Research in International Business and Finance 55 (2021) 101338

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Research in International Business and Finance

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Full length article

Can fintech improve the efficiency of commercial banks? —An

analysis based on big data
Yang Wang a, *, Sui Xiuping b, Qi Zhang c
a
School of Economics, Capital University of Economics and Business, China
b
School of International Economics, University of International Business and Economics, China
c
China institute for WTO Studies, University of international Business and Economics

A R T I C L E I N F O A B S T R A C T

Keywords: This paper assess the potential impact of Fintech on the banking industry. Results suggest that, for
Fintech commercial banks, development of Fintech leads to increased profitability, financial innovation,
Commercial Bank and improved control of risk. Overall, by using financial technology, commercial banks can
TFP
improve their traditional business model by reducing bank operating costs, improving service
efficiency, strengthening risk control capabilities, and creating enhanced customer-oriented
business models for customers; thereby improving comprehensive competitiveness. We also
find that levels of such outcomes vary with levels of respective bank’s use of technological
innovation.

1. Introduction

With the rapid development of network technology and Internet, various traditional industries innovations are booming in China.
Since 2014, a new term has entered the public’s vocabulary: financial technology, or fintech (Pedersen, 2015). Broadly speaking, fintech
involves the application of a variety of advanced technologies to support the development of the finance industry (Darolles, 2016).
Related fields include big data, artificial intelligence (AI), cloud computing, blockchain, and quantum computing. Fintech is a new type
of computer algorithm that is currently difficult to implement (Barocas and Selbst, 2016; Dhar, 2016). Fintech encompasses both
digital innovations and technology-enabled business model innovations in the financial sector. Such innovations can disrupt existing
industry structures, blur industry boundaries, facilitate strategic disintermediation, change how existing firms create and deliver
products and services, provide new gateways for entrepreneurship, and democratise access to financial services (Admati and Hellwig,
2013; Philippon, 2016). Fintech include areas of finance in which technology is widely used, such as front-end consumer products,
competition between new entrants and existing players, and even innovative block chain technology, and cryptocurrencies such as
bitcoin (Dranev et al., 2019). Examples of innovations that are central to fintech today include various applications of blockchain
technologies, new digital advisory and trading systems, artificial intelligence and machine learning, peer-to-peer lending, equity
crowdfunding, and mobile payment systems (Philippon, 2015; Darolles, 2016). The development of fintech has enhanced competi­
tiveness of commercial banks, as digital technologies have played a significant role in improving the efficiency of services provided by
banks and other financial institutions to small and micro enterprises, and to private enterprises (Bodenhorn, 2000; Berg et al., 2019).
Banks and other financial institutions are seeking to minimise the costs of customer acquisition and risk control, reduce operating costs

* Corresponding author at: School of International Economics, University of International Business and Economics, China.
E-mail addresses: rucwy@126.com (Y. Wang), sui_xp@163.com (S. Xiuping), Zhangqi3226608@163.com (Q. Zhang).

https://doi.org/10.1016/j.ribaf.2020.101338
Received 17 January 2020; Received in revised form 13 September 2020; Accepted 27 September 2020
Available online 3 October 2020
0275-5319/© 2020 Published by Elsevier B.V.
Y. Wang et al. Research in International Business and Finance 55 (2021) 101338

and improve efficiency, and enhance the user experience for a wider range of consumers, leading to increasingly strong demand for
fintech applications.
Intelligent decision-making, marketing, risk control, operations, and customer service enabled by fintech can optimise an in­
stitution’s credit process and customer evaluation model (Aylin and Ahmet, 2020), enable the quick lending of money, reduce the
overall cost of corporate financing, and enhance the economic efficiency of financial services (Lucey and Roubaud, 2020). For
example, after a firm submits a loan application online, a fintech system can automatically decide whether to provide a loan or not
without manual intervention (Bazot, 2013a, 2013b). Banks can model and analyse user data through customer profile regression, filter
users according to nearly 10,000 rules, refer to Google’s PageRank and analyse the weights of ordinary users on social networks.
Furthermore, fintech in China has accumulated more than a billion anonymous samples encompassing six major categories of core
data, such as social and e-commerce data, and has verified and optimised the customer identification model in practice (Bickenbach
et al., 2009a, 2009b). The impact and challenges of fintech for commercial banks are primarily reflected in the impact of online
payments (including third-party and mobile payments) and intermediary services such as payment and settlement (Chamley et al.,
2012). At the same time, the traditional asset and liability business of commercial banks has been challenged by the trend toward
financial disintermediation. Therefore, fintech has immediately affected both commercial banks’ customer base and their market
competition (Dhar, 2016). This study assesses the potential impact of fintech on the banking industry by examining the relationship
between the use of fintech and commercial banks’ total factor productivity (TFP) (Bergstresser et al., 2009). This paper is structured as
follows: Section 2 briefly reviews the related literature and presents hypotheses; Section 3 introduces the model, data, and method­
ology for the study; Section 4 provides my empirical results and analysis; and Section 5 summarises the main findings and presents the
conclusion.

