Fintech and the economic capital of chinese commercial banks risk based on theory and evidence

Received: 11 August 2020 Revised: January 2021 Accepted: January 2021 DOI: 10.1002/ijfe.2528 RESEARCH ARTICLE Fintech and the economic capital of Chinese commercial bank's risk: Based on theory and evidence Ting Yao1,2 | Liangrong Song1,3 Business School, University of Shanghai for Science and Technology, Shanghai, China Guangxi University Xingjian College of Science and Liberal Arts, Nanning, China Business School, University of Shanghai for Science and Technology, Shanghai, China Correspondence Ting Yao, Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China Email: yaoting_99@163.com Funding information National Natural Science Foundation of China, Grant/Award Number: 71871144 Abstract This article examines the impact of “finance + technology” (Fintech) on different sizes of banks economic capital through the application of Fintech perspective in China during the period January 2011 and September 2019, using a dynamic panel generalized method of moments (GMM) estimation technique The study found compared with small and medium-sized banks, large stateowned commercial banks have advantages in scale, capital and experience There is a negative correlation between the scale of assets of commercial banks and economic capital Further tests reveal the impact of Fintech on the profitability of different types of commercial banks shows significant heterogeneity KEYWORDS economic capital, Fintech, Fintech application index, GMM, risk management | INTRODUCTION In March 2014, “Fintech” first appeared in the Chinese government work report In August 2019, the People's Bank of China issued a plan for the development of FinTech in the next years, raising the application of Fintech in the field of financial risk management to an unprecedented level In December 2019, the People's Bank of China announced the launch of a Fintech innovation supervision pilot Beijing took the lead in launching the Chinese version of the regulatory model, which shows that the Chinese government attaches great importance to the status of Fintech in the field of financial risk control As the main body of China's financial system, commercial banks need to seize the opportunity of the development of Fintech to achieve self-upgrading and transformation However, few directly focus on how Fintech affects banks' risk The changes in the economic capital of commercial bank's risk under the impact of Fintech This study aims to fill this gap in the literature We also investigate whether Fintech will have different effects on Int J Fin Econ 2021;1–15 banks of different sizes to present new conclusions on these differences Fintech (Milian, de Spinola, & de Carvalho, 2019; Puschmann, 2017; Thakor, 2020) combines the characteristics of “finance + technology” and innovates the Fintech ecosystem, using emerging technologies such as robotic investment advisors, distributed accounting, face recognition and brainwave payment, etc., innovated various new models, application scenarios, processes, new products and rich new visual experiences (Pi, Liu, & Wu, 2018), breaking the existing industry structure and blurring the industry boundaries (Shim & Shin, 2016) Fintech can provide convenient access to services and cost reduction (Li, Spigt, & Swinkels, 2017; Vasiljeva & Lukanova, 2016) Fintech can also fill the original financial industry gap (Truong, 2016), but it also poses challenges to supervision and law enforcement (Philippon, 2016) The essence of economic capital is funds to make up for unexpected losses It directly reflects the overall risk of the bank and is an effective tool for banks to conduct comprehensive risk management Economic capital is the amount of capital a bank needs wileyonlinelibrary.com/journal/ijfe © 2021 John Wiley & Sons, Ltd to resist business risks It is not the bank's own capital or actual capital, but just a kind of capital demand Economic capital is a variable, which changes in the same direction as the risks faced by the bank That is, when the risk is high, the demand for economic capital is high; when the risk decreases, the demand for economic capital also decreases The essence of “Fintech + Commercial Bank” is to change the original profit model of commercial banks through the comprehensive three-dimensional integration between Fintech and commercial banks The data is the core of operation Fintech can provide diversifieddimensional risk operation innovative ideas for commercial banks The impact of the application of Fintech on the risks of commercial banks can be summarized in two aspects: (a) The application of Fintech has reduced the cost of information asymmetry and improved the bank's profitability and risk-taking Fintech uses advanced technologies (predictive algorithms, distributed accounting, visual recognition, natural language processing, etc.) to provide commercial banks with new solutions to the problems of information asymmetry, the need to expand the original data capacity and data quality is low Fintech cuts emerging technologies into the field of financial services, connects the asset side of the financial industry with other links, accurately matches the capital side and the asset side, realizes efficient pricing, service and product innovation and reduces operating costs and achieves reasonable allocation of funds (Gong, Yang, & Qu, 2017; Yang, 2018; Zhang & Jiang, 2018; Zhou & Li, 2016) The financial sector is shifting towards smart finance, convenience and efficiency Fintech can not only provide convenient financial services but also fill the gaps in the retail customers and low-end customers service areas of traditional banks Fintech analyzes massive data sets to measure, track and describe consumer behaviour patterns and can provide customers with personalized solutions (Liao, 2016; Qi & Xiao, 2018; Thakor, 2020; Yi, 2017) (b) Fintech makes various market risks constantly superimposed, and commercial banks have to face sudden changes Once a decision error is made, the risk of commercial bank bankruptcy will increase Ba and Bai (2016) found that with the diversification of participants in the financial sector (including traditional financial institutions, financial institutions that have undergone transformation and upgrading, etc.), the service areas have also expanded to multiple areas, including traditional credit, deposit and fundraising, bank intermediary business, but also investment management services and insurance, etc.