# Application of Fusion of Fuzzy Mathematical Clustering Analysis in Enterprise Financial Management Cloud Platform

Authors: Xianli Zhang
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

The enterprise financial management cloud platform can conveniently obtain computing resources such as network, storage, and services according to actual needs based on the network. The maintenance of resources can be realized in a general way, and various computers and their storage resources can be effectively integrated and effectively utilized. But internal conflicts within the company, such as asset management issues, can seriously hinder the company’s long-term growth. In order to solve the above problems, this paper analyzes the actual needs of the current financial information management personnel of enterprises and the business process of financial management. Combined with the characteristics and requirements of the current enterprise financial management, the integrated fuzzy mathematics cluster analysis was introduced to complete the design and implementation of the financial IMS, and to build a modern, systematic, and scientific enterprise management system. It greatly improves the financial level of the enterprise and the intelligent level of enterprise management, provides objective and strong support for enterprise decision-making through the enterprise’s financial management ability, and finally realizes the long-term development of the enterprise. The prediction effect of the samples under the FCM is shown in Table 3. The prediction effect of the training samples of the normal company is 83.33%, and the prediction effect of the training samples of the ST company is 80%. The test sample prediction effect of normal company is 83.33%, the test sample prediction effect of ST company is 80%, and the comprehensive prediction effect under the FCM is 85.28%.


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## Body

## 1. Introduction

Although Chinese enterprises have achieved many achievements in the construction of financial informatization, there are still many deficiencies, especially in the aspect of financial institutionalized supervision and lack of systematic management. In addition, the problems of asset management and control caused by the internal contradictions of the enterprise are seriously restricting the long-term development of the enterprise. In this situation, how to improve efficiency and reduce overhead costs, as well as how to establish an enterprise financial information management model, so that financial information can be fully shared and truly play a financial management function, these are undoubtedly the key issues that enterprises urgently need to solve. Efficient and scientific financial management can help enterprises establish correct business goals and conduct reasonable evaluation and analysis of enterprise income and expenditure, thereby effectively promoting the healthy and orderly development of enterprises. Financial management is the key link of enterprise operation and management. The business audit opinions of various departments will eventually be consolidated into the financial department. It is an urgent need for enterprises to truly exert their financial management functions in the information age. Improving the informatization level of the enterprise financial management system can effectively reduce the operating cost of the enterprise and improve the efficiency of the financial department.Cloud computing has the advantage of reducing the characteristics of financial information technology and at the same time can increase the rate of return. Chen and Metawa solved the problem of corporate financial management based on cloud computing technology, using a business-driven value approach to research [1]. As a cloud computing economy, online finance can reduce the operating cost of enterprises. Yue and Tian analyzed the construction and implementation optimization of e-commerce financial management system. The enterprise financial model can optimize the capital flow and information flow of the enterprise, so that the organization can maintain the competitiveness in the financial operation level [2]. However, there is still a problem of insufficient application in practical use. Cloud computing technology can improve the ability of enterprises to process financial data. Shang et al. determined the frequent fuzzy option sets through FCM, parallel rules, and parallel mining algorithms, and selected the relevant data of listed companies to analyze the financial risks of enterprises [3]. Wei and Yang research found that financial sharing management is more standardized and efficient, reducing business operating costs and improving business and service quality [4]. In order to make financial management uniform and balanced, and for management to be compatible and expandable, Xu et al. will propose that the data model is an asset platform based on cloud computing management [5]. However, the traditional financial information system has high implementation costs and difficult upgrades, and cannot flexibly face the problems of rapidly updating economic business and financial standards. And cloud technology can have positive significance for the optimization of financial information system in terms of technical support, data security, finance, and business strategy.Modeling and forecasting the volatility of equity assets provide an important reference value for a range of operations such as venture capital management. Maciel et al. conducted computational experiments and statistical analysis by modeling and forecasting financial market volatility based on an extension of likelihood FCM and a model based on functional fuzzy rules [6]. The assessment of a retailer’s financial performance in an environment of ambiguity and uncertainty is based on multiple levels of criteria. Wang and Han research found a model under uncertain environment to evaluate the FMCDM problem and solved the relationship between the multiplication of two fuzzy numbers [7]. Efficiency and simplicity are the advantages of FCM. Bharill et al. proposed a hybrid fuzzy clustering method (FCM) to provide an important reference for corporate financial management [8]. Batoo et al. proposed optimal neural network for optimal management of financial management system using FCM and analyzed the weights and ranks [9]. Lu et al. used fuzzy clustering to study the multi-objective group decision-making problem in the project bidding process [10]. However, none of the above studies link the fuzzy mathematics cluster analysis and the enterprise financial management cloud platform for analysis.Combining cloud technology and virtualization can achieve efficient integration and maximized utilization of computing resources. Taking this as the technical background and foundation of the subject, this paper chooses cloud technology and virtualization technology as the development technology. We use J2EE technology to build the system architecture, use Web services to complete integration and interaction, and finally use fuzzy mathematical clustering analysis to improve the intelligence and informatization of the enterprise financial management cloud platform.


## 2. Application Method of Fusion Fuzzy Mathematics Clustering in Enterprise Financial Management Cloud Platform

### 2.1. Enterprise Financial Management Cloud Platform

At present, major enterprises are in a period of rapid development, and the scope of their business development is also deepening and expanding. At the same time, the scale of the enterprise is also expanding. This requires that the work efficiency of enterprise financial management staff must be qualitatively improved in order to meet the development needs of enterprises. Compared with the early enterprise financial management system, this is undoubtedly a huge challenge or an impossible task [11]. Based on the contradiction between demand and reality, people’s thinking about solving the contradiction is triggered: how to improve financial efficiency while reducing administrative expenses; how to achieve data sharing; and how to build an enterprise financial information management model, so that financial information can be fully shared, so as to truly play the role of financial management.Cloud technology refers to the use of Internet technology to provide computing resources. The overall structure includes three levels of resources, platforms, and applications. It can improve object availability through shared resource storage channels and convenient resource invocation channels [12]. The realization of virtualization belongs to the abstract application mode of computing resources, mainly for desktops, applications, servers, etc. It can be understood as the realization of multiple operating systems based on basic physical devices such as computer keyboards and processors. Eliminate differences in attributes and operations by abstracting interfaces, so as to complete the maintenance of computing resources.