2. Literature review

The rise of fintech has had a major impact on the traditional business of commercial banks (Petralia et al., 2019). In key areas such
as residential mortgages, commercial banks have lost market share to shadow banks and fintech lenders, which are subject to different
regulations and enjoy technological advantages (Buchak et al., 2018). Fintech lenders serve more creditworthy borrowers than shadow
banks but charge higher interest rates (14–16 basis points), which supports the idea that consumers are willing to pay more for a better
user experience and faster lending decisions. Another difference between fintech lenders and traditional lenders in the mortgage
market is that the former process applications 20 % faster, without increasing loan risk (Fuster et al., 2019). Fintech lenders also
respond more elastically to demand shocks and have a higher propensity to refinance, especially for borrowers who are likely to benefit
from it. In this way, fintech lenders have improved the efficiency of financial intermediation in mortgage markets (Klaus et al., 2020).
The advent of fintech is often considered a promising avenue for reducing unequal access to credit. Misalignment of incentives
within finance firms can lead to biased lending decisions (Dobbie et al., 2018). Fintech lenders may alleviate discrimination in
mortgage markets; traditional lenders charge minorities more for purchase and refinance mortgages, and fintech algorithms
discriminate 40 % less than do face-to-face lenders (Bartlett et al., 2018). New financial technologies and data may offer superior
capability for screening borrowers (Berg et al., 2019). The predictive power of data collected by fintech, which is based on consumers’
digital footprints, equals or exceeds that of traditional credit bureau scores when it comes to predicting consumer default (Pagnotta
and Philippon, 2018).
Fintech companies are also competing in the market for wealth management. The United States is the leading market for robo-
advisors, and accounts for more than half of all investments managed by robo-advisors in 2017 (Abraham et al., 2019). Neverthe­
less, the assets managed by robo-advisors are still a small portion of the total assets under management, with average client wealth
much less than the average in the industry (Garleanu and Pedersen, 2018). Because they save on fixed costs (such as the salaries of
financial advisors and maintenance of physical offices), robo-advisors can reduce minimum investment requirements and lower fees
(Abraham et al., 2019).
Finally, fintech has also intensively impacted the intermediary businesses of commercial banks, as well as the incentives within
organisations. Payment settlement has always been one of the most basic and traditional intermediate businesses of commercial banks.
According to information asymmetry theory, commercial banks, as financial intermediaries, help alleviate information asymmetry to a
certain extent (Kelly et al., 2016). Their information-based advantage and the resulting monopoly position have granted commercial
banks long-term and unique advantages. Fintech, which enables third-party and mobile payments, has reduced these advantages
(Berger et al., 1999). Third-party and mobile payments have far lower costs than the services provided by banks. Cloud computing
support and other technologies can efficiently store and manage customer data, thereby more effectively alleviating information
asymmetry, and can realise payment and settlement more conveniently and efficiently than traditional methods (Baker and Wurgler,
2015).
In conclusion, fintech influences the efficiency of commercial banks in several different ways. In this study, I apply a data
envelopment analysis (DEA) Malmquist non-parametric method to evaluate the multi-input and multi-output effects on the banking
industry and to calculate the TFP of commercial banks, then analyse the impact of fintech on the efficiency of commercial banks.
Based on this, the hypotheses are:

1 Fintech promotes the TFP of commercial banks.

2 The positive impact of fintech on the TFP of commercial banks varies according to the degree to which banks apply the
technology.

