IDEAS home Printed from https://ideas.repec.org/a/spr/fininn/v1y2015i1d10.1186_s40854-015-0014-5.html
   My bibliography  Save this article

A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance

Author

Listed:
  • Lang Zhang

    (School of Economics and Administration, Xi’an University of Technology)

  • Haiqing Hu

    (School of Economics and Administration, Xi’an University of Technology)

  • Dan Zhang

    (School of Economics and Administration, Xi’an University of Technology)

Abstract

Background Supply chain finance (SCF) is a series of financial solutions provided by financial institutions to suppliers and customers facing demands on their working capital. As a systematic arrangement, SCF utilizes the authenticity of the trade between (SMEs) and their “counterparties”, which are usually the leading enterprises in their supply chains. Because in these arrangements the leading enterprises are the guarantors for the SMEs, the credit levels of such counterparties are becoming important factors of concern to financial institutions’ risk management (i.e., commercial banks offering SCF services). Thus, these institutions need to assess the credit risks of the SMEs from a view of the supply chain, rather than only assessing an SME’s repayment ability. The aim of this paper is to research credit risk assessment models for SCF. Methods We establish an index system for credit risk assessment, adopting a view of the supply chain that considers the leading enterprise’s credit status and the relationships developed in the supply chain. Furthermore, We conducted two credit risk assessment models based on support vector machine (SVM) technique and BP neural network respectly. Results (1) The SCF credit risk assessment index system designed in this paper, which contained supply chain leading enterprise’s credit status and cooperative relationships between SMEs and leading enterprises, can help banks to raise their accuracy on predicting a small and medium enterprise whether default or not. Therefore, more SMEs can obtain loans from banks through SCF. (2) The SCF credit risk assessment model based on SVM is of good generalization ability and robustness, which is more effective than BP neural network assessment model. Hence, Banks can raise the accuracy of credit risk assessment on SMEs by applying the SVM model, which can alleviate credit rationing on SMEs. Conclusions (1)The SCF credit risk assessment index system can solve the problem of banks incorrectly labeling a creditworthy enterprise as a default enterprise, and thereby improve the credit rating status in the process of SME financing. (2)By analyzing and comparing the empirical results, we find that the SVM assessment model, on evaluating the SME credit risk, is more effective than the BP neural network assessment model. This new assessment model based on SVM can raise the accuracy of classification between good credit and bad credit SMEs. (3)Therefore, the SCF credit risk assessment index system and the assessment model based on SVM, is the optimal combination for commercial banks to use to evaluate SMEs’ credit risk.

Suggested Citation

  • Lang Zhang & Haiqing Hu & Dan Zhang, 2015. "A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-21, December.
  • Handle: RePEc:spr:fininn:v:1:y:2015:i:1:d:10.1186_s40854-015-0014-5
    DOI: 10.1186/s40854-015-0014-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40854-015-0014-5
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1186/s40854-015-0014-5?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
    ---><---

