Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 15, No. 2, 2024 

 

86 

Research on the Mechanism and Path of Science and 
Technology Finance to Support the High-quality 
Development of Private Enterprises 

Le Yu *, Yu Sun, Wen Hao, Yaping Tai, Junjie Qian 

Anhui University of Finance and Economics, Bengbu 233030, China 

* Corresponding author: Le Yu (Email: 18955948716@163.com) 

 

Abstract: With the rapid progress of science and technology in China and the transformation and upgrading of the national 
economy, science and technology finance is gradually showing its role in helping the high-quality development of private 

enterprises. This paper takes science and technology finance as the supporting force and private enterprises as the research object, 

and explores the effective support for the high-quality development of private enterprises from three aspects: mechanism, path 

and policy, and then proposes a high-quality development model of private enterprises. By using random forest algorithm to test 

the effect, the feasibility and scientificity of the development model are qualitatively and quantitatively analyzed. The research 

shows that through the effective support of science and technology finance, the high-quality development model of private 

enterprises has great development potential, so that private enterprises can better cope with market challenges and enhance their 

competitiveness. 

Keywords: Science and technology, Finance, Private enterprises, High-quality development. 

 

1. Introduction 

As an important part of China's economy, private 

enterprises bear the important responsibility of promoting 

employment, promoting economic growth and promoting 

social stability. As a combination of technology and finance, 

science and technology finance provides a new development 

path and opportunities for private enterprises. Through the 

application of advanced technologies such as artificial 

intelligence and big data analysis, science and technology 

finance can help private enterprises reduce financing costs, 

improve financing efficiency, promote scientific and 

technological innovation and enhance competitiveness. At the 

same time, fintech can also provide more diversified 

financing channels and services for private enterprises, 

helping them better cope with market challenges and risks. 

Therefore, in-depth research on how science and 

technology finance can support private enterprises to achieve 

high-quality development and explore its mechanisms and 

ways will not only help provide more development 

opportunities for private enterprises, but also provide strong 

support for the sustainable growth of China's economy. By 

strengthening the in-depth cooperation between science and 

technology finance and private enterprises, we will jointly 

promote the high-quality development of private enterprises 

and realize the optimization and transformation and 

upgrading of the economic structure. 

2. Literature Review 

2.1. Research on the Development of Science 
and Technology Finance 

Zhao Changwen (2009) pointed out that to increase the 

power of science and technology finance, it is necessary to 

promote its reform from the root that is, to accelerate financial 

investment in science and technology, and encourage 

financial institutions, entrepreneurs, and the public to 

participate in it. Therefore, he proposed that measures should 

be taken to develop more financial products, improve the 

service experience, and establish a more complete 

information exchange platform, so that the scientific and 

technological innovation chain can be better linked and better 

meet the changing social and economic needs. According to 

Fang Hanting (2010), fintech can be summarized in four 

simple aspects: first, it is an innovative activity that mobilizes 

resources by applying science and technology to the business 

field; Second, it will form a model that closely links science 

and technology with the economy, and this model will be 

beneficial to both parties; Moreover, it may lead to more 

investment of financial capital, which may bring more returns; 

In the end, it may lead to a tremendous increase in people's 

productivity. Zhou Changfa (2011) proposed that through the 

establishment of a sound science and technology financial 

system, the gap in the market mechanism can be filled, the 

high degree of synergy of resource elements can be promoted, 

scientific research and innovation can be stimulated, and 

social development can be promoted, so as to achieve the 

important goals in four aspects. 

2.2. Research on the Relationship between 
Science and Technology Finance and 

Innovative Technology 

The interaction between fintech and innovative 

technologies is multidimensional and far-reaching. Hicks 

(2010) argues that efficient financial markets promote 

technological innovation, and that investment, not technology, 

is the fundamental prerequisite for an industrial revolution. 

Efficient financial markets often bring dynamism to 

technological innovation, which in turn drives technological 

development and ultimately applies to financial markets. 