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3. Model and data

Based on the above theoretical analysis, the development of fintech has resulted in increased profitability, financial business in­
novations, and improved risk control for commercial banks. That is, by using fintech, commercial banks can improve their traditional
business models, reduce operating costs, improve service efficiency, strengthen risk control capabilities, and directly create more
attractive business models for customers, thereby improving their comprehensive competitiveness. Because commercial banks of
different sizes and types are affected differently by the development of fintech, econometric models should fully consider the impact of
variables on the heterogeneity of commercial banks on regression. Therefore, in developing my model, it was necessary to introduce
heterogeneity control variables. The competitiveness of commercial banks involves many factors which are difficult to quantify.
Therefore, I used the TFP of commercial banks as a proxy for their competitiveness. Because TFP has a certain viscous effect, it was
necessary to use a lagging TFP.

3.1. Model

TFPit = α + βFintechit + γXit + δTFPit− 1 + μit + εit

In the above model, the main explanatory variable, TFPit , indicates the change in the TFP of bank i in year t. When it is greater than
1, the bank’s TFP in that year improved over the previous year, and when it is less than 1, this indicates a decrease compared with the
previous year. When it is equal to 1, it is the same as in the previous year. Fintechit indicates China’s fintech index (i.e. China’s fintech
development indicators) for t years. Xit is the control variable, including macroeconomic indicators, the maturity of the financial
market, monetary policy indicators (i.e. the strength of monetary policy), capital market development indicators, and some bank-level
heterogeneity indicators, such as sector concentration at the industry level, bank size, risk-bearing capacity, profitability, resource
allocation ability, innovation ability, and whether it is a listed bank. Because of the lagging period of TFPit− 1 , I form a dynamic data
panel model. To solve the endogenous problem, the baseline regression discussed in this paper uses two methods: systematic gener­
alised moment estimation (SYS-GMM) and differential generalised moment estimation (DIF-GMM). On this basis, I use regression as a
robustness test.

3.1.1. TFP
My analysis uses TFP as the proxy variable for the competitiveness of commercial banks. When calculating the TFP of a commercial
bank, two choices must be considered: the TFP calculation method and the input and output (Brei and Gambacorta, 2016).
Measuring the inputs and outputs of banks is a difficult problem, because the characteristics of banks are distinctive (O’Mahony and
Timmer, 2009). Unlike, for example, companies in the manufacturing industry, which produce tangible goods, banks produce products
that are both intangible (i.e. intermediate services) and composite (i.e. composed of a range of products). Bank efficiency studies have
used many methods of measuring bank output, such as the number of deposit and loan accounts and the revenue in each account
(Bolton et al., 2011). Because my analysis emphasises the basic nature of banks’ production processes, rather than stock changes, I
consider the services provided to customers as bank outputs.
There are two main methods for measuring service products: production and intermediary methods (Chamley et al., 2012). The
production method considers banks to be companies that produce different deposit and loan accounts. The number and type of
transactions and vouchers are considered the best measure of bank output (Favara, 2009). However, it is generally difficult to obtain
this data. Therefore, in practice, the number of deposit and loan accounts alone is usually used to measure the output of banks. The
intermediary method considers banks to be financial intermediaries that transfer funds between depositors and lenders (Dell’Ariccia
et al., 2016). In the production method, a bank’s number of bank loans and investments represent its output, while labour and deposits
represent inputs. The intermediary method, on the other hand, uses deposits as an input while taking into account operating and
interest costs. Neither method is perfect, and they may complement each other. Each of the above methods emphasises the functions of
a certain aspect of a bank, and they can be used to analyse efficiency on different levels. Given that the production method considers
the operating costs of banks, it is most suitable for studying cost efficiency. As the intermediary method assesses the overall cost of
banks, it is most suitable for analysing the economic differences between banks. This method takes interest costs into account and is
useful for assessing bank efficiency and undertaking boundary analyses. However, there are different theoretical interpretations of the
way commercial banks operate, as well as differences in the definitions of the input and output variables of banks.
In the existing literature, there are five commonly used methods for defining bank inputs (Levine, 2005): the production method,
the intermediary method, the asset method, the user cost method, and the added value method (Glode et al., 2012). The main dif­
ference between these methods is the existence of a rationality of the bank and a different understanding of the role of the bank, and the
resulting choice of input (Greenwood and Scharfstein, 2013). As discussed above, according to the production method, the output of
banks is measured by the number of their deposit and loan accounts. A bank’s inputs are usually capital and fixed labour costs (Kelly
et al., 2016). In contrast, the intermediary method, as discussed above, considers that banks pool idle funds and distribute them to the
parties who need them, acting as an intermediary between fund suppliers and demanders (Lixin, 2020).
The asset method also considers banks as intermediaries between cash suppliers and demanders, and defines the output of a bank as
assets on its balance sheet, mainly including loans (Kovner et al., 2014). Under this method, deposits are viewed as liabilities rather
than outputs. This method considers that assets should be regarded as outputs when the opportunity cost for the bank is lower than its
return on the assets. Deposits are also considered to be liabilities when the opportunity cost for the bank is higher than the value of the

