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金融研究  2026, Vol. 555 Issue (9): 151-169    
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金融科技投资、信贷优化与供应链效率
陈雨轩, 段文奇
FinTech Investment, Credit Optimization, and Supply Chain Efficiency
CHEN Yuxuan, DUAN Wenqi
School of Economics and Management, Tsinghua University; School of Economics, Shandong University
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摘要 提升供应链效率是增强经济韧性与安全稳定的重要途径。不同于已有研究多从金融机构供给端切入,本文立足企业需求侧,聚焦企业主动开展金融科技投资对其供应链效率的影响,旨在揭示从“被动缓解融资约束”向“主动优化信贷资源配置”转变的作用机制,并进一步讨论金融科技与供应链金融的协同效应。研究发现,金融科技投资能够显著提升供应链效率,且这一提升作用在规模较小、处于成长期和竞争性行业、所在城市传统金融机构密度较低与营商环境较优的企业中更为突出,体现出鲜明的普惠属性。就作用机制而言,金融科技投资通过增加信贷可得性、提升信贷灵活性、降低信贷成本与缓释信贷风险四重路径,实现了信贷体系的系统优化。进一步地,金融科技投资通过赋能供应链金融,协同促进链上参与主体的资源配置与运营效率,揭示出其价值实现路径从“服务企业内部”向“重塑外部生态”的拓展。
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陈雨轩
段文奇
关键词:  金融科技投资  信贷优化  供应链效率    
Summary:  Enhancing supply chain efficiency has become a strategic imperative for strengthening China's global economic competitiveness and ensuring economic security in the context of external shocks and domestic structural transformation. While existing literature predominantly examines FinTech from the supply side, emphasizing how banks and FinTech firms reshape credit allocation, this study shifts to the corporate demand side and investigates whether proactive corporate FinTech investment influences supply chain efficiency. We posit a novel mechanism that moves beyond the conventional financing-constraints narrative: Fin Tech investment enables firms to shift from passive relief to proactive optimization of their internal credit architecture. We further extend the analysis to the network level, exploring the synergistic interaction between Fin Tech investment and supply chain finance.
Using a hand-collected dataset of FinTech-related job postings by Chinese listed firms from 2010 to 2022, we construct a firm-level measure of substantive FinTech investment. This metric captures genuine resource commitment, by treating hiring talent as the critical enabler of technological adoption, thereby avoiding measurement errors and “free-rider” issues inherent in aggregate indices or patent proxies. OLS estimates with firm and year fixed effects yield strong evidence that FinTech investment significantly enhances supply chain efficiency. This result is robust to a battery of robustness checks, including instrumental variable estimation, alternative specifications, and different estimators, leaving the baseline findings unchanged.
In our mechanism analysis, we identify a fourfold pathway through which FinTech investment systematically optimizes the firm's credit system: (1) increasing credit availability by mitigating information asymmetries and expanding financing channels; (2) enhancing credit flexibility by accelerating the adjustment of capital structure and refining the choice of credit maturities; (3)reducing direct and indirect credit costs by strengthening market competition and lowering risk premiums; and (4) mitigating credit risk through real-time monitoring and predictive analytics that lower default probabilities. Collectively, these four channels constitute a systematic upgrade from passive borrowing to active credit architecture management, which significantly extends the conventional unidimensional narrative of financing constraints.
Crucially, heterogeneity analysis reveals that the positive effect is significantly more pronounced among smaller firms, growth-stage enterprises, those in highly competitive industries, and firms located in cities with lower traditional financial institution density and more favorable business environments. This pattern underscores the inclusive, equalizing nature of FinTech-driven efficiency gains, suggesting it serves as a compensatory mechanism in regions underserved by conventional banking while thriving in institutional contexts marked by sound rule of law.
