# Research on Enterprise Financial Management and Prediction System Based on SaaS Model

Authors: Qianying Zhang; Fang Zhou
Journal: Security and Communication Networks
 (2022)

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## Abstract

In order to supervise and forewarn the sustainable operation ability of enterprises efficiently and accurately, this paper proposes an enterprise financial management and forecasting system based on SaaS model. First of all, in order to continue to effectively predict and analyze the enterprise finance, first analyze and extract the report data in the financial system. Then, by building a deep belief network model to predict the enterprise financial data, in order to reduce the cost of enterprises, the financial system designed in this paper chooses the cloud technology service framework based on SaaS model. Finally, in order to analyze the risk identification performance of the financial management and prediction system in this paper, the risk sample data of an enterprise’s financial system is selected for simulation test. The results show that the correct rate of risk identification of the financial management system designed in this paper is higher than other comparison systems, which speeds up the speed of risk identification of the financial information management system, and has certain practical application value.


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## Body

## 1. Introduction

With the continuous development of economy, financial management is widely used in enterprises. Due to the increasing probability of economic crisis, the probability of bankruptcy of enterprises is getting higher and higher. Therefore, improving financial management level can reduce the probability of bankruptcy of enterprises [1]. Financial risk identification is an important branch of financial management. Financial risk identification can help enterprises develop certain risk control measures and control financial risk identification within a certain range. It can better promote the development of enterprises, so financial risk identification has always been regarded as a major research topic [2].In the past few decades, domestic and overseas scholars have conducted extensive research on financial risk identification. At first, people used the theory of statistics to conduct financial risk identification research [3], such as the financial risk identification method based on decision tree, the financial risk identification method based on random forest algorithm, the financial risk identification method based on XGBoost algorithm [4], and the financial risk identification method based on multivariate discriminant analysis. They assume that financial risk is a fixed law of change, such as linearity and periodicity. However, there are many factors involved in financial risk, which make the change of financial risk complicated and time-varying. Traditional statistical theory is difficult to accurately grasp the characteristics of financial risk changes, resulting in low accuracy of financial risk identification [5]. Soon afterwards there are financial risk identification methods based on modern statistical theory, such as BP neural network financial risk identification method [6] and RBF neural network financial risk identification method which support vector machine financial risk identification method [7]. Their financial risk identification effect is obviously better than traditional statistical theory. In the process of financial risk identification modelling [8], the neural network requires a large number of samples, while the financial risk samples are usually small, which makes difficult to meet the requirements of large samples. As a result, the financial risk identification results of neural network often show overfitting phenomenon, and the credibility of financial risk identification results is low. Although support vector machine will not produce “overfitting” financial risk identification results, the low modelling efficiency leads to a long time of financial risk identification [9].In recent years, with the continuous development of data mining technology, deep belief network has been greatly developed, which provides a new research tool for risk identification modelling of financial information management system [10]. Because of the complexity of risk change in financial information management system, in order to improve the accuracy of risk identification in financial information management system, this paper proposes a prediction model based on improved deep belief network. Two-dimensional rotation crossing (TRC) strategy and adaptive mechanism are introduced to improve the accuracy of model prediction.This paper mainly has the following innovations:(1)
The report data in the financial system is analyzed and extracted(2)
The parameters of hidden layer are optimized by deep belief network combined with improved differential evolution algorithm(3)
The financial system designed in this paper chooses the cloud technology service framework based on SaaS model to reduce the cost of enterprisesThis paper consists of five main parts: the first part is the introduction, the second part is prediction model based on improved deep belief network, the third part is financial management system based on SaaS model, the fourth part is the experiments, and the fifth part is the conclusion; besides there are abstracts and references.


## 2. Prediction Model Based on Improved Deep Belief Network

### 2.1. Data Preprocessing

In order to continue to forecast and analyze effectively, it is necessary to analyze and extract various forms and statements data from the financial system of the enterprise [11]. Data cleaning is used to generate comma-separated values (CSV) data as required.


