Research article Special Issues

Network structure of urban digital financial technology and its impact on the risk of commercial banks

  • Received: 08 September 2022 Revised: 21 October 2022 Accepted: 24 October 2022 Published: 23 December 2022
  • In the context of the development of digital finance, the complexity of the network formed by urban digital financial technology has been deepening. Based on Chinese city data from 2010 to 2019, this paper conducts a dynamic evaluation of urban digital financial technology through grey target theory and uses social network analysis methods to study the network structure characteristics of urban digital financial technology and its impact on commercial bank risks. The study found that the spatial network of urban digital financial technology shows a trend of complexity and closeness, developed cities occupy a central position in the network of digital financial technology linkages and are net spillovers of urban digital financial technology. Further research on the impact of urban digital financial network structure on commercial bank risk found that both the overall network structure of urban digital financial technology and individual network structure have a significant inhibiting effect on commercial bank risk. Therefore, this paper focuses on the balanced development of digital financial technology in cities, while seeking to further exert the demonstration role of developed cities and achieve the reduction of risk level of commercial banks through the increase of overall network density and the decrease of network efficiency and network hierarchy.

    Citation: Jiaqi Chang, Xuhan Xu. Network structure of urban digital financial technology and its impact on the risk of commercial banks[J]. Electronic Research Archive, 2022, 30(12): 4740-4762. doi: 10.3934/era.2022240

    Related Papers:

  • In the context of the development of digital finance, the complexity of the network formed by urban digital financial technology has been deepening. Based on Chinese city data from 2010 to 2019, this paper conducts a dynamic evaluation of urban digital financial technology through grey target theory and uses social network analysis methods to study the network structure characteristics of urban digital financial technology and its impact on commercial bank risks. The study found that the spatial network of urban digital financial technology shows a trend of complexity and closeness, developed cities occupy a central position in the network of digital financial technology linkages and are net spillovers of urban digital financial technology. Further research on the impact of urban digital financial network structure on commercial bank risk found that both the overall network structure of urban digital financial technology and individual network structure have a significant inhibiting effect on commercial bank risk. Therefore, this paper focuses on the balanced development of digital financial technology in cities, while seeking to further exert the demonstration role of developed cities and achieve the reduction of risk level of commercial banks through the increase of overall network density and the decrease of network efficiency and network hierarchy.