### 2.2. Fuzzy C-Means Clustering (FCM)

Denote a set containingn samples as X = {x1,x2, …, xn,}. Each sample contains p indicators. Denote yi as the cluster center of the ith class, and use the sample point xj and the membership degree αij of the sample prototype of the ith class as the weight for weighting processing. Then, the objective function of the C-means algorithm based on the sum of squared errors within the class is(1)FJ=∑i=1n∑j=1ciαijaxji−yi2.Among them, a represents the fuzzy weighting index, which is used to control the fuzzy degree of fuzzy clustering. The larger the a is, the more fuzzy it is, and the smaller the a is, the less fuzzy it is.The objective functionFL takes the minimum value. Using the Lagrangian method to solve, the process is as follows:Let the Lagrangian function be(2)FL=∑i=1n∑j=1ciαijaxj−yi2−∑j=1ciλj∑i=1nαij−1.LetFL take the partial derivative with respect to yi, αij, and λj and equal zero:(3)∂FL∂λj=n∑i=1nαij−1=0,∂FL∂λi=2∑j=1ciαijaxj−yi=0,∂FL∂αij=∑j=1ca∑i=1nαja−1xj−yi2=0.The results are(4)αij=xj−yi2/1−a∑i=1nxj−yi2/1−a,(5)yi=∑i=1nαijaxj∑i=1ciαija.The FCM algorithm first initializes each cluster center by setting the number of clustersn, the weighting index a, and the iteration stop threshold ε [13, 14].


### 2.3. Enterprise Financial Management Cloud Platform System Implementation

System Cloud Architecture: This paper builds an enterprise financial information management system (IMS) based on cloud technology, which is essentially a cloud-based operation of the IMS. Combined with the inherent characteristics of various information management software on the cloud platform and the analysis results of the development needs of the financial IMS, this paper deploys a multi-level and multi-application architecture, as shown in Figure1.Figure 1
System cloud platform architecture.As can be seen from Figure1, the financial IMS architecture covers four levels: infrastructure layer, data layer, management platform layer, and application layer. System users log in to the system through a computer or mobile phone to implement functional applications. Among them, system users are mainly divided into three categories, namely, enterprise staff, enterprise management, and system administrators. The latter has the highest system authority. The infrastructure layer provides components and equipment support for the operation of the financial IMS. The data layer provides data resource support for the operation of the financial IMS. The management platform layer can be understood as the maintenance center of the financial IMS. The application layer can be understood as the service provider of the financial information management system. The physical structure of the financial IMS is shown in Figure 2.Figure 2
System physical architecture.As shown in Figure2, it can be seen that the system port equipment includes a master control platform and a client terminal, and the master control platform is connected to the system through a virtual private network; the client terminal is divided into two forms, namely, the PC terminal and the Android terminal, which connects to the system via Ethernet. The system realizes data transmission and communication between servers through Web Service, which can respond quickly, and users can have a good experience when using it. In addition, because the authority setting only allows administrators to access the database server, the system data security can be effectively guaranteed to a large extent.System Virtualization Architecture: Virtual technology is equivalent to an application. The virtual host program is mainly composed of software technology that can abstract the hardware function and a physical container that can install the server host. It consists of its core foundation. Virtual servers can then be configured to serve different needs [15, 16]. The virtual server can divide and arrange functions according to different needs and can perform reasonable process allocation to system resources such as CPU. Virtual servers have the characteristics of distributed work, and their work does not interfere with each other and does not affect each other, maintaining the independence of information [17]. The virtual server can accept a variety of operating systems, and the financial IMS installs the commonly used operating systems on different virtual servers. Software with different functions is also reasonably managed and divided, making it more convenient to manage these resources. After the virtualized architecture is built, resources can be managed reasonably, the investment in hardware equipment can be reduced, the utilization rate of resources can be improved, and the operating cost of the company can be reduced [18]. The storage framework under the virtualization architecture is shown in Figure 3.Figure 3
System storage frame under the virtualization architecture.As shown in Figure3, a virtual machine is mainly composed of an environment configuration file and a disk file that records and stores data. The disk file corresponds to the disk-like function in the virtual host, and the size is determined by the actual situation of the virtual host. The initial storage file may be simpler. But the data are more and more, when it reaches a certain level, the scalability of the storage file has reached the limit, and it needs to be shared storage [19]. After connecting with the virtual host, build a virtual storage system like a database system. For data interaction and data management between multiple hosts, the cost of constructing such a storage system is relatively high at that time, and it is suitable for large-scale enterprises with irritable data processing [20]. The other is the configuration file. The virtual machine needs to be configured just like the system software or other software that is usually used in order to build it successfully.In the operation of the enterprise, multiple servers may be used, in order to facilitate management, improve the level of management, and improve work efficiency. Design a server management platform, which is a large server independent of all servers. It can carry out daily management of the server, and the financial IMS can also carry out more advanced management of services through this [21, 22].