(Thakor, 2020; Wang, 2018), diversified participants and service areas make risks complex and diverse The potential risks of Fintech application are divided into two aspects: macro and micro The YAO AND SONG micro includes financial institutions' process, network, legal risks, etc.; The macro includes risks such as reputation, excessive volatility and procyclicality (Li & Jiang, 2017; Liu et al., 2017) The continuous development and evolution of risks has made it more difficult for commercial banks to predict, evaluate and measure risks Once commercial banks are not managed well, they are more likely to face bankruptcy risks Fintech is a product of the continuous development and evolution of commercial banks, and it has a counterproductive effect on commercial banks Correctly grasping the law of risk evolution of commercial banks needs to be based on a deep understanding of Fintech Through the perspective of Fintech application changes, clarify the internal logic and important issues of China's Fintech operation and commercial bank risks This will be of great significance for improving the commercial banks risk management and promoting the healthy development of China's financial system In response to the above issues, this article makes marginal contributions in the following points: First, the Fintech is incorporated into the analysis framework of the comprehensive risk management of commercial banks, and the understanding of the comprehensive risk management of commercial banks is expanded from the macro level, and the degree of Fintech application is used to explain the internal logic and evolutionary mechanism of the comprehensive risk of commercial banks and Fintech have made the mechanism of Fintech's impact on the overall risk of commercial banks clearer, which will help to provide a theoretical basis for the comprehensive risk management of commercial banks in the future; second, the application of Fintech in various fields of the banking industry, combined with the data provided by the regular reports published by commercial banks, and the use of text mining technology and principal component analysis to innovatively construct a Fintech application index Since the index is constructed by combining the data regularly announced by commercial banks, the index is more relevant to the actual situation of commercial banks, and it is easier to obtain real data It is also easy to operate and implement flexibly according to the commercial banks' own conditions The quantitative analysis of economic capital required for comprehensive risk management of the bank industry is more accurate, enriching the relevant literature on the quantitative analysis of Fintech; third, studying the degree of Fintech application to commercial banks' risk tolerance is helpful and examines the changes in commercial bank risks under the background of Fintech from a new perspective, which has special policy significance for the design of banking supervision and policy, and provides micro-empirical evidence for commercial banks to YAO AND SONG formulate reasonable response measures for comprehensive risk management The remaining part is arranged as follows: The second part explains the internal logic and evolution of the Fintech application level and commercial bank risk; The third part draws on the text mining method and the principal component analysis method to construct the Fintech application index and empirically analyzes the impact of the Fintech application on the business bank risk-taking and the heterogeneity of different types of commercial banks The fourth part proposes corresponding improvement policy suggestions | LITERATURE R EVIEW, THEORY AND HYPOTHESIS There is an intensive debate about the essence of Fintech and the economic capital of commercial bank risk (Ma & Zhang, 2019) The internal logic and evolution mechanism of Fintech application and commercial bank risk can be summarized as follows: (1) The application of Fintech improves commercial banks' risk-taking and changes profit models Fintech can optimize banking business processes and reduces overall risk levels (Xue & Hu, 2020) In terms of credit granting and rating to customers: Fintech can use data mining, machine learning, neural network and other advanced technologies to provide data sources references for bank credit and solve non-linear problems that cannot be handled by traditional rating models and improve commercial banks' credit granting capabilities In terms of market risk management process: Fintech can improve the accuracy of capital position forecasts, strengthen capital operation efficiency, analyse customer risks in a timely manner and track reports In terms of operational risk process management, Fintech can use advanced technologies such as biometrics, voice recognition and intelligent robots to reduce manpower, capital and time costs, improve data accuracy and reduce internal personnel fraud risks and systemic risks (Fuster, Plosser, Schnabl, & Vickery, 2019) In addition, in terms of finance inclusive: Fintech's blockchain technology, digital intelligence and supply chain finance can provide precise risk control and supply chain financing services in the loan process of small and micro enterprises (SMEs) and achieve professional and real-time monitoring and risk-tracking service (Blythin & Cooten, 2017; Basel Committee on Banking Supervision, 2018; TSAI, 2017; Loukoianova & Yang, 2018; Shen, Hueng, & Hu, 2020; Jin, Li, & Liu, 2020; Hasan, Lu, & Mahmud, 2020) Fintech overthrows the original profit model of banks The original profitability of commercial banks mainly relied on the spread between loans and deposits Under the reform of interest rate marketization, the profitability of commercial banks has declined, and commercial banks urgently need to change the original single profit model In 2018, global investment in Fintech reached US$55.3 billion, of which China's participation in transactions reached US$25.5 billion Commercial banks have invested heavily in Fintech for the purpose of Fintech to bring huge profits to commercial banks The essence of Fintech lies in innovation While the Fintech innovation can not only bring more innovative products and services to customers but also bring more new ways for commercial banks to obtain funds For example, commercial banks can use technologies such as blockchain and distributed accounting to process the transaction data of SMEs and individuals Fintech can solve the problem of the high cost of acquiring information from longtail customers by the original financial institutions and the long and cumbersome transaction chain Fintech