Article Sidebar

Download
PDF
Statistic
Read Counter : 91 Download : 80

Main Article Content

Abstract

This study investigates how AI-driven supply chain creditworthiness assessment transforms commercial banks' credit policies for small and medium-sized enterprises (SMEs), addressing the persistent SME financing gap through technological innovation. Using structural equation modeling, we analyzed data from 360 commercial banking professionals across China to test five hypotheses grounded in information asymmetry theory, relationship lending theory, group lending theory, and supply chain finance theory. SME credit status and core enterprise influence significantly impact bank credit policies (β = 0.285 and β = 0.317, p < 0.001), with AI-enhanced bank cognition serving as a partial mediator (indirect effects: β = 0.167 and β = 0.193, p < 0.001). Critically, AI assessment accuracy moderates these relationships, with higher-accuracy systems demonstrating stronger policy effects (β = 0.124 and β = 0.138, p < 0.001). AI fundamentally transforms SME credit evaluation by enhancing risk assessment accuracy, effectively leveraging supply chain relationships, and augmenting banks' cognitive capabilities. The moderating role of AI precision emphasizes the importance of technological sophistication for maximizing benefits. This research provides empirical evidence that AI-powered supply chain finance offers a viable solution to global SME financing constraints while maintaining robust risk management standards.

Keywords

AI-driven assessment Supply chain finance SME credit Commercial banks Creditworthiness evaluation

Article Details

Author Biographies

Feng Wu, Bansomdejchaopraya Rajabhat University, Bangkok, Thailand

FENG WU is currently pursuing his DBA degree at Bansomdejchaopraya Rajabhat University and his research interest is in financial technology.

Nusanee Meekaewkunchom, Bansomdejchaopraya Rajabhat University, Bangkok, Thailand

Prof. Dr. Nusanee Meekaewkunchom is currently working as a PhD supervisor at Bansomdejchaopraya Rajabhat University and his research interest is in financial technology.

Chaiyawit Muangmee, Bansomdejchaopraya Rajabhat University, Bangkok, Thailand

Asst.Prof. Dr. Chaiyawit Muangmee is currently working as a PhD Supervisor at Bansomdejchaopraya Rajabhat University and his research interest is in the direction of marketing.

Tatchapong Sattabut, Bansomdejchaopraya Rajabhat University, Bangkok, Thailand

Asst.Prof. Dr. Tatchapong Sattabut is currently working as a PhD Supervisor at Bansomdejchaopraya Rajabhat University and his research interest is in the direction of Human Resource Management.

How to Cite
Wu, F., Meekaewkunchom, N., Muangmee, C., & Sattabut, T. (2025). Research on the impact mechanism of AI-driven supply chain creditworthiness assessment on commercial banks’ credit policies for SMEs. Future Technology, 4(3), 45–53. Retrieved from https://fupubco.com/futech/article/view/363
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Bookmark and Share