## 2.1. Enterprise Financial Management Cloud Platform

At present, major enterprises are in a period of rapid development, and the scope of their business development is also deepening and expanding. At the same time, the scale of the enterprise is also expanding. This requires that the work efficiency of enterprise financial management staff must be qualitatively improved in order to meet the development needs of enterprises. Compared with the early enterprise financial management system, this is undoubtedly a huge challenge or an impossible task [11]. Based on the contradiction between demand and reality, people’s thinking about solving the contradiction is triggered: how to improve financial efficiency while reducing administrative expenses; how to achieve data sharing; and how to build an enterprise financial information management model, so that financial information can be fully shared, so as to truly play the role of financial management.Cloud technology refers to the use of Internet technology to provide computing resources. The overall structure includes three levels of resources, platforms, and applications. It can improve object availability through shared resource storage channels and convenient resource invocation channels [12]. The realization of virtualization belongs to the abstract application mode of computing resources, mainly for desktops, applications, servers, etc. It can be understood as the realization of multiple operating systems based on basic physical devices such as computer keyboards and processors. Eliminate differences in attributes and operations by abstracting interfaces, so as to complete the maintenance of computing resources.


## 2.2. Fuzzy C-Means Clustering (FCM)

Denote a set containingn samples as X = {x1,x2, …, xn,}. Each sample contains p indicators. Denote yi as the cluster center of the ith class, and use the sample point xj and the membership degree αij of the sample prototype of the ith class as the weight for weighting processing. Then, the objective function of the C-means algorithm based on the sum of squared errors within the class is(1)FJ=∑i=1n∑j=1ciαijaxji−yi2.Among them, a represents the fuzzy weighting index, which is used to control the fuzzy degree of fuzzy clustering. The larger the a is, the more fuzzy it is, and the smaller the a is, the less fuzzy it is.The objective functionFL takes the minimum value. Using the Lagrangian method to solve, the process is as follows:Let the Lagrangian function be(2)FL=∑i=1n∑j=1ciαijaxj−yi2−∑j=1ciλj∑i=1nαij−1.LetFL take the partial derivative with respect to yi, αij, and λj and equal zero:(3)∂FL∂λj=n∑i=1nαij−1=0,∂FL∂λi=2∑j=1ciαijaxj−yi=0,∂FL∂αij=∑j=1ca∑i=1nαja−1xj−yi2=0.The results are(4)αij=xj−yi2/1−a∑i=1nxj−yi2/1−a,(5)yi=∑i=1nαijaxj∑i=1ciαija.The FCM algorithm first initializes each cluster center by setting the number of clustersn, the weighting index a, and the iteration stop threshold ε [13, 14].


## 2.3. Enterprise Financial Management Cloud Platform System Implementation

System Cloud Architecture: This paper builds an enterprise financial information management system (IMS) based on cloud technology, which is essentially a cloud-based operation of the IMS. Combined with the inherent characteristics of various information management software on the cloud platform and the analysis results of the development needs of the financial IMS, this paper deploys a multi-level and multi-application architecture, as shown in Figure1.Figure 1
System cloud platform architecture.As can be seen from Figure1, the financial IMS architecture covers four levels: infrastructure layer, data layer, management platform layer, and application layer. System users log in to the system through a computer or mobile phone to implement functional applications. Among them, system users are mainly divided into three categories, namely, enterprise staff, enterprise management, and system administrators. The latter has the highest system authority. The infrastructure layer provides components and equipment support for the operation of the financial IMS. The data layer provides data resource support for the operation of the financial IMS. The management platform layer can be understood as the maintenance center of the financial IMS. The application layer can be understood as the service provider of the financial information management system. The physical structure of the financial IMS is shown in Figure 2.Figure 2
System physical architecture.As shown in Figure2, it can be seen that the system port equipment includes a master control platform and a client terminal, and the master control platform is connected to the system through a virtual private network; the client terminal is divided into two forms, namely, the PC terminal and the Android terminal, which connects to the system via Ethernet. The system realizes data transmission and communication between servers through Web Service, which can respond quickly, and users can have a good experience when using it. In addition, because the authority setting only allows administrators to access the database server, the system data security can be effectively guaranteed to a large extent.System Virtualization Architecture: Virtual technology is equivalent to an application. The virtual host program is mainly composed of software technology that can abstract the hardware function and a physical container that can install the server host. It consists of its core foundation. Virtual servers can then be configured to serve different needs [15, 16]. The virtual server can divide and arrange functions according to different needs and can perform reasonable process allocation to system resources such as CPU. Virtual servers have the characteristics of distributed work, and their work does not interfere with each other and does not affect each other, maintaining the independence of information [17]. The virtual server can accept a variety of operating systems, and the financial IMS installs the commonly used operating systems on different virtual servers. Software with different functions is also reasonably managed and divided, making it more convenient to manage these resources. After the virtualized architecture is built, resources can be managed reasonably, the investment in hardware equipment can be reduced, the utilization rate of resources can be improved, and the operating cost of the company can be reduced [18]. The storage framework under the virtualization architecture is shown in Figure 3.Figure 3
System storage frame under the virtualization architecture.As shown in Figure3, a virtual machine is mainly composed of an environment configuration file and a disk file that records and stores data. The disk file corresponds to the disk-like function in the virtual host, and the size is determined by the actual situation of the virtual host. The initial storage file may be simpler. But the data are more and more, when it reaches a certain level, the scalability of the storage file has reached the limit, and it needs to be shared storage [19]. After connecting with the virtual host, build a virtual storage system like a database system. For data interaction and data management between multiple hosts, the cost of constructing such a storage system is relatively high at that time, and it is suitable for large-scale enterprises with irritable data processing [20]. The other is the configuration file. The virtual machine needs to be configured just like the system software or other software that is usually used in order to build it successfully.In the operation of the enterprise, multiple servers may be used, in order to facilitate management, improve the level of management, and improve work efficiency. Design a server management platform, which is a large server independent of all servers. It can carry out daily management of the server, and the financial IMS can also carry out more advanced management of services through this [21, 22].