Chen Jieying (2020) started from the data of the Greater Bay 

Area and believed that the innovation of technology and 

finance is inseparable from the ecological coordination of 

industrial development. Wang Weibin et al. (2012) found that 



 

87 

scientific and technological innovation is a derivative of the 

development of science and technology finance, so it can be 

used as an intermediary bridge for science and technology 

finance to promote the development of high-tech industries. 

And the mode of high-tech industrialization development will 

change due to the different ways of resource allocation, that 

is to say, if a region gathers more scientific and technological 

financial resources, then its high-tech emerging industries 

will develop more vigorously, and it will also have a good role 

in promoting the economic level of the entire region. 

2.3. Research on the Promotion of Private 
Economic Development by Science and 

Technology Finance 

With the deepening of modern digital transformation, the 

development of innovative market economy, and the 

development of private enterprises, it is necessary to 

fundamentally improve their financing difficulties. Therefore, 

the use of science and technology financial means to reduce 

the financial burden of private enterprises is an effective way 

that is generally recognized by the current society. Cui Yin 

(2024) believes that technology finance can provide 

necessary financial support for private enterprises. Wang 

Xuejun (2019) pointed out that the application of financial 

technology, such as big data, to achieve digital transformation 

can effectively alleviate the financing difficulties of small and 

micro enterprises. Fan Xiaoyun (2015) proposed that science 

and technology finance has the function of accelerator, and 

science and technology finance promotes the development of 

innovation ability by providing financial resource support for 

innovative enterprises and new quality productivity industries. 

Studies have shown that the use of advanced scientific and 

technological means, such as technological revolution and 

financial reform, can effectively support private enterprises, 

so that they can obtain less investment, less expenses, and 

have rich investment channels, thereby greatly reducing their 

financial pressure and promoting them to achieve a high level 

of economic growth. 

3. Relevant Theoretical Basis 

3.1. Economic Principles for the Development 
of Private Enterprises 

The core of the growth of private enterprises lies in the 

effective operation of the market mechanism. As part of the 

market, private enterprises can flexibly adjust the scale of 

production and product structure, so as to achieve the optimal 

allocation and efficient use of resources. 

In the market competition, private enterprises can achieve 

efficient allocation of resources through independent 

operation and self-financing. Private enterprises can better 

grasp market opportunities, and compared with other state-

owned enterprises, they show stronger innovation momentum 

and more innovative thinking. 

3.2. Theoretical Basis of Science and 
Technology Finance 

3.2.1. Theoretical Definition of Science and Technology 
Finance 

Schumpeter's Theory of Economic Development 

emphasized the central role of innovation in economic 

development. His innovation theory focuses on the 

introduction of the "five new" (new products, new methods, 

new markets, new sources of supply, and new organizational 

forms), and explains the close relationship between financial 

capital and innovation. From Schumpeter's point of view, 

credit and capital are enormous drivers of economic 

innovation. For start-ups, credit support is key to their 

development. The development of fintech is based on this 

theory. 

Compared with traditional financial management, science 

and technology finance is more prominent in terms of 

innovative development and guiding characteristics. 

Moreover, science and technology finance will continue to 

innovate with the development of scientific and technological 

innovation in terms of service methods, and its ability to adapt 

to the financing needs of science and technology enterprises 

at different stages will be better than that of traditional finance. 

The orientation of sustainable development is one of the 

important characteristics of fintech. 

3.2.2. Financial Development Theory 
This theory emphasizes the importance of the financial 

system to achieve the allocation of financial resources in an 

uncertain environment. This allocation has the attributes of 

spanning time and space, which helps to optimize the use of 

financial resources and promote economic development. In 

the field of science and technology finance, the theory of 

financial development provides broader financing channels 

and more efficient financial services for science and 

technology enterprises, which is conducive to promoting 

scientific and technological innovation and industrial 

upgrading. 

3.2.3. Theoretical and Technical Support Brought By 
Interdisciplinarity 

Fintech requires a certain degree of interdisciplinarity, such 

as computer science, statistics and finance. These disciplines 

have given financial support to science and technology on the 

basis of technical support and theory. To provide the 

necessary technical support for science and technology 

finance, computer science involves the design, development 

and management of computer systems, as well as the design 

and management of databases. Statistics is closely related to 

data analysis and modeling, as well as the use of statistical 

analysis that is useful for fintech applications in risk 

management and decision support. As an important core 

theoretical foundation of financial technology, finance 

provides a wealth of core theoretical guidance and practical 

applications for technology finance. 

