Enterprise Credit Risk Management Model Based on Super_SBM

Model

Xiangdong Zhang

College of Arts& Information Engineering Dalian Polytechnic University, Dalian, China

Keywords: Risk Management, Super_SBM Model, Credit Risk.

Abstract: The current risk management model has the problem that the credit risk type is not clear, which leads to the

poor goodness of fit performance of the model the credit risk management model based on SBM model.

Calculate the enterprise exploration factor index, study the correlation degree between the observation

variables, divide the credit risk types, establish the credit system, and use Super_ SBM model obtains index

weight, extracts the importance of risk factors, establishes credit risk management model, and forecasts the

decision-making results of enterprises. Experimental results: the mean goodness of fit of the risk management

model and the other two risk management models is 0.4139, 0.4989, 0.807, proving that the fusion of Super_

SBM model has higher practical value in risk management.

1 INTRODUCTION

Generally speaking, when we discuss the basic

meaning of credit risk, because the economic

meaning of the concept of credit risk is stronger than

that of other angles, we usually study it from the

economic level. However, the definition of the

concept of credit risk has not yet been completely

determined (Oliveira, Mexas, Meirino, et al. 2019).

Credit is an invisible contractual relationship among

the participants in economic activities, which is based

on equality and reciprocity. In the economic activities

of an enterprise, the specific performance may be that

the enterprise obtains money, products or services

with the same physical assets (Kuo, Lin, Chien 2020).

To grasp the definition of enterprise credit risk, we

need to understand the meaning of risk. To

understand risk, we need to pay attention to two

important factors: firstly, risk refers to the situation

that may have bad expectations, and the risk manager

is to prevent and deal with the occurrence of bad

situations, which will bring losses to people the

existence of "risk" means that there are worse than

expected results in all the results (Silva, Fernandes

2019). If the probability of those worse than expected

results is zero, then the risk does not exist, and the

expected good results can always be achieved;

secondly, the probability of the expected result or loss

is not zero, and the probability of occurrence is a

random value. To sum up, risk includes two

important factors: the expected bad loss and the

probability of the expected bad result. Traditionally,

credit risk is the risk that the borrower fails to repay

the debt within the specified period and causes losses

to the lender, so it is also called default risk. With the

continuous maturity of the financial market and the

high attention paid to the credit risk of enterprises,

when the repayment ability of borrowers or

counterparties declines, that is, the credit quality

declines, the price of relevant assets in the market

also decreases, resulting in the loss of credit risk

(Hiebl, Duller, Neubauer 2019, Corrêa, Neto, Souza,

et al. 2019). To define credit risk in a broad sense, we

should first define the credit relationship. Where

there is credit in the relationship between the two,

whether it is on the level of ethics, economy, law or

currency, there is a credit relationship between them.

In a broad sense, credit risk refers to the possible loss

caused by the failure of the other party to perform the

contract (Polly, Stevens, Jordan, et al. 2019). From

the narrow economic perspective, credit risk is

defined as the possible loss to the creditor if the other

party defaults in the expectation of obtaining a series

of income in a fixed period of time. Generally

speaking, a creditor's debt can't be recovered or can't

be recovered in time due to the debtor's breach of

contract, which may bring losses to the creditor. In

short, credit risk refers to the potential loss to the

credit provider when the party accepting the credit

Zhang, X.

Enterprise Credit Risk Management Model Based on Super

BMModel.

DOI : 10.5220/0011768200003607

InProceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology (ICPDI 2022), pages813 − 819

ISBN : 978− 989 − 758 − 620 − 0

 2023bySCIT EPRESS˘ScienceandTechnologyPublications, Lda.UnderCClicense(CCBY − NC − ND4.0)

813

refuses or is unable to repay the debt on time and in

full. Enterprise credit generally refers to the credit

relationship between enterprises due to credit sales.