improves the efficiency of commercial bank loans and increase bank revenue (Gu & Zhang, 2018) In addition, Fintech can use emerging technologies to provide one-toone differentiated services to high-quality customers of commercial banks and use the open banking model to implant various scenarios of “ clothing, food, accommodation, transportation” and “office services” to provide retail customers with a better experience, improve the operating efficiency of retail business Fintech can solve the dilemma of poor original data quality Although commercial banks have a large amount of national consumption and fund storage data, a large amount of data (such as customer information, product information, credit behavior, third-party platforms, etc.) cannot be used rationally Because the original technology and data are not perfect, the quality of the data and the way of generating and obtaining are also different Commercial banks are unable to make good comprehensive evaluations and judgements of customers in transactions It is extremely difficult to segment customer groups and to judge fraud in transactions Nowadays, Chinese credit investigation system is not perfect, and there are uncertainties in the source and reliability of customer data Fintech can use text mining, cluster analysis, iris recognition and other technologies to compare and analyse customers' online behaviours, user preferences and public data and through learning and correction methods, the original data can be efficiently analyzed to improve the quality of data (2) Fintech reduces banks' risk-taking and increases the probability of commercial bank bankruptcy The superposition and aggregation effects of various risks generated by the application of Fintech are unprecedented, and the probability of bank bankruptcy risks has increased Various products and services of Fintech innovation are accompanied by various risks, which are dispersed in various markets Fintech combines the boundaries of multiple markets, such as currency, derivatives, foreign exchange, etc., resulting in the continuous superposition and integration of various risks, and the form of change is different from the past, various capital chains have become complicated and various financial institutions have increased The space–time and dimension of transmission are also constantly changing The uncertainty and relevance of global financial risks due to the development of Fintech applications is unprecedented In the face of the continuous development of Fintech, the cross-risks generated by national, industry and regional risks have become complicated, and the customers' own situation and expectations of the economic situation have also become complicated Fintech is constantly innovating, and risks are constantly accumulating Once a risk occurs, it may produce a “herd effect” that will have a huge destructive effect on commercial banks The application of Fintech has increased the risks of commercial banks In terms of credit risk management: while Fintech continues to innovate products and services, commercial banks' credit risks are also changing The original credit business process and risk mitigation technology need to be revised according to actual conditions Whether credit ratings, industry standards, guarantees, authorizations, reviews and asset disposal policies meet the new development requirements, and how to modify them, are also major issues that the bank's senior management must face Commercial banks have a credit preference Their investments in credit rating, small and micro enterprises(SME) and movable property financing are relatively weak An undeveloped or even neglected value network will cause commercial banks to lose some high-quality customers(Anagnostopoulos, 2018) At the same time, with the development of finance inclusive and increasing coverage of low-end customers, the default probability of credit risk may increase In terms of market risk management, financial innovation and tools are becoming increasingly diversified, various market risk components are complex, the total portfolio investment risk is increasing and the identification, evaluation, measurement and verification of market risks are becoming more difficult The capital, currency, foreign exchange and commodity market price fluctuations affect commercial banks' market risk will also become complicated during investment and asset-liability mismatches period Various collections, payments, settlements, product transactions and even investment decision making, wealth management and interest rate pricing are very different In terms of operational risk management, YAO AND SONG traditional profit methods have led to a decline in commercial banks' profits Commercial banks need to invest in riskier assets with higher profits to obtain more profits to make up for losses Risk preference to the asset side has increased, leading to increased risks on the asset side (Qi & Xiao, 2018) In addition, the fraud of internal personnel of commercial banks, the management of internal control and operating procedures, operational risk information management system failure, collapse, loopholes, error risks, etc., will bring investors' capital loss or information leakage and other operational risks Fintech produces technical risks (Gu & Shi, 2020; Ma, Q L, & Dai, 2020) As Fintech has created a new financial ecosystem, all-day service methods and diversification of participants, massive amounts of data are constantly increasing at the power level, a large amount of information data has been generated Improper handling and storage of this information will lead to information and data leakage, increase the difficulty of risk identification and increase the risk of technology out of control Due to the lack of both rich financial knowledge and excellent computer technology professionals, the processing of unprecedented massive data generated by Fintech has encountered a bottleneck Whether the original technology can carry the capacity and processing speed of these data can be followed up? The emerging technology will also have risks in the storage, reading and processing of the data during the running-in period with the original technology Incomplete and distorted data processing improperly will also bring huge risks, leading to a significant impact on the information management system of commercial banks Through the analysis of the internal logic mechanism of the above Fintech applied to commercial bank risks, Hypothesis 1a and Hypothesis 1b are obtained: Hypothesis 1a Fintech can reduce the economic