Extending our analysis to the supply chain network, we find evidence of spillover effects. The internal credit optimization generated by FinTech investment amplifies the effectiveness of supply chain finance. Core firms with advanced FinTech capabilities can build shared digital infrastructures such as dynamic discounting platforms and blockchain-based receivable confirmation systems. These infrastructures integrate transaction, logistics, and fund flows across tiers, enhancing the operating efficiency of all participating entities, particularly upstream SMEs facing themost severe financing frictions. This reveals a critical value-expansion trajectory: from serving the internal firm to reshaping the external ecosystem.
This paper makes three primary contributions. First, it introduces a demand-side perspective on FinTech, documenting how firms actively harness technology to transform financial operations, complementing the vast supply-side literature. Our job-posting-based metric offers a precise and forward-looking proxy for real FinTech activity. Second, it extends FinTech research into supply chain management, demonstrating that technology's benefits transcend individual firm performance to enhance inter-organizational coordination and network resilience. Third, theoretically, it introduces a dynamic “credit system optimization” framework, replacing the static “financing constraints” approach, and articulates a clear internal-to-external value diffusion mechanism with four distinct transmission channels.
Our findings carry significant policy implications. They advocate for policies encouraging corporate technology upgrading through tax incentives, digital infrastructure development supporting inter-firm data sharing, and regulatory frameworks fostering synergies between FinTech and supply chain finance, especially in underserved regions.
Keywords:  FinTech Investment    Credit Optimization    Supply Chain Efficiency
JEL分类号:  O33 G21 M11  
基金资助: *本文感谢国家自然科学基金(72303135)的资助。感谢匿名审稿人的宝贵意见,文责自负。
通讯作者:  段文奇,经济学博士,助理研究员,山东大学经济学院,E-mail:duanwenqi@sdu.edu.cn.   
作者简介:  陈雨轩,博士研究生,清华大学经济管理学院,E-mail:yxchen22@mails.tsinghua.edu.cn.
引用本文:    
陈雨轩, 段文奇. 金融科技投资、信贷优化与供应链效率[J]. 金融研究, 2026, 555(9): 151-169.
CHEN Yuxuan, DUAN Wenqi. FinTech Investment, Credit Optimization, and Supply Chain Efficiency. Journal of Financial Research, 2026, 555(9): 151-169.
链接本文:  
http://www.jryj.org.cn/CN/  或          http://www.jryj.org.cn/CN/Y2026/V555/I9/151
[1] 曹廷求和庞念伟,2025,《政府白名单的信贷引导效应研究》,《金融研究》第6期,第114~132页。
[2] 丁娜、金婧和田轩,2020,《金融科技与分析师市场》,《经济研究》第9期,第74~89页。
[3] 李春涛、闫续文,宋敏和杨威,2020,《金融科技与企业创新——新三板上市公司的证据》,《中国工业经济》第1期,第81~98页。
[4] 李万利、刘晓剑和许均平,2024,《中小银行网点扩张与企业信贷期限结构》,《金融研究》第6期,第114~130页。
[5] 李逸飞、李茂林和李静,2022,《银行金融科技、信贷配置与企业短债长用》,《中国工业经济》第10期,第137~154页。
[6] 彭俞超和马思超,2022,《非银行金融科技与上市公司借贷成本——竞争压力还是信息溢出?》,《金融研究》第12期,第93~111页。
[7] 邱晗和刘巍,2018,《金融科技发展对银行风险承担的影响研究》,《金融研究》第8期,第111~127页。
[8] 宋敏、周鹏和司海涛,2021,《金融科技与企业全要素生产率——“赋能”和信贷配给的视角》,《中国工业经济》第4期,第138~155页。
[9] 唐松和谢雪妍,2021,《企业持股金融机构如何服务实体经济——基于供应链溢出效应的视角》,《中国工业经济》第11期,第116~134页。
[10] 王红建、张科和李青原,2023,《金融科技的经济稳定器作用:金融加速器理论的视角》,《经济研究》第12期,第4~21页。
[11] 尹振涛和李泽广,2021,《竞争规避与银行金融创新扩散——基于同质化视角的实证检验》,《管理世界》第11期,第71~89页。
[12] 张同斌和刘文龙,2024,《留抵退税改革、融资约束与企业产业链关联》,《管理世界》第3期,第94~115页。
[13] Alfaro, L., M. Brussevich, C. Minoiu and A. Presbitero, 2025, “Bank Financing of Global Supply Chains”, CEPR Discussion Paper No. 20164.