## 3. Results Analysis

### 3.1. Application Deconstruction of Financial Management Early Warning Model

#### 3.1.1. Sample Selection and Data Sources

This article takes the listed companies of ST as the selected sample of financial crisis. At the same time, from the perspective of the special treatment system, the special treatment of listed companies in Shanghai and Shenzhen did not happen in a short period of time. Rather, it is a conditional, continuous, and progressive dynamic process. That is, if the company is subject to special treatment in yeart, it can be reflected by the financial status of the listed company in years t − 1 and t − 2. Therefore, as long as the financial report of a company in the previous year and two years before ST is obtained, it can be judged whether the company will be specially treated. However, if the financial data of years t − 1 and t − 2 are used as the basis for constructing the model, the predictive ability of the model will be overestimated. Year t − 3 is generally considered to be a turning point, using the financial data for that year as forecast data is an ideal choice, and this is the approach taken in this article.This paper selects the companies that were firstly treated by ST in the Shanghai and Shenzhen A-share markets in 2019 as the training samples, and the companies that were specially treated for the first time in 2020 as the test samples. As of the end of 2020, there are hundreds of ST stocks in Shanghai and Shenzhen stock exchanges. However, through the analysis of listed companies in the manufacturing industry, ST companies caused by equity shrinkage, audit denial, information disclosure, etc., are excluded. At the same time, after excluding B-share ST manufacturing listed companies, a total of 27 ST manufacturing listed companies caused by two-year losses in 2020 were screened as testing samples. Since the number of companies with first ST in 2018 and 2019 is relatively small, this paper uses a total of 30 companies with first ST in two years as the training sample. In the selection of samples, there are two methods of paired and unpaired extraction. This paper takes into account that most of the literature on financial early warning in China adopts the method of paired sample selection. For the convenience of comparison, this paper also adopts the method of paired sampling, and at the same time, it is based on the principle that the total asset size of the same industry does not differ by more than 5%.


#### 3.1.2. Screening of Financial Indicators

Since the ratios were originally chosen to give a complete picture of the company’s financial health, it is unlikely that all of these ratios will be included in the model forecast, so more ratios are not better. Valid financial ratios are necessary, and these ratios need to be controlled because too many invalid ratios contain unnecessary information, only add complexity, and affect the detection results of the model. First, performT-test on these 30 financial indicators to test whether there is a significant difference in the mean of financial indicators of financial crisis companies and financial normal companies. In this way, we can preliminarily judge whether these indicators can reveal the financial crisis. If the test results show that there is no significant difference, it indicates that such indicators cannot reveal the financial crisis well, and the indicators with insignificant differences need to be eliminated. In the early stage of financial distress, a single indicator cannot accurately reflect whether the company is in crisis. For the data of t − 3 year, T-test cannot detect the difference. Therefore, when initially using T-test to filter the indicators, the data of t − 3 year are not selected, but the data of the special treatment year are directly used. The T-test is based on the following assumptions, there is no significant difference between the financial early warning indicators of ST companies and normal companies, and the significance level of the overall variance of the T-test was 5%. This paper uses SPSS statistical software to conduct paired T-test on 30 indicators of ST company and normal company, and the test results are shown in Figure 4.Figure 4
T-test results.
(a)(b)As shown in Figure4, according to the above T-test analysis, it can be seen that the average value of 17 indicators can significantly distinguish ST companies from normal companies. In addition, there are sometimes great correlations between some indicators, so we need to conduct further correlation analysis and test on the data.In correlation analysis, in order to reduce the degree of multicollinearity between variables, highly correlated financial indicators should be eliminated. Statistical theory generally believes that when the correlation coefficient between variables is above 0.9, there must be multicollinearity, which will seriously affect the prediction results. If the correlation between variables is above 0.8, there may be a collinearity problem.The results of the Pearson correlation analysis show that the current ratio ofx1 is highly correlated with the quick ratio of x2. Compared with the current ratio, the current ratio on hand is considered to better reflect the short-term solvency of the company, so the former is excluded, and the current ratio on hand of x2 is retained. Return on equity, return on assets, and return on sales are obviously related, and only one of them should be kept. But since ROE is the most comprehensive, only ROE x14 is kept. Other ratios considered will not be discussed separately due to space constraints. After an initial screening using statistical methods, 12 financial indicators were finally selected.


#### 3.1.3. Data Preprocessing of Financial Indicators

Since rough set attribute reduction can only be used for discrete data, the financial indicators collected in this paper are all continuous data. Therefore, before performing the next attribute reduction on the indicators, the data are discretized and preprocessed. Since rough set attribute reduction can only be used for discrete data, and the financial indicators collected in this paper are all continuous data, the data are discretized and preprocessed before the next attribute reduction is performed on the indicators. This paper directly uses the Boolean reasoning method to discretize the financial data in the text. The discretization results are shown in Tables1 and 2.Table 1
Discretization results of financially distressed companies.
Serial numberX2X3X5X7X10X11X14X21X21X22X23X271233111212133221312222231231331122113324113112233131513112221121161331122133127112132131112Table 2
Discretization results of financially healthy companies.
Serial numberX2X3X5X7X10X11X14X21X21X22X23X271232113111111221311321112331211113313134111111211111513212231311161231123331217113131211111As shown in Tables1 and 2, after discretizing the index value, the rough set software package Rosetta can be used to reduce the attribute value. When fuzzy clustering the data, the fuzzy matrix requires the data to be normalized.