capital of commercial banks Hypothesis 1b Fintech increase the economic capital of commercial banks A large number of scholars are studying whether bank risks are heterogeneous Most of them believe that large state-owned commercial banks have scale effects compared to small and medium banks in risk management (Gu & Shi, 2020; Gu & Zhang, 2018; Ma et al., 2020; Sheng, 2020) Therefore, from the perspective of the application of Fintech to study whether Fintech is heterogeneous to commercial bank risks of different asset scales, it can provide a new research perspective and enrich the relevant literature On the one hand, large state-owned commercial banks have the advantages of YAO AND SONG scale, talent, technology and more practical experience in upgrading self-risk management technologies and processes In 2017, with the help of the internet and other technological developments, the five state-owned commercial banks of China, Agriculture Bank of China, Industry and Commercial Bank of China, China Construction Bank and Bank of Communications established strategic cooperation with the Fintech giant “BATJS” to rapidly expand their Fintech field Among them, Alibaba and Ant Financial and China Construction Bank cooperates in the areas of electronic payment and QR code mutual recognition and scanning; JD and Industrial and Commercial Bank of China cooperate in financing, corporate credit and asset management; Baidu and Agricultural Bank of China cooperate in intelligent banking and finance inclusive; Tencent and Bank of China established a unified financial big data platform; Bank of Communications and Suning cooperate in smart finance By cooperating with Fintech companies, large state-owned banks can quickly adjust their asset structure to keep up with the pace of economic development and market competition On the other hand, due to the limitations of capital and talents, small- and medium-sized banks have difficulties in the layout of the Fintech field Therefore, it needs to be treated differently According to their own characteristics, joint-stock banks have different layouts in the field of Fintech For example, the Bank of Nanjing is mainly deployed in the small- and medium-sized bank ecosystem and connected e-commerce, express delivery and other industry platforms Shanghai Pudong Development Bank adopts a “technological community” approach, with technology companies, scientific research institutions, upstream and downstream suppliers and consumers participating in cooperation China Merchants Bank and Industrial Bank have incorporated “long-tail customers” into their Fintech development strategies Urban commercial banks and rural commercial banks have limited investment in Fintech due to capital, business scope, geographical and customer groups, and it is difficult to keep up with the pace of Fintech Therefore, it is necessary to study the heterogeneity of the impact of Fintech on different types of commercial banks(Chunbing et al., 2014; Fang, Lau, Lu, Tan, & Zhang, 2019; Lorenc & Zhang, 2020; Sleimi, 2020; Xie & Ling, 2018) Hypothesis 2a and Hypothesis 2b are proposed: Hypothesis 2a The impact of Fintech on commercial banks of different sizes is heterogeneous Hypothesis 2b The impact of Fintech on commercial banks of different sizes is not heterogeneous | EMPIRICAL DESIGN Based on the theoretical hypothesis described above, this article adopts the model of Phan, Narayan, Rahman, and Hutabarat (2020) The major interest of this article is the coefficient of Fintech in the Equations (1) and (2) The study attempts to testify whether the Fintech has significant effects on the economic capital of commercial bank risk Based on this, it is modified according to specific conditions to obtain the following model: EC it = α0 + α1 Fintechit + α2 GGDPi + α3 GM2i + α4 SIZE it + α5 ROE it + α6 COST it + α7 NI it + α8 CARit + α9 Liquidityit + μi + εit ð1Þ EC it = β0 + β1 Fintechit + β2 Fintechit − + β3 GGDPi + β4 GM2i + β5 SIZE it + β6 ROEit + β7 COST it + β8 NI it + β9 CARit + β10 Liquidityit + β11 Fintechit × SIZE it + β12 Fintechit × COST it + β13 Fintechit × NI it + 14 Fintechit ì ROE it + 15 Fintechit 2ị × Liquidityit + μi + εit The explained variables in Equations (1) and (2) are the economic capital of commercial bank risk (EC), the core explanatory variables are the Fintech Application Index (Fintech) and the control variables are the bank's profitability (ROE), asset size (SIZE), the growth rate of gross domestic product (GGDP), the growth rate of currency supply (GM2), CAR (Capital adequacy ratio), NI (Non-interest income), COST (Cost to revenue ratio) and Liquidity ratio Among them, i = 1, 2, …, N represents the number of banks, t = 1, 2, …, T represents time, μ is the fixed effect of individual commercial banks and ε is the random disturbance term 3.1 | Variables 3.1.1 | Explained variable The explained variable of economic capital of commercial bank risk (EC), this article uses the VaR model to measure As mentioned above, the economic capital of commercial bank risk is the capital required by the bank's board of directors to compensate for unexpected losses within a certain period according to its asset risk status and preferences It is a measure of risk, and the essence is to cover the risk with capital Value at risk (VaR) as a standard measure of risks has been widely implemented by financial institutions Therefore, this article will adopt YAO AND SONG the VaR model to measure the economic capital of commercial banks' risk In July 1993, G30 Group proposed a standard method for measuring market value at risk (VaR) VaR has been widely implemented by financial institutions VaR is called the value at risk, which depends on the absolute level of risk, the manager's tolerance to risk and the length of the risk period VaR refers to the measurement of the maximum possible loss faced by a particular investment combination within a certain confidence level and holding period It is a forward-looking risk measurement method (Drenovak et al., 2017) This method is a standard method designated by the Basel Agreement to measure the market risk of financial institutions, and its application has become the international banking risk management standard In order to measure risk more accurately, this article uses the ThaiVaR (Conditional Tail Expectation, CTE) method proposed by Artzner, Delbaen, Jean-Marc, and Heath (1999) to estimate the economic capital of 16 listed commercial banks from China Statistical formula to express the definition, namely ProbðΔV ≤ −VaRÞ = −α ð3Þ That is: ð − VaR −∞ f ðx