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Research on measurement of supply chain finance credit risk based on Internet of Things

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  • Waseem Ahmed Abbasi
  • Zongrun Wang
  • Yanju Zhou
  • Shahzad Hassan

Abstract

This article first expounds the concept of supply chain finance and its credit risk, describes the hierarchical structure of the Internet of Things and its key technologies, and combines the unique functions of the Internet of Things technology and the business process of the inventory pledge financing model to design the supply chain financial model based on the Internet of Things. Then it studies the credit risk assessment under the supply chain financial model based on the Internet of Things, and uses the support vector machine algorithm and Logistic regression method to establish a credit risk measurement model considering the subject rating and debt rating. Finally, an example analysis shows that the credit risk measurement model has a high accuracy rate for determining whether small and medium-sized enterprises in the supply chain financial model based on the Internet of Things are trustworthy. This will facilitate the revision and improvement of the existing credit evaluation system and improve the accuracy of measuring the current financial risk of supply chain. This research adopts the Internet of Things to measure financial credit risk in supply chain and provides a reference for the following researches.

Suggested Citation

  • Waseem Ahmed Abbasi & Zongrun Wang & Yanju Zhou & Shahzad Hassan, 2019. "Research on measurement of supply chain finance credit risk based on Internet of Things," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:9:p:1550147719874002
    DOI: 10.1177/1550147719874002
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    References listed on IDEAS

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    1. Berger, Allen N. & Udell, Gregory F., 2006. "A more complete conceptual framework for SME finance," Journal of Banking & Finance, Elsevier, vol. 30(11), pages 2945-2966, November.
    2. Gour Chandra Mahata, 2012. "Analysis of partial trade credit financing in a supply chain by EOQ-based inventory model for exponentially deteriorating items," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 15(1), pages 94-124.
    3. Guillen, Gonzalo & Badell, Mariana & Puigjaner, Luis, 2007. "A holistic framework for short-term supply chain management integrating production and corporate financial planning," International Journal of Production Economics, Elsevier, vol. 106(1), pages 288-306, March.
    4. Brkic, Sabina & Hodzic, Migdat & Dzanic, Enis, 2017. "Fuzzy Logic Model of Soft Data Analysis for Corporate Client Credit Risk Assessment in Commercial Banking," MPRA Paper 83028, University Library of Munich, Germany, revised Nov 2017.
    5. Heckmann, Iris & Comes, Tina & Nickel, Stefan, 2015. "A critical review on supply chain risk – Definition, measure and modeling," Omega, Elsevier, vol. 52(C), pages 119-132.
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    Cited by:

    1. Zhang, Yuanyuan & Zhao, Huiru & Li, Bingkang & Zhao, Yihang & Qi, Ze, 2022. "Research on credit rating and risk measurement of electricity retailers based on Bayesian Best Worst Method-Cloud Model and improved Credit Metrics model in China's power market," Energy, Elsevier, vol. 252(C).
    2. Soni, Gunjan & Kumar, Satish & Mahto, Raj V. & Mangla, Sachin K. & Mittal, M.L. & Lim, Weng Marc, 2022. "A decision-making framework for Industry 4.0 technology implementation: The case of FinTech and sustainable supply chain finance for SMEs," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    3. Dhirendra Prajapati & Felix T. S. Chan & H. Chelladurai & Lakshay Lakshay & Saurabh Pratap, 2022. "An Internet of Things Embedded Sustainable Supply Chain Management of B2B E-Commerce," Sustainability, MDPI, vol. 14(9), pages 1-14, April.

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