### 3.2. Application of Financial Early Warning Model

Cluster the calculated fuzzy similarity matrix according to the netting method, take the lower triangle of the fuzzy similarity matrix R1, rewrite the “1” on the diagonal into the symbol of the corresponding sample, and give the clustering level. If the elementrij=λ in the matrix R, replace the element rij with the “∗” sign; if it is rij<λ, delete it. The longitude and latitude lines are introduced to the main diagonal of the matrix by the “∗” sign, and the elements on the diagonal lines tied to the ends of the longitude and latitude lines are grouped into one class. When 0.997 < λ ≤ 1, the training samples are clustered into 60 categories; when 0.54 < λ ≤ 0.67, the training samples are clustered into 2 categories; when 0 < λ < 0.17, the training samples are clustered into 1 category. To save space, the clustering results under different threshold levels are not listed one by one. Different threshold levels correspond to different clustering effects, but it has no practical significance for the study of this paper to cluster the samples into one category. Since the samples selected in this paper are composed of normal companies and ST companies, in order to facilitate the study of the clustering accuracy of fuzzy clustering, only the cases of two clusters are selected for analysis.


### 3.3. Validation of the Early Warning Model

#### 3.3.1. Validity Test of Fuzzy Clustering Model

The trained model for fuzzy clustering of detection samples is used, and the same method is used to cluster detection samples. The effect of clustering training samples and testing samples is comprehensively analyzed, and the sample prediction effect under the FCM is shown in Table3.Table 3
Sample prediction effect under FCM.
Prediction effect (%)Normal companyST companyComprehensive effectTraining samples83.338081.67Inspection sample88.988.988.9Average value86.184.4585.28The prediction effect of the samples under the FCM is shown in Table3. The prediction effect of the training samples of the normal company is 83.33%, and the prediction effect of the training samples of the ST company is 80%. The test sample prediction effect of normal company is 83.33%, the test sample prediction effect of ST company is 80%, and the comprehensive prediction effect under the FCM is 85.28%.


#### 3.3.2. Comparison of Model Validity Test

Since discriminant analysis is essentially a kind of clustering, it differs from traditional clustering in that there is at least one “training sample” whose class is already known explicitly. The data of the known training samples are used to determine the discriminant criteria, while the observed values of the unknown categories are determined by the predictor variables. The sample prediction results under the Fisher discriminant method are shown in Table4.Table 4
Sample prediction results under Fisher discriminant method.
Prediction effect (%)Normal companyST companyComprehensive effectTraining samples56.77063.3Inspection sample66.781.574.1Average value61.775.7568.7Table4 shows the prediction results of the training samples and the prediction results of the test samples under the Fisher discriminant method, the comprehensive analysis of the prediction accuracy under the Fisher discriminant method reaches 68.7%, and the results show that the prediction effect is not as good as that under the FCM.(1) System Performance Test. As mentioned above, test whether the system performance based on the high-intensity access environment meets the expected design response goals, such as operation response time and system pressure resistance. Combined with the analysis results of system performance requirements, it can be seen that the financial IMS should meet the following requirements: under the pressure of different concurrent users, the delay response time is less than 5 s. The main contents of performance testing include the selection of testing tools and the writing of test scripts. The financial IMS in this paper uses the test software EVEREST to simulate the performance and gradually increases the number of concurrent users to approach the upper limit of the system’s withstand voltage and the system’s response time under this limit. Through this method, the performance indicators of the system are tested.The operation process of the performance test of the financial IMS in this paper is briefly described as follows: install EVEREST software, and write and run different system operation performance test script languages. The user pressure increase unit is defined here as 30/second, the average value is taken according to the test results, and the corresponding data recording is performed by using the tools built in the system. The results of the performance test of the financial IMS are shown in Figure5.Figure 5
System stress test.
(a)(b)From the data in Figure5, it can be seen that the financial IMS is in the state of 480 concurrent users, the average data query response time is 4.2, the data access response time is 5.0, and the average data size per second is 103.51. Therefore, it can be judged that the performance test results meet the expected goals and are suitable for the financial business needs of enterprises of different scales.


## 3.1. Application Deconstruction of Financial Management Early Warning Model

### 3.1.1. Sample Selection and Data Sources

This article takes the listed companies of ST as the selected sample of financial crisis. At the same time, from the perspective of the special treatment system, the special treatment of listed companies in Shanghai and Shenzhen did not happen in a short period of time. Rather, it is a conditional, continuous, and progressive dynamic process. That is, if the company is subject to special treatment in yeart, it can be reflected by the financial status of the listed company in years t − 1 and t − 2. Therefore, as long as the financial report of a company in the previous year and two years before ST is obtained, it can be judged whether the company will be specially treated. However, if the financial data of years t − 1 and t − 2 are used as the basis for constructing the model, the predictive ability of the model will be overestimated. Year t − 3 is generally considered to be a turning point, using the financial data for that year as forecast data is an ideal choice, and this is the approach taken in this article.This paper selects the companies that were firstly treated by ST in the Shanghai and Shenzhen A-share markets in 2019 as the training samples, and the companies that were specially treated for the first time in 2020 as the test samples. As of the end of 2020, there are hundreds of ST stocks in Shanghai and Shenzhen stock exchanges. However, through the analysis of listed companies in the manufacturing industry, ST companies caused by equity shrinkage, audit denial, information disclosure, etc., are excluded. At the same time, after excluding B-share ST manufacturing listed companies, a total of 27 ST manufacturing listed companies caused by two-year losses in 2020 were screened as testing samples. Since the number of companies with first ST in 2018 and 2019 is relatively small, this paper uses a total of 30 companies with first ST in two years as the training sample. In the selection of samples, there are two methods of paired and unpaired extraction. This paper takes into account that most of the literature on financial early warning in China adopts the method of paired sample selection. For the convenience of comparison, this paper also adopts the method of paired sampling, and at the same time, it is based on the principle that the total asset size of the same industry does not differ by more than 5%.


