A Study on the Application of Big Data in Credit Risk Management
Shuning Cao
1
School of Financial Science and Technology, Shenzhen University, Shenzhen, Guangdong, 518000, China
Keywords:
Big Data, Credit Risk Management, Commercial Banks, MSMEs.
Abstract: Credit in the modern economy has a vital role. In today's increasingly complex financial markets, the problems
of traditional manual credit scoring in credit risk assessment have become more prominent. The emergence
of big data technology, on the other hand, has enabled some financial institutions to discover its potential
value. This paper analyzes the value of applying big data in credit risk management and the challenges that
need to be faced. Therefore, this paper aims to analyze the value of the application of big data in credit risk
management and the challenges that need to be faced. For current credit risk problems, big data technology
can produce corresponding countermeasures to effectively reduce credit risk. At the same time, the application
of big data in credit risk management is also characterized by the risks and challenges of privacy leakage, lack
of talent, and dominance of traditional credit thinking. Comprehensively speaking, there is great value in
applying big data to credit risk management. The technology can provide effective solutions to the current
problems, and it can also provide some reference for other risk management research.
1 INTRODUCTION
Credit is an important part of the modern economy,
providing a partial source of funding for individuals
as well as businesses. With the increasing complexity
of the global financial market and the gradual rise of
Internet finance, some credit risk management
challenges have arisen. Traditional credit risk
management methods often rely on manual credit
scoring, so it is difficult to capture changes in
information and prone to subjective bias accurately.
With the gradual transition of credit business to an
online model, financial institutions face new
challenges and opportunities.The institutional reform
of the State Council and the establishment of the
National Digital Agency have made digitalization a
central force driving economic growth. As the core
technology of this change, big data is gaining
attention for its potential value for credit risk
management. The use of big data for credit risk
management is of great practical significance. Big
data can provide richer data samples and more precise
model analysis, thus improving the accuracy and
reliability of risk assessment and prediction (Zhong,
2024). At the same time, big data can optimize the
1
https://orcid.org/0009-0002-3314-3772
credit decision-making process through the
comprehensive analysis of multi-dimensional
information on borrowers, providing financial
institutions with a more scientific and objective basis
for credit decision-making. Big data can also track
credit evaluation dynamically regularly to ensure the
continuity, accuracy, and timeliness of project
database updates (Lin, 2024).
Currently, relevant researchers have analyzed
the current status of the application of big data in
credit risk management. For example, Tingting Wang
(Wang, 2024) summarized the credit business and
risk management innovation of commercial banks in
the context of big data. In addition, Yu Hongfei (Yu,
2023) summarized the research on credit risk
assessment and control of micro, small and medium
enterprises (MSMEs) in the big data environment.
However, it is necessary to summarize and review the
existing studies again and look forward to the future
research direction due to the following three reasons.
First, the rapid development of big data technology
and the proliferation of related studies require
constant updating of the review. Second, most of the
existing reviews focus on specific areas or issues and
lack a comprehensive analysis of overall credit risk
management. Finally, with the advancement of digital
Cao, S.
A Study on the Application of Big Data in Credit Risk Management.
DOI: 10.5220/0013205300004568
In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence (ECAI 2024), pages 19-26
ISBN: 978-989-758-726-9
Copyright © 2025 by Paper published under CC license (CC BY-NC-ND 4.0)
19
transformation, enterprises need to build a more
comprehensive digital system in credit risk
management.
This paper aims to analyze the application of big
data in credit risk management. The paper is
developed along the following lines. Firstly, this
paper will give an overview of big data technology.
Then it will sort out the current problems of big data
in credit risk management in the literature. Further,
the value of big data in credit risk management is
analyzed. In addition, it analyzes the main challenges
of big data in credit risk management, such as privacy
protection and other issues. Finally, summarize the
whole paper and look forward to the research
prospect and direction.
2 OVERVIEW OF BIG DATA
TECHNOLOGY
DEVELOPMENT
With the increasing complexity of global financial
markets and the deepening of China's digital
economy, financial institutions face a number of
credit risk management challenges. Traditional credit
risk assessment methods often rely on limited data
and expert experience. The data sources mainly rely
on limited information, such as financial statements
and credit reports provided by borrowers, so it is
difficult to accurately capture the impact of market
fluctuations, changes in the economic environment,
and changes in the behavior of individual borrowers
on the risk of credit defaults, and at the same time,
because it is an artificial score, it is susceptible to
subjective bias, which all increase the credit risk.
