Supply chain finance model and credit risk analysis of E-commerce platform ——Taking JingDong as an example
Yutong Jiang
Jilin University of Finance and Economics
DOI: https://doi.org/10.59429/bam.v7i2.10521
Keywords: Supply chain finance; E-commerce platform; Credit risk analysis; Complex network analysis; Deep reinforcement learning (DRL)
Abstract
In recent years, digital economy and real economy have been deeply integrated, e-commerce platform has been transformed into an ecological hub of supply chain resource integration, and supply chain finance has highlighted its value in solving the financing problems of small and medium-sized enterprises. As a leading e-commerce and technology enterprise in China, Jingdong has built a full-chain financial service system by virtue of its data and technological advantages, becoming a model of industry innovation. However, in the case of rapid business expansion and complex supply chain, the dynamic and concealment of credit risk put forward higher requirements for the risk control ability of platforms. This paper takes Jingdong supply chain Finance as the research object, uses the method of case analysis and quantitative modeling to systematically deconstruct its business model and risk management mechanism, aiming to provide replicable experience and risk prevention enlightenment for the industry, and help improve the financial infrastructure in the era of digital economy.
References
[1]Cen, Z., Cai, X., & Xiao, J. (2017). Credit Spillover Effect in Supply Chain Finance. Journal of Financial Research, 42(3), 125-140.
[2]Fuster, A., Plosser, M., & Schnabl, P. (2022). Digital Data and Credit Risk Prediction. Review of Financial Studies, 35(6), 2876-2915.
[3]Cong, L., He, W., & Li, Y. (2021). Smart Contracts and Operational Risk in Supply Chain Finance. Journal of Management Science, 28(4), 567-582.
[4]Zuboff, S. (2019). The Age of Surveillance Capitalism. Profile Books.
[5]Guan, X., Chen, Y., & Wang, H. (2023). Risk Contagion in E-commerce Supply Chain Networks: An AgentBased Modeling Approach. Journal of Computational Social Science, 16(2), 345-362.
[6]Chen, X., & Zhou, Y. (2022). Algorithmic Discrimination in Credit Scoring Models: Evidence from Small and Micro Enterprises. Journal of Financial Ethics, 18(1), 78-95.
[7]Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1 minus 8.
[8]Gordon, D. M., & Loeb, M. P. (2002). A cost-benefit model of information security investment. Journal of Information Systems, 16(1), 43-58.
[9]Porter, M. E., & Kramer, M. R. (2011). Creating shared value. Harvard Business Review, 89(1/2), 62-77.