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Table 1
Fintech dimensions.
Payment dimension Online payment

mobile payment
Third party payment
QR code payment
Network payment
Resource allocation dimension internet loan
Internet lending
Network investment
Online lending
P2P loan
Risk management dimension Internet insurance
Internet financing
Network financing
Online financing
Network insurance
Network channel dimension Mobile banking
Online Banking Service
Internet Banking
E-bank
E-bank
Big data dimension Big data
Big data
data mining
Big data analysis
Big data application
AI dimension AI
Artificial intelligence
intelligent robot
natural language processing
machine learning
Distributed technology dimension cloud computing
Cloud platform
Digital currency
Bitcoin
Blockchain Technology
Internet technology dimension Internet of things
Vehicle interconnection
Mobile Internet
5G
Mobile communication
Security technology dimension Biometrics
fingerprint identification
Iris Recognition
Face recognition
Voice recognition

liability.
The added value method considers that a bank’s inputs include labour and physical capital to purchase funds, and its outputs are
the activities that generate high added value, such as loans, demand deposits, and time deposits (Kumar, 2016).
The first three methods mentioned above are currently the main methods used in the literature. The same calculation method may
use different input and output variables due to different data sources or focus points. The production method requires an analysis of the
volume of business done by a bank, and this information is difficult to obtain, so I do not use this method. However, whether the asset
method or intermediary method is used, production is the most important factor, and the final result of production is the attainment of
income and maximisation of profits. Therefore, my research focusses on the production results of banks (Sun et al., 2020).
I define bank input variables as their labour force costs and registered capital. I consider a bank’s labour force to be the number of
full-time employees in a given year, including managers, sales staff, and other personnel at all levels within the head office and
branches, and define capital as the owner equity component of a bank’s income statement, to include physical and financial capital.
As discussed above, at present, there is no unified view of the definition of bank output. Based on existing research both in China
and abroad, I combine the characteristics of China’s banking industry to define bank output variables as follows: loan amount, profit
amount, and deposit amount. As special financial enterprises, the business objective of banks is to maximise income and profit.
Therefore, I include loan and profit amounts as outputs. Due to the liquidity and security of some deposits, it is difficult to judge
whether a bank’s deposit amount should be attributed to output or input. However, China’s commercial banks do not charge any fees
when handling deposits, and this intermediary behaviour has a positive external effect on society. Therefore, I consider it reasonable to
also use deposits as an output.
As mentioned above, I use the DEA Malmquist non-parametric method (Fare, 1994) to evaluate the multiple inputs and outputs of

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Table 2
Data Description.
Variables Obs mean Standard min max

TFP 1243 1003 0.124 0 2.547

Fintech index 1130 0.594 0.319 0 1
GDP% 1130 7.96 1.346 6.6 10.6
Extend of finance 1130 59.125 12.526 42.02 78.5
Assets scale 1130 7.599 1.624 4.417 12.532
Capital adequacy ratio 1130 13.013 2.313 3.24 40.3
Deposit ratio 1130 69.011 182.405 13.81 61.81
Proportion of non-interest income 1130 11.195 8.766 0 47.1
ROA 1130 5.093 23.206 − 59 209
listed 1130 0.212 0.409 0 1

Table 3
Multicollinearity matrix.
TFP Fintech GDP growth maturity of asset Capital adequacy deposit deposit ROA
index rate financial market scale ratio ratio ratio