These challenges provide new opportunities for
applying big data technology in credit risk
management. Due to the massive, diverse, and fast
nature of big data can provide more comprehensive
and dynamic data support to help financial
institutions identify and manage credit risk more
effectively (Wang, 2024). The value density of big
data is low, and generally valuable data accounts for
no more than 10% of all data. Finally, big data
technology has efficient processing capabilities and
can quickly mine high-quality information from
massive amounts of data.
With the continuous development of big data
technology, financial institutions have begun to
realize the huge potential for big data in credit risk
management. Big data has features that make it
capable of integrating and processing data from
multiple channels, including but not limited to
comprehensively capturing all types of information
about borrowers, including transaction records, social
media behavior, consumption behaviour, credit
history and other multi-dimensional data (Chen,
2022). This data provides financial institutions with
unprecedented insights that enable more
comprehensive, in-depth and accurate credit risk
assessment.
In addition, with the rise of Internet finance, credit
business has gradually shifted from offline to online,
with a broader customer base and more diversified
credit needs. Financial institutions must have more
flexible and efficient credit default risk control
mechanisms to cope with the complex and changing
credit market environment. The application of big
data technology provides financial institutions with
such a possibility. Financial institutions can quickly
identify potential risks and realize precise prevention
and control of credit default risks by monitoring,
analyzing and adjusting massive data in real time.
Therefore, the study of big data in credit default risk
control is of great significance.
The comparative differences between manual
credit scoring and big data techniques are noted in
Table 1. The table focuses on the comparison in terms
of data sources, processing power, accuracy,
efficiency and timeliness.
Table 1: Comparison of manual credit scoring and big data techniques
Data sources Processing
capabilit
y
Accuracy Efficiency Timeliness
Manual
credit
scoring
Limited data
sources
Weak
processing
capacity
Accuracy is
subjective
Less efficient Limited ability
to monitor data
and lag in
adjustments
Big data
technology
Wide range of
data sources
High processing
power
High accuracy High
efficiency
Real-time
monitoring,
dynamic
ad
j
ustment
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3 PROBLEMS IN CREDIT RISK
MANAGEMENT
Credit risk refers to the possibility that the borrower
cannot repay the loan's principal and interest on time
due to various reasons in the course of the loan, which
leads to the creditor (usually the bank or other
financial institutions) to suffer losses. The objects
facing credit risk mainly include borrowers and
creditors. Borrowers include MSMEs, large
enterprises and individuals. Creditors are mainly
financial institutions. Large enterprises are generally
not exposed to credit risk because of their strong
economic strength and ability to repay debts.
Individuals contain diverse and complex groups that
need to be treated appropriately and will not be
discussed here. And MSMEs face credit risk because
of their small production scale, illiquid capital and
other problems. So, this paper will discuss the credit
risk problems faced by MSMEs as representatives of
borrowers. Meanwhile, the representative of financial
institutions is commercial banks, and this paper will
discuss commercial banks as the representative of
creditors.
3.1 Main Credit Risks of Commercial
Banks
Under the traditional credit risk management system,
commercial banks mainly face the problems of
insufficient credit system, difficulty obtaining
comprehensive, accurate and real customer
information and difficulty in post-loan risk control.
3.1.1 The Credit System Is not yet Perfect
In credit risk management, having a perfect credit risk
system is key to reducing credit risk. The credit
business development of China's commercial banks
started late, and the immaturity of the credit market
led to the difficulty of managers to adapt to market
changes, and the credit risk management system
needs to be improved (Du, 2021). As a result, some
problems have arisen. Due to the lack of exhaustive
customer information checking, the credit credit
system can not give full play to its role, which makes
it difficult for banks to assess customer risk
accurately (Yin, 2024). Compared with developed
countries with hundreds of years of management
experience, there are still obvious deficiencies in the
setup of credit market structure and the usefulness of
information management platform in China. This
affects the efficiency of credit risk management and
increases the possibility of credit default (Huang,
2023). At the same time, there are a series of problems
in the regulatory system, such as the regulatory
content not being detailed, the regulatory power not
being clear, the regulatory efforts not being strong
enough, and the regulatory duties not being strictly
fulfilled.
