Research on the Credit Risk Assessment of Small and Medium
Enterprises Based on BP Neural Network
Xiaoying Zhang
a
and Xing Tan
b
School of Management, Wuhan University of Technology, Wuhan, China
Keywords: Small and Medium-sized Enterprises, BP Neural Network, Credit Risk, Evaluation Model.
Abstract: Small and medium-sized enterprises play an important role in China's economic development. However, they
face many problems in their development, among which credit financing is particularly important. This paper
mainly focuses on the correlation of small and medium-sized enterprise credit problems. Combined with the
characteristics of small and medium-sized enterprises in China, this paper builds a more scientific and
reasonable evaluation model based on the evaluation index system and uses BP neural network to grade the
credit capacity and risk of small and medium-sized enterprises. It also puts forward relevant suggestions from
the perspective of enterprises and banks to help banks make reasonable decision-making plans and find the
optimal credit strategy.
1 INTRODUCTION
With the rapid development of China's economy and
the adjustment of economic structure, China's small
and medium-sized enterprises have developed rapidly
and played an important role. In the development
process of small and medium-sized enterprises, there
are often various problems due to their scale and
organizational structure. High credit risk, difficult
financing, and expensive financing are still the
primary problems faced by China's small and
medium-sized enterprises. Through investigation and
research, the existing internal management
regulations of commercial banks in China are not
very perfect and lack scientific and reasonable credit
risk assessment methods and systems for the
evaluation and prevention of small and medium-sized
enterprise creditability. Therefore, it is particularly
important to establish a perfect credit risk evaluation
model for small and medium-sized enterprises in
commercial banks.
Based on the theoretical research at home and
abroad, Wu Jingmei puts forward an evaluation
system suitable for the company's financial quality
and solvency. She is the first economist in China to
deeply study the credit evaluation index system.
a
https://orcid.org/0000-0001-5251-9718
b
https://orcid.org/0000-0002-4631-9987
Based on a commercial bank in Zhejiang Province,
Shi Zhen and Xu Feng used BP neural network
training sample model to predict and evaluate their
risk evaluation. Through empirical research, the
model can be used as the basis for credit risk decision-
making of urban commercial banks. Based on the
credibility research theory, Liu Cheng and Liu
Xiangdong combined analytic the hierarchy process
with credibility measurement to establish a
comprehensive risk index system from multiple
levels and form a framework and model that can
reflect the characteristics of small and medium-sized
enterprises. Wu Jingru selects and modifies the
indicators that can reflect its characteristics,
establishes and tests the credit evaluation index
system of small and medium-sized enterprises by
using fuzzy analytic hierarchy process.
Based on previous studies, combined with the
characteristics of small and medium-sized enterprises
in China, this study intends to build a more scientific
and reasonable evaluation model based on the
establishment of the evaluation index system, and use
BP artificial network to evaluate the credit capacity
and risk of small and medium-sized enterprises in
China.
82
Zhang, X. and Tan, X.
Research on the Credit Risk Assessment of Small and Medium Enterprises based on BP Neural Network.
DOI: 10.5220/0011159900003440
In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 82-86
ISBN: 978-989-758-593-7
Copyright
c
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
2 DATA SOURCES AND INDEX
SELECTION
2.1 Index System Construction
This paper will build a credit strategy system from
two aspects of enterprise-strength and credit level
(see Table 1). The hierarchy under "credit level" is
divided into the following two categories: credit level
and default. The hierarchy of "enterprise-strength" is
divided into the following four categories: capital
flow, transaction vitality, profitability, and supply,
and demand. To analyze and solve the problems more
specifically, these four aspects are further subdivided,
and it is decided to select 10 quantities such as total
annual profit, profit margin, price and tax stability of
purchase (Sales), the efficiency of purchase (Sales)
invoice and invoicing stability of purchase (Sales)
invoice as the evaluation index.
Table 1: Index System.
Primary index Secondary index
Enterprise strength
Input price tax stability
Output price tax stability
Input invoicing stability
Output invoicing stability
Total profit for the year
Profit margin
Credit level
Credit rating
Default or not
2.2 Data Sources and Assumptions
The data in this paper are all from the relevant invoice
data of 123 enterprises with credit records and 302
enterprises without credit records provided in the
appendix of question C of the "2020 Higher
Education Association Cup National Mathematical
Contest in Modeling for College Students" released
by the organizing Committee.
In order to ensure the scientificity and rationality
of the system construction, the following assumptions
are established:
(1) It is assumed that the screening data is true and
effective without deviation.
(2) It is assumed that the enterprise has a certain
risk control ability.
(3) It is assumed that in addition to the selected
index system, other factors have little impact on risk
assessment and decision-making.
(4) It is assumed that the optimal loan amount
should maintain the same change rate as the
enterprise as a whole.
3 ESTABLISHMENT AND
SOLUTION OF THE MODEL
3.1 Model Overview
BP neural network was proposed by McClelland and
Rumelhart in 1986. It is a kind of multilayer feed-
forward neural network trained by an error
backpropagation algorithm. BP neural network does
not need to determine the mathematical equation of
the mapping relationship between input and output in
advance but can get the result closest to the expected
output value after giving the input value through its
training and learning of corresponding rules. Its core
idea is the gradient descent method, which uses
gradient search technology to minimize the error
mean square deviation between the actual output
value and the expected output value of the network.
The structure of the BP neural network is shown in
Figure 1:
(1)
Figure 1: Structure of multilayer neural network.
3.2 Model Establishment
For a single neuron, its structure includes input,
synaptic weight, sum plus bias, activation function,
and output.
Figure 2: Schematic diagram of the single-neuron structure.
Research on the Credit Risk Assessment of Small and Medium Enterprises based on BP Neural Network
83
1) Input vector of the input layer
𝑋=
(
𝑋
,𝑋
,…,𝑋
)
as 𝑋
=(𝑋

