Research on the Development of Data Application under the
Background of Financial Technology
Wenjing Hu
School of Economics, Sichuan University, Chengdu, China
Keywords: Digital Economy, Financial Technology, Production Factor.
Abstract: In the context of the rapid development of digital economy, how can the financial sector make effective use
of data as a production factor, solve the problem of information asymmetry between financial institutions and
"long tail customers", improve the efficiency of data application, strengthen the protection of the legitimate
rights and interests and privacy of information subjects, and give better play to the important value of data in
promoting economic and financial development, It is worthy of in-depth research.
The Fourth Plenary Session of the 19th Central
Committee of the Chinese Party Central proposed to
improve the mechanism that labor, capital, land,
knowledge, technology, management, data and other
production factors contribute by market evaluation
and determine remuneration according to
contribution. This is the first time that Chinese Party
Central Committee proposed to take data as a
production factor to participate in income
distribution, which reflects the keeping pace with the
times of the basic socialist economic system under
the background of the rapid development of digital
economy. It is a major theoretical innovation. In the
financial field, the People's Bank of China has built
credit investigation system with financial data. On
this basis, using non-financial data other than
financial data to solve the problem of information
asymmetry between financial institutions and "long
tail customers" is of great significance for giving play
to the basic role of data in leveraging financial
resources and serving inclusive finance. Therefore, in
the current era of big data with the rapid development
of financial technology, based on the pilot
establishment of data exchanges in some regions, the
financial field tries to use cloud computing,
blockchain, internet of things and other technologies
to further break the non-financial data barriers, but it
also leads to a series of problems such as excessive
use of data and infringement of the legitimate rights
and interests of data subjects. How to effectively and
reasonably apply data as a production factor under the
background of financial technology is worthy of in-
depth research.
We strongly encourage authors to use this document
With the accelerated breakthrough and application of
new generation data technologies such as cloud
computing, big data, blockchain, internet of things,
industrial internet, 5G and artificial intelligence,
human society has ushered in the era of digital
economy after agricultural economy and industrial
economy. In 2020, the scale of Chinese digital
economy will reach 39.2 trillion yuan, ranking second
in the world, accounting for 38.6% of Chinese GDP.
It is expected that the proportion of digital economy
in Chinese GDP will exceed 50% by 2025. Making
good use of data resources, strengthening the mining
of data value and giving full play to the role of data
as a key production factor will effectively promote
the high-quality development of Chinese economy.
Hu, W.
Research on the Development of Data Application under the Background of Financial Technology.
DOI: 10.5220/0011156900003440
In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 55-60
ISBN: 978-989-758-593-7
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Table 1: The scale of Chinese Internet development.
Internet of
2020 39.2 trillion 37.2 trillion 1.7 trillion 303.1billion
2019 35.8 trillion 34.8 trillion —— ——
2018 —— 31.6 trillion 1.2 trillion 33.9 billion
Data sources: China Internet development report 2021, China Internet development report
2020, China Internet development report 2019.
Under the background of financial technology,
the financial field has become one of the earliest and
most widely used fields of data resources. After the
People's Bank of China built the credit investigation
system with financial data to help credit risk control,
the financial field began to further mine non-financial
data in order to improve the ability to prevent and
control credit risks. This trend is basically the same
as that of developed countries. The United States first
proposed and applied non-financial data in the
financial field. Especially after the rapid development
of financial technology, a large number of non-
traditional standard data outside the financial field are
cleaned, sorted and processed by technical means and
applied to prevent and control credit risk in the
financial field. This phenomenon is regarded as the
breakthrough application of "non-traditional data" in
the United States. Since then, non-traditional
standard data has been widely used in the
development of all walks of life. In China, the value
mining of data, especially non-traditional standard
data, is mainly concentrated in a number of service
industries such as finance, retail, clothing, food,
housing and transportation, and its application in
agriculture and industry is still in its infancy. In other
words, the data application in the financial field is
exploratory, which is mainly reflected in the
following aspects:
First, help the financial institutions to achieve
precision marketing, by accurately classifying
customers through existing customer behavior data,
and predicting credit preferences of different types of
customers, so the financial institutions can conduct
more targeted marketing, recommend more suitable
financial products to customers, and improve
marketing efficiency. In addition, data application
also can help the financial institutions to reduce
ineffective costs in traditional marketing methods,
and improve customer satisfaction. Second, after
been provided data support for the development of
intelligent credit products, the financial institutions
can accurately match the customer credit amount and
interest rate for different types of customers,
implement low interest rate for low-risk customers
and high interest rate for high-risk customers, and
carry out automatic marketing and lending, so as to
effectively shorten the customer loan time and reduce
the manual input cost in the credit process. Third,
effectively prevent and control credit risk. For
example, in terms of anti fraud, compare existing
customer data to identify possible fraud by using the
advanced technology of big data, and mark more
suspicious fraud clues such as identity forgery by
applicating desensitization data shared among
financial institutions through blockchain and other
Sources: McKinsey, The value of open data for individuals and institutions.