3.1.2 Difficulty in Obtaining
Comprehensive, Accurate and
Truthful Customer Information
The main risk in credit business originates from the
repayment ability of customers. Therefore, in the
process of credit business, banks need to check and
record customer information in detail to reduce the
repayment risk (Yin, 2024). Chinese commercial
banks have a single channel for obtaining data and
information, the cost of obtaining them is high, and
the data are not updated in a timely manner. And
Chinese banks are easily affected by changes in the
external market environment in the process of credit
management (Hu, 2023). The backwardness of the
data information acquisition and processing mode has
led to the inability of commercial banks to accurately
and timely identify external market risks, and the
failure to take timely and reasonable interventions has
resulted in interest rate losses, affecting profitability.
At the same time, the indicators for obtaining
information under the traditional credit risk
management system are relatively single, including
credit, records of violations, economic disputes and
so on. For enterprises and individuals associated with
the financial / material / human data can not be
obtained, can not be cross-validation and correlation
analysis of all aspects of information, resulting in the
credit front-line staff and credit approving officers to
determine the strength of the customer's repayment
ability, it is difficult to pass the data and other
objective records, the determination is more
subjective (Huang, 2023). At the same time, the
relevant information is also easy to tamper with, so
the data that can not pass the approval can be
approved, which leads to the bank is difficult to grasp
the customer's real business and financial situation,
and can not accurately measure the credit risk of the
lender.
A Study on the Application of Big Data in Credit Risk Management
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3.1.3 Difficulty in Post-Loan Risk Control
According to the field research, the research bank
after the loan carries out risk control mainly through
the phone, door to door, contact with customer units
and other ways to collect, at the same time to
encourage the borrower through the monthly,
quarterly, semi-annual and other ways to settle the
interest rate, the use of risk clues to monitor the
system of regular decentralization of suspicious loans
(Xue, 2023). Numerous commercial banks, however,
have shortcomings in financial data monitoring,
making it difficult to capture and follow up on key
data information quickly. This limits the ability of
commercial banks to predict risks in a timely manner,
take stop-loss measures, or develop reasonable
response strategies. Although China's commercial
banks have set up a post-loan risk warning
mechanism, by technology, data and other factors
constraints, only based on loan overdue, deposit and
loan ratios and other direct indicators to set early
warning rules, early warning matters are not
comprehensive enough (Huang, 2023), or failed to
take the results of post-loan risk control.
3.2 The Main Credit Risks of MSMEs
Under the traditional credit risk management system,
MSMEs are mainly faced with the problems of low
information transparency, high financial management
risk and high market competition credit risk. These
problems affect the quality of assets affecting
financial institutions, the development of MSMEs
and the stability of the overall financial market.
3.2.1 Low Information Transparency
Low information transparency is a significant
disadvantage for MSMEs in the financial market.
Specifically, due to their small size and limited
resources, MSMEs often find it difficult to establish
a sound financial management system and
information system, leading to deficiencies or lags in
the collection, organization and disclosure of
financial information and operational data. This
information opacity firstly affects financial
institutions' credit assessment of MSMEs. In the pre-
credit review stage, financial institutions need to
judge the repayment ability and operational stability
of MSMEs on the basis of sufficient and accurate
information. Still, the opacity of information makes
this process complicated and uncertain. Financial
institutions may make conservative credit decisions
because they are unable to fully understand the real
situation of MSMEs, thus limiting the financing space
for MSMEs. In addition, at the post-loan supervision
stage, information opacity also poses a challenge to
financial institutions. It is difficult for financial
institutions to monitor the operation status and capital
flow of MSMEs in real time, and discover potential
risk factors in time and take effective measures to
prevent and control them. This information opacity
makes credit institutions have blind spots in pre-credit
review and post-credit supervision, thus increasing
the riskiness of credit investment (Zhang, 2024).
3.2.2 High Risk of Financial Management
Cash flow stability is the key to the survival of
MSMEs, and its impact is extremely significant.
Insufficient monitoring of cash flow by MSMEs may
lead to the risk of capital chain breakage (Zhang,
2024). When the enterprise faces cash flow problems,
its daily operations will be seriously constrained,
resulting in a tense or even broken capital chain,
which not only puts the enterprise itself in a difficult
situation, making it difficult to fulfill its payment
obligations to suppliers and employee payroll but also
triggers credit defaults due to the inability to repay
bank loans on time, which in turn affects the banking
system that provides it with credit support, resulting
in a decline in the asset quality of the bank, which
may ultimately cause This could lead to a decline in
the quality of bank assets and ultimately lead to asset
losses, affecting financial stability. The credit review
and accurate risk assessment can effectively control
costs and correctly recognize the feasibility of return
on earnings, which is the basis of credit risk
management (Zhang, 2024). Financial information is
the core of credit assessment. However, the
asymmetry of financial information makes risk
assessment more complicated, and it is difficult for
credit institutions to grasp the true and accurate
condition of the enterprise accurately, thus increasing
the risk of default. In addition to financial
management risks, MSMEs face unique challenges in
market competition, which also have an impact on
credit risk.
