Summary:
The Fourth Plenary Session of the 20th Central Committee of the Communist Party of China has recommended that during the 15th Five-Year Plan period, we should draw on a full range of resources and means to address risks and develop the system for preventing and defusing risks, so as to ensure stable operations in the financial sector. In this context, preventing and mitigating financial risks has become a critical task to ensure economic stability. As both a major source and the ultimate bearer of regional financial risks, the real economy plays a central role in the formation and transmission of the risks. Among the three sectors of the real economy, the non-financial enterprise sector holds the highest macro leverage ratio, and is the primary source of overall financial risk. Its assets (and liabilities) largely correspond to the liabilities (and assets) of the financial sector. When non-financial enterprises face insolvency risk, they may be unable to repay loans, thereby triggering credit risks for financial institutions. In recent years, the widespread adoption of digital technology has driven profound transformations in financial service models. Among these, digital credit, characterized by its immediacy, automation, and remote accessibility, has emerged as an essential tool for financing the real economy. Systematically examining the impact of digital credit development on regional financial risk is thus of substantial theoretical and policy significance for promoting financial support to the real economy and preventing systemic risks. This study utilizes micro-level data from 5,679 listed non-financial enterprises to construct macro balance sheets and contingent equity balance sheets for 31 provincial-level administrative regions in China. Based on four dimensions:operational risk, liquidity risk, insolvency risk, and market risk, we develop a regional financial risk index using data primarily sourced from the CSMAR database. Unlike existing studies that mainly rely on financial system indicators, this research adopts a microeconomic enterprise perspective to reveal the endogenous mechanisms underlying the generation of regional financial risk, thereby enhancing the effectiveness and forward-looking nature of risk measurement. We then apply a two-way fixed effects spatial Durbin model to empirically examine the direct and spatial spillover effects of digital credit development on regional financial risk during 2011~2022, followed by a mechanism analysis to explore the underlying transmission pathways. The results reveal three key findings. First, the constructed regional financial risk index effectively captures the impact of macroeconomic shocks on the real economy and financial system. Major events such as the 2015 stock market turbulence and the 2020 COVID-19 outbreak are clearly reflected as significant fluctuations in the index. Second, digital credit development significantly alleviates regional financial risks, not only within local regions but also on neighboring regions through spatial spillover effect, with the mitigating impact accumulating over time. At the risk-dimension level, digital credit primarily operates by easing firms’ operational and liquidity risks. Spatial heterogeneity analysis further indicates the existence of spillover barriers among urban clusters. Third, mechanism analysis shows that digital credit reduces regional financial risk by lowering information asymmetry and enhancing financial competition within regions. Additionally, by strengthening interregional capital flows and trade linkages, digital credit amplifies the spatial spillover of risk mitigation effects. On the basis of the above results and from a policy perspective, the study suggests: (1) accelerating the construction of infrastructure for digital finance through government investment, fiscal subsidies, and tax incentives to expand coverage and improve service efficiency of digital credit; and (2) promoting the development of digital credit comprehensively, removing administrative barriers, enhancing interregional capital flow and trade linkages, and improving information and capital flow channels to foster deeper integration between the digital and real economies. This would enable digital credit to serve as a sustainable driving force for mitigating regional financial risk. The contributions of this research are threefold. First, it innovatively constructs a regional financial risk index from the micro perspective of non-financial enterprises, offering a new approach for measuring financial risks, providing a scientific basis for promoting financial support to the real economy and mitigating financial risks. Second, by employing spatial econometric methods, the study systematically examines the cross-regional transmission of the effects of digital credit development on financial risk, advancing research on financial risk spillovers. Third, it reveals both the direct and indirect mechanisms through which digital credit influences financial risk, offering new insights into the formation and transmission of financial risks in the digital era. Future research may further extend this work by incorporating larger and more diverse enterprise samples with longer time spans to test the robustness of the conclusions; obtaining richer regional digital credit data to explore different digital credit models and their risk mitigation effects; and examining the interactions between digital credit and emerging financial forms such as green finance and inclusive finance to build a more comprehensive framework for digital finance and regional risk management.
宋鹭, 李佳林, 方意. 数字信贷发展对区域金融风险的影响——基于非金融企业部门的视角[J]. 金融研究, 2026, 549(3): 95-113.
SONG Lu, LI Jialin, FANG Yi. The Impact of Digital Credit Development on Regional Financial Risks: Based on the Perspective of the Non-Financial Corporate Sector. Journal of Financial Research, 2026, 549(3): 95-113.