TFP 1.000
Fintech index 0.043 1.000
GDP growth rate − 0.007 − 0.615 1.000
maturity of financial − 0.053 − 0.014 − 0.060 1.000
market
asset scale 0.032 0.329 − 0.304 0.004 1.000
Capital adequacy 0.052 − 0.058 0.093 − 0.072 − 0.204 1.000
ratio
deposit ratio 0.024 0.006 − 0.013 − 0.034 − 0.005 0.011 1.000
deposit ratio 0.067 0.167 − 0.162 0.106 0.016 − 0.001 − 0.020 1.000
ROA 0.022 − 0.049 0.049 − 0.040 − 0.182 0.078 − 0.003 − 0.019 1.000

the banking industry. This method calculates the total factor productivity of a commercial bank and then analyses the impact of fintech
on the efficiency of the bank.

3.1.2. Fintech
Fintech can be evaluated across different dimensions, including payment calculation, resource allocation, risk management, and
network channels. These dimensions can be combined with fintech performance and big data indices.
I use factor analysis to comprehensively calculate China’s fintech index (i.e. China’s fintech development indicators) based on the
following five dimensions: big data (Vu Le and Leiviska, 2020), artificial intelligence (Shay-Kee et al., 2020), distributed technology,
the interconnectedness of technology (Shaker et al., 2020), and the security of technology. The application layer used another four
dimensions, based on those suggested by Asktitas (2009) and listed above.

3.1.3. Bank size

I calculated bank size based on the log value of a bank’s total assets. Research has shown that large banks take advantage of
economies of scale and scope to reduce transaction costs and improve operating efficiency. However, some studies have suggested that
the expansion of banks makes management more complicated and reduces efficiency. Considering that fintech impacts all aspects of
the banking business, commercial banks with a larger asset scale are more capable of introducing fintech and reforming and innovating
traditional business models, and are thus more likely to improve their operating efficiency.

3.1.4. Capital adequacy ratio

Most recent empirical studies have used the capital adequacy ratio to measure the robustness of banks. Considering that the capital
adequacy ratio was not available for most commercial banks in the sample, and that the calculation method for the ratio was also
adjusted by the CBRC in 2004, I use the ratio of owner equity to total assets as the risk control agent variable to calculate the robustness
of each bank.

3.1.5. Bank return on assets (ROA)

Banks retain capital to increase capital and improve their ability to manage risks, and therefore the current profitability of banks
has a positive impact on TFP. This study adopts the same method as Rime and replaces the bank’s current profitability with its total
asset income replacement rate (ROA) as the profitability proxy variable.

3.1.6. Bank loan-to-deposit ratio (LDR)

I used bank LDR as the bank resource allocation capability proxy variable. To calculate the LDR, I divided a bank’s total amount of

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Table 4
Baseline regression after winsor.
DFF-GMM SYS-GMM

(1) (2) (3) (4) (5) (6)

Fintech index
TFP
GDPgrowth rate
maturity of financial market
asset scale
Capital adequacy ratio
deposit ratio
deposit ratio
ROA
listed
constant
obs 678 678 678 791 791 791

Note: The values in parentheses are the T statistics after correction of heteroscedasticity: *, **, *** represent the significance levels of 10 %, 5%, and
1%, respectively.

loans by its total amount of deposits for the same period. I drew these figures from bank balance sheets, on which loans are listed as
assets, while deposits are listed as liabilities. A bank’s LDR shows its ability to cover loan losses and withdrawals by its customers.
Investors monitor the LDR of banks to make sure they have adequate liquidity to cover loans in the event of an economic downturn
resulting in loan defaults (Tables 1–4).

3.1.7. Deposit ratio

In my model, the proxy variable for a commercial bank’s innovation capability is equal to its non-interest income divided by its
total revenue ratio. An increase in a bank’s cash deposit ratio leads to a decrease in its money multiplier. An increase in deposit rates
induces depositors to deposit more, thereby leading to a decrease in the cash-to-aggregate-deposit ratio.

3.1.8. Other macro-level control variables

I also use the GDP growth rate or monetary policy to control for the impact of macroeconomic shifts on TFP.

