of Operational Research, Elsevier, vol. 263(2), pages 679-689.
    6. Medina-Olivares, Victor & Calabrese, Raffaella & Dong, Yizhe & Shi, Baofeng, 2022. "Spatial dependence in microfinance credit default," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1071-1085.
    7. Fernandes, Guilherme Barreto & Artes, Rinaldo, 2016. "Spatial dependence in credit risk and its improvement in credit scoring," European Journal of Operational Research, Elsevier, vol. 249(2), pages 517-524.
    8. Wilson X. B. Li & Tina He, 2023. "Banking structure and government policies regarding SMEs financing," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 21(3), pages 387-402, July.
    9. Chiara Pederzoli & Grid Thoma & Costanza Torricelli, 2013. "Modelling Credit Risk for Innovative SMEs: the Role of Innovation Measures," Journal of Financial Services Research, Springer;Western Finance Association, vol. 44(1), pages 111-129, August.
    10. Shi, Baofeng & Chi, Guotai & Li, Weiping, 2020. "Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach," Economic Modelling, Elsevier, vol. 85(C), pages 420-428.
    11. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    12. Zhan, Yuanzhu & Tan, Kim Hua, 2020. "An analytic infrastructure for harvesting big data to enhance supply chain performance," European Journal of Operational Research, Elsevier, vol. 281(3), pages 559-574.
    13. Yi Cao & Jia Zhai, 2022. "A survey of AI in finance," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 20(2), pages 125-137, April.
    14. Dahms, Sven, 2019. "Foreign-owned subsidiary knowledge sourcing: The role of location and expatriates," Journal of Business Research, Elsevier, vol. 105(C), pages 178-188.
    15. Bai, Chunguang & Shi, Baofeng & Liu, Feng & Sarkis, Joseph, 2019. "Banking credit worthiness: Evaluating the complex relationships," Omega, Elsevier, vol. 83(C), pages 26-38.
    16. Nana Chai & Bi Wu & Weiwei Yang & Baofeng Shi, 2019. "A Multicriteria Approach for Modeling Small Enterprise Credit Rating: Evidence from China," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(11), pages 2523-2543, September.
    17. Wamba, Samuel Fosso & Gunasekaran, Angappa & Akter, Shahriar & Ren, Steven Ji-fan & Dubey, Rameshwar & Childe, Stephen J., 2017. "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 356-365.
    18. Antonio D'Amato & Emiliano Mastrolia, 2022. "Linear discriminant analysis and logistic regression for default probability prediction: the case of an Italian local bank," International Journal of Managerial and Financial Accounting, Inderscience Enterprises Ltd, vol. 14(4), pages 323-343.
    19. Sun, Yue & Chai, Nana & Dong, Yizhe & Shi, Baofeng, 2022. "Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1158-1172.
    20. Glennon, Dennis & Nigro, Peter, 2005. "Measuring the Default Risk of Small Business Loans: A Survival Analysis Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(5), pages 923-947, October.
    21. Chaitanya Baru, 2018. "How to deliver translational data-science benefits to science and society," Nature, Nature, vol. 561(7724), pages 464-464, September.
    22. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    23. Altman, Edward I., 1998. "The importance and subtlety of credit rating migration," Journal of Banking & Finance, Elsevier, vol. 22(10-11), pages 1231-1247, October.
    24. Chunguang Bai & Simonov Kusi-Sarpong & Sharfuddin Ahmed Khan & Diego Vazquez-Brust, 2021. "Sustainable buyer–supplier relationship capability development: a relational framework and visualization methodology," Annals of Operations Research, Springer, vol. 304(1), pages 1-34, September.
    25. Samuel Fosso Wamba & Angappa Gunasekaran & Rameshwar Dubey & Eric W. T. Ngai, 2018. "Big data analytics in operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 1-4, November.
    26. Girish Santosh Bagale & Venkata Ramana Vandadi & Deepmala Singh & Dilip Kumar Sharma & Durga Venkata Kusuma Garlapati & Ravi Kumar Bommisetti & Ravi Kumar Gupta & Roy Setsiawan & V. Subramaniyaswamy &, 2023. "RETRACTED ARTICLE: Small and medium-sized enterprises' contribution in digital technology," Annals of Operations Research, Springer, vol. 326(1), pages 3-4, July.
    27. Angilella, Silvia & Mazzù, Sebastiano, 2015. "The financing of innovative SMEs: A multicriteria credit rating model," European Journal of Operational Research, Elsevier, vol. 244(2), pages 540-554.
    28. Bai, Chunguang & Dhavale, Dileep & Sarkis, Joseph, 2016. "Complex investment decisions using rough set and fuzzy c-means: An example of investment in green supply chains," European Journal of Operational Research, Elsevier, vol. 248(2), pages 507-521.