Figure 1: Potential Gains from Open Data for Finance by 2030.
BDEDM 2022 - The International Conference on Big Data Economy and Digital Management
However, the support level of data application in
the financial field in different countries is different,
and the data application modes are also different
among countries. In addition, there are great
differences in the value generated by data application
due to the differences in market conditions, the
robustness of digital financial infrastructure and
Sources: McKinsey, The value of open data for individuals and
Figure 2: Potential GDP Impact by 2030 by Broad
Attribution to Market Participants, % of GDP.
On June 24, 2021, McKinsey released the
research report Financial data unbound: The value of
open data for individuals and institutions. Combined
with the actual situation of different economies such
as the European Union, India, the United States and
the United Kingdom, the report analyzes the main
mechanisms and values of openning and sharing
financial data. According to the report, by 2030, the
wide use of openning data ecosystem in the EU, the
UK and the United States, may promote the economy
as high as 1.5% of GDP, and India may be as high as
4% to 5%. However, due to the lack of breadth of data
sharing in the European Union and the lack of
standardization of financial data in the United States,
the potential value that the United States and the
European Union can obtain from financial data is
expected to less than 10%; The UK's data ecosystem
is more perfect, but the breadth of data sharing is still
not enough. The potential value that can be obtained
is expected to account for 30-40%; India's data
aggregator provides a high level of standardization
and wide sharing for sharing data, enabling India to
obtain 60% - 70% of the potential value from
financial data. In other words, for emerging
economies, due to the current low level of financial
access and financial depth, the credit needs of
individual business and enterprises have not been
met. With the more data application in the financial
field, the possibility of obtaining loans has been
improved. Once these economies obtain loans and put
them into production and operation, every unit of real
capital will be increased, it will bring greater
economic growth potential, and create great value. As
a developing country, China has huge data that can be
used as a production factor and applied in the
financial field. At present, on the premise of the
legitimate rights and interests protection of data
subjects, we need to seize the opportunity of the rapid
development of financial science and technology,
make more data resources and create more economic
value in the financial field, and make finance truly
serve economic development.
3.1 After Data Became a Production
Factor, the Constraint of Data
Application Has Increased
The application of massive data has subverted the
traditional credit risk control mode and efficiency in
the financial field. Financial institutions obtain
customer data authorization by signing agreements
for credit risk control, and use the data to post loan
management and other links of credit activities. More
and more non-traditional standard data has been
applied in inclusive finance. It enterprise financing
and the internet finance have gradually expanding the
scope of financial services to "long tail customers"
without credit data, increasing the loan availability of
them, and effectively promote the in-depth
development of the financial market. However, after
data becomes a production factor, customers also put
forward more demands on the ownership and even
derivative value of their own data, by requiring
stricter standardization and management of data. If
the data is applied arbitrarily in the financial field
without restriction and its economic value is obtained
by the financial institutions, the legitimate interests of
the data subject will be damaged, especially when the
data is applied without authorization and consent,
which will affect the feasibility of data application.
Therefore, the data application in distribution as a
new production factor also puts forward higher
compliance and value-added requirements for data
flowing, sharing, trading and other behaviors in the
financial field (Chang 2018).