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    [1] F. Guo, T. Kong, J. Wang, Analysis of the spatial agglomeration effect of Internet finance-evidence from the Internet finance development index, Int. Financ. Stud., 8 (2017), 75–85.
    [2] M. M. Hasan, Y. Lu, A. Mahmud, Regional development of China's inclusive finance through financial technology, SAGE Open, 10 (2020). https://doi.org/10.1177/2158244019901252 doi: 10.1177/2158244019901252
    [3] G. Liao, D. Yao, Z. Hu, The spatial effect of the efficiency of regional financial resource allocation from the perspective of internet finance: Evidence from Chinese provinces, Emerging Mark. Finance Trade, 56 (2019), 1211–1223. https://doi.org/10.1080/1540496X.2018.1564658 doi: 10.1080/1540496X.2018.1564658
    [4] F. Meng, W. Zhang, Digital finance and regional green innovation: evidence from Chinese cities, Environ. Sci. Pollut. Res. Int., 2022 (2022). https://doi.org/10.1007/S11356-022-22072-2 doi: 10.1007/S11356-022-22072-2
    [5] J. P. Elhorst, Dynamic panels with endogenous interaction effects when T is small, Regional Sci. Urban Econ., 40 (2010), 272–282. https://doi.org/10.1016/j.regsciurbeco.2010.03.003 doi: 10.1016/j.regsciurbeco.2010.03.003
    [6] G. Feng, M. Zhang, A literature review on digital finance, consumption upgrading and high-quality economic development, J. Risk Anal. Crisis Response, 11 (2022). https://doi.org/10.54560/JRACR.V11I4.312 doi: 10.54560/JRACR.V11I4.312
    [7] D. Yang, P. Chen, F. Shi, C. Wen, Internet finance: Its uncertain legal foundations and the role of big data in its development, Emerging Mark. Finance Trade, 54 (2018), 721–732. https://doi.org/10.1080/1540496X.2016.1278528 doi: 10.1080/1540496X.2016.1278528
    [8] A. Metzler, Y. Zhou, C. Grace, Learning about financial health in Canada, Quant. Finance Econ., 5 (2021), 542–570. https://doi.org/10.3934/QFE.2021024 doi: 10.3934/QFE.2021024
    [9] T. Li, J. Ma, Does digital finance benefit the income of rural residents? A case study on China, Quant. Finance Econ., 5 (2021), 664–688. https://doi.org/10.3934/QFE.2021030 doi: 10.3934/QFE.2021030
    [10] S. N. Mohd Daud, A. H. Ahmad, K. Airil, W. N. W. Azman-Saini, FinTech and financial stability: Threat or opportunity, Finance Res. Lett., 47 (2022), 102667. https://doi.org/10.1016/J.FRL.2021.102667 doi: 10.1016/J.FRL.2021.102667
    [11] H. W. S. Koffi, The fintech revolution: an opportunity for the west african financial sector, Open J. Appl. Sci., 6 (2016), 771–782. https://doi.org/10.4236/ojapps.2016.611068 doi: 10.4236/ojapps.2016.611068
    [12] T. Philippon, Has the US finance industry become less efficient? On the theory and measurement of financial intermediation, Am. Econ. Rev., 105 (2015), 1408–1438. https://doi.org/10.1257/aer.20120578 doi: 10.1257/aer.20120578
    [13] M. Wang, R. Gu, M. Wang, J. Zhang, Research on the impact of finance on promoting technological innovation based on the state-space model, Green Finance, 3 (2021), 119–137. https://doi.org/10.3934/GF.2021007 doi: 10.3934/GF.2021007
    [14] L. Yang, S. Wang, Do fintech applications promote regional innovation efficiency? Empirical evidence from China, Socio-Econ. Plann. Sci., 83 (2022), 101258. https://doi.org/10.1016/J.SEPS.2022.101258 doi: 10.1016/J.SEPS.2022.101258
    [15] W. Qiu, Research of the current situation and future trend of internet small-credit companies in China—Take Chongqing as a case study, Appl. Econ. Finance, 6 (2019). https://doi.org/10.11114/aef.v6i3.4210 doi: 10.11114/aef.v6i3.4210
    [16] M. N. Khatun, S. Mitra, M. N. I. Sarker, Mobile banking during COVID-19 pandemic in Bangladesh: A novel mechanism to change and accelerate people's financial access, Green Finance, 3 (2021), 253–267. https://doi.org/10.3934/GF.2021013 doi: 10.3934/GF.2021013
    [17] Z. Li, H. Chen, B. Mo, Can digital finance promote urban innovation? Evidence from China, Borsa Istanbul Rev., in press, 2022. https://doi.org/10.1016/j.bir.2022.10.006
    [18] S. Zhao, D. Peng, H. Wen, Y. Wu, Nonlinear and spatial spillover effects of the digital economy on green total factor energy efficiency: evidence from 281 cities in China, Environ. Sci. Pollut. Res., 2022 (2022). https://doi.org/10.1007/S11356-022-22694-6 doi: 10.1007/S11356-022-22694-6
    [19] M. Demertzis, S. Merler, W. B. Guntram, Capital markets union and the fintech opportunity, J. Financ. Regul., 4 (2018), 157–165. https://doi.org/10.1093/jfr/fjx012 doi: 10.1093/jfr/fjx012
    [20] Y. Su, Z. Li, C. Yang, Spatial interaction spillover effects between digital financial technology and urban ecological efficiency in China: An empirical study based on spatial simultaneous equations, Int. J. Environ. Res. Public Health, 18 (2021), 8535. https://doi.org/10.3390/IJERPH18168535 doi: 10.3390/IJERPH18168535
    [21] T. Li, X. Li, K. Albitar, Threshold effects of financialization on enterprise R & D innovation: A comparison research on heterogeneity, Quant. Financ. Econ., 5 (2021), 496–515. https://doi.org/10.3934/QFE.2021022 doi: 10.3934/QFE.2021022