Þdx = −α ð4Þ ThaiVaR = EẵXjXVaR Xị quantile of cumulative probability is the q So the TailVaR of the general normal distribution X is equal the mean plus the result of multiplying the standard deviation of X and the TailVaR value of the standard normal distribution Y, namely TailVaRx = μ + σTailVaRy ð7Þ This paper uses the return on assets (ROA) instead of profit, namely ROAt = profitt × 100% ðthe total assetst − + the total assetst Þ=2 ð8Þ The bank's net loss rate is the negative value of the ROA We use SPSS software to the Kolmogorov − Smirnov (K − S) test on ROA If the p value is less than 1, the bank net loss rate does not follow the normal distribution, otherwise, it follows the normal distribution The results show that ROA obeys a normal distribution So we can calculate the TailVaR value according to formula (7) The value of μ and σ is shown in n Table The value of the standard normal distribution TailVaR is shown in Table The confidence level in this paper selected is 99.99% Finally, we multiply the total bank assets of a certain period by calculating the TailVaRx to get the current bank's economic capital value = VaR X ị + E ẵX VaR X ịjXVaR Š 3.1.2 | Core explanatory variable: Fintech application index That is: ThaiVaR = ð1 −αÞ − ð +∞ VaRα ðX Þ xf x ðx Þdx ð5Þ where fx(x) is the distribution density of the random variable X, and VaR is the value at risk under the confidence level α The confidence level selected is 99.99% in this article so that more risks can be accommodated The larger the value, the poorer risk-taking capacity of commercial banks This paper uses a simplified method of normal distribution assumption to calculate ThaiVaR When the loss of the bank (that is, the opposite of profit) X follows a normal distribution, namely À Á TailVaRx = E XjX > x q = μ + ασ ð6Þ f ðx q Þ where α = − F x ;f( ) is the density function of X;F( ) is ð qÞ the cumulative distribution function of X; xq is the The construction of Fintech application index is a very critical step in this article Existing research is mainly constructed through text mining and index synthesis methods This article refers to Guo (2015) Internet financial index construction method to construct the Fintech application index The index includes four secondary indicators: Fintech composite index, personal deposits of commercial banks, personal loans of commercial banks, new credit card issuance.1 The raw data of the Fintech composite index comes from Baidu Search Index and Baidu Consulting The construction steps are as follows: First, select keywords This article selects keywords based on the application of Fintech to commercial banks' thirdparty payment, wealth management, open banking and other fields, combined with the data provided by financial reports of commercial bank, and finally selects Fintech, electronic payment, internet finance, third-party payment, mobile phones payment and e-banking YAO AND SONG TABLE Descriptive statistics for ROA Bank Mean (negative) Std.dev The p value of K-S test Bank of China −0.6952 0.3127 0.597 Agricultural Bank of China −0.6900 0.2994 0.728 Industrial and Commercial Bank of China −0.6952 0.3685 0.704 Bank of Communications −0.6016 0.2729 0.891 China Construction Bank −0.8339 0.3637 0.666 Ping An Bank −0.5371 0.2480 0.664 Hua Xia Bank −0.5435 0.2786 0.672 Industrial Bank −0.6754 0.2949 0.797 China Minsheng Banking Corp., Ltd −0.6911 0.3198 0.984 China CITIC Bank −0.5829 0.2823 0.937 China Everbright Bank −0.6068 0.844 0.943 China Merchants Bank −0.7956 0.3387 0.509 Shanghai Pudong Development Bank −0.6436 0.3110 0.683 Bank of Beijing −0.6418 0.2716 0.874 Bank of Ningbo −0.6756 0.2985 0.578 Bank of Nanjing −0.6306 0.2870 0.819 T A B L E The value of VaR and TailVaR under different confidence levels under the standard normal distribution Confidence level (%) 0.95 0.99 0.995 0.999 0.9995 0.999 VaR 1.645 2.326 2.576 3.090 3.291 3.719 TailVaR 2.063 2.665 2.892 3.367 3.554 3.959 6000 5000 4000 3000 2000 1000 FIGURE Internet financial third-party payment fintech electronic payment electronic banking mobile payment Trends in the various component of Fintech indexes [Colour figure can be viewed at wileyonlinelibrary.com] 2019.7 2019.4 2019.1 2018.7 2018.10 2018.1 2018.4 2017.7 2017.10 2017.4 2017.1 2016.10 2016.7 2016.4 2016.1 2015.7 2015.10 2015.4 2015.1 2014.7 2014.10 2014.4 2014.1 2013.10 2013.7 2013.4 2013.1 2012.7 2012.10 2012.1 2012.4 2011.7 2011.10 2011.4 2011.1 YAO AND SONG construct a basic dimension Second, according to the relevant data and factor analysis, the monthly average of the keyword attention is obtained and the trend chart of each component of the Fintech composite index from January 2011 to September 2019 is drawn (Figure 1) Finally, through the use of SPSS software for principal component analysis and factor analysis, to reduce the dimensionality of keywords, get the common factors of keywords and then calculate the Fintech composite index Then use the same method to integrate the four secondary indicators and finally establish a Fintech application index When using SPSS for data processing, the KMO test value is 0.599 In the Bartlett test, the Sig value is less than 0.001, indicating that the selection method in this article is reasonable 3.1.3 | Control variables We choose several bank and country level characteristics as control variables In terms of the country characteristics, the control variables include (a) The growth rate of GDP (the logarithm of Gross national product) Studies suggest when the economy is on an upward trend, banks are willing to invest more funds to obtain higher returns Once the economic situation reverses, the market risk of commercial banks also increases In order to draw lessons from past experiences, Basel III proposed macroprudential principles and management of reverse economic cycles to reduce previous “short-sighted behaviours” and improve the risk management level of commercial banks (b) The growth rate of M2 (the logarithm of currency supply) The monetary authority implements monetary policy mainly through commercial banks, and the change in the money supply represents the intention of the monetary authority to adjust and control the market With the continuous development of Fintech, the speed and cost of transactions have decreased Recently, the monetary authorities have begun to try to issue digital currencies, and the money (paper currency) supply may decrease in the future In terms of the bank-specific characteristics, the controls include (a) ROE (net assets per share) On the one hand, Fintech can reduce the asymmetric