[14] Babina, T., A. Fedyk, A. He and J. Hodson, 2024, “Artificial Intelligence, Firm Growth, and Product Innovation”, Journal of Financial Economics, 151, 103745.
[15] Banerjee, S., S. Dasgupta and Y. Kim, 2008, “Buyer–supplier Relationships and the Stakeholder Theory of Capital Structure”, Journal of Finance, 63(5), pp. 2507~2552.
[16] Berg, T., V. Burg, A. Gombovic' and M. Puri, 2020, “On the Rise of FinTechs: Credit Scoring Using Digital Footprints”, The Review of Financial Studies, 33(7), pp. 2845~2897.
[17] Bharath, S. and T. Shumway, 2008, “Forecasting Default with the Merton Distance to Default Model”, The Review of Financial Studies, 21(3), pp. 1339~1369.
[18] Bollaert, H., F. Lopez-de-Silanes and A. Schwienbacher, 2021, “Fintech and Access to Finance”, Journal of Corporate Finance, 68, 101941.
[19] Buchak, G., G. Matvos, T. Piskorski and A. Seru, 2018, “Fintech, Regulatory Arbitrage, and the Rise of Shadow Banks”, Journal of Financial Economics, 130(3), pp. 453~483.
[20] Buzacott, J. and R. Zhang, 2004, “Inventory Management with Asset-Based Financing”, Management Science, 50, pp.1274~1292.
[21] Chu, Y. and J. Wei, 2023, “Fintech Lending and Credit Market Competition”, Journal of Financial and Quantitative Analysis, pp. 59(5): 1~50.
[22] Cunat, V., 2007, “Trade Credit: Suppliers as Debt Collectors and Insurance Providers”, The Review of Financial Studies, 20(2), pp. 491~527.
[23] Dickinson V., 2011, “Cash Flow Patterns as a Proxy for Firm Life Cycle”, The Accounting Review, 86(6), pp. 1969~1994.
[24] Ding, N., L. Gu and Y. Peng, 2022, “Fintech, Financial Constraints and Innovation: Evidence from China”, Journal of Corporate Finance, 73, 102194.
[25] Fazzari, S., R. Hubbard and B. Petersen, 1988, “Investment, Financing Decisions, and Tax Policy”, American Economic Review, 78(2), 200~205.
[26] Feng, J., W. Jiao and Y. Wang, 2025, “Bank Fintech and Lending Distance: The Role of Information Asymmetry and Risk Management Channels”, Economic Modelling, 151, 107154.
[27] Fuster, A., M. Plosser, P. Schnabl and J. Vickery, 2019, “The Role of Technology in Mortgage Lending”, Review of Financial Studies, 32(5), pp. 1854~1899.
[28] Griffin, J., S. Kruger, and P. Mahajan, 2023, “Did FinTech Lenders Facilitate PPP Fraud”? Journal of Finance, 78(3), pp. 1777~1827.
[29] Jiang, W., Y. Tang, R. Xiao and V. Yao, 2025, “Surviving the Fintech Disruption”, Journal of Financial Economics, 171, 104071.
[30] Lin, M., N. Prabhala and S. Viswanathan, 2013, “Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending”, Management Science, 59(1), pp. 17~35.
[31] Oster, E., 2019, “Unobservable Selection and Coefficient Stability: Theory and Evidence”, Journal of Business and Economic Statistics, 37(2), pp. 187~204.
[32] Thakor, A., 2020, “Fintech and Banking: What Do We Know”? Journal of Financial Intermediation, 41, 100833.
[33] Zhu, C., 2019, “Big Data as a Governance Mechanism”, Review of Financial Studies, 32(5), pp.2021~2061.
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