### 3.1.2. Screening of Financial Indicators

Since the ratios were originally chosen to give a complete picture of the company’s financial health, it is unlikely that all of these ratios will be included in the model forecast, so more ratios are not better. Valid financial ratios are necessary, and these ratios need to be controlled because too many invalid ratios contain unnecessary information, only add complexity, and affect the detection results of the model. First, performT-test on these 30 financial indicators to test whether there is a significant difference in the mean of financial indicators of financial crisis companies and financial normal companies. In this way, we can preliminarily judge whether these indicators can reveal the financial crisis. If the test results show that there is no significant difference, it indicates that such indicators cannot reveal the financial crisis well, and the indicators with insignificant differences need to be eliminated. In the early stage of financial distress, a single indicator cannot accurately reflect whether the company is in crisis. For the data of t − 3 year, T-test cannot detect the difference. Therefore, when initially using T-test to filter the indicators, the data of t − 3 year are not selected, but the data of the special treatment year are directly used. The T-test is based on the following assumptions, there is no significant difference between the financial early warning indicators of ST companies and normal companies, and the significance level of the overall variance of the T-test was 5%. This paper uses SPSS statistical software to conduct paired T-test on 30 indicators of ST company and normal company, and the test results are shown in Figure 4.Figure 4
T-test results.
(a)(b)As shown in Figure4, according to the above T-test analysis, it can be seen that the average value of 17 indicators can significantly distinguish ST companies from normal companies. In addition, there are sometimes great correlations between some indicators, so we need to conduct further correlation analysis and test on the data.In correlation analysis, in order to reduce the degree of multicollinearity between variables, highly correlated financial indicators should be eliminated. Statistical theory generally believes that when the correlation coefficient between variables is above 0.9, there must be multicollinearity, which will seriously affect the prediction results. If the correlation between variables is above 0.8, there may be a collinearity problem.The results of the Pearson correlation analysis show that the current ratio ofx1 is highly correlated with the quick ratio of x2. Compared with the current ratio, the current ratio on hand is considered to better reflect the short-term solvency of the company, so the former is excluded, and the current ratio on hand of x2 is retained. Return on equity, return on assets, and return on sales are obviously related, and only one of them should be kept. But since ROE is the most comprehensive, only ROE x14 is kept. Other ratios considered will not be discussed separately due to space constraints. After an initial screening using statistical methods, 12 financial indicators were finally selected.


### 3.1.3. Data Preprocessing of Financial Indicators

Since rough set attribute reduction can only be used for discrete data, the financial indicators collected in this paper are all continuous data. Therefore, before performing the next attribute reduction on the indicators, the data are discretized and preprocessed. Since rough set attribute reduction can only be used for discrete data, and the financial indicators collected in this paper are all continuous data, the data are discretized and preprocessed before the next attribute reduction is performed on the indicators. This paper directly uses the Boolean reasoning method to discretize the financial data in the text. The discretization results are shown in Tables1 and 2.Table 1
Discretization results of financially distressed companies.
Serial numberX2X3X5X7X10X11X14X21X21X22X23X271233111212133221312222231231331122113324113112233131513112221121161331122133127112132131112Table 2
Discretization results of financially healthy companies.
Serial numberX2X3X5X7X10X11X14X21X21X22X23X271232113111111221311321112331211113313134111111211111513212231311161231123331217113131211111As shown in Tables1 and 2, after discretizing the index value, the rough set software package Rosetta can be used to reduce the attribute value. When fuzzy clustering the data, the fuzzy matrix requires the data to be normalized.


## 3.1.1. Sample Selection and Data Sources

This article takes the listed companies of ST as the selected sample of financial crisis. At the same time, from the perspective of the special treatment system, the special treatment of listed companies in Shanghai and Shenzhen did not happen in a short period of time. Rather, it is a conditional, continuous, and progressive dynamic process. That is, if the company is subject to special treatment in yeart, it can be reflected by the financial status of the listed company in years t − 1 and t − 2. Therefore, as long as the financial report of a company in the previous year and two years before ST is obtained, it can be judged whether the company will be specially treated. However, if the financial data of years t − 1 and t − 2 are used as the basis for constructing the model, the predictive ability of the model will be overestimated. Year t − 3 is generally considered to be a turning point, using the financial data for that year as forecast data is an ideal choice, and this is the approach taken in this article.This paper selects the companies that were firstly treated by ST in the Shanghai and Shenzhen A-share markets in 2019 as the training samples, and the companies that were specially treated for the first time in 2020 as the test samples. As of the end of 2020, there are hundreds of ST stocks in Shanghai and Shenzhen stock exchanges. However, through the analysis of listed companies in the manufacturing industry, ST companies caused by equity shrinkage, audit denial, information disclosure, etc., are excluded. At the same time, after excluding B-share ST manufacturing listed companies, a total of 27 ST manufacturing listed companies caused by two-year losses in 2020 were screened as testing samples. Since the number of companies with first ST in 2018 and 2019 is relatively small, this paper uses a total of 30 companies with first ST in two years as the training sample. In the selection of samples, there are two methods of paired and unpaired extraction. This paper takes into account that most of the literature on financial early warning in China adopts the method of paired sample selection. For the convenience of comparison, this paper also adopts the method of paired sampling, and at the same time, it is based on the principle that the total asset size of the same industry does not differ by more than 5%.