3.2.3 High Credit Risk of Market
Competition
MSMEs themselves are small in production scale and
not strong enough in production strength (Yu, 2023).
In the fierce market competition, MSMEs often have
limited resources, which causes it to be more difficult
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to obtain credit compared with large enterprises
(Zhang, 2024). Banks and other financial institutions
tend to be more cautious about the repayment ability
of MSMEs when assessing the risk of loans.However,
the financial needs of MSMEs also continue to
increase (Feng, 2023).The existence of these reasons
has led to the persistence of the problem of difficult
and expensive financing for MSMEs. Moreover,
MSMEs are often at the end of the supply chain, with
a high degree of dependence on upstream suppliers
and downstream buyers. Once a problem occurs in
one part of the supply chain, such as price increase by
upstream suppliers or default by downstream buyers,
it will significantly impact the production and
operation of MSMEs. This transmission effect of
supply chain risks makes MSMEs more vulnerable in
the face of market changes, and their credit risks are
further amplified.
4 THE VALUE OF BIG DATA FOR
CREDIT RISK MANAGEMENT
Big data used in credit risk management has certain
value. For example, big data can provide richer data
samples and more accurate modelling analysis,
making it easier and more comprehensive to obtain
data, and more scientific and objective to process the
data results. At the same time, big data has high
efficiency, which can quickly process data processing
data and efficiently find valuable data from massive
data. Big data also allows for regular dynamic
tracking of credit evaluations, ensuring that problems
can be identified and interventions taken promptly.
The paper will expand and analyze the value of big
data for commercial banks and MSMEs.
4.1 The Value of Big Data Used in
Credit Risk Management of
Commercial Banks
Applying big data to commercial banks' credit risk
management system has certain value. Firstly, it can
build a complete credit system and enhance the effect
of risk management. Secondly, it can effectively
mine, process and follow up the data information.
Finally, it can solve the problem of post-credit
management.
4.1.1 Constructing a Complete Credit
System and Enhancing the Effect of
Risk Management
For commercial banks, credit risk is the main risk
factor (Feng, 2023).Applying big data technology in
credit credit system is more conducive to building a
complete credit system and enhancing the effect of
credit risk management (Yin, 2024). Big data helps
build a credit credit system by analyzing a large
amount of data, identifying customer information and
repayment ability, to accurately judge their borrowing
qualifications and reduce credit risk. Big data can also
innovate the bank risk decision management mode. In
the actual development of risk management, banks
should first identify the risk, the impact of the risk,
and the risk level, which should be scientifically
divided, and do a good job of risk decision
management (Wang, 2024). At the same time, big
data can establish a more complete risk monitoring
mechanism, which has a positive role in risk
management innovation and internal risk
management of banks, is an important initiative for
the development and innovation of commercial bank
credit business (Yin, 2024).
4.1.2 Effectively Mining, Processing and
Following up Data Information
Big data can establish a data sharing platform and
break data silos. Banks can strengthen cooperation
among themselves to eliminate data barriers and
realize data sharing. The government can take the
lead in establishing a data sharing platform,
integrating credit data from government departments
and various credit bureaus, and granting and
regulating access to financial institutions (Xue,
2023). The sharing platform can improve the
efficiency of data mining, while making data
collection more comprehensive. And the
synchronization of data to regulatory agencies
strengthens the credibility and accuracy of data, thus
enhancing the accuracy of risk assessment.
Meanwhile, the correlation between the data can
enhance the power of risk prediction. Commercial
banks should make full use of the large amount of
customer information they have accumulated, and
classify and process it with the specific situation to
further explore the correlation between the sample
data and customer behavior, such as the connection
between the non-performing loan rate and the
borrower's investment behavior, the type of work, and
A Study on the Application of Big Data in Credit Risk Management
23
other factors, in order to comprehensively improve
the level of controlling credit risk (Xue, 2023).