,𝑋

,…,𝑋

)
2) Hidden layer vector of layer 1:
𝐻
=
,ℎ
,…,ℎ
,…,ℎ
as 𝑙=2,3,,𝐿 − 1,𝑗=1,2,,𝑠
3) Output layer output vector:
𝑌=
(
𝑦
,𝑦
,…,𝑦
)
4) Set
𝑤

is the connection weight from the
𝑖th neuron in layer 𝑙−1 to the 𝑗th neuron
in the layer, is the
𝑏
offset of the 𝑗th
neuron in the layer, including:
=𝑓(𝑛𝑒𝑡
)
𝑛𝑒𝑡
=𝑤



+𝑏
as
𝑛𝑒𝑡
is the input of the 𝑗th neuron in the lth
layer, and is the activation function. The activation
function can introduce nonlinear function. In this
paper, the Sigmod function is selected as the
activation function, that is:
𝑓
(
𝑥
)
=
1
1+𝑒
5) The error function is introduced to measure
the gap between the output result and the
expected output. Assuming that there are 𝑝
training samples of enterprise data, 𝑑(𝑖) is
the expected output corresponding to,
assuming that a single training sample has 𝑛
outputs, 𝐸(𝑖) is defined as the training
error of a single sample:
𝐸
(
𝑖
)
=
1
2
𝑑
(
𝑖
)
−𝑦
(
𝑖
)

therefore, the global error function:
𝐸=
1
2𝑝
𝑑
(
𝑖
)
−𝑦
(
𝑖
)


The weight and bias are updated. In BP neural
network, gradient descent method is generally used to
realize this step:
𝑤