3.2. Data

Based on data availability, I selected a data panel to include 113 domestic commercial banks from 2009 to 2018. However, TFP, the
main explanatory variable for this study, has a certain sticky effect. Generally, when performing a regression analysis, it is necessary to
include a TFP lag. Given that I use the DEA Malmquist method to calculate TFP, I had to obtain input-output information for the year
prior to the beginning of the dataset (i.e. to calculate the change in TFP from 2009 to 2018, it would be necessary to obtain data from
2008 to 2018). Thus, I used data from 2008 to 2017.
I analyse the multicollinearity problem between variables by describing the correlation coefficient matrix of the main variables.
The correlation coefficient for each variable reported in the table below was less than 0.7, indicating that there is no serious multi­
collinearity problem.

4. Empirical testing

First, I conducted an empirical test of Hypothesis 1 using the dependent and independent variables outlined in Section 3 for the
benchmark regression. To make the results in this section more robust, I used two regression methods: SYS-GMM and DFF-GMM.

4.1. Baseline regression

The following table shows the benchmark regression results. The regression results using DIF-GMM estimation are reported in
columns (1)-(3). The fintech index is significantly positive at 1%, indicating that the development of fintech has increased the TFP of
commercial banks. The regression results of the SYS-GMM are reported in columns (4)–(6), showing that the fintech index is still
significantly positive, and the coefficient of the variable’s estimation is always significant when the control variable is included, thus
confirming the basic hypotheses of this article.
As shown in columns (5) and (6), the asset scale is significantly positive, indicating that the larger the asset scale, the higher the TFP
of a commercial bank. As discussed, large-scale commercial banks can generate economies of scale and are thus more capable of
introducing fintech to improve their operating efficiency.
The estimated coefficient of capital adequacy ratio is significantly positive, indicating that the higher the risk-bearing capacity of a
commercial bank, the higher the TFP. This is consistent with the findings of existing research. For example, John et al. noted that risk-
bearing capacity was related directly to corporate sales growth and investment in innovation. Appropriate risk-bearing capacity can

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Table 5
DFF-GMM subsample regression results.
National City Rural

(1) (2) (3) (4) (5) (6)

Fintech
TFP
GDPgrowth rate
maturity of financial market
asset scale
Capital adequacy ratio
deposit ratio
deposit ratio
ROA
obs 108 108 432 432 138 138

Note: The values in parentheses are the T statistics after correction of heteroscedasticity: *, **, *** represent the significance levels of 10 %, 5%, and
1%, respectively.

Table 6
SYS-GMM subsample regression results.
National City Rural

(1) (2) (3) (4) (5) (6)

Fintech
TFP
GDPgrowth rate
maturity of financial market
asset scale
Capital adequacy ratio
deposit ratio
deposit ratio
ROA
constant
obs 126 126 504 504 161 161

Note: The values in parentheses are the T statistics after correction of heteroscedasticity: *, **, *** represent the significance levels of 10 %, 5%, and
1%, respectively.

strengthen the ability of commercial banks to launch new businesses, thereby improving TFP.
The estimated deposit ratio coefficient is significantly positive, indicating that commercial banks with a stronger ability to allocate
resources have a higher TFP. The deposit ratio reflects the ability of a commercial bank to allocate funds. After the central bank relaxes
the deposit ratio requirements for a commercial bank, the bank can put more funds into their asset business, increasing its TFP.
As shown in columns (5) and (6), the deposit ratio is significantly positive, indicating that the more innovative the commercial
bank, the higher its TFP. This is logical; highly innovative commercial banks are better able to accept technological changes to
traditional business models.
The estimated coefficient of GDP growth rate is significantly negative, indicating an inverse relationship between the speed of
economic development and the TFP of commercial banks. This may be because when the economy is developing rapidly, commercial
banks can easily acquire a large number of high-quality deposit and loan customers, and stable interest rate spreads reduce the
willingness of banks to innovate and restructure their businesses, leading to a decline in TFP.
The estimated coefficient of capital market deepening is significantly negative, indicating that the development of the capital
market has a negative impact on the TFP of commercial banks. As a country’s capital market becomes mature, more funds flow from
commercial banks to the capital market, making it more difficult for commercial banks to absorb savings, and lowering their TFP.
The estimated coefficients of ROA and listed status are not significant, indicating that in the domestic banking system, these factors
have only a small impact on the TFP of commercial banks.