cost of transaction information and increase profitability On the other hand, in accordance with the principle of high risk and high return, commercial banks will be encouraged to participate in high-risk behaviours in order to pursue higher returns (b) COST (the cost to revenue ratio, operating expenses and depreciation account for the proportion of operating income), it reflects bank's ability to obtain income The higher the proportion, the higher the cost and expense and the poorer profitability of the bank In the environment of Fintech innovation, commercial banks can develop and provide more new products and services In the process of innovation, they can expand their business scope and increase the channels for obtaining funds, which will reduce operating costs and increase economic benefits, and increase profitability of commercial banks (c) NI(the Non-interest income) is also an important indicator for measuring bank profitability The original single profit model of commercial banks' deposit-loan spreads limited the profitability of commercial banks under interest rate marketization Fintech can help banks vigorously develop capital transactions and clearing, asset management and robot consulting services These non-interest income businesses can improve the profitability of banks However, some scholars have found that the expansion of non-interest income business will lead to increased instability of bank income, which will increase bank market risks And these risks are heterogeneous for different types of banks (Jin, 2018; Xu & Zheng, 2018) For example, large stateowned commercial banks have advantages in terms of asset scale and customer sources, which can partially reduce instability Joint-stock banks are subject to geographical and scale restrictions, but due to their relatively small business volume, the overall risk is lower than that of state-owned banks (d) Asset size (the logarithm of total assets) The relationship between the size of bank assets and the level of risk management is temporarily inconclusive On the one hand, “too big to falling,” the larger the scale of bank assets, the diversified investment can diversify risks and improve risk management and profitability On the other hand, due to the huge dividends generated at the beginning of the Fintech era, commercial banks tend to invest in high-risk areas, and the probability of commercial bankruptcy has increased (e) CAR (Capital adequacy ratio) It reflects the capital adequacy of bank and is a comprehensive index that measures the ability of bank to bear risks The higher the capital adequacy ratio, the higher the capital required for market risks and the reduction in bank funds available, but the more robustness (f) Liquidity ratio, it is due to the mismatch in the maturity of bank assets and liabilities, and the high liquidity ratio indicates that the bank is safe, but it also indicates that the bank's capital utilization is insufficient Since the units of different variables are different, if they are used directly, it will cause errors in the empirical test results Therefore, this article uses the mean-SD method to process the original data of the above variables to avoid the influence of incorrect results due to different original data units in the comprehensive evaluation process YAO AND SONG X it − Xj Sj xit is the original data of the i_th risk indicator in the tperiod, x j represents the mean of the i_th risk indicator, Sjrepresents the SD of the i_th risk indicator Construction Bank joint-stock commercial banks: Ping An Bank, Hua Xia Bank, Industrial Bank, China Minsheng Banking Corp., Ltd., Shanghai Pudong Development Bank, China Everbright Bank, China Merchants Bank, China CITIC Bank Three city commercial banks: Bank of Beijing, Bank of Ningbo, Bank of Nanjing The data comes from the Wind database and the financial reports of commercial banks, the control variable data comes from the EPS database, and the missing data comes from online collection 3.2 | Data and methodology 3.2.2 | 3.2.1 Table lists the descriptive statistical results of the main variables in the model From this, we can notice several important statistics The average value of Bank asset scale of bank market risk is 1.902, and the SD is 0.917, which indicates that the range of change during the sample period is large and the stability is weak The Fintech application index is 3.19, and the SD is 0.46 The EC variation range is also large The SD ROE, COST and GGDP are also large, indicating that their stability is also weak Moreover, there is a big difference between the X it = n n À Á2 1X X Among them : Xj = X it Sj = X ij − Xj ð9Þ n i=1 n−1 i=1 | Data To identify the impact of Fintech on the market risk of commercial banks, this article used a database that contains 16 listed commercial banks in China from January 2011 to September 2019 It is the reflection of Chinese situation but not suitable for other countries Among the 16 listed banks, large state-owned commercial banks: Bank of China, Agricultural Bank of China, Industrial and Commercial Bank of China, China TABLE Descriptive statistics Descriptive statistics for model variables Variable type Variable name Symbol Variable design Mean Explained variable Bank risk management level EC Economic capital 1.866 Core explanatory variable Internet finance index JJKJ1 Financial technology index JRKJ2 Fintech application index Third-party payment index JRKJ3 E-banking index JRKJ4 Mobile banking index JRKJ5 Electronic payment index JRKJ6 Personal loan amount DK Personal deposit CK Credit card issuance Credit car Profitability ROE Net assets per share Control variable Min Max 0.8152 0.03 4.56 3.190 0.459 0.805 7.249 8.097 4.058 1.81 SD 21.18 COST Cost to revenue ratio 28.725 1.9146 25.3 32.9 NI Non-interest income 23.8193 1.4347 21.15 26.84 Bank asset scale SIZE Asset size 1.902 0.917 RISK CAR Capital adequacy ratio LIQUIDITY Liquidity ratio DM2 The growth rate of M2 GGDP The growth rate of GDP Macroeconomic level 13.26 0.129 0.7035 12.13 3.435 14.64 1.9548 1.102 −4.15 14.0926 0.3043 13.5061 14.5740 1.913 0.935 0.421 3.793 4.72 10 YAO AND SONG maximum and minimum values of all variables, which indicates that there may be heterogeneity in the sample 3.2.3 | Methodology General econometric methods such as ordinary least squares, fixed effects, random effects and generalized least squares may not meet our estimation requirements, resulting in biased results To solve this problem, we use the generalized method of moments (GMM) proposed by Arellano and Bond (1991) This method has less