3.3. The Relationship between Science and 
Technology Finance and the Development 

of Private Small, Medium and Micro 

Enterprises 

3.3.1. The role of Science and Technology Finance in the 
Development of Private Enterprises 

The role of science and technology finance in the 

development of private enterprises is becoming increasingly 

prominent, providing diversified financing channels for 

private enterprises and promoting the upgrading and 

innovation of their industries and technologies. 

Fintech provides a wealth of financing options for private 

enterprises. Traditional financing channels often have many 

restrictions for private enterprises, such as cumbersome 

approval processes and limited financing amounts. Through 

the use of modern information technologies such as the 



 

88 

Internet and big data, science and technology finance has 

innovated financing methods and service models, so that 

private enterprises can obtain funds more conveniently. 

3.3.2. The impact of Private Enterprises on Science and 
Technology Finance 

The positive impact of technology finance in the 

development of private enterprises is very significant. 

(1)The rapid development of private enterprises provides 

the market with a broad vision of technology and financial 

demand. With the increasing number and scale of private 

enterprises, these enterprises have an increasing demand for 

financial services, such as financing, investment, risk 

management, etc. This market demand promotes the 

innovation and development of science and technology 

finance, so that science and technology financial institutions 

continue to introduce more products and services that meet 

the needs of private enterprises, so as to meet the diversified 

needs of the market. At the same time, it will bring rounds of 

innovation and development to science and technology 

finance. 

(2) In the process of development, the influence of private 

enterprises on risk management has a positive effect on 

science and technology finance. Private enterprises need to 

establish a sound risk management system to improve their 

financing ability and market competitiveness. In the 

cooperation between science and technology financial 

institutions and private enterprises, they can jointly build a 

risk management system and credit system, reduce financial 

risks, and improve the stability of the financial market. This 

kind of cooperation has a positive effect on the healthy 

development of private enterprises, and also has a certain role 

in promoting the steady operation of science and technology 

finance. 

(3) At the same time of the international development of 

private enterprises, the science and technology financial belt 

has also gained a broader space for development. With the 

acceleration of globalization, more and more private 

enterprises have begun to enter the international market, and 

technology financial institutions can promote the 

globalization of the financial market by providing cross-

border financial services for these enterprises, so that the 

influence and competitiveness of science and technology 

finance can be further enhanced. 

3.4. Case Study of the Benefits Brought By 
Science and Technology Finance 

Ant Financial's fintech model is a comprehensive financial 

ecosystem that integrates multiple financial business areas 

such as payment, financing, investment, and insurance with 

technology at its core: 

Payment: As one of the core products of Ant Gold, Alipay 

meets users' needs for fast, safe and convenient payment 

through online and offline payment services. Alipay not only 

provides basic functions such as QR code payment and 

transfer, but also continuously expands payment scenarios 

through technological innovation, such as non-inductive 

payment, face payment, etc. 

Financing: Ant Financial uses big data and cloud 

computing technology to provide accurate financing services 

for small and micro enterprises and individuals. By analysing 

data on users' behaviour on the Alipay platform, Ant Financial 

is able to assess the credit profile of its customers and provide 

more accurate lending services. This model makes it easier 

for small and micro enterprises to obtain financing. 

Investment: In addition to money market fund products, 

Ant Financial also provides users with a full range of 

diversified wealth management services. Users can deposit 

their idle funds in various wealth management products 

provided by Ant Financial for rapid growth. In addition, for 

users with different investment preferences and risk 

tolerances, Ant Financial has also launched a variety of 

wealth management products for them to choose from. 

Insurance: As one of the representatives of Internet finance 

companies, Ant Financial has entered the insurance industry 

to provide convenient insurance purchase services. Users can 

easily purchase various insurance products such as travel 

insurance and health insurance through the Alipay platform. 

