How does fintech affect the revenue and risk of commercial banks? Evidence from China

IF 0.4 4区经济学 Q4 BUSINESS, FINANCE

Journal of Operational Risk Pub Date : 2023-01-01 DOI:10.21314/jop.2023.008

Lixia Yu, Zhenghan Li, Liujue Li

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Abstract

Financial technology (fintech) has driven a profound transformation in the finance industry, and the application of various new technologies in the financial sector has brought about changes in the operating environment faced by commercial banks. To assess the impact of fintech on commercial banks, we selected Chinese commercial banks as sample data, calculated the fintech application index at the individual bank level using text mining methods and principal component analysis, and conducted empirical tests using fixed-effect panel regression models. Our results indicate that the application of fintech can effectively increase a bank’s revenue. In terms of risk, using binned groups analysis we found that the development of fintech has a nonlinear, roughly L-shaped relationship with the risk of commercial banks. Further, we also considered the characteristics of the Chinese banking industry for further analysis of the impact of bank-type heterogeneity and to control for sample differences. In addition, we enhanced the robustness of the empirical results using substitution variables, instrumental variable methods and propensity score matching. In conclusion, starting from a new perspective on fintech, using the Chinese banking industry as an example, we offer suggestions for developing the fintech capabilities of banks in developing countries.

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金融科技如何影响商业银行的收入和风险?来自中国的证据

金融科技(fintech)推动了金融行业的深刻变革，各种新技术在金融领域的应用带来了商业银行所面临的经营环境的变化。为了评估金融科技对商业银行的影响，我们选择中国商业银行作为样本数据，利用文本挖掘方法和主成分分析计算金融科技在单个银行层面的应用指数，并使用固定效应面板回归模型进行实证检验。我们的研究结果表明，金融科技的应用可以有效地增加银行的收入。在风险方面，我们使用分类组分析发现，金融科技的发展与商业银行的风险呈非线性的、大致为l型的关系。此外，我们还考虑了中国银行业的特点，以进一步分析银行类型异质性的影响，并控制样本差异。此外，我们使用替代变量、工具变量方法和倾向得分匹配来增强实证结果的稳健性。综上所述，本文从金融科技的新视角出发，以中国银行业为例，对发展中国家银行金融科技能力建设提出建议。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Operational Risk BUSINESS, FINANCE-

CiteScore

1.00

自引率

40.00%

发文量

期刊介绍： In December 2017, the Basel Committee published the final version of its standardized measurement approach (SMA) methodology, which will replace the approaches set out in Basel II (ie, the simpler standardized approaches and advanced measurement approach (AMA) that allowed use of internal models) from January 1, 2022. Independently of the Basel III rules, in order to manage and mitigate risks, they still need to be measurable by anyone. The operational risk industry needs to keep that in mind. While the purpose of the now defunct AMA was to find out the level of regulatory capital to protect a firm against operational risks, we still can – and should – use models to estimate operational risk economic capital. Without these, the task of managing and mitigating capital would be incredibly difficult. These internal models are now unshackled from regulatory requirements and can be optimized for managing the daily risks to which financial institutions are exposed. In addition, operational risk models can and should be used for stress tests and Comprehensive Capital Analysis and Review (CCAR). The Journal of Operational Risk also welcomes papers on nonfinancial risks as well as topics including, but not limited to, the following. The modeling and management of operational risk. Recent advances in techniques used to model operational risk, eg, copulas, correlation, aggregate loss distributions, Bayesian methods and extreme value theory. The pricing and hedging of operational risk and/or any risk transfer techniques. Data modeling external loss data, business control factors and scenario analysis. Models used to aggregate different types of data. Causal models that link key risk indicators and macroeconomic factors to operational losses. Regulatory issues, such as Basel II or any other local regulatory issue. Enterprise risk management. Cyber risk. Big data.