Research on the Development of Data Application under the Background of Financial Technology
3.2 The Data Standard Has Not Been
Established, Which Reduces the
Efficiency of Data Application
At present, the data application in finance field is
carried out by financial institutions relying on their
own credit system, and the credit system among
different financial institutions have different
structures, that will result in some differences in the
application processes such as data acquisition and
cleaning. In particular, the data acquisition boundary
has not been unified, and some small financial
institutions with weak risk control capabilities are at
the stage of blind obedience in data application, and
a large number of useless data are added to the scope
of application. On the one hand, the accuracy and
efficiency of data application may be reduced,
resulting in rights protection events. On the other
hand, the structure and continuity of data is very
important for the efficient and beneficial of data
analysis, if a large number of fragmented and
unstructured data is incorporated into the application
scope, the corresponding work links such as
collection, calculation and cleaning will lead to a
significant increase in the cost of data application.
The fragmented and unstructured of data may also
lead to the change or disappearance of many signals
hidden in the data over time, and that will change the
accuracy of the model.
3.3 The Algorithm Opacity Brought by
Technology will Affect the Data
The most distinctive feature of digital economy is
taking data as the key production factor, participating
in wealth creation, adopting the operation mode of
"data + algorithm + product", and finally tending to
be "intelligent" form. The data application in
financial field, such as credit scoring, often based on
the data model of complex calculus, that will lead to
the lack of explicability of the application methods,
and unequal treatment or discrimination in financial
services (Ba, Hou, Tang, 2016). In addition,
fragmented data often lacks historical records, and
can not be accessed publicly, so it is difficult to carry
out backtracking test, which also challenges the
accuracy of data application. And due to the different
data application methods among different financial
institutions, some data are collected and accessed in
real time, some data are stored regularly by technical
means, that will result in uncertainty and low
economic efficiency of data application.
3.4 Credit Agencies May Bring New
Independent Credit Risks
With the rapid development of financial technology,
credit agencies, relying on their technical advantages
such as data mining, begin to collect and process
various behavior data in a comprehensive, multi
angle and multi-level manner, and as the risk control
party, to attract customers and provide credit services
for financial institutions, such as providing
suggestions of credit decision, sharing a part of credit
profits and bearing a part of credit risks. With the
gradual refinement of social division of labor, the
technical advantages of credit agencies become more
prominent. However, with the deeper and deeper
involvement of credit agencies in credit business,
especially when their role changes from the provider
of data to the processor of data products for financial
institutions, and even replaces the credit risk control
role of financial institutions, a series of independence
risks will be caused by interest conflicts. Especially if
the financial institutions only bear a small part of the
credit business risk, and most of the risk is borne by
government departments or guarantee companies, the
financial institutions will reduce their input costs in
data collection, processing and other links as far as
possible from the perspective of driving profit, and
rely on credit agencies to make credit decisions (Ye
Financial technology is a double-edged sword for
data application, which needs to be treated
dialectically. We should not only recognize its
advantages in expanding traditional data sources, but
also recognize its disadvantages in personal privacy
and data security, so as to give better play to the value
of data as a production factor in promoting economic
and financial development.
4.1 Strengthen the Protection of the
Legitimate Rights of Data Subjects
First, improve legislation. Personal Information
Protection Act, Data Security Act, Data Transaction
Rule and other laws, clarify the rights and obligations
of data, infringement form and compensation system
of data, and realize the legal using of data and the
rights protection of the data subjects. Especially in the
BDEDM 2022 - The International Conference on Big Data Economy and Digital Management
context of trade internationalization, we should pay
attention to the data application of the process of
cross-border data flow. For example, increase the
research on data opening and sharing, actively
participate in the research and formulation of
international rules, promote the legislation of cross-
border data flow, and strengthen the classified
management and risk assessment of cross-
border data.
Second, make full use of financial technology to
protect the business secrets and privacy of data
subjects. Build a complete and effective data security
guarantee system and data management system to
ensure the security of the whole life cycle of data,
such as data collection, processing, using and sharing.