[1] 陈国进、丁赛杰和赵向琴,2023,《气候政策、银行风险与宏观审慎监管创新》,《金融研究》第9期,第38~57页。 [2] 丁志国、丁垣竹和金龙,2021,《违约边界与效率缺口:企业债务违约风险识别》,《中国工业经济》第4期,第175~192页。 [3] 方意、杨勇和宋鹭,2022,《空间维度视角下的系统性金融风险生成机制研究》,《南开经济研究》第9期,第151~168页。 [4] 郭峰、王靖一、王芳、孔涛和张勋,2020,《测度中国数字普惠金融发展:指数编制与空间特征》,《经济学(季刊)》第4期,第1401~1418页。 [5] 黄益平和黄卓,2017,《中国的数字金融发展:现在与未来》,《经济学(季刊)》第4期,第1489~1502页。 [6] 黄益平和邱晗,2021,《大科技信贷:一个新的信用风险管理框架》,《管理世界》第2期,第12~21页+第50页。 [7] 刘超和孙晓鹏,2023,《金融结构对金融风险影响的空间外溢性与区域异质性研究》,《统计研究》第4期,第73~87页。 [8] 刘景卿、车维汉和夏方杰,2021,《全球价值链贸易网络分析与国际风险传导应对》,《管理科学学报》第3期,第1~17页。 [9] 刘晓光和刘元春,2019,《杠杆率、短债长用与企业表现》,《经济研究》第7期,第127~ 141页。 [10] 马勇、张靖岚和陈雨露,2017,《金融周期与货币政策》,《金融研究》第3期,第33~53页。 [11] 欧阳资生、熊家毅和马文博,2024,《数字金融如何影响我国区域性金融风险——基于空间杜宾模型的分析》,《统计研究》第5期,第24~35页。 [12] 王晓婷、沈沛龙和杨静雯,2022,《中国金融稳定评估——基于31个省区市宏观资产负债表的分析》,《国际金融研究》第11期,第43~53页。 [13] 夏杰长、李銮淏和刘怡君,2023,《数字经济如何打破省际贸易壁垒——基于全国统一大市场建设的中国经验》,《经济纵横》第2期,第43~53页。 [14] 谢绚丽和王诗卉,2022,《中国商业银行数字化转型:测度、进程及影响》,《经济学(季刊)》第6期,第1937~1956页。 [15] 杨子晖、陈里璇和陈雨恬,2020,《经济政策不确定性与系统性风险的跨市场传染——基于非线性网络关联的研究》,《经济研究》第1期,第65~81页。 [16] 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+6页。 [17] 张金清、李柯乐和张剑宇,2022,《银行金融科技如何影响企业结构性去杠杆?》,《财经研究》第1期,第64~77页。 [18] 甄红线和王三法,2021,《企业精准扶贫行为影响企业风险吗?》,《金融研究》第1期,第131~149页。 [19] 钟凯、梁鹏、董晓丹和王秀丽,2022,《数字普惠金融与商业信用二次配置》,《中国工业经济》第1期,第170~188页。 [20] 周开国和季苏楠,2023,《中央政策统筹下的区域金融风险形成体系研究》,《财贸经济》第3期,第71~85页。 [21] 周晔和丁鑫,2022,《“激化”还是“缓释”？数字金融对区域金融风险的影响研究——跨区效应、机制识别与结构特征》,《国际金融研究》第10期,第26~37页。 [22] Asgharian, H., W. Hess, and L. Liu, 2013, “A Spatial Analysis of International Stock Market Linkages”, Journal of Banking & Finance, 37(12), pp. 4738~4754. [23] Berg, T., V. Burg, A. Gombovi, and M. Puri, 2020, “On the Rise of Fintechs: Credit Scoring Using Digital Footprints”, The Review of Financial Studies, 33(7), pp. 2845~2897. [24] Blasques F., S.J. Koopman, A. Lucas, and J. Schaumburg, 2016, “Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial time Series Models”. Journal of Econometrics, 195(2), pp.211~23. [25] Bostanci, G., and K. Yilmaz, 2020, “How Connected is the Global Sovereign Credit Risk Network? ”, Journal of Banking & Finance, 113, pp. 105761. [26] 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. [27] Burlando, A., M. A. Kuhn, and S. Prina, 2025, “Too Fast, Too Furious? Digital Credit Delivery Speed and Repayment Rates”, Journal of Development Economics, 174, pp. 103427. [28] Dai, Y., H. Z,and Lu, 2023, “Regional Digital Finance and Corporate Financial Risk: Based on Chinese Listed Companies”, Emerging Markets Finance and Trade, 59(2), pp. 296~311. [29] Drukker, D.M., I.R. Prucha, and R. Raciborski, 2013. “Maximum Likelihood and Generalized Spatial Two-stage Least-squares Estimators for a Spatial-autoregressive Model with Spatial-autoregressive Disturbances”, The Stata Journal, 13(2), pp.221~241. [30] Gao, C., and Q. Wang, 2023, “Does Digital Finance Aggravate Bank Competition? Evidence from China”, Research in International Business and Finance, 66, p. 102041. [31] Wang X., and J. Guan, 2017, “Financial Inclusion: Measurement, Spatial Effects and Influencing Factors”, Applied Economics, 49(18), pp. 1751~1762. [32] Yang, J., Y. Wu, and B. Huang, 2023, “Digital Finance and Financial Literacy: Evidence from Chinese Households”, Journal of Banking & Finance, 156, p. 107005. [33] Zhai H., M. Yang, K. and C. Chan,2022, “Does Digital Transformation Enhance a Firm's Performance? Evidence from China”, Technology in Society, 68, p. 101841.