#### 2.1.1. Data Cleaning

Aiming at the problem of low integrity and much overlap of expenditure funds in corporate financial institutions, the data cleaning adopted in this paper is divided into four steps.(1)
Missing value cleaning: Set the threshold value of miss judgment to 80%, and select the original data according to the standard, remove the feature columns exceeding the threshold value, and fill the missing value in the area with “0” value(2)
Format content cleaning: Set the imported data to a unified storage format, for example, 2019-02-21(3)
Repeated content cleaning: Then, the data is filtered again, and multiple feature columns with high content repetition are deleted to keep only one of them, which is conducive to dimensionality reduction(4)
Nonrequired data cleaning: The irrelevant data that are not in the predicted time span are deleted, and only the sample data with the minimum time span of 1 month are retainedAfter the above four steps, all the processed data is saved in the required CSV format


#### 2.1.2. Feature Selection

The characteristics of each column in the data sample need to be reasonably selected, so as to reflect the required relational mapping and avoid overfitting as much as possible and to strengthen the ability of multiple model generalization.In this paper, L1 norm regularization method is used for feature selection, which can be effectively applied to nonlinear scenes [12]. The L1 norm scores of all statistical features were calculated. 0.6 was set as the selection threshold in this paper, and features with scores less than 0.6 were deleted to complete the feature selection process.


#### 2.1.3. Normalization

After cleaning the financial data, it is also necessary to unify the value range of the actual sample values in order to unify the scale of the sample characteristics. This paper adopts mean variance normalization to process all data samples, which are uniformly expressed as numbers between [0, 1], as shown in the following equation:(1)Iscale=i−minmax−min,where min represents the minimum eigenvalue and max represents the maximum eigenvalue.


#### 2.1.4. Generation of Sliding Samples

Since financial forecasting is a periodic work, time span needs to be set, similar to the window frame in the graphic image processing mechanism. In this paper, 2 years is set as the time span for sliding selection of data samples. Too short or too long time span will have a certain impact on the performance of the prediction, and 2 years is the empirical value of multiple experiments.


### 2.2. Deep Belief Networks

DBN is a neural network classification, recognition, and prediction model with multiple hidden layers. Compared with shallow machine learning and traditional neural network, DBN with a large number of hidden layers has good feature extraction and recognition ability for abnormal data flow of enterprise finance. Through multilayer nonlinear transformation, deep abstract features are trained from complex enterprise financial data, and the internal correlation of data is described. The layer-by-layer training method overcomes the defect that feedforward neural network is easy to fall into local minimum value and obtains better prediction results and faster convergence speed.


#### 2.2.1. Sample Input

This paper adopts Support Vector Classification (SVC) model to collect four kinds of historical sample data sets and obtains the current enterprise financial situation assessment grade setV=vx according to the evaluation method of existing literature.vx is the situation assessment grade value of enterprise finance at moment x, and then the assessment set V is converted into the input data set N of DBN network. As shown in formula (2) ia is the input feature vector, ja is the label value, a is the serial number of the sliding window, t is the size of the sliding window, the sliding step of the window is 1, t ≤ T, a ≤ T − t.(2)N=ia,ja⇒lia,…,lia+t−2,lja,…,lja+t−1.


#### 2.2.2. Training Phase

DBN uses two steps of unsupervised pretraining and supervised global fine-tuning to adjust the weight of neural network. In the pretraining, Restricted Boltzmann machine (RBM) was trained separately, layer by layer from low level to high level. In fine-tuning stage, BP neural network is used to fine-tune the weight and bias of DBN. The DBN structure is shown in Figure1.Figure 1
Structure of DBN.RBM obtains the weights of the generated model through pretraining in an unsupervised, layer-by-layer greedy manner. During the neural network training, the visual value is mapped to the hidden layer node, and the hidden layer node is reconstructed as the visible node. The value of each node is in the set {0, 1}; that is, there exists anyx, y such that qx∈0,1, by∈0,1, mx,y are the weights between the visible node and the hidden node, and the offset of the visible node c=c1,c2,…,cx, and the offset of hidden layer node h=h1,h2,…,hy. For RBM with x visible nodes and y hidden layer nodes, b and q represent the states of hidden layer nodes and visible nodes, respectively. Set a set of states (b, q), and the energy function of RBM is defined as the following formula:(3)Eq,b|ϕ=−∑cxqx−∑hyby−∑∑mxyqxby,where ϕ=cx,hy,mxy represents the joint distribution law of (b, q) obtained by formula (3) for RBM parameters, as shown in the following formula:(4)uq,b|ϕ=Fϕ−1expq,b,where E is the expected value and Fϕ is the normalized factor, as shown in the following formula:(5)Fϕ=∑∑exp−Eq,b|ϕ.When the visible layer is known, all nodes of the hidden layer are independent of each other, and the probability distribution of they-th node in the hidden layer is shown in the following formula:(6)uQ|B=∏xuby|q,uby=1|q=fhx+∑mxyqx,uby=0|q=1−uby=1|q,where u is the probability and f is the sigmoid activation function. Similarly, if the hidden layer is known, the probability distribution of the xth node in the visible layer is obtained. Because the data in the sample training set has the label of enterprise financial situation data source, in the RBM training at the top level, in addition to the dominant neurons, there should also be neurons representing classification labels in the visible layer. Here, for each group of training data, the corresponding label neurons are opened as 1, while the others are closed and set as 0. The algorithm of DBN training process is obtained.