After the credit sales activities, the enterprise will

give the customer the corresponding credit line, that

is, a part of the accounts receivable of the customer

can be paid in the future, which is a financing activity

for the customer (González, Santomil, Herrera 2020,

Johnston, Soileau 2020, Altuntas, Berry-Stlzle,

Cummins 2019). This kind of credit relationship is

based on the premise that the enterprise thinks that

the customer can repay all the money before the due

date. It is a loan relationship guaranteed by the

customer's credit. If the enterprise thinks that the

customer cannot repay the debt when it matures, it

will not grant a credit line. Credit sale is the basis of

accounts receivable and the cause of credit risk. At

present, the academic circles are concerned about

Super_ SBM model, applied to the construction of

enterprise credit risk management model literature is

not very rich, still need to be further explored.

2 ENTERPRISE CREDIT RISK

MANAGEMENT MODEL

BASED ON SUPER_SBM

MODEL

2.1 Calculation of Enterprise

Exploration Factor Index

Exploratory factor is to explore the basic structure of

observation data by studying the internal dependence

of many variables. The basic idea is to find common

factors, and ultimately achieve the purpose of

dimension reduction. Confirmatory factor analysis

(CFA) uses prior information to test whether the

previously collected data work according to the

predetermined structure. First, we need to use

exploratory factors to group financial indicators, then

get preliminary factors after analysis, and then carry

out confirmatory factor research. The growth rate of

total assets is mainly used to reflect the growth degree

of enterprise's operating strength, the expression

formula is as follows:

100%

=×

(1)

In formula (1),

represents the growth of the

total assets of the enterprise in the current year, and

represents the total assets at the beginning of the

year. The important basis to judge whether an

enterprise is out of the expansion period is whether

its asset scale is expanded or not. There are two ways

for enterprises to expand assets: one is to expand the

scale of liabilities, the other is to increase the owner's

equity. Companies with single operation and

outstanding main business usually have higher

growth (Huang 2019, Shi, Wang, Wang 2019). This

indicates that the company has a large market demand

for its products and a strong ability to expand its

business. If a company can maintain a growth rate of

more than 30% of its main business income for

several consecutive years, it can basically be

considered as a growth company. The continuous

growth of net profit is the basic feature of a

company's growth, the expression formula is as

follows:

100%

−

=×

(2)

In formula (2),

is the net profit of the

enterprise last year, 𝑟



is the net profit of the

enterprise this year, 𝑟



is the net profit of the

previous year. If the increase is large, it indicates that

the company has outstanding business performance

and strong market competitiveness. On the contrary,

if the index is small or negative, then the growth of

the enterprise is very poor. For most production-

oriented enterprises, the growth rate of fixed assets

reflects the expansion of production capacity.

Therefore, when calculating the growth rate of fixed

assets, we should pay attention to the composition of

the increased fixed assets, the expression formula is

as follows:

100%

−

=×

(3)

In formula (3),

represents the net assets at the

end of the period,

represents the net assets at the

beginning of the period, and

represents the total

fixed assets at the beginning of the period. If most of

the increased parts are under construction, it is

necessary to pay attention to the completion time,

because the completion will have a significant impact

on the current profits. If the growth part has been

completed earlier in this year, then there is no need to

expect a substantial increase in future earnings on the

current basis (Abu-Qarn 2019). It is closely related to

the level of credit risk. The study of solvency can

obtain the ability of an enterprise to deal with risks

and the efficiency of financing. Based on the above

calculation, the steps of obtaining enterprise

exploration factor indicators are completed.

ICPDI 2022 - International Conference on Public Management, Digital Economy and Internet Technology

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2.2 Classification of Credit Risk Types

From the perspective of sources, credit risk can be

divided into two types: the risk of counterparties and

the risk of issuers. The former is mainly produced in

commercial bank loans and financial derivatives

transactions, while the latter is mainly related to

bonds. In terms of composition, credit risk consists of

two parts. One part is default risk, which refers to the

possibility that one party of the transaction is

unwilling or unable to pay the agreed amount,

resulting in the loss of the other party. In terms of

level, credit risk can be divided into three levels: one

is transaction level, which is related to a single

financial transaction. The second is the level of

counterparties or issuers, which is generated in all

transactions with one counterparties or issuers. The

third is the level of asset portfolio, which is related to

all transactions of market entities, all counterparties

and issuers. The most important step of credit risk

management is to examine the credit of the enterprise.

