Online supply chain financial risk assessment based on improved random forest

Abstract

This article applies the improved stochastic forest algorithm to online supply chain financial risk assessment and establishes the index system and corresponding model of the online supply chain financial risk assessment based on improved random forest. Data analysis proves the feasibility and accuracy of improved random forest applied to an online financial risk assessment of a supply chain, which provides a new risk assessment method.

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References

  1. Basu P, Nair SK (2012) Supply chain finance enabled early pay: unlocking trapped value in B2B logistics. Int J Logis Syst Manag 12(3):334–353

    Google Scholar 

  2. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  3. Gornall W, Strebulaev IA (2018) Financing as a supply chain: the capital structure of banks and borrowers. J Financ Econ 129:510–530

    Article  Google Scholar 

  4. Guo J, Shi J, Wang Z (2014) Research on the mode evolution and risk management of the online supply chain finance based on the third-party B2B E-commerce platform. J Bus Econ 267(1):13–22

    Google Scholar 

  5. Heng M (2001) Implications of e-commerce for banking and finance. IFIP Advances in Information and Communication Technology 74(6):317–327

    Google Scholar 

  6. Kaplan S, Sawhney M (2000) E-hubs: the new B2B (business-to-business) marketplaces. Harv Bus Rev 78(3):97–103

    Google Scholar 

  7. Kouvelis P, Zhao W (2012) Financing the newsvendor: supplier vs. Bank, and the structure of optimal trade credit contracts. Oper Res 60(3):566–580

    MathSciNet  Article  Google Scholar 

  8. Nejad MG (2016) Challenges and opportunities for innovation in financial services. Int J Bank Mark 34(1):2–8

    Article  Google Scholar 

  9. Shimizu T, Park Y, Hong P (2013) Supply chain risk management and organisational decision making: a case study of a major Japanese automotive firm. Int J Serv Oper Manag 15(3):293–312

    Google Scholar 

  10. Sun WB, Jiang Y, He WZ (2012) Core competitiveness cultivation in small-medium construction enterprises. Appl Mech Mater 174-177:3309–3312

    Article  Google Scholar 

  11. Wu, Y., Li, Y., and Li, P. SMEs' financing decision: Based on the supply chain finance. ICSSSM12. IEEE, 2012

  12. Zhang G, Zhang QJ (2019) Multiportfolio optimization with CVaR risk measure. Journal of Data, Information and Management, pp 1–16

    Google Scholar 

  13. Zhao L, Huo B, Sun L, Zhao X (2013) The impact of supply chain risk on supply chain integration and company performance: a global investigation. Supply Chain Manag 18(2):115–131

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by 2018 Beijing Talents foundation of organization department of Beijing Municipal Committee of the CPC (No.2018000026833ZS09), Science and technology innovation service capacity provincial-ministerial scientific research platform construction social science provincial-ministerial scientific research platform construction project (No.19008020111, No.19002020217).

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Correspondence to Yuxin Shi.

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Zhang, H., Shi, Y. & Tong, J. Online supply chain financial risk assessment based on improved random forest. J. of Data, Inf. and Manag. (2021). https://doi.org/10.1007/s42488-021-00042-6

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Keywords

  • Stochastic forest improvement algorithm
  • Online supply chain finance
  • Risk assessment