strict requirements on the assumptions than the least square method The loose assumptions of the GMM method make it widely used in Econometrics The bias of the finite sample in the GMM estimation is negligible, and the variance is much smaller | EMPIRICAL TEST 4.1 | Result analysis Before presenting and interpreting our test results, we first check the possible multicollinearity between the model variables Multicollinearity can distort the accuracy of regression coefficients and make their estimated fluctuate to the data The results of the multicollinearity test are shown in Table Table shows that state is highly correlated with several variables: GGDP, GM2 and NI Except for the three cases, the relationship between the other variables is weakly, so there is a problem of multicollinearity We conducted empirical tests according to Equations (1) and (2) and found that the empirical results were not ideal, and the coefficients of some indicators were contrary to economic significance Therefore, we decided to modify the original model to eliminate TABLE Correlation matrix CAR CAR COST variables that are both highly correlated and contrary to economic significance The final results are shown in Tables and Table presents the results of Fintech on the economic capital of bank risks This article uses panel mixed regression, fixed effects and random effects, the differential GMM method of the GMM method and the system GMM (Sys-GMM) to test Because economic capital is a continuously adjusted variable, the first-order lag period of economic capital is introduced as an explanatory variable in the model At the same time, other variables will influence each other, which will cause the explanatory variable to be related to the disturbance term, and there will be endogenous problems In order to solve the corresponding endogeneity problem and avoid the loss of sample information caused by the differential GMM, we use the dynamic panel system generalized estimation method to estimate the model, namely SyS-GMM This article will focus on the experimental results of the SyS-GMM In order to ensure the applicability of the estimation method, AR(1) statistics and AR(2) statistics are used to test the autocorrelation of the disturbance items, and he Sargan test is used to analyse the exogeneity of the instrumental variables The p value is greater than 5% The validity of the instrumental variable was tested by Sargan test, which confirmed the validity of the instrumental variable because its p value was greater than 5% That is the choice of this model is reasonable Empirical results are displayed in Table 5, the Fintech has a negative and significance effect at the 1% level of significance on the performance of China banks, indicating that the higher the development of Fintech, the lower the risk economic capital (measured by EC) required by commercial banks This is consistent with the Hypothesis 1a The coefficient of GGDP has a significant negative correlation with the economic capital of commercial bank risk It shows that Fintech can provide commercial COST FINTECH GGDP GM2 Liquidity NI SIZE ROE 1.000 −0.109 1.000 FINTECH 0.4145 −0.387 1.000 GGDP 0.504 −0.262 0.841 1.000 GM2 0.444 −0.433 0.862 0.949 1.000 Liquidity 0.220 −0.228 0.421 0.413 0.418 1.000 NI 0.186 −0.276 0.491 0.528 0.615 0.334 1.000 SIZE 0.046 −0.430 0.872 0.948 0.988 0.44 0.620 1.000 ROE 0.439 −0.349 0.651 0.742 0.762 0.339 0.394 0.751 1.000 YAO AND SONG TABLE Variables 11 Result of TEST (Dependent Variable: EC) Mixed effect Fixed effect Random effect EC(−1) Diff-GMM 0.9546 * 0.9217* (.0000) Fintech GGDP GM2 SIZE CAR ROE NI COST 0.095 ** 0.195 * Sys-GMM (.0000) −0.0943 −0.2746 (.0000) * −0.2540* (.0251) (.0042) (.0266) * 1.2870 * 0.7599 * (.0000) (.0000) (.0000) 0.0615* −3.196* −3.0598* (.0000) (.0000) (.0005) −0.266 0.515* 0.4021* (.093) (.0003) (.000) (.000) (.000) 0.0256 −0.034 −0.008 −0.044* −0.038* (.5014) (.4159) (.836) (.0000) 0.5911 *** (.0000) −1.5754 * −1.488* (.0000) (.0000) −0.041* −0.039* (.0000) −0.065 0.014 0.008 0.29995 (.6157) (.7944) (.8792) (.0000) (.0000) 0.15* 0.1587* 0.1685* (.0000) (.0010) (.0005) 10.2 33.7 −0.1368* −0.1774* −0.1725* (.0016) (.0010) (.0005) −0.0020 0.0045 (.9537) (.8950) 44.16 42.31 Liquidity Cons * 0.2968* (.0000) (.0004) (−.584) (1.132) 560 560 560 528 528 0.57 0.603 0.5919 AR(1) 0.09 0.08 AR(2) 0.42 0.186 Sargan-p 0.20 0.23 N R F 0.0000 Hasuman 0.0018 Note: (1) *, **, and *** indicate significant at 1%, 5% and 10% significance levels, respectively; (2) p-value in brackets banks with a variety of advanced technologies and algorithms, it can effectively learn and correct the information and data of economic activities and comprehensively evaluate the overall economic situation，so the economic capital needed decreases There is a significant negative correlation between the scale of commercial banks and the economic capital of risk, indicating that Fintech provides advanced technology to analyze market data Large state-owned commercial banks have the advantage of asset scale, so they can provide more financial innovation products and service, less expensive, and increase income, so the risk is reduced, the required economic capital decreases The larger the bank's assets, the lower the requirements for risk economic capital of commercial bank The coefficient of the ROE is significantly positive, indicating that banks with strong profitability have a preference to chase profits, and the investment amount that can be obtained is large, so the economic capital needed increases In order to further study the impact of Fintech on the risk of different asset scales of commercial banks, this article adds the cross term of Fintech and multiple control variables to test on the basis of Equation (2) and also adds a Fintech lag term to test whether Fintech has continuous and heterogeneous effects on banks of different asset sizes The AR(2) test shows that there is no second-order serial correlation between the differences of the disturbance items, and the Sargan test shows that the test results are valid.2 12 YAO AND SONG TABLE Heterogeneity test Variables Mixed effect Fixed effect Random effect Diff-GMM * EC (−1) 0.7863* 0.7326 (.0000) FINTECH 0.0267 0.096 (.5877) DFINTECH GGDP GM2 SIZE ROE −0.3725 0.033 (.3251) (.5008) 0.3408 −0.2915 −0.2695 (.0033) (.7666) 0.2762 (.0582) * 0.9400 * * (.0000) (.0000) 0.058 * * −0.4886 0.9697 (.0000) −3.7517 * −3.5803 (.8000) * (.0002) (.0000) * (.0000) (.0004) −0.3472** 0.3817 0.3911 (.0416) (.1505) (.1396) −0.0146 0.0124 0.011 −0.4740* −0.4191* (.8348) (.0000) (.0000) * 