Furthermore, the timeliness characteristics of big data
technology can deliver customer information to the
bank in real time, so that if the latest data show that
the customer's credit risk is too high, it can stop the
loss in time and reduce the credit risk.
4.1.3 Solve the Problem of Post-Loan
Management
China's banking industry in the loan supervision often
focusing on pre-loan and loan strict review, but the
post-loan management attention is insufficient,
mainly due to information asymmetry. After the
customer loan, the actual use of funds is difficult to
control, often deviating from the original direction,
such as business loans for investment, increasing the
risk of repayment, affecting the bank's wind control.
Big data can solve the problems of post-loan
management (Huang, 2023). It can track the flow of
funds in real time, provide timely warning of illegal
use, and reduce loan risks; at the same time, record
information about customers and their affiliates to
help approve new loans; and improve the efficiency
of interdepartmental communication to solve the time
gap and collaboration problems in traditional post-
loan management. In addition, big data technology
accumulates rich post-loan management data through
data traces, providing strong support for risk analysis
and customer resource mining.
4.2 The Value of Big Data Used in the
Credit Risk Management of
MSMEs
Applying big data to MSMEs' credit risk management
system has certain value. First, it can solve the
limitations brought by low information transparency.
Secondly, it can reduce the risks brought by
insufficient financial management. Finally, it can
enhance market competitiveness.
4.2.1 Addressing Constraints Brought
About by Low Information
Transparency
Restricted by their size and financial transparency,
these MSMEs often face heavy obstacles in obtaining
credit funds, which largely binds their development
potential. As a result, how to scientifically and
efficiently assess the credit risk of MSMEs has
become a common issue for both financial institutions
and MSMEs. In this context, the application of big
data technology shows irreplaceable value (Zhang,
2024). Traditional credit assessment favors financial
reports and ignores unstructured information such as
online activities, social media reputation, and
geographic location. Big data technologies, on the
other hand, excel at mining these pieces of Internet
information and applying advanced algorithms to
identify credit risk signals. For example, analyzing
online purchases to predict cash flow stability, or
social media public opinion to assess a company's
public image and potential risks. This provides
financial institutions with a comprehensive and multi-
dimensional view of credit assessment, helping them
identify risks and accurately make more informed
lending decisions. Even though information
transparency is low, big data can still mine
information from other sources to effectively address
its limitations.
4.2.2 Reducing the Risk of Inadequate
Financial Management
On the one hand, big data can solve the risks brought
about by financial information asymmetry. The big
data platform can promote data sharing and
integration among the government, financial
institutions, enterprises and other parties. By
integrating data from different sources, credit
institutions can gain a more comprehensive
understanding of the real situation of MSMEs and
reduce the risks brought by information asymmetry.
In addition to traditional financial data, big data
technology can also mine and analyze non-financial
information of MSMEs, such as online behavior and
social media reputation. This information helps credit
institutions to more comprehensively assess the
creditworthiness and operational capacity of
enterprises, thus making more informed lending
decisions. On the other hand, big data also monitors
cash flow in real time to prevent businesses from
getting into trouble and thus avoiding property
damage. Big data technology monitors key financial
data such as online transactions, bank account
changes, supply chain payments, etc. of MSMEs in
real time, and analyzes and predicts cash flow trends
through algorithms. This real-time monitoring helps
enterprises identify potential cash flow shortage risks
in a timely manner and take measures to address them
in advance. This can effectively solve the problems
caused by insufficient cash flow monitoring.
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4.2.3 Enhancing Market Competitiveness
MSMEs can actively utilize the big data platform to
accumulate their own credit data. By timely and
accurately recording the enterprise's transaction
behavior, repayment records and other information, it
can gradually build up a good credit record and
improve its credit rating among financial institutions.
At the same time, MSMEs can establish a data
sharing mechanism with other enterprises in the
supply chain, and use big data technology to monitor
and analyze data changes in each supply chain link.
This helps enterprises identify potential risk points in
a timely manner and take appropriate measures to
prevent and respond to them. The big data-based risk
early warning system is also able to monitor
anomalies in the supply chain in real time, such as
price increases by upstream suppliers and defaults by
downstream buyers. When the system detects a
potential risk, it will immediately issue an early
warning signal to remind the enterprise to pay
attention and take corresponding measures to reduce
the impact of supply chain risks on the enterprise's
production and operation, thus increasing market
competitiveness.