References

  1. L. Liu, J. Z. Zhang, W. He, and W. Li, "Mitigating information asymmetry in inventory pledge financing through the Internet of things and blockchain," Journal of Enterprise Information Management, vol. 34, no. 5, pp. 1429-1451, 2021. https://doi.org/10.1108/JEIM-12-2020-0510.
  2. S. Chen, D. R. Eyers, J. Gosling, and Y. Huang, "Supply chain risks for SMEs in construction projects: a structured literature review and research agenda," The International Journal of Logistics Management, 2025. https://doi.org/10.1108/IJLM-12-2023-0548.
  3. K. Wang, F. Yan, Y. Zhang, Y. Xiao, and L. Gu, "Supply chain financial risk evaluation of small‐and medium‐sized enterprises under smart city," Journal of advanced transportation, vol. 2020, no. 1, p. 8849356, 2020. https://doi.org/10.1155/2020/8849356.
  4. Y. Zhu, L. Zhou, C. Xie, G.-J. Wang, and T. V. Nguyen, "Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach," International Journal of Production Economics, vol. 211, pp. 22-33, 2019. https://doi.org/10.1016/j.ijpe.2019.01.032.
  5. L. Oldemeyer, A. Jede, and F. Teuteberg, "Investigation of artificial intelligence in SMEs: a systematic review of the state of the art and the main implementation challenges," Management Review Quarterly, pp. 1-43, 2024. https://doi.org/10.1007/s11301-024-00405-4.
  6. S. Barja-Martinez, M. Aragüés-Peñalba, Í. Munné-Collado, P. Lloret-Gallego, E. Bullich-Massagué, and R. Villafafila-Robles, "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, vol. 150, p. 111459, 2021. https://doi.org/10.1016/j.rser.2021.111459.
  7. K. Jiang, X. Chen, J. Li, and M. Zhou, "Technology adoption and extreme stock risk: Evidence from digital tax reform in China," Humanities and Social Sciences Communications, vol. 12, no. 1, pp. 1-20, 2025. https://doi.org/10.1057/s41599-025-04483-x.
  8. N. Drydakis, "Artificial Intelligence and reduced SMEs’ business risks. A dynamic capabilities analysis during the COVID-19 pandemic," Information Systems Frontiers, vol. 24, no. 4, pp. 1223-1247, 2022. https://doi.org/10.1007/s10796-022-10249-6.
  9. F. Francesco and R. Tiziana La, "Does the bank-firm human relationship still matter for SMEs The game-changing role of digitalization," ed: Springer, 2023. https://doi.org/10.1007/s11187-023-00758-2.
  10. N. Madhavika et al., "Navigating economic crisis: Factors shaping resilience in Sri Lankan construction SME supply chains," International Journal of Construction Management, pp. 1-13, 2024. https://doi.org/10.1080/15623599.2024.2415158.
  11. S. Bakhtiari, R. Breunig, L. Magnani, and J. Zhang, "Financial constraints and small and medium enterprises: A review," Economic Record, vol. 96, no. 315, pp. 506-523, 2020. https://doi.org/10.1111/1475-4932.12560.
  12. W. Duan, H. Hu, and Y. Zhang, "What determines the performance of small and medium-sized enterprises supply chain financing? A qualitative comparative analysis of fuzzy sets based on the technology–organization–environment framework," Frontiers in psychology, vol. 13, p. 976218, 2022. https://doi.org/10.3389/fpsyg.2022.976218.
  13. J. Kaur, S. Kumar, and R. Joshi, "Is supply chain finance an antidote to SMEs in the economic crisis?-A qualitative inquiry," The International Journal of Logistics Management, vol. 34, no. 6, pp. 1890-1910, 2023. https://doi.org/10.1108/IJLM-10-2021-0496.
  14. Z. Lou, Q. Xie, J. H. Shen, and C.-C. Lee, "Does supply chain finance (SCF) alleviate funding constraints of SMEs? Evidence from China," Research in International Business and Finance, vol. 67, p. 102157, 2024. https://doi.org/10.1016/j.ribaf.2023.102157.
  15. S. H. Bae, S. Saberi, M. Kouhizadeh, and J. Sarkis, "Examining blockchain's role in supply chain finance structure and governance," International Review of Financial Analysis, p. 103955, 2025. https://doi.org/10.1016/j.irfa.2025.103955.
  16. L. Liu, J. Z. Zhang, W. He, and W. Li, "Mitigating information asymmetry in inventory pledge financing through the Internet of things and blockchain," Journal of Enterprise Information Management, vol. 34, no. 5, pp. 1429-1451, 2021. https://doi.org/10.1108/JEIM-12-2020-0510.
  17. S. Chen, D. R. Eyers, J. Gosling, and Y. Huang, "Supply chain risks for SMEs in construction projects: a structured literature review and research agenda," The International Journal of Logistics Management, 2025. https://doi.org/10.1108/IJLM-12-2023-0548.
  18. K. Wang, F. Yan, Y. Zhang, Y. Xiao, and L. Gu, "Supply chain financial risk evaluation of small‐and medium‐sized enterprises under smart city," Journal of advanced transportation, vol. 2020, no. 1, p. 8849356, 2020. https://doi.org/10.1155/2020/8849356.
  19. Y. Zhu, L. Zhou, C. Xie, G.-J. Wang, and T. V. Nguyen, "Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach," International Journal of Production Economics, vol. 211, pp. 22-33, 2019. https://doi.org/10.1016/j.ijpe.2019.01.032.