Enterprises should control credit risk from the source,

require customers to provide business reputation,

financial statements and other information within the

scope of the overall strategic objectives of the

enterprise, strictly review the authenticity and

integrity of the information, and grant corresponding

credit line according to the evaluation results. If it

exceeds the ordinary approval authority, it needs the

approval of the company's higher level management

personnel (Saeidi, Saeidi, Sofian, et al. 2019, Oliveira,

Mexas, Meirino, et al. 2019). Only by establishing a

strict credit system can we minimize the risk from the

source and reduce unnecessary losses for enterprises.

If it is difficult for an enterprise to obtain the

corresponding financial statements and other

information when examining the customer's credit, it

can turn to an independent credit rating agency for

help. In some cases, it can ask the enterprise to

provide property guarantee or third-party guarantee

to ensure the safe recovery of creditor's rights. The

establishment of credit system is an effective way to

reduce the bad debt rate of accounts receivable. On

the one hand, it is strictly controlled from the initial

stage to fully understand the credit risk level of

customers and reduce the credit risk to the lowest

level as far as possible. On the other hand, it increases

mutual trust and mutual understanding with

customers, standardizes the credit relationship

between enterprises and customers, and increases

mutual information, laying a foundation for further

cooperation between enterprises and customers. As a

current asset of an enterprise, the quality of accounts

receivable is related to the authenticity of the

financial situation of the enterprise. The enterprise

shall make an agreement with the debtor that when

the debtor fails to repay the relevant debts on time,

the creditor shall have the right to recover the money

in advance or mortgage the relevant assets with a

third party, so as to ensure the safety of the creditor's

rights. Statistical method is the summary of human-

machine operation and experience (Ojeka, Adegboye,

Adegboye, et al. 2019). The use of modern statistical

methods combined with computer technology can

greatly improve the efficiency of evaluation. With the

development of statistical theory and computer

technology, many new risk assessment methods have

emerged in recent years, such as neural network,

support vector machine and so on. Although they also

have a high correct rate of discrimination, it is

difficult to understand the evaluation process, and the

evaluation results are lack of explanation and

persuasion; other traditional statistical methods, such

as multivariate discriminant analysis, require the

sample distribution to conform to multivariate normal

distribution, which also limits the practical

application. There is no special requirement for the

distribution of sample data, and the result is a

probability value belonging to a certain risk category,

which has a clear meaning and is easy to compare

different evaluation objects. Based on the above

description, complete the credit risk type step.

2.3 Super_ SBM Model to Obtain

Index Weigh

In 2002, tone proposed the DEA efficiency analysis

method based on SBM, that is, when building the

DEA model, we should consider its relaxation

variables, and then study the influence of angle and

radial selection on the input-output relaxation (Huang,

Liu 2020, Zhu, Zhu, Shan 2021). All DMUs are

divided into efficient DMUs and inefficient DMUs to

form Pareto boundary. As a non radial and non angle

data envelopment analysis (DEA), it follows the basic

idea of DEA and envelops the input-output data set

with the most effective technology frontier (An, Wu,

Li, et al. 2019, Lin, Zhu, Han, et al. 2020). Different

from the previous methods, super SBM model puts

the relaxation variables into the objective function to

solve the problem of input and output relaxation.

Super SBM model can be solved directly by software,

without setting production function in advance, and

without considering the problems of index dimension.