5 CHALLENGES OF BIG DATA
IN CREDIT RISK
MANAGEMENT
In this era of rapid development of information
technology, the application of big data to credit risk
management system also has the risks and challenges
of privacy leakage, lack of talent traditional credit
thinking dominates.
Currently, in the traditional credit field, risky
cases of selling personal credit reports are common.
However, with the arrival of the Internet of Things
(IoT) era, any behavior may be collected and utilized
by big data (Luo, 2019)]. Many other associated data
will be involved once part of the data is leaked.
Moreover, big data is characterized by sea
quantization and huge amount of data, so it is
extremely difficult to preserve and protect it. For
these reasons, in the field of big data credit, it is easier
to obtain information through the Internet, and the
cost of crime is lower, which is more likely to
increase the chance of the risk of information leakage
(Luo, 2019)]. Privacy security is not negligible and
difficult to solve, which is undoubtedly a great
challenge for big data in credit risk management.
Applying big data to credit risk management also
has the problem of lack of talents. This is because
integrating credit risk control with big data requires a
group of composite talents familiar with credit
business, data mining technology and system
programming ability at the same time. However, at
present, talents with experience in both fields are
extremely scarce, resulting in financial institutions
facing technical bottlenecks and talent gaps in the
implementation of big data strategies. According to
the current view, there is a great lack of talents that
meet the needs in this area, resulting in commercial
banks and MSMEs being unable to scientifically and
effectively utilize big data technology, which is not
conducive to their development. On the other hand,
commercial banks and MSMEs also lack the
corresponding talent training mechanism. As a result,
the ability and quality of the employees of
commercial banks and MSMEs have not been
sufficiently improved, and it is difficult to effectively
integrate big data technology into credit risk
management, thus limiting the further development
and expansion in the field of credit business.
In the traditional credit approval mode of
commercial banks, human subjective judgment still
occupies a dominant position (Xue, 2023). The use of
big data technology is mostly limited to providing
auxiliary information for manual decision-making,
failing to give full play to its potential for risk
identification, so that the approval process lacks
fundamental changes compared with the past. In
addition, although the concept of comprehensive risk
management has been put forward, the
implementation of the bank's internal implementation
is not satisfactory, often on the surface, failed to
penetrate into the various levels and business
processes, resulting in the risk management of
grassroots employees with a weak sense of risk
management, and insufficient practical experience. In
terms of specific operation, when approving loans,
banks often tend to select key industries with high
maturity and development potential as well as
customers with rich experience in production and
operation in order to minimize credit risks. However,
this preference inevitably ignores those in the early
stages of growth or small-scale "long-tail customers",
whose financing needs are difficult to fully satisfy,
which limits the expansion of the bank's revenue scale
and the enhancement of credit risk control
capabilities. Therefore, realising more
A Study on the Application of Big Data in Credit Risk Management
25
comprehensive and accurate risk assessment with the
support of big data technology, while considering the
financing needs of long-tail customers, has become
an important issue for commercial banks to solve.
6 CONCLUSIONS
Credit is an important part of the modern economy
and is widely concerned. This paper analyzes the
study of big data in credit risk management. From it,
the paper can conclude that there are many problems
in traditional credit risk management, and the
application of big data in it has a chance to solve these
problems. However, applying big data also brings
some risks and challenges, and people urgently need
a corresponding policy system, technology and
relevant professional talents.
Existing studies have comprehensively and
deeply analyzed the opportunities and challenges of
big data in credit risk management, etc., and have
achieved rich research results, but there are still some
niche areas that need more exploration.
First, improving the accuracy of the big data
model is subject to more in-depth research. Although
the current credit risk assessment model has achieved
certain results, there is still room for improvement in
terms of prediction accuracy and stability. How to
further optimize the model algorithm to improve the
accuracy and timeliness of risk identification is an
area that requires in-depth study of big data in credit
risk management.
Secondly, with the continuous development of big
data technology, relevant laws and regulations are
also constantly improving. However, in the field of
credit risk management, balancing the contradiction
between data security and data utilization and
ensuring the effective implementation of laws and
regulations is still a topic that needs to be studied in
depth.
Third, with the development of financial
technology, new credit business scenarios continue to
emerge, such as Internet finance and supply chain
finance. These emerging business scenarios are
characterized by complexity and uncertainty, putting
forward higher requirements for credit risk
management. Utilizing big data technology to
effectively identify and manage risks in these
emerging business scenarios is another gap area that
requires in-depth research.
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