=𝑤

−𝛼
𝜕𝐸
𝜕𝑤

b
=b
−α
∂E
∂b
3.3 Solving the Model
In this paper, 123 known credit rating and default
record data are used as the training set and prediction
set. The proportion of training and prediction samples
is about 80% and 20% respectively. That is, the data
of the first 100 enterprises are used as the training set
and the data of the last 23 enterprises are used as the
prediction set. The training times are determined as
15000 times, the convergence error is 0.1 and the
learning rate is 0.0001. After the results of the
training set converge, the data records of the last 23
enterprises are put into the trained neural network for
the test, and the accuracy is 82.6%.
The prediction accuracy is high, so further
prediction can be carried out. The data of 302
enterprises with unknown credit ratings can be
brought into the trained BP neural network. The final
results are shown in Table 2:
Table 2: Evaluation Results.
Score Level Number
>0.9 Excellent 21
0.75-0.9 Good 114
0.6-0.75 Commonly 143
<0.6 Poor 24
Based on the analysis of the advantages of the
model algorithm, the value of AUC is 0.74, which
indicates that the model has a predictive value and the
establishment of the model is more reasonable
4 CONCLUSIONS
4.1 Conclusion
In real life, due to the scale and structure of small and
medium-sized enterprises, banks will evaluate the
loan risk of each enterprise when providing loans.
Banks prefer to provide loans to enterprises with
strong strength and stable supply-demand
relationships, and will give preferential interest rate
policies to enterprises with high reputations and low
credit risk. To do a good job in loan risk management,
banks need to establish corresponding models
according to the actual situation of enterprises,
comprehensively consider through the model
solution, and judge the risk of each enterprise, to
effectively avoid the emergence of non-performing
loans.
BDEDM 2022 - The International Conference on Big Data Economy and Digital Management
84
This study starts with the identification of credit
risk of small and medium-sized enterprises, and
mainly studies the application of the BP neural
network model in credit customer risk management
of commercial banks. This study takes the credit
rating of each enterprise as the evaluation standard of
loan risk, and the relationship between impact index
and credit rating can be trained through BP neural
network. It has the characteristics of simple structure,
fast convergence speed, easy implementation, and
high prediction accuracy. The establishment of this
model is convenient for banks to estimate the credit
rating of small and medium-sized enterprises more
scientifically and reasonably, and then formulate the
Credit Strategies of enterprises with different ratings
to achieve a win-win situation between banks and
enterprises.
4.2 Suggestion
(1) Enterprises should strengthen their
construction and improve their awareness and
ability of risk prevention and control.
Small and medium-sized enterprises should first start
from themselves, establish a set of management
structures suitable for the long-term development of
enterprises, and improve the internal control and
management system of enterprises. At the same time,
by constantly improving the enterprise system and
standardizing the financial system, enterprises can
improve the transparency and authenticity of
financial information, smooth the enterprise capital
chain, fundamentally control the generation of non-
performing loans and reduce the credit risk of
commercial banks. In terms of credit, enterprises
should repay the principal and interest on time,
strengthen communication and cooperation with
banks, and provide detailed and accurate financial
information and enterprise information to banks in
time. Enterprises should form a good cooperative
relationship with banks and maintain their own
reputation, so as to achieve win-win cooperation.
At the same time, enterprises should establish a
corresponding incentive system and supervision
system. Small and medium-sized enterprises through
the implementation of reward and punishment
measures to improve the enthusiasm of employees
and create more benefits for enterprises. The
corresponding supervision mechanism is conducive
to eliminating the improper behavior that is not
conducive to the development of enterprises.
(2) Banks should improve the credit evaluation
system and optimize the way of credit
management.
First, strengthen the pre-loan and post loan
supervision of small and medium-sized enterprises,
reduce the problem of information asymmetry with
enterprises, and establish a perfect information
disclosure platform to ensure the openness and
transparency of information. Second, optimize the
lending process of small and medium-sized
enterprises to achieve efficiency and effectively
avoid risks. Third, establish and improve the credit
risk early warning mechanism of small and medium-
sized enterprises in commercial banks, improve the
level of risk management, and use scientific and
effective risk identification and early warning models
to improve the Scientific and applicability of credit
decision-making.
(3) Improve the quality and ability of evaluation
personnel and optimize post setting
Banks should improve the quality of employees and
cultivate their risk assessment ability in various ways.
On the one hand, introduce high-quality risk
assessment talents, especially those with rich
experience, who can use and continuously improve
the risk assessment model to adapt to the actual
situation of different enterprises. On the other hand,
improve the bank's employee training system and
enhance employees' risk management awareness.
Relevant staff must master relevant legal knowledge,
credit rules and regulations, and deeply understand
the actual situation of the enterprise to evaluate and
handle credit business, so as to improve their own risk
management level.
A bank shall improve the comprehensive quality
of all staff in the whole process of credit business
handling, and improve their own ability and
professionalism. Only by strengthening the
awareness of relevant personnel from the level of
consciousness can they significantly improve their
personal ability and strengthen the risk control of
commercial banks.
At the same time, the bank shall improve the risk
management structure and clarify the responsibilities
of each department and post. Not only in quantity but
also in quality to ensure the matching of posts, so as
to form the organizational guarantee of risk
prevention and control.
(4) Strengthen external supervision and
communication and obtain data from multiple
channels
Banks should speed up the establishment of a perfect
information disclosure platform to ensure the
openness and transparency of market information.
Research on the Credit Risk Assessment of Small and Medium Enterprises based on BP Neural Network
85
Banks should constantly strengthen the information
exchange between enterprises and financial
institutions such as banks, implement effective
communication, and reduce the problem of
information asymmetry with enterprises, so as to
reduce financial risks and provide high-quality credit
services for enterprises.
A bank shall establish a responsibility system for
the authenticity of financial information, appoint
special personnel to supervise and implement an
accountability system. Banks should also strengthen
the management of the authorization system, clarify
the authorized personnel and operators, and conduct
strict supervision and inspection. At the same time,
with the advent and development of the big data era,
many information industries dominated by big data
are developing rapidly and playing an important role
in the modern Internet. Banks can use big data to
make credit data more specific, so as to promote the
construction of credit system more and more
comprehensive.
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