The company combines its own advantages in technology and 

data to contribute to improving the operational efficiency and 

user experience of the insurance industry. At the same time, it 

is also a combination of Internet finance and traditional 

insurance industry. 

Credit assessment services: In addition to existing credit 

assessment services such as Sesame Credit Score, Ant 

Financial also conducts a more comprehensive assessment of 

users' credit status by analyzing big data such as users' 

borrowing and spending behavior on Alipay, providing all-

round coverage of personalized financial services. 

Ant Financial's technology finance model is a 

comprehensive technology-driven financial system that 

combines fintech with traditional financial services, covering 

many areas such as payment, financing, investment, insurance, 

etc. The integration of the advantages of finance and science 

and technology has brought more convenient financial 

services to the people, and at the same time, the efficiency and 

security of finance have been greatly improved, thus playing 

a pivotal role in promoting the inclusiveness of financial 

services. 

4. Models and Methods 

4.1. Sample Selection and Data Sources 

According to the notice on determining the second batch of 

pilot projects to promote the combination of science and 

technology and finance, the cities involved in the notice will 

be taken as the cities for the implementation of science and 

technology finance policies, including Zhengzhou City in 

Henan Province, Xiamen City in Fujian Province, Yinchuan 

City in Ningxia Province, Baotou City in Inner Mongolia 

Province, Shenyang City in Liaoning Province, and other 

seven cities that have not implemented science and 

technology finance policies, including Nanjing City in 

Jiangsu Province, Hangzhou City in Zhejiang Province, Xi'an 

City, Shaanxi Province, etc. The characteristic data of the city 

is collected through the China City Statistical Yearbook, and 

the characteristic data of specialized and special new 

enterprises are retrieved through the Guotaian database, and 

then screened and matched according to the set time. 

4.2. Model Settings 

In 2016, China launched the second batch of science and 

technology finance pilot cities, a total of 9 cities, in order to 

meet the control group and the experimental group, after 

screening and matching, we used 16, including 9 science and 

technology finance pilot cities and 7 non-science and 



 

89 

technology finance pilot cities. At the same time, we match 

the characteristic data of private enterprises with the 

characteristic data of cities, and analyze the path of science 

and technology finance to the development of private 

enterprises. The specific model settings are as follows: 

0 1 2
* *

i tij t jt t i i
d t Eci y C         

   
 

Where, i represents the enterprise, j represents the city, and 

t represents the time. ijdcity is a dummy variable for the 

implementation of urban policies, which represents the 

explanatory variable, which is a binary variable and is 

assigned according to whether the city is implemented by the 

science and technology financial policy; itE represents the 

main variables selected at the enterprise level, and jtC

represents the representative variables selected at the city 

level. tη  denotes a fixed effect of time; i  represents the 

individual fixed effect of the firm, which can control the 

differences between the firms and ensure that the influence of 

individual characteristics on the research results is eliminated 

in the time dimension and before and after treatment. 
1

  and 

2
  are the coefficients of the interaction term, which 
measure the impact of science and technology finance on the 

high-quality development of private enterprises in terms of 

cities and enterprises, respectively, if 0 , it means that 
science and technology finance promotes the high-quality 

development of private enterprises, if 0 , it means that 
science and technology finance is not conducive to the high-

quality development of private enterprises, and if 0 , it 
means that the policy effect of science and technology finance 

is not obvious. 

4.3. Variable Description 

4.3.1. Explanatory Variables 
We select the dummy variable of whether the city 

implements science and technology policy as the explanatory 

variable of the model, which is a binary variable, and the 

choice of the dummy variable as the explanatory variable is 

based on its binary variable, which makes the policy effect 

more intuitive. By comparing the differences between cities 

that implement science and technology finance policies and 

those that do not, we can understand more clearly the impact 

of science and technology finance policies on the high-quality 

development of private enterprises. 