    29. Ortiz de Guinea, Ana & Raymond, Louis, 2020. "Enabling innovation in the face of uncertainty through IT ambidexterity: A fuzzy set qualitative comparative analysis of industrial service SMEs," International Journal of Information Management, Elsevier, vol. 50(C), pages 244-260.
    30. Ciampi, Francesco, 2015. "Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms," Journal of Business Research, Elsevier, vol. 68(5), pages 1012-1025.
    31. Alireza Hooman & Govindan Marthandan & Wan Fadzilah Wan Yusoff & Mohana Omid & Sasan Karamizadeh, 2016. "Statistical and data mining methods in credit scoring," Journal of Developing Areas, Tennessee State University, College of Business, vol. 50(5), pages 371-381, Special I.
    32. Raffaella Calabrese & Galina Andreeva & Jake Ansell, 2019. "“Birds of a Feather” Fail Together: Exploring the Nature of Dependency in SME Defaults," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 71-84, January.
    33. Tom Caneghem & Geert Campenhout, 2012. "Quantity and quality of information and SME financial structure," Small Business Economics, Springer, vol. 39(2), pages 341-358, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sun, Yue & Chai, Nana & Dong, Yizhe & Shi, Baofeng, 2022. "Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1158-1172.
    2. Nana Chai & Mohammad Zoynul Abedin & Xiaoling Wang & Baofeng Shi, 2025. "Growth potential of machine learning in credit risk predicting of farmers in the industry 4.0 era," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(3), pages 2163-2185, July.
    3. Nana Chai & Baofeng Shi & Bin Meng & Yizhe Dong, 2023. "Default Feature Selection in Credit Risk Modeling: Evidence From Chinese Small Enterprises," SAGE Open, , vol. 13(2), pages 21582440231, April.
    4. Francesco Ciampi & Alessandro Giannozzi & Giacomo Marzi & Edward I. Altman, 2021. "Rethinking SME default prediction: a systematic literature review and future perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2141-2188, March.
    5. Medina-Olivares, Victor & Calabrese, Raffaella & Dong, Yizhe & Shi, Baofeng, 2022. "Spatial dependence in microfinance credit default," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1071-1085.
    6. Jingjing Long & Cuiqing Jiang & Stanko Dimitrov & Zhao Wang, 2022. "Clues from networks: quantifying relational risk for credit risk evaluation of SMEs," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-41, December.
    7. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    8. Jiang, Cuiqing & Yin, Chang & Tang, Qian & Wang, Zhao, 2023. "The value of official website information in the credit risk evaluation of SMEs," Journal of Business Research, Elsevier, vol. 169(C).
    9. Huaming Du & Xingyan Chen & Yu Zhao & Qing Li & Fuzhen Zhuang & Fuji Ren & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised Mar 2025.
    10. Yang Lu & Lian Yang & Baofeng Shi & Jiaxiang Li & Mohammad Zoynul Abedin, 2025. "A novel framework of credit risk feature selection for SMEs during industry 4.0," Annals of Operations Research, Springer, vol. 350(2), pages 425-452, July.
    11. Wang, Weiqing & Chen, Yuxi & Wang, Liukai & Xiong, Yu, 2025. "Developing the value of legal judgments of supply chain finance for credit risk prediction through novel ACWGAN-GPSA approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 196(C).
    12. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    13. Zhang, Wen & Yan, Shaoshan & Li, Jian & Tian, Xin & Yoshida, Taketoshi, 2022. "Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    14. Li, Zhe & Liang, Shuguang & Pan, Xianyou & Pang, Meng, 2024. "Credit risk prediction based on loan profit: Evidence from Chinese SMEs," Research in International Business and Finance, Elsevier, vol. 67(PA).
    15. Maarouf, Abdurahman & Feuerriegel, Stefan & Pröllochs, Nicolas, 2025. "A fused large language model for predicting startup success," European Journal of Operational Research, Elsevier, vol. 322(1), pages 198-214.
    16. Chai, Nana & Shi, Baofeng & Hua, Yiting, 2023. "Loss given default or default status: Which is better to determine farmers’ credit ratings?," Finance Research Letters, Elsevier, vol. 53(C).
    17. Chunguang Bai & Kannan Govindan & Dileep Dhavale, 2024. "Optimal selection and investment-allocation decisions for sustainable supplier development practices," Annals of Operations Research, Springer, vol. 335(1), pages 1-31, April.
    18. Mohammad S. Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib, 2022. "Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3713-3729, July.
    19. Shi, Baofeng & Chi, Guotai & Li, Weiping, 2020. "Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach," Economic Modelling, Elsevier, vol. 85(C), pages 420-428.
    20. Chai, Nana & Abedin, Mohammad Zoynul & Yang, Lian & Shi, Baofeng, 2025. "Farmers' credit risk evaluation with an explainable hybrid ensemble approach: A closer look in microfinance," Pacific-Basin Finance Journal, Elsevier, vol. 89(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:350:y:2025:i:2:d:10.1007_s10479-024-06054-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.