    [22] Y. Wang, X. Sui, Q. Zhang, Can fintech improve the efficiency of commercial banks?—An analysis based on big data, Res. Int. Bus. Finance, 55 (2021), 101338. https://doi.org/10.1016/j.ribaf.2020.101338 doi: 10.1016/j.ribaf.2020.101338
    [23] Y. Yao, D. Hu, C. Yang, The impact and mechanism of fintech on green total factor productivity, Green Finance, 3 (2021), 198–221. https://doi.org/10.3934/GF.2021011 doi: 10.3934/GF.2021011
    [24] W. Liao, Research on the impact of internet finance on risk level of commercial banks, Am. J. Ind. Bus. Manage., 8 (2018), 992. https://doi.org/10.4236/ajibm.2018.84068 doi: 10.4236/ajibm.2018.84068
    [25] G. Liao, P. Hou, X. Shen, K. Albitar, The impact of economic policy uncertainty on stock returns: The role of corporate environmental responsibility engagement, Int. J. Finance Econ., 26 (2020), 4386–4392. https://doi.org/10.1002/IJFE.2020 doi: 10.1002/IJFE.2020
    [26] F. C. Bett, J. B. Bogonko, Relationship between digital finance technologies and profitability of banking industry in Kenya, Int. Acad. J. Econ. Finance, 2 (2017), 34–56.
    [27] B. Hu, L. Zheng, Digital finance: Definition, models, risk, and regulation, in Development of China's Financial Supervision and Regulation, Palgrave Macmillan, New York, (2016), 31–58. https://doi.org/10.1057/978-1-137-52225-2_2
    [28] H. Qiao, M. Chen, Y. Xia, The effects of the sharing economy: How does internet finance influence commercial bank risk preferences, Emerging Mark. Finance Trade, 54 (2018), 3013–3029. https://doi.org/10.1080/1540496X.2018.1481045 doi: 10.1080/1540496X.2018.1481045
    [29] K. Bauer, S. E. Hein, The effect of heterogeneous risk on the early adoption of internet banking technologies, J. Banking Finance, 30 (2006), 1713–1725. https://doi.org/10.1016/j.jbankfin.2005.09.004 doi: 10.1016/j.jbankfin.2005.09.004
    [30] J. Dong, L. Yin, X. Liu, M. Hu, X. Li, L. Liu, Impact of internet finance on the performance of commercial banks in China, Int. Rev. Financ. Anal., 72 (2020), 101579. https://doi.org/10.1016/j.irfa.2020.101579 doi: 10.1016/j.irfa.2020.101579
    [31] T. Li, X. Li, G. Liao, Business cycles and energy intensity. Evidence from emerging economies, Borsa Istanbul Rev., 22 (2022), 560–570. https://doi.org/10.1016/J.BIR.2021.07.005 doi: 10.1016/J.BIR.2021.07.005
    [32] F. Guo, J. Wang, F. Wang, T. Kong, X. Zhang, Z. Cheng, Measuring the development of digital inclusive finance in China: index compilation and spatial characteristics, Econ. Q., 19 (2020), 1401–1418. https://doi.org/10.13821/j.cnki.ceq.2020.03.12 doi: 10.13821/j.cnki.ceq.2020.03.12
    [33] X. Ren, H. Zhang, R. Hu, Y. Qiu, Location of electric vehicle charging stations: A perspective using the grey decision-making model, Energy, 173 (2019), 548–553. https://doi.org/10.1016/j.energy.2019.02.015 doi: 10.1016/j.energy.2019.02.015
    [34] J. Zhu, Z. Li, Can digital financial inclusion effectively stimulate technological Innovation of agricultural enterprises?—A case study on China, Natl. Account. Rev., 3 (2021), 398–421. https://doi.org/10.3934/NAR.2021021 doi: 10.3934/NAR.2021021
    [35] Y. Liu, P. Failler, Y. Ding, Enterprise financialization and technological innovation: Mechanism and heterogeneity, PloS One, 17 (2022), e0275461. https://doi.org/10.1371/journal.pone.0275461 doi: 10.1371/journal.pone.0275461
    [36] X. Huang, W. Wang, Evaluating industrial economy-ecology-coordinated development level by grey target theory, Grey Syst. Theory Appl., 3 (2013), 338–348. https://doi.org/10.1108/GS-08-2013-0020 doi: 10.1108/GS-08-2013-0020
    [37] Q. Yu, X. Chen, X. Li, C. Zhou, Z. Li, Using grey target theory for power quality evaluation based on power quality monitoring data, Energy Eng., 119 (2022), 359–369. https://doi.org/10.32604/EE.2022.015397 doi: 10.32604/EE.2022.015397
    [38] S. Pathan, Strong boards, CEO power and bank risk-taking, J. Banking Finance, 33 (2009), 1340–1350. https://doi.org/10.1016/j.jbankfin.2009.02.001 doi: 10.1016/j.jbankfin.2009.02.001
    [39] H. T. T. Le, R. P. Narayanan, L. Van Vo, Has the effect of asset securitization on bank risk taking behavior changed, J. Financ. Serv. Res., 49 (2016), 39–64. https://doi.org/10.1007/s10693-015-0214-1 doi: 10.1007/s10693-015-0214-1
    [40] V. Bruns, M. Fletcher, Banks' risk assessment of Swedish SMEs, Venture Capital, 10 (2008), 171–194. https://doi.org/10.1080/13691060801946089 doi: 10.1080/13691060801946089
    [41] S. Chen, Y. Wang, K. Albitar, Z. Huang, Does ownership concentration affect corporate environmental responsibility engagement? The mediating role of corporate leverage, Borsa Istanbul Rev., 21 (2021), S13–S24. https://doi.org/10.1016/J.BIR.2021.02.001 doi: 10.1016/J.BIR.2021.02.001
    [42] Z. Li, J. Zhong, Impact of economic policy uncertainty shocks on China's financial conditions, Finance Res. Lett., 35 (2020), 101303. https://doi.org/10.1016/j.frl.2019.101303 doi: 10.1016/j.frl.2019.101303
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