4.3.2. Explanatory Variables 
Explanatory variables are mainly composed of two parts. 

Enterprise characteristics: (1) enterprise scale, total assets is 

the sum of all assets owned by the enterprise, a larger total 

asset scale means that the enterprise has a wide range of 

business in the market, so it is expressed by the logarithm of 

total assets; (2) Capital liquidity, current assets refer to assets 

owned by enterprises that can be converted into cash or cash 

equivalents in a relatively short period of time. Therefore, we 

use current assets to represent the liquidity of the company's 

assets; (3) Equity ratio, that is, total equity/total assets, this 

ratio can clarify the asset structure of the enterprise, and a 

higher equity ratio means that the enterprise has strong 

solvency and financial stability. Urban characteristics: (1) 

Industrial structure, which reflects the changing trend of 

urban industrial structure through the ratio of the added value 

of the secondary industry (industry and manufacturing) to the 

added value of the tertiary industry (service industry); (2) the 

level of economic development, which uses the city's GDP as 

an intuitive indicator to measure the overall economic 

development of the city; (3) education level, which is 

measured by education expenditure/expenditure within the 

general budget of local finance; (4) The level of financial 

development can reflect the scale and activity of the credit 

business of financial institutions, and the balance of loans of 

financial institutions at the end of the year is selected as an 

effective indicator to evaluate the level of financial 

development. 

5. Empirical Research 

5.1. Empirical Research Based on Multiple 
Linear Regression 

5.1.1. Descriptive Statistics 

Table 1. Descriptive statistics of variables
 

Variabl

e 

Sam

ple 

Size 

Mean S.D. Mini Max 

dcity 414 0.3188 0.4666 0 1 

Total1 414 
262723.

6 
405890 22980 4463393 

ER 414 0.6962 0.2085 0.0590 2.8301 

TAGR 414 0.2425 0.3942 -0.3366 3.2642 

Mainin

come 
414 

141355.

5 

351869.

8 

10132.5

5 
4304281 

ecolevel 414 7.9979 0.1988 7.2089 8.2695 

edulevel 414 0.1510 0.0290 0.0015 0.1940 

finlevel 414 8.3013 0.1836 7.3808 8.5548 

indlevel 414 0.7740 0.2080 0.0050 1.2430 

 

5.1.2. Correlation Analysis 
The correlation analysis matrix shows that the correlation 

coefficient between the level of economic development and 

the level of financial development is above 0.74, so the model 

has a certain collinearity problem, which may be due to the 

fact that when the economic growth of a region is relatively 

fast, the demand for funds of enterprises and individuals will 

also increase. This will push financial institutions to issue 

more loans, leading to an increase in loan balances. In 

addition, there are also some collinearity problems between 

total assets and main business income, which may be due to 

the fact that enterprises in different industries may have 

different asset allocation and main business models. 

Therefore, we modify the variables: since the level of 

financial development and total assets are not the main 

explanatory variables of the model, we remove the level of 

financial development and total assets, retain the level of 

economic development and the main business income, and 

replace the growth rate of total assets with the logarithm of 

the growth rate of total assets in the model, and re-analyze the 

correlation of the model, as shown in the following table. 

 

 

 

 

 

 



 

90 

Table 2. Variable correlation analysis 

Nam

e 

dcit

y 
ER 

lnTA

GR 

Mainin

come 

ecol

evel 

edul

evel 

indl

evel 

dcity 
1.0

000 
      

ER 
0.0

856 

1.0

000 
     

lnTA

GR 

-

0.0

933 

-

0.1

315 

1.000

0 
    

Main

in-

come 

0.0

431 

-

0.2

656 

0.062

8 
1.0000    

ecole

vel 

-

0.6

481 

-

0.1

076 

-

0.009

3 

0.0764 
1.00

00 
  

edule

vel 

-

0.1

600 

0.0

054 

0.151

1 
-0.0473 

0.06

77 

1.00

00 
 

indle

vel 

0.2

826 

0.0

923 

-

0.033

5 

0.0197 

-

0.04

25 

0.18

29 

1.00

00 

5.1.3. Model analysis 
In this paper, we make OLS estimates of the explanatory 

variable and the explanatory variable, and establish two 

models at the same time, model_1 all the explanatory 

variables are added to the model, but the results show that the 

coefficient of the explanatory variable-ER is not significant, 

so we remove this variable from the model_2, and the results 

show that the coefficients of the explanatory variable pass the 

significance F-statistic test. So our model estimates as follows: 

 

 

 

 

 

 

 

 

 

0 1 2 3 4 5
*ln * * * *

ij it it jt jt jt t i it
dcity TAGR Mainincome ecolevel edulevel indlevel                       

 

 

The model estimates are shown in the table below. 