  20. L. Oldemeyer, A. Jede, and F. Teuteberg, "Investigation of artificial intelligence in SMEs: a systematic review of the state of the art and the main implementation challenges," Management Review Quarterly, pp. 1-43, 2024. https://doi.org/10.1007/s11301-024-00405-4.
  21. S. Barja-Martinez, M. Aragüés-Peñalba, Í. Munné-Collado, P. Lloret-Gallego, E. Bullich-Massagué, and R. Villafafila-Robles, "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, vol. 150, p. 111459, 2021. https://doi.org/10.1016/j.rser.2021.111459.
  22. K. Jiang, X. Chen, J. Li, and M. Zhou, "Technology adoption and extreme stock risk: Evidence from digital tax reform in China," Humanities and Social Sciences Communications, vol. 12, no. 1, pp. 1-20, 2025. https://doi.org/10.1057/s41599-025-04483-x.
  23. N. Drydakis, "Artificial Intelligence and reduced SMEs’ business risks. A dynamic capabilities analysis during the COVID-19 pandemic," Information Systems Frontiers, vol. 24, no. 4, pp. 1223-1247, 2022. https://doi.org/10.1007/s10796-022-10249-6.
  24. F. Francesco and R. Tiziana La, "Does the bank-firm human relationship still matter for SMEs The game-changing role of digitalization," ed: Springer, 2023. https://doi.org/10.1007/s11187-023-00758-2.
  25. N. Madhavika et al., "Navigating economic crisis: Factors shaping resilience in Sri Lankan construction SME supply chains," International Journal of Construction Management, pp. 1-13, 2024. https://doi.org/10.1080/15623599.2024.2415158.
  26. S. Bakhtiari, R. Breunig, L. Magnani, and J. Zhang, "Financial constraints and small and medium enterprises: A review," Economic Record, vol. 96, no. 315, pp. 506-523, 2020. https://doi.org/10.1111/1475-4932.12560.
  27. W. Duan, H. Hu, and Y. Zhang, "What determines the performance of small and medium-sized enterprises supply chain financing? A qualitative comparative analysis of fuzzy sets based on the technology–organization–environment framework," Frontiers in psychology, vol. 13, p. 976218, 2022. https://doi.org/10.3389/fpsyg.2022.976218.
  28. J. Kaur, S. Kumar, and R. Joshi, "Is supply chain finance an antidote to SMEs in the economic crisis?-A qualitative inquiry," The International Journal of Logistics Management, vol. 34, no. 6, pp. 1890-1910, 2023. https://doi.org/10.1108/IJLM-10-2021-0496.
  29. Z. Lou, Q. Xie, J. H. Shen, and C.-C. Lee, "Does supply chain finance (SCF) alleviate funding constraints of SMEs? Evidence from China," Research in International Business and Finance, vol. 67, p. 102157, 2024. https://doi.org/10.1016/j.ribaf.2023.102157.
  30. S. H. Bae, S. Saberi, M. Kouhizadeh, and J. Sarkis, "Examining blockchain's role in supply chain finance structure and governance," International Review of Financial Analysis, p. 103955, 2025. https://doi.org/10.1016/j.irfa.2025.103955.
  31. Q. Wang, S. Yuan, D. Ostic, and L. Pan, "Supply chain finance and innovation efficiency: An empirical analysis based on manufacturing SMEs," Plos one, vol. 18, no. 7, p. e0286068, 2023. https://doi.org/10.1371/journal.pone.0286068.
  32. H. Sadok, F. Sakka, and M. E. H. El Maknouzi, "Artificial intelligence and bank credit analysis: A review," Cogent Economics & Finance, vol. 10, no. 1, p. 2023262, 2022. https://doi.org/10.1080/23322039.2021.2023262.
  33. Y. Xia, T. Xu, M.-X. Wei, Z.-K. Wei, and L.-J. Tang, "Predicting chain’s manufacturing SME credit risk in supply chain finance based on machine learning methods," Sustainability, vol. 15, no. 2, p. 1087, 2023. https://doi.org/10.3390/su15021087.
  34. C. Yin, C. Jiang, H. K. Jain, and Z. Wang, "Evaluating the credit risk of SMEs using legal judgments," Decision Support Systems, vol. 136, p. 113364, 2020. https://doi.org/10.1016/j.dss.2020.113364.
  35. P. E. De Lange, B. Melsom, C. B. Vennerød, and S. Westgaard, "Explainable AI for credit assessment in banks," Journal of Risk and Financial Management, vol. 15, no. 12, p. 556, 2022. https://doi.org/10.3390/jrfm15120556.
  36. M. El Khair Ghoujdam, R. Chaabita, O. Elkhalfi, K. Zehraoui, H. Elalaoui, and S. Idamia, "Consumer credit risk analysis through artificial intelligence: a comparative study between the classical approach of logistic regression and advanced machine learning techniques," Cogent Economics & Finance, vol. 12, no. 1, p. 2414926, 2024. https://doi.org/10.1080/23322039.2024.2414926.
  37. A. Belhadi, S. S. Kamble, V. Mani, I. Benkhati, and F. E. Touriki, "An ensemble machine learning approach for forecasting credit risk of agricultural SMEs’ investments in agriculture 4.0 through supply chain finance," Annals of Operations Research, vol. 345, no. 2, pp. 779-807, 2025. https://doi.org/10.1007/s10479-021-04366-9.