This process is the first step in the process of credit

risk management. Enterprises need to conduct a

detailed study on the credit of the opposite party of

credit sales, and also conduct market risk research on

Enterprise Credit Risk Management Model Based on Super

BMModel

815

the related industries, because different industries

have different market risks. Some industries are

cyclical and fluctuate with the change of the cycle. If

we do not pay close attention to the change of market

risk, it may cause great credit risk loss to the credit

seller. For example, as a kind of high-quality non

renewable energy, the mining scale of coal industry

will be affected by government policies, and will be

greatly affected by seasonal changes. In contrast, the

higher the solvency, the lower the corresponding

credit risk. If we want to get credit risk income, we

must adopt scientific credit risk management

methods, seriously and comprehensively collect

customers' credit information, and pay close attention

to customers' business dynamics. The characteristic

analysis of enterprise credit risk factors can establish

a special language to describe customer

characteristics, which is commonly used by sales,

marketing, credit and other departments of the

enterprise, so as to strengthen the understanding and

cooperation among various departments. The entropy

weight method is used to determine the index weight

in the index layer, the expression formula is as

follows:

Skdk

=−



(4)

In formula (4), 𝑘 is the enterprise credit score

vector,

is the standardized value,

is the

indicator vector,

is the criterion layer, and

the number of indicators. Enterprises are not

professional evaluation institutions, so in the

selection of characteristic indicators, we should fully

consider the incomplete information collection,

information distortion and other factors, try to make

the technical indicators easier to understand, the

conclusion is relatively accurate and easy to use. Due

to the difficulty and poor accuracy of obtaining credit

information in China, we should focus on qualitative

index analysis, supplemented by quantitative index

analysis.

2.4 Establishing Credit Risk

Management Model

According to the credit management department's

investigation of customers' credit, enterprises identify

and analyze the risk degree of credit sales, formulate

corresponding contract conditions such as collection

method and credit line in combination with their own

control objectives of financial risk, and then track the

financial status and market risk changes of the

enterprise to be granted credit in time before

recovering all funds, so as to ensure the safe recovery

of funds. Perfect credit risk management measures

can effectively reduce the risk of financial loss caused

by credit sales. When a shareholder of a company

borrows a debt, it is equivalent to buying an asset

whose underlying asset is the value of the company's

assets. The principle of the call option whose default

point is the exercise value is shown in Figure 1.

According to Figure 1, to establish a credit risk

management model, we should follow the following

steps: 1. Credit risk identification and evaluation. The

enterprise conducts a detailed investigation and

Research on the financial status, solvency and other

information of customers, further studies and

analyzes the factors that may cause development and

change, analyzes and evaluates with the evaluation

model, and links the evaluation data with the

corresponding authorization mechanism of the

time

Three years

Current

Average value of assets

Asset value

Expected growth

rate of assets

Book value of debt

repayment

Standard

deviation of

asset value

after three

years

Asset value

distribution after

three years

Figure 1: Schematic diagram of enterprise anticipatory breach of contract.

ICPDI 2022 - International Conference on Public Management, Digital Economy and Internet Technology

816

enterprise, so as to give customers the corresponding

credit line. Enterprises revise credit risk management

methods and procedures. (Vij 2019). All members of

the enterprise need to create their own unique credit

culture, which can increase the cohesion of

employees, improve the company's reputation and

market competitiveness. Due to the neglect of the

influence of credit behavior, the credit risk awareness

of Chinese enterprises is generally not high. On the

basis of identifying and measuring enterprise credit

risk, credit risk managers must take appropriate credit

risk control strategies to achieve the goal of credit risk

management. The control before the occurrence of

enterprise credit risk is the control of credit risk in

advance. In modern credit risk management, this kind

of control method is more and more valued and

widely used by people. Before making business

decisions, the credit risk factors inside and outside the

enterprise are analyzed in detail, various risk factors

are estimated comprehensively, and the trend of the

decision results of the enterprise is predicted. It is

assumed that when the asset value of an enterprise is

at a certain value level, the default probability of

credit risk is higher, the default probability of an

enterprise is expressed as:

𝐺 =















√



(5)

In formula (5), 𝑚 is the asset value of the

enterprise, 𝑓 is the critical asset value of credit risk

default, 𝑞 is the standard distribution value, 𝑛 is the

initial asset value of the enterprise, and

is the

market risk coefficient. If the possible credit risk

factors are found, preventive corrective measures

should be taken in advance to ensure that the

enterprise's business decisions are always on the right

track, so as to achieve the enterprise goals. To a

certain extent, it can avoid and prevent the mistakes

of control in the event and control after the event.