    References listed on IDEAS

    as
    1. Altman, Edward I., 1980. "Commercial Bank Lending: Process, Credit Scoring, and Costs of Errors in Lending," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(4), pages 813-832, November.
    2. Feldmann, Martin & Müller, Stephanie, 2003. "An incentive scheme for true information providing in Supply Chains," Omega, Elsevier, vol. 31(2), pages 63-73, April.
    3. Gupta, Sushil & Dutta, Kaushik, 2011. "Modeling of financial supply chain," European Journal of Operational Research, Elsevier, vol. 211(1), pages 47-56, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dariush Akbarian, 2021. "Network DEA based on DEA-ratio," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-26, December.
    2. Mao, Jinzhou & Yang, Siying, 2024. "Changes in supply chain relationships and the enterprise internationalization process," Research in International Business and Finance, Elsevier, vol. 67(PB).
    3. Zhang, Lu & Cui, Li & Chen, Lujie & Dai, Jing & Jin, Ziyi & Wu, Hao, 2023. "A hybrid approach to explore the critical criteria of online supply chain finance to improve supply chain performance," International Journal of Production Economics, Elsevier, vol. 255(C).
    4. Crovini, Chiara & Ossola, Giovanni & Britzelmaier, Bernd, 2021. "How to reconsider risk management in SMEs? An Advanced, Reasoned and Organised Literature Review," European Management Journal, Elsevier, vol. 39(1), pages 118-134.
    5. Lorenzo Reus & Guillermo Alexander Sepúlveda-Hurtado, 2023. "Foreign exchange trading and management with the stochastic dual dynamic programming method," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-38, December.
    6. Karim Amzile & Mohamed Habachi, 2022. "Assessment of Support Vector Machine performance for default prediction and credit rating," Post-Print halshs-03643738, HAL.
    7. Ratri Parida & Manoj Kumar Dash & Anil Kumar & Edmundas Kazimieras Zavadskas & Sunil Luthra & Eyob Mulat‐weldemeskel, 2022. "Evolution of supply chain finance: A comprehensive review and proposed research directions with network clustering analysis," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(5), pages 1343-1369, October.
    8. Kong, Lingxuan & Zheng, Ge & Brintrup, Alexandra, 2024. "A federated machine learning approach for order-level risk prediction in Supply Chain Financing," International Journal of Production Economics, Elsevier, vol. 268(C).
    9. Bastien Lextrait, 2021. "Scaling up SME's credit scoring scope with LightGBM," EconomiX Working Papers 2021-25, University of Paris Nanterre, EconomiX.
    10. Parimal Kumar Giri & Sagar S. De & Sachidananda Dehuri & Sung‐Bae Cho, 2021. "Biogeography based optimization for mining rules to assess credit risk," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 35-51, January.
    11. Xin Liu & Bangxin Zhao & Wenqing He, 2020. "Simultaneous Feature Selection and Classification for Data-Adaptive Kernel-Penalized SVM," Mathematics, MDPI, vol. 8(10), pages 1-22, October.
    12. Chih-Hung Hsu & Ru-Yue Yu & An-Yuan Chang & Wan-Ling Liu & An-Ching Sun, 2022. "Applying Integrated QFD-MCDM Approach to Strengthen Supply Chain Agility for Mitigating Sustainable Risks," Mathematics, MDPI, vol. 10(4), pages 1-41, February.
    13. Lu Xiang & Renyong Hou, 2023. "Research on Innovation Management of Enterprise Supply Chain Digital Platform Based on Blockchain Technology," Sustainability, MDPI, vol. 15(13), pages 1-13, June.
    14. 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.
    15. Paulo Cesar Schotten & Leydiana Sousa Pereira & Danielle Costa Morais, 2022. "Credit granting sorting model for financial organizations," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.
    16. Ying Chen & Krystel K. Castillo-Villar & Bing Dong, 2021. "Stochastic control of a micro-grid using battery energy storage in solar-powered buildings," Annals of Operations Research, Springer, vol. 303(1), pages 197-216, August.
    17. Yubin Yang & Xuejian Chu & Ruiqi Pang & Feng Liu & Peifang Yang, 2021. "Identifying and Predicting the Credit Risk of Small and Medium-Sized Enterprises in Sustainable Supply Chain Finance: Evidence from China," Sustainability, MDPI, vol. 13(10), pages 1-19, May.

