The Impact of FinTech on Commercial Bank Credit Supply under Economic Policy Uncertainty

DOI

10.2991/978-94-6239-602-9_2How to use a DOI?

Keywords

Financial Technology (FinTech); Manufacturing Credit; Shifting from the Virtual to the Real Economy

Abstract

Under the impact of global financial crises, trade disputes, and public health events, economic policy uncertainty has intensified, leading to prominent phenomena of liquidity hoarding and credit rationing among commercial banks. This has caused credit resources to shift from the real to the virtual economy, creating financing difficulties for the real economy. Existing research has largely focused on the effects of economic policy uncertainty on corporate investment, credit structure imbalances, and FinTech risk management. However, there is insufficient research on how FinTech can optimize banks’ credit behavior and promote resource allocation to the real economy in an uncertain environment. This paper, based on a panel dataset of 462 observations from 42 commercial banks between 2014 and 2024, constructs a FinTech index (Fintech) and a liquidity hoarding (LHT) measure as explanatory variables, with the proportion of manufacturing loans (LMR) as the dependent variable. Control variables include bank-level microeconomic factors (asset size, return on assets, cost-to-income ratio) and macroeconomic factors (economic growth, inflation, economic policy uncertainty). A fixed-effects model is used for regression analysis, and the results are validated for robustness by using an alternative digitalization index and conducting regional heterogeneity analysis. The study finds that the application of FinTech significantly increases the proportion of manufacturing loans, supporting the hypothesis. In the baseline regression, the coefficient for Fintech is positive and significant at the 1% level, supporting its mechanism of action in alleviating information asymmetry, reducing liquidity hoarding, and promoting the shift of credit from the virtual to the real economy. The results remain consistent after robustness checks. Heterogeneity analysis reveals that this effect is strongest in the eastern region at 0.0409 (p < 0.01), followed by the central region at 0.0232 (p < 0.05) and the western region at 0.0218 (p < 0.1), reflecting the influence of regional development disparities on technology transmission.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ze-Hao Dong
PY  - 2026
DA  - 2026/03/13
TI  - The Impact of FinTech on Commercial Bank Credit Supply under Economic Policy Uncertainty
BT  - Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025)
PB  - Atlantis Press
SP  - 4
EP  - 20
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-602-9_2
DO  - 10.2991/978-94-6239-602-9_2
ID  - Dong2026
ER  -