Credit risk control is also a necessary form of control.

When credit risk problems occur, necessary measures

should be taken in time to control the increase of

losses and reduce losses. Because credit risk may

occur at any time, and the occurrence time of risk

events is extremely short, the credit risk control

requires enterprise decision-makers to have a high

degree of risk perception, and be able to deal with risk

events in time.

3 EXPERIMENTAL ANALYSIS

3.1 Experiment Content

Firstly, the most significant regression coefficient of

the variable with the largest partial correlation

coefficient was tested to determine whether the

variable entered the regression equation. Then, each

variable in the equation is taken as the last variable

selected into the equation to calculate the index

weight, and the variable with the smallest index

weight is tested to determine whether it remains in the

equation. Repeat this process until no variables are

introduced and no variables can be eliminated. In this

way, the application of stepwise regression method

can introduce and eliminate variables, and the

original eliminated variables may be introduced into

the regression equation later. Because the stepwise

regression method is a general term of a large

category, the selection forward LR method in SPSS

software is mainly selected to test the goodness of fit

of the model under different iterations.

3.2 Experimental Result

In the experiment, the enterprise credit risk

management model based on machine learning and

the enterprise credit risk management model based on

logistic are selected to compare with the designed risk

management model. The goodness of fit of the three

models is tested under different iteration times. The

closer the goodness of fit is to 1, the better the fitting

degree of the regression line to the observed value is.

Table 1: Experimental results of goodness of fit.

Iterations

(

times

)

Enterprise credit risk management

model based on machine learnin

Enterprise credit risk management

model based on Lo

istic

Risk management

model of desi

100 0.3016 0.6445 0.7849

200 0.3699 0.7031 0.6638

300 0.2647 0.5432 0.7934

400 0.4458 0.6019 0.8025

500 0.5537 0.3376 0.6974

600 0.6029 0.4116 0.8569

700 0.4631 0.3871 0.7774

800 0.3397 0.4036 0.8639

900 0.6080 0.6003 0.9468

1000 0.6682 0.4319 0.8533

Enterprise Credit Risk Management Model Based on Super

BMModel

817

Table 2: Average goodness of fit of. three models.

Number of

experiments (group)

Enterprise credit risk

management model based on

machine learnin

Enterprise credit risk

management model based on

istic

Risk management

model of design

1 0.4618 0.5011 0.7984

2 0.4839 0.5026 0.8001

3 0.3364 0.4967 0.8948

4 0.4012 0.5113 0.7866

5 0.3207 0.4837 0.8631

6 0.3489 0.5006 0.6485

7 0.4068 0.4988 0.7933

8 0.4816 0.5022 0.7204

9 0.4553 0.4886 0.8546

10 0.4431 0.5036 0.9102

The experimental results are shown in Table 1.

According to Table 1, the mean goodness of fit of

the three risk management models can be obtained

under different iterations, as shown in Table 2.

It can be seen from Table 2 that the average

goodness of fit of the enterprise credit risk

management model based on machine learning, the

enterprise credit risk management model based on

logistic and the design risk management model are

0.4139, 0.4989 and 0.807 respectively. The average

goodness of fit of the risk management model

designed in the three models is closest to 1, which

proves that the design model has better performance.

4 CONCLUSION

The risk management model designed in this paper

has better performance than the current model, and

enriches the academic literature on enterprise risk

management. At the same time, it broadens the scope

of Super_ SBM model, it provides new feasible ideas

for enterprise risk management. Due to my limited

ability, the article is about Super_ SBM model in

other fields is not comprehensive enough, in the

future, I will continue to work on related research.

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