    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. Mou, W.M. & Wong, W.-K. & McAleer, M.J., 2018. "Financial Credit Risk and Core Enterprise Supply Chains," Econometric Institute Research Papers EI2018-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. WeiMing Mou & Wing-Keung Wong & Michael McAleer, 2018. "Financial Credit Risk Evaluation Based on Core Enterprise Supply Chains," Sustainability, MDPI, vol. 10(10), pages 1-17, October.
    3. Seock-Jin Hong & Hossein Najmi, 2020. "The Relationships between Supply Chain Capability and Shareholder Value Using Financial Performance Indicators," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
    4. Wang, Deshen & Chen, Bintong & Chen, Jing, 2019. "Credit card fraud detection strategies with consumer incentives," Omega, Elsevier, vol. 88(C), pages 179-195.
    5. Roy Cerqueti & Francesca Pampurini & Annagiulia Pezzola & Anna Grazia Quaranta, 2022. "Dangerous liasons and hot customers for banks," Review of Quantitative Finance and Accounting, Springer, vol. 59(1), pages 65-89, July.
    6. Faranak Emtehani & Nasim Nahavandi & Farimah Mokhatab Rafiei, 2021. "A joint inventory–finance model for coordinating a capital-constrained supply chain with financing limitations," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-39, December.
    7. Changjoon Lee & Byoung-Chun Ha, 2021. "Interactional Justice, Informational Quality, and Sustainable Supply Chain Management: A Comparison of Domestic and Multinational Pharmaceutical Companies," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    8. Zahid Hussain & Ahmad Bin Jusoh & Muhammad Sarfraz & Khalil Ur Rehman Wahla, 2018. "Uncovering the Relationship of Supply Chain Management and Firm Performance: Evidence from Textile Sector of Pakistan," Information Management and Business Review, AMH International, vol. 10(2), pages 23-29.
    9. Ivakina, A. & Zenkevich, N., 2018. "Working capital optimization under liquidity constraints in collaborative supply chains," Working Papers 15110, Graduate School of Management, St. Petersburg State University.
    10. Wu, Jianghua & Zhai, Xin & Huang, Zhimin, 2008. "Incentives for information sharing in duopoly with capacity constraints," Omega, Elsevier, vol. 36(6), pages 963-975, December.
    11. Wuttke, David A. & Blome, Constantin & Sebastian Heese, H. & Protopappa-Sieke, Margarita, 2016. "Supply chain finance: Optimal introduction and adoption decisions," International Journal of Production Economics, Elsevier, vol. 178(C), pages 72-81.
    12. Rodgers, Waymond, 1999. "The influences of conflicting information on novices and loan officers' actions," Journal of Economic Psychology, Elsevier, vol. 20(2), pages 123-145, April.
    13. Roberto Chavez, 2014. "Customer integration, information quality and operational performance: A social capital view," Working Papers 57, Facultad de Economía y Empresa, Universidad Diego Portales.
    14. Gabriela Kuvikova, 2015. "Does Loan Maturity Matter in Risk-Based Pricing? Evidence from Consumer Loan Data," CERGE-EI Working Papers wp538, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    15. Hahn, G.J. & Brandenburg, M. & Becker, J., 2021. "Valuing supply chain performance within and across manufacturing industries: A DEA-based approach," International Journal of Production Economics, Elsevier, vol. 240(C).
    16. Zedda, Stefano & Modina, Michele & Gallucci, Carmen, 2024. "Cooperative credit banks and sustainability: Towards a social credit scoring," Research in International Business and Finance, Elsevier, vol. 68(C).
    17. Bitetto, Alessandro & Cerchiello, Paola & Filomeni, Stefano & Tanda, Alessandra & Tarantino, Barbara, 2023. "Machine learning and credit risk: Empirical evidence from small- and mid-sized businesses," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
    18. Cheng, Yuxiang & Wen, Fenghua & Wang, Yiming & Olson, David L., 2023. "Who should finance the supply chain? Impact of accounts receivable mortgage on supply chain decision," International Journal of Production Economics, Elsevier, vol. 261(C).
    19. K. T. Shibin & Rameshwar Dubey & Angappa Gunasekaran & Benjamin Hazen & David Roubaud & Shivam Gupta & Cyril Foropon, 2020. "Examining sustainable supply chain management of SMEs using resource based view and institutional theory," Annals of Operations Research, Springer, vol. 290(1), pages 301-326, July.
    20. Banerjee, Mohua & Mishra, Manit, 2017. "Retail supply chain management practices in India: A business intelligence perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 34(C), pages 248-259.

    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:fininn:v:1:y:2015:i:1:d:10.1186_s40854-015-0014-5. 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.