Table 3. Model estimates 

 (1) (2) 

 model_1 model_2 

ER 0.0123  

 (0.885)  

lnTAGR -0.0266* -0.0269* 

 (0.062) (0.058) 

Mainincome 0.000000105** 0.000000103** 

 (0.030) (0.027) 

ecolevel -1.480*** -1.481*** 

 (0.000) (0.000) 

edulevel -2.519*** -2.519*** 

 (0.000) (0.000) 

indlevel 0.638*** 0.639*** 

 (0.000) (0.000) 

_cons 11.97*** 11.98*** 

 (0.000) (0.000) 

N 351 351 

df_m 6 5 

F 63.14 75.98 

R2 0.524 0.524 

R2_a 0.516 0.517 

 

The estimation results model_2 in the table show that the P 

value of the coefficients of the explanatory variables (except 

for the explanatory variable lnTAGR) is less than 0.05 in the 

significance test of the linear relationship of the overall 

regression model, and we reject the null hypothesis from the 

P-value rule, but the explanatory variable lnTAGR can be 

considered significant at the significance level of 10%, so the 

coefficients of the explanatory variables in the model are 

significant. According to the results of the model, the 

education level of enterprises will decrease after the 

implementation of the science and technology finance policy, 

that is, the local government education expenditure/total 

expenditure will decrease, which may be due to the fact that 

the science and technology finance policy may attract more 

funds and resources to flow to the field of science and 

technology innovation and development, and reduce the 

investment in the field of education. At the same time, science 

and technology finance policies are likely to focus on 

supporting technological innovation and technological 

upgrading in the industrial sector to enhance their 

competitiveness and production efficiency. This technology-

intensive nature has led to a tilt in the secondary sector of 

science and technology finance policies; the negative 

coefficient of the economic development level and the growth 

rate of total assets may be due to the small sample size of the 

selected data, which is not representative of the results and 

has a certain error. However, the implementation of science 

and technology finance policies has increased the main 

business income of enterprises, and increasing the main 

business income may mean that enterprises have obtained a 

strong competitive position in the market and have a large 

market share, thereby improving the market valuation and 

shareholders' equity of enterprises. This increase in market 

value is reflected in the total equity of the company, which 

increases the equity ratio. Therefore, the implementation of 

science and technology policies for private enterprises will 

also promote the high-quality development of enterprises to a 

certain extent. 

5.2. Risk Prediction—Intelligent Risk Control 
Model Based on Random Forest 

5.2.1. Risk Rules 
A risk rule is a set of guiding principles developed for a 

specific sector or industry to identify, assess, and manage 

potential adverse situations or uncertainties. Therefore, we 

select relevant financial data from the dataset to assess the risk 

of the enterprise: 

 

 

 

 

 



 

91 

Table 4. Risk Rules Table 

Indicator Risk Rules 

Total Assets 

(Total1) 

below 50,000 may indicate that the 

business is not doing well 

Main business 

income 

(Mainincome) 

Below 30,000 may indicate that the 

company has lost market share and is 

facing a squeeze from competitors 

Equity Rate (ER) 

below 0.2 or above 0.8 may indicate 

an imbalance in the company's 

financial structure 

Capital Liquidity 

(CA) 

below 10,000 may indicate a 

company's liquidity problems 

 

As for the rule of equity ratio, an excessively high equity 

ratio may indicate that the company is overly reliant on 

shareholder capital rather than debt financing. This can lead 

to companies not being able to take full advantage of the 

leverage effects of debt financing, missing out on 

opportunities to scale and improve profitability; A low equity 

ratio may expose the company to liquidity risk, i.e., it will not 

be able to repay its maturing debts or meet short-term 

operating needs in the short term, which may lead to a tight 

cash situation. Therefore, an excessively high or too low 

equity ratio of a company will put the company in a risk 

situation. 