## 3.1.2. Screening of Financial Indicators

Since the ratios were originally chosen to give a complete picture of the company’s financial health, it is unlikely that all of these ratios will be included in the model forecast, so more ratios are not better. Valid financial ratios are necessary, and these ratios need to be controlled because too many invalid ratios contain unnecessary information, only add complexity, and affect the detection results of the model. First, performT-test on these 30 financial indicators to test whether there is a significant difference in the mean of financial indicators of financial crisis companies and financial normal companies. In this way, we can preliminarily judge whether these indicators can reveal the financial crisis. If the test results show that there is no significant difference, it indicates that such indicators cannot reveal the financial crisis well, and the indicators with insignificant differences need to be eliminated. In the early stage of financial distress, a single indicator cannot accurately reflect whether the company is in crisis. For the data of t − 3 year, T-test cannot detect the difference. Therefore, when initially using T-test to filter the indicators, the data of t − 3 year are not selected, but the data of the special treatment year are directly used. The T-test is based on the following assumptions, there is no significant difference between the financial early warning indicators of ST companies and normal companies, and the significance level of the overall variance of the T-test was 5%. This paper uses SPSS statistical software to conduct paired T-test on 30 indicators of ST company and normal company, and the test results are shown in Figure 4.Figure 4
T-test results.
(a)(b)As shown in Figure4, according to the above T-test analysis, it can be seen that the average value of 17 indicators can significantly distinguish ST companies from normal companies. In addition, there are sometimes great correlations between some indicators, so we need to conduct further correlation analysis and test on the data.In correlation analysis, in order to reduce the degree of multicollinearity between variables, highly correlated financial indicators should be eliminated. Statistical theory generally believes that when the correlation coefficient between variables is above 0.9, there must be multicollinearity, which will seriously affect the prediction results. If the correlation between variables is above 0.8, there may be a collinearity problem.The results of the Pearson correlation analysis show that the current ratio ofx1 is highly correlated with the quick ratio of x2. Compared with the current ratio, the current ratio on hand is considered to better reflect the short-term solvency of the company, so the former is excluded, and the current ratio on hand of x2 is retained. Return on equity, return on assets, and return on sales are obviously related, and only one of them should be kept. But since ROE is the most comprehensive, only ROE x14 is kept. Other ratios considered will not be discussed separately due to space constraints. After an initial screening using statistical methods, 12 financial indicators were finally selected.


## 3.1.3. Data Preprocessing of Financial Indicators

Since rough set attribute reduction can only be used for discrete data, the financial indicators collected in this paper are all continuous data. Therefore, before performing the next attribute reduction on the indicators, the data are discretized and preprocessed. Since rough set attribute reduction can only be used for discrete data, and the financial indicators collected in this paper are all continuous data, the data are discretized and preprocessed before the next attribute reduction is performed on the indicators. This paper directly uses the Boolean reasoning method to discretize the financial data in the text. The discretization results are shown in Tables1 and 2.Table 1
Discretization results of financially distressed companies.
Serial numberX2X3X5X7X10X11X14X21X21X22X23X271233111212133221312222231231331122113324113112233131513112221121161331122133127112132131112Table 2
Discretization results of financially healthy companies.
Serial numberX2X3X5X7X10X11X14X21X21X22X23X271232113111111221311321112331211113313134111111211111513212231311161231123331217113131211111As shown in Tables1 and 2, after discretizing the index value, the rough set software package Rosetta can be used to reduce the attribute value. When fuzzy clustering the data, the fuzzy matrix requires the data to be normalized.


## 3.2. Application of Financial Early Warning Model

Cluster the calculated fuzzy similarity matrix according to the netting method, take the lower triangle of the fuzzy similarity matrix R1, rewrite the “1” on the diagonal into the symbol of the corresponding sample, and give the clustering level. If the elementrij=λ in the matrix R, replace the element rij with the “∗” sign; if it is rij<λ, delete it. The longitude and latitude lines are introduced to the main diagonal of the matrix by the “∗” sign, and the elements on the diagonal lines tied to the ends of the longitude and latitude lines are grouped into one class. When 0.997 < λ ≤ 1, the training samples are clustered into 60 categories; when 0.54 < λ ≤ 0.67, the training samples are clustered into 2 categories; when 0 < λ < 0.17, the training samples are clustered into 1 category. To save space, the clustering results under different threshold levels are not listed one by one. Different threshold levels correspond to different clustering effects, but it has no practical significance for the study of this paper to cluster the samples into one category. Since the samples selected in this paper are composed of normal companies and ST companies, in order to facilitate the study of the clustering accuracy of fuzzy clustering, only the cases of two clusters are selected for analysis.


## 3.3. Validation of the Early Warning Model

### 3.3.1. Validity Test of Fuzzy Clustering Model

The trained model for fuzzy clustering of detection samples is used, and the same method is used to cluster detection samples. The effect of clustering training samples and testing samples is comprehensively analyzed, and the sample prediction effect under the FCM is shown in Table3.Table 3
Sample prediction effect under FCM.
Prediction effect (%)Normal companyST companyComprehensive effectTraining samples83.338081.67Inspection sample88.988.988.9Average value86.184.4585.28The prediction effect of the samples under the FCM is shown in Table3. The prediction effect of the training samples of the normal company is 83.33%, and the prediction effect of the training samples of the ST company is 80%. The test sample prediction effect of normal company is 83.33%, the test sample prediction effect of ST company is 80%, and the comprehensive prediction effect under the FCM is 85.28%.