4.2. Heterogeneous impact on different types of commercial banks

Based on benchmark regression, I next divided the sample of 113 banks into three sub-samples to study the impact of fintech on the
TFP of different types of commercial banks. The three sub-samples were national banks (a total of 18, including 6 state-owned large
banks and 12 joint-stock banks), urban commercial banks (a total of 72), and rural commercial banks (a total of 23). The banks were
classified into these categories on the following basis:

1) National banks have branches across the whole country;

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Table 7
DFF-GMM Robustness Regression Results.
DFF-GMM DFF-GMM DFF-GMM DFF-GMM
(1) (2) (3) (4)

LagTFP
Fintech
GDPgrowth rate
maturity of financial market
asset scale
Capital adequacy ratio
deposit ratio
deposit ratio
ROA
obs 678 108 432 138

Note: The values in parentheses are the T statistics after correction of heteroscedasticity: *, **, *** represent the significance levels of 10 %, 5%, and
1%, respectively.

Table 8
SYS-GMM robustness regression results.
SYS-GMM SYS-GMM SYS-GMM SYS-GMM
(5) (6) (7) (8)

Lag TFP
Fintech
GDPgrowth rate
maturity of financial market
asset scale
Capital adequacy ratio
deposit ratio
deposit ratio
ROA
constant
obs 791 126 504 161

2) Urban commercial banks are local commercial banks that can only open branches in specific regions, and generally provide
financial services to urban enterprises and residents; and
3) Rural commercial banks are local commercial banks that can only open branches in specific regions and generally provide financial
services to rural enterprises and farmers.

Tables 5 and 6report the regression results using DIF-GMM and SYS-GMM, respectively. As can be seen from the tables, fintech has
the most positive impact on the TFP of urban commercial banks, followed by national banks, and finally by rural commercial banks. A
reasonable explanation is that because of the restrictions on opening a new branch, urban commercial banks are at a disadvantage in
terms of acquiring offline customers compared with national commercial banks.
Therefore, they are more eager to use fintech to offset this disadvantage and cultivate their competitive advantage. Rural com­
mercial banks have the same needs as urban commercial banks, but are generally small in scale and cannot afford the upfront costs of
fintech applications. Moreover, compared with national commercial banks, the level of organisation and strength of human capital of
rural commercial banks are relatively low. A lack of understanding of fintech and its applications in rural commercial banks has
resulted in a low rate of adoption of new technologies and new business models.

4.3. Robustness test

Based on the above analysis, I next used lagged TFP as a robustness test. The results are reported in the table below. The results in
columns (1)-(6) are identical to those in the table above. The sign of the estimated coefficient of the control variable was consistent
across the columns of the robustness test. To some extent, this shows that the selected control variables can control the impact of
various factors on bank performance with relative stability (Tables 7 and 8).

5. Conclusion

Based on the above analysis, the development of fintech has increased profitability, led to innovations, and improved risk control
for commercial banks. By adopting fintech, commercial banks can improve their traditional business models, reduce operating costs,
improve service efficiency, strengthen risk control capabilities, and create more attractive business models for customers, thereby
improving their comprehensive competitiveness. Different sizes and types of commercial banks are affected differently by the
development of fintech. The adoption of fintech is likely to decrease the costs of financial intermediation, but also to create new

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regulatory issues. In this paper, I have highlighted two relevant forces that will shape the impact of fintech on inequality. In the case of
robo-advisors, I have argued that the new model of fixed costs is likely to improve participation by lower-income households.
However, this may not lower inequality across all groups. In relation to the credit market, alternate data sources are likely to reduce
non-statistical discrimination. The most basic prerequisite for a commercial bank to achieving in-depth integration with fintech and
improving its professionalism, responsiveness, and inclusivity is to have the required hardware and software infrastructure in place.
Hardware requirements include information network facilities, high-performance computers and cloud servers, and large-capacity
storage, whereas the required software capacity includes powerful data-mining and calculation, distributed storage, batch process­
ing, and advanced artificial intelligence.

Acknowledgements

This research was supported by Beijing Municipal Social Science Foundation (No. 18JDYJB001), Ministry of Education of Hu­
manities and Social Science Youth Project (No. 18YJC790212), Ministry of Education of Humanities and Social Science Project (No.
18YJC790154).

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