0.1549* (.8160) FINTECH*ROE 0.1609 (.0000) 0.0276 (.4800) Cons N R F −0.4799* (.0000) (.7820) CAR −0.5201* (.0000) (.0070) 0.7740 (.0000) * (.0000) * 0.3146 *** Sys-GMM −0.0243 (.5758) 51.70 * (.0000) −0.0284* −0.01 −0.0324 (.8021) (.0141) (.0041) 496 496 * * 49.35 (.0002) (.0003) 512 512 512 0.57 0.8692 0.59 0.0000 Hasuman 0.0018 AR(1) 0.092 0.058 AR(2) 0.42 0.38 Sargan 0.25 0.20 Note: (1) *, **, and *** indicate significant at 1%, 5% and 10% significance levels, respectively; (2) p-value in brackets According to the estimation results of panel mixed regression, fixed effects and random effects, the differential GMM method of the GMM method and the system GMM (Sys-GMM), the following conclusions are obtained: According to the results in Table 6, the cross-term of the Fintech application index and commercial bank ROE passed the significance test, indicating that the risk level of Fintech for different types of commercial banks is different, and the heterogeneity test passed The lag term of the Fintech Application Index is not significant, which may be due to the small sample size and short inspection period In addition, the coefficient of CAR is negatively correlated with economic capital, which is in line with economic significance The test in Table meets the Hypothesis 2a 4.2 | Robustness test In order to ensure the robustness and reliability of the model test results, this article replaces the nonperforming loan rate with the explanatory variables of the model Although the results of the model test have different absolute values of the coefficients, the results are basically consistent with Equations (1) and (2) | CONCLUSION AND IMPLICATIONS This article aims to provide a new perspective on the debate about the economic capital of commercial bank risk using quantitative analysis To this, this article YAO AND SONG used data related to a sample of 16 listed commercial banks in China observed during the period January 2011 and September 2019 collected from both banks' financial reports and Wind database The Generalized Method of Moments (GMM) was used to estimate the parameters of our econometric model As far as we know, there are no published empirical studies that combine Fintech and economic capital of bank risk This article provides a new perspective to research the changes in commercial bank risk economic capital under the influence of Fintech Empirical results show that Fintech can reduce the economic capital of commercial banks' risk and has a significant negative relationship with it With the continuous development of Fintech, commercial banks' information acquisition methods, speed and transaction costs have decreased Commercial banks can provide more personalized services to satisfied customer needed, also enlarge their profit margins and reduce risk Large state-owned commercial banks have advantages in capital, experience and scale, the scale of commercial bank assets and risk economic capital show a significant negative relationship Banks with strong profitability can have more funds to invest, and the required economic capital will increase The application of Fintech has produced significant heterogeneity in the impact of different types of banks' profitability Our results have some interesting implications From the perspective of banks, commercial banks need to increase their investment in capital and manpower, adopt self-development or cooperation with Fintech companies and deeply integrated with Fintech as soon as possible Due to capital and area constraints, city commercial banks can adopt a mode of selectively imitating and copying the Fintech reforms of large state-owned commercial banks based on their own characteristics, reducing transaction costs and data analysis costs, in order to improve their competitiveness (Arner, Barberis, & Buckley, 2017) From the perspective of government regulation: On one hand, government needs to make corresponding toplevel3 design in various aspects such as the regulatory system and laws, industry standards, technical standards and risk monitoring systems In addition, in order for small and medium banks to have more space for development, the design must take into account fairness Smalland medium-sized banks have limited integration with Fintech due to their own limitations Since 2013, the central bank's base currency delivery channels have clearly favoured large banks The central bank explicitly requires banks to provide high-quality collateral or pledges for all types of loans, and large national banks are better able to provide these pledges than small regional commercial banks; These discriminated small- and medium-sized 13 banks in the short term it is difficult to obtain the funds of this new channel through business competition, for the growth and growth of small- and medium-sized banks there are capital constraints On the other hand, referring to the British-style sandbox4 regulatory model(Arner, Barberis, & Buckley, 2017), the new financial products and services are evaluated in advance to guard against systemic financial risks caused by technological innovation and to control the risks within control Design the processes, tools, access, evaluation, etc., formulate the code of conduct for regulators and participants and seek a balance between supervision and innovation development (Bernards, 2019; Demertzis, et al., 2018) CONFLICT OF INTEREST No potential conflict of interest was reported by the authors DATA AVAILABILITY STATEMENT The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study ORCID Ting Yao https://orcid.org/0000-0002-5683-910X ENDN OTE S The basis for selecting secondary indicators: (1) The availability of index The index in the sample comes from the bank's public financial report; (2) It can reflect the characteristics of Fintech's significant impact on commercial banking business First, Fintech makes commercial banks low cost and high 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Information System Engineering, (7), 49–51 How to cite this article: Yao T, Song L Fintech and the economic capital of Chinese commercial bank's risk: Based on theory and evidence Int J Fin Econ 2021;1–15... diversifieddimensional risk operation innovative ideas for commercial banks The impact of the application of Fintech on the risks of commercial banks can be summarized in two aspects: (a) The application of Fintech. .. about the essence of Fintech and the economic capital of commercial bank risk (Ma & Zhang, 2019) The internal logic and evolution mechanism of Fintech application and commercial bank risk can be summarized