### 3.3.2. Comparison of Model Validity Test

Since discriminant analysis is essentially a kind of clustering, it differs from traditional clustering in that there is at least one “training sample” whose class is already known explicitly. The data of the known training samples are used to determine the discriminant criteria, while the observed values of the unknown categories are determined by the predictor variables. The sample prediction results under the Fisher discriminant method are shown in Table4.Table 4
Sample prediction results under Fisher discriminant method.
Prediction effect (%)Normal companyST companyComprehensive effectTraining samples56.77063.3Inspection sample66.781.574.1Average value61.775.7568.7Table4 shows the prediction results of the training samples and the prediction results of the test samples under the Fisher discriminant method, the comprehensive analysis of the prediction accuracy under the Fisher discriminant method reaches 68.7%, and the results show that the prediction effect is not as good as that under the FCM.(1) System Performance Test. As mentioned above, test whether the system performance based on the high-intensity access environment meets the expected design response goals, such as operation response time and system pressure resistance. Combined with the analysis results of system performance requirements, it can be seen that the financial IMS should meet the following requirements: under the pressure of different concurrent users, the delay response time is less than 5 s. The main contents of performance testing include the selection of testing tools and the writing of test scripts. The financial IMS in this paper uses the test software EVEREST to simulate the performance and gradually increases the number of concurrent users to approach the upper limit of the system’s withstand voltage and the system’s response time under this limit. Through this method, the performance indicators of the system are tested.The operation process of the performance test of the financial IMS in this paper is briefly described as follows: install EVEREST software, and write and run different system operation performance test script languages. The user pressure increase unit is defined here as 30/second, the average value is taken according to the test results, and the corresponding data recording is performed by using the tools built in the system. The results of the performance test of the financial IMS are shown in Figure5.Figure 5
System stress test.
(a)(b)From the data in Figure5, it can be seen that the financial IMS is in the state of 480 concurrent users, the average data query response time is 4.2, the data access response time is 5.0, and the average data size per second is 103.51. Therefore, it can be judged that the performance test results meet the expected goals and are suitable for the financial business needs of enterprises of different scales.


## 3.3.1. Validity Test of Fuzzy Clustering Model

The trained model for fuzzy clustering of detection samples is used, and the same method is used to cluster detection samples. The effect of clustering training samples and testing samples is comprehensively analyzed, and the sample prediction effect under the FCM is shown in Table3.Table 3
Sample prediction effect under FCM.
Prediction effect (%)Normal companyST companyComprehensive effectTraining samples83.338081.67Inspection sample88.988.988.9Average value86.184.4585.28The prediction effect of the samples under the FCM is shown in Table3. The prediction effect of the training samples of the normal company is 83.33%, and the prediction effect of the training samples of the ST company is 80%. The test sample prediction effect of normal company is 83.33%, the test sample prediction effect of ST company is 80%, and the comprehensive prediction effect under the FCM is 85.28%.


## 3.3.2. Comparison of Model Validity Test

Since discriminant analysis is essentially a kind of clustering, it differs from traditional clustering in that there is at least one “training sample” whose class is already known explicitly. The data of the known training samples are used to determine the discriminant criteria, while the observed values of the unknown categories are determined by the predictor variables. The sample prediction results under the Fisher discriminant method are shown in Table4.Table 4
Sample prediction results under Fisher discriminant method.
Prediction effect (%)Normal companyST companyComprehensive effectTraining samples56.77063.3Inspection sample66.781.574.1Average value61.775.7568.7Table4 shows the prediction results of the training samples and the prediction results of the test samples under the Fisher discriminant method, the comprehensive analysis of the prediction accuracy under the Fisher discriminant method reaches 68.7%, and the results show that the prediction effect is not as good as that under the FCM.(1) System Performance Test. As mentioned above, test whether the system performance based on the high-intensity access environment meets the expected design response goals, such as operation response time and system pressure resistance. Combined with the analysis results of system performance requirements, it can be seen that the financial IMS should meet the following requirements: under the pressure of different concurrent users, the delay response time is less than 5 s. The main contents of performance testing include the selection of testing tools and the writing of test scripts. The financial IMS in this paper uses the test software EVEREST to simulate the performance and gradually increases the number of concurrent users to approach the upper limit of the system’s withstand voltage and the system’s response time under this limit. Through this method, the performance indicators of the system are tested.The operation process of the performance test of the financial IMS in this paper is briefly described as follows: install EVEREST software, and write and run different system operation performance test script languages. The user pressure increase unit is defined here as 30/second, the average value is taken according to the test results, and the corresponding data recording is performed by using the tools built in the system. The results of the performance test of the financial IMS are shown in Figure5.Figure 5
System stress test.
(a)(b)From the data in Figure5, it can be seen that the financial IMS is in the state of 480 concurrent users, the average data query response time is 4.2, the data access response time is 5.0, and the average data size per second is 103.51. Therefore, it can be judged that the performance test results meet the expected goals and are suitable for the financial business needs of enterprises of different scales.


## 4. Conclusion

The application of integrated fuzzy mathematics cluster analysis in the enterprise financial management cloud platform is combined with the current research status, the function design of the system is always based on the overall requirements of the system and the characteristics of business processes, and the main functional modules are determined for this paper. In the development demand analysis stage of the financial IMS, this paper comprehensively considers the characteristics of the enterprise’s financial business, the general requirements of the system, etc., and uses the use case diagram to summarize the sub-units of each functional module and elaborate the actual content of the sub-units. Based on the demand analysis of the financial IMS, this paper rationally designs the system database to provide exact support for the realization of the system. The realization of financial IMS includes two major prerequisites: first, the system architecture is built, combining cloud technology and virtualization to build the overall cloud architecture, physical architecture, virtualization architecture, and cloud platform architecture. The second is to build a functional support model, and design and implement a financial information quality evaluation model based on machine learning, to ensure that the financial information has high quality and the functional operation results are credible. In addition, following the software development process, this paper, respectively, tests the function and performance of the financial IMS, and the test results show that the system is qualified. The scalability of the financial information management system in this paper is relatively insufficient, and it is difficult to match the flexibility requirements of financial work. In the later stage, the scalability of the system should be improved.



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*Source: 5354573-2022-09-10.xml*