For example, use the blockchain technology to make
the data exchange between nodes follow a fixed
algorithm, ensure data security and the privacy of
data subjects from the underlying technical
architecture, and solve the problem of trust between
nodes. At the same time, improve the data
authorization and objection handling mechanism, and
strictly protect the credit rights and interests of data
subjects, such as the right to know, the right to
objection, the right to correction and the right to
4.2 Promote the Establishment of the
Right Confirmation and Pricing
Mechanism of Data as a Production
First, clarify the data property rights. Explore and
improve the data property rights identification rules
according to the characteristics of data generation
conditions and processing methods, establish a
perfect data transaction mechanism on the basis of
clear property rights, and build reasonable transaction
rules and equity distribution model, establish a
trading market of all kinds of data, promote the
smooth flow and efficient application of data in the
financial field, and give play to the important role of
data in improving productivity and promoting
economic development. Second, improve intellectual
property protection. Effectively ensure the innovative
development of data mining, analysis, modeling and
other technologies, and promote the effective
transformation of data resources into data products,
which support the development of information
economy. Promote professional data application
institutions such as credit agencies to play a more
important role in collecting market transactions and
other economic activity data, encourage them to
participate in the collection, transaction and
application of data, provide better credit investigation
products for enterprises and farmers, and promote the
healthy and vigorous development of digital
economy (Pian, Xie, 2020).
4.3 Clarify the Data Collection and
Application Standards
First, promote the standardization of data collection.
In view of the different of formats and quality of
unstructured data, establish a unified data collection
and processing mode, strengthen the anonymization
and de identification of data, ensure the scientificity
and unity of data collection, and prevent differential
discrimination caused by data processing or
infringement of the rights and interests of data
subjects (Yang, Tian, Liu, 2021). Second, establish a
unified data application specification according to the
risk characteristics presented by the type and
application of data. Establish a new application mode
composed of computer related technologies such as
point-to-point transmission, distributed storage,
consensus mechanism and encryption algorithm. In
this mode, distributed data storage generates
interconnected data blocks and stamped with time
stamps to form an open and transparent time series
chain and improve the efficiency of data application
(Talin, Li, 2018). Third, strengthen the construction
of financial infrastructure. Promote the
interconnection of various infrastructures, give play
to the advantages of financial infrastructure in data
circulation and security protection. And on this basis,
research and demonstrate the establishment of a data
sharing center or the pilot of financial data sharing
relying on the existing system to promote the security
protection and open utilization of financial data (Ju,
Zou, Fu, 2018).
4.4 Strengthen the Supervision of Data
Application in the Financial Field
First, credit agencies establishe independent system
of department, personnel and salary. Credit agencies
should establish a clear internal organizational
structure and a responsible firewall system, ensure
the collection of customer information, the design of
risk model and other credit departments are
independent of other departments such as business
marketing, and ensure that the compensation of credit
department personnel is not related to the scale of
financial institution credit business. At the same time,
credit agencies shall establish an independent
compliance department to supervise the compliance
status of credit reporting department and personnel.
Second, financial institution should deeply
Research on the Development of Data Application under the Background of Financial Technology
participate in the whole process of designning the risk
control mode, and negotiate with credit agenies on
key elements such as specific data items and model
weighting coefficient of risk control model.
Especially when the defect rate of credit business
exceeds a certain value, both parties should find out
the cause in time and adjust the model parameters.
Third, financial institution should independently
carry out secondary risk control. The risk control
function of credit agenies can not completely replace
the offline investigation and secondary risk control of
financial institution. Financial institution should
make independent credit decisions on the basis of
comprehensive analysis of the credit reports provided
by credit agenies and Credit Reference Center of The
People's Bank of China, and comprehensive
understanding of the actual business situation of
customers, so as to reduce the dependence on the
credit agenies.
At present, the data application in Chinese financial
field is still in its infancy, but with the rapid
development of financial technology, data
application will usher in explosive development. The
improvement of data subjects' awareness of their own
rights and interests protection, the lack of data
application standards, the opacity of application
algorithms, and the independence of professional data
application institutions such as credit agenies will
become obstacles to the development of data
application. Therefore, we proposes to strengthen the
protection of the legitimate rights and privacy of
data subjects by improving legislation and scientific
and technological support, protect data property
rights by establishing the right confirmation and
pricing mechanism of data, promote the security
protection and open utilization of financial data by
clarifying the data collection and application
standards, and ensure the independence and
effectiveness of data application in the financial field
by increasing supervision.
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BDEDM 2022 - The International Conference on Big Data Economy and Digital Management