5.2.2. Model Establishment and Solving 
Identify companies based on set risk rules to get new data 

sets. When constructing the decision tree, feature engineering 

is carried out, and numerical features are defined for node 

splitting, so as to increase the diversity of the model. Data pre-

processing of numerical features-filling and normalization of 

missing values; A complete preprocessing and model training 

pipeline is constructed, 20% of the data is selected as the 

training set, and the model generates multiple different 

training subsets by random sampling of the original dataset. 

These subsets are often referred to as "bootstrap samples" and 

thus train the model. When all decision trees are built, the 

random forest integrates the predictions for each tree. Finally, 

the established random forest model was evaluated and tuned 

to improve the generalization ability and prediction 

performance of the model. 

From the predictions of the model, we derive the feature 

importance. Feature importance refers to the measure of the 

contribution or influence of each feature to the prediction 

results of the model in a machine learning model, and for a 

tree-based random forest model, the importance of features 

can be calculated by evaluating the change in the information 

gain or Gini coefficient brought about by each feature in the 

node splitting in the decision tree. 

From the feature importance table, the ER (equity ratio) can 

be obtained: the most important feature with an importance of 

0.495269. Mainincome: The second most important 

characteristic with an importance of 0.187674. Total1 (Total 

Assets): The third most important feature with an importance 

of 0.085773. Total2: Importance is 0.058009. CA (Capital 

Liquidity): Importance is 0.042621. The results show that the 

equity ratio, main revenue and total income are the main 

factors affecting the company's risk assessment. A clear 

understanding of feature importance can be used to detect 

anomalies or outliers in the data. If the importance of a feature 

suddenly decreases or increases, it may indicate that the 

feature data is abnormal and needs to be further examined and 

processed, which is the basis for the decision implementation 

of the so-called "intelligent risk control" model. 

5.2.3. Model Evaluation 
From the confusion matrix, we can obtain TN=60, which 

means that the model correctly predicts the number of 

samples without risk as no risk is 60. FP=0, which means that 

the number of samples in which the model mispredicts the 

enterprise without risk as risk is 0; FN=2, which means that 

the model predicts the number of samples of risky enterprises 

as no risk is 2; TP=21, indicating that the model predicts the 

risk as risky with the number of samples is 21. 

Therefore, the classification report of the model is obtained: 

Table 5. Model classification report 

 
Preci

sion 
Recall 

F1 

scores 

Number of 

samples 

Model 

Accurac

y=97.6

% 

There 

is no 

risk 0 

0.97 1.00 0.98 60 

There 

is a risk 

1 

1.00 0.91 0.95 23 

 

From the classification report, the accuracy rate for non-

risk enterprises is 97%, and the accuracy rate for risk 

enterprises is 100%; The recall rate, also known as sensitivity, 

means that the proportion of enterprises that are correctly 

predicted by the model as risk-free is 100%, and the 

proportion of risky enterprises that are predicted by the model 

to be risky is 91%; The F1 score refers to the harmonic 

average of precision and recall, and he combines the 

performance of both, and the F1 score for both risk-free and 

risky predictions is close to 1, indicating that the model has 

superior performance and has achieved a good balance 

between accuracy and recall. The prediction accuracy of the 

whole model is also as high as 90%, indicating that the model 

has superior effect. 

6. Summary 

6.1. Conclusions 

This paper selects the dummy variable of whether the city 

implements science and technology policy as the explanatory 

variable, selects seven explanatory variables from the 

perspectives of enterprise characteristics and city 

characteristics, and analyzes the data through a variety of 

models, and concludes that there is a two-way causal 

relationship between science and technology finance and the 

high-quality development of private enterprises. Science and 

technology finance provides private enterprises with richer 

financing services and technical resources, improves their 

own technical level and comprehensive competitiveness, and 

promotes the high-quality development of enterprises. With 

the development of private enterprises, the demand for 

scientific and technological support is also increasing, which 

will promote the continuous innovation and improvement of 

service models of scientific and technological financial 

institutions to meet the needs of enterprise development. 

6.2. Policy Recommendations 

With the rapid development of science and technology and 

the transformation and upgrading of the national economy, 

science and technology finance, as an important means to 

promote the high-quality development of private enterprises, 

has received more and more attention. By studying the 

specific impact of fintech on private enterprises, this paper 

draws the following important policy implications: 



 

92 

(1) Build an innovative financial ecosystem. The 

government can create ways to set up science and technology 

finance science and technology innovation funds, innovative 

business incubators and other institutions to use technology 

to drive the financial system, lower the threshold for financial 

services, provide private enterprises with all kinds of financial 

support they need, and help enterprises make breakthroughs 

in scientific and technological innovation. 

(2) Increase efforts in innovative research and development, 

venture capital, etc. In addition to increasing capital 

investment in financial innovation and research and 

development, we should create a good atmosphere for the 

development of science and technology finance. It is also 

necessary to seize the opportunity of national financial 

technology development and actively promote cooperation 

between the two sides to attract more funds to invest in the 

development of private enterprises. 

(3) Improve the financial service system of science and 

technology. Policy measures should be implemented to 

balance the risk relationship between finance and private 

enterprises. Establish a scientific and technological financial 

service platform, integrate all kinds of capital, technology and 

human resources, and provide private enterprises with a full 

range of financial services and compound talent policies to 

promote technological innovation, including financing, R&D 

support, marketing and other aspects of help. 

(4) Strengthen the supervision of science and technology 

finance to ensure the sustainable and healthy development of 

the science and technology finance industry. There are certain 

risks in the technology and finance industry, and it is 

necessary to strengthen supervision. The government should 

establish and improve the management system of science and 

technology finance, improve the relevant regulatory system 

and legal system, improve the efficiency of supervision, and 

ensure that science and technology finance supports the 

sustainable and healthy development of private enterprises. 

6.3. Future Prospects 

With the development of artificial intelligence and big data, 

technology finance will play a more important role in 

supporting the high-quality development of private 

enterprises. It can not only change the way enterprises operate, 

improve efficiency and expand the market, but also bring 

innovative financing methods to enterprises, and also provide 

private enterprises with the opportunity to directly connect 

with investors around the world, which will reduce the cost of 

private enterprises in the financing intermediate link and 

obtain more competitive financing conditions. Of course, the 

technology financial platform also provides a variety of 

financial management tools and data analysis services to help 

enterprises better manage finances, optimize resource 

allocation, and improve operational efficiency. Through the 

technology and financial platform, private enterprises can 

have wider access to the global market, develop overseas 

business, and expand international trade cooperation, so as to 

achieve business growth and risk diversification. 

First of all, science and technology finance will create more 

new financial products and services to meet the diversified 

financing needs of private enterprises. For example, by 

introducing blockchain technology, cross-border payments 

and supply chain financial services can be more convenient 

and efficient. Second, science and technology finance will 

stimulate more vitality of scientific and technological 

innovation. This will provide more opportunities and 

incentives for private enterprises to achieve breakthroughs 

and competitive advantages in the field of technology. In 

addition, it plays an important role in strengthening data 

security and risk management. In the future, the government 

and financial institutions will adopt stricter risk prevention 

and control measures to ensure the security of enterprises' 

financial and transaction data. At the same time, we will 

strengthen the data risk prediction and prevention and control 

mechanism, detect and prevent risks in a timely manner, and 

reduce non-performing loans and losses. In addition, fintech 

will also promote the deepening of international cooperation. 

Its development will not be limited to the domestic market, 

but will also expand to the international market. The Chinese 

government will strengthen cooperation with other countries 

and regions in science and technology finance, which will 

provide more international financing channels and market 

opportunities for private enterprises and promote their 

development on a global scale. 

Acknowledgments 

This paper is supported by Anhui University of Finance 

and Economics Undergraduate Research and Innovation Fund 

project (Grant No: XSKY24087). 

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