### 2.3. Prediction Model

Because the layer number of hidden layer unit of DBN network is mostly set by experience, and the related parameters of hidden layer are sensitive to this, improper selection may lead to serious decline in accuracy of prediction results or too long training time. In order to solve these problems, Differential Evolution (DE) algorithm is integrated into deep belief network to simplify network structure and build a good deep learning model [13]. The core problem of the enterprise financial situation prediction model based on IDE-DBN is to determine the number of hidden node layers and the weight and bias between layers. The improved DE algorithm enhanced the global search performance and further reduced the local extremum [14]. The basic idea is to map the parameters of hidden layer nodes of DBN network used for enterprise financial situation prediction to the initial target individuals of differential vector space evolution [15]. By judging the fitness level of the new individuals and the old individuals, the weak ones are selected and the strong ones are retained. After several iterations, the target search is guided to the optimal solution with low error. The mathematical description of DE algorithm is shown in the following formula:(7)DE=a0,w0,l0,v0,f0,h0,c0,where a0 is the number of nodes in hidden layer of DBN network, w0 is the parameter of hidden layer of DBN, l0 is the population size, v0 is the fitness function of individual, and three operations are defined: f0 is the replication operation, h0 is the crossover operation, and c0 is the mutation operation.


#### 2.3.1. Standard DE Algorithm

The main process of standard DE algorithm is shown in Figure2.Figure 2
Standard DE algorithm flow.In the standard DE algorithm, the difference vector between the parent individuals is generated, and it is superimposed on a random target individual. The optimal individual is retained and added to the next generation population through the crossover and selection operation between the new offspring and the parent. Algorithm optimization is a process of constant transformation in search space. However, it is easy to fall into local optimality, so the crossover process is improved in this paper. Population initialization: Set TU to represent population size andD to represent individual dimension and randomly generate the initial population in the problem decision space according to the following formula:(8)ix,0=ir1,0,ir2,0,…,irD,0x=1,2,…,TU,where ix,0 is the ith individual of the population of the 0th generation, and the individual is valued in each dimension according to the following formula:(9)ixy,0=Ly+randBy−Ly,y=1,2,…,D,where By,Ly represents the value range of the yth dimension, ixy,0 represents the yth dimension component of the xth individual of the initial generation, and RAND represents the random number evenly distributed on the interval (0, 1).Mutation means that, after initialization, DE algorithm generates a population composed of NP experimental vectors through mutation and recombination operation of the population and produces mutant individuals by perturbation of target individuals. Common mutation strategies are as follows: the DE/RAND/1 differential mutation operation adds a scalable and randomly selected vector to a third-party vector, ss shown in the following formula:(10)vi,G=xr1,G+F⋅xr2,G−xr3,G.In DE/ Best/1 strategy, the differential variation operation with a scalable and randomly selected vector is added to the vector with the best performance of the basis vector in the current population, as shown in the following formula:(11)qx,A=ibest,A+F⋅ir1,A−ir2,A.In DE/current-to-best/1 strategy, the differential mutation operation and two randomly selected scalable vectors are added to the third-party vector, and the vector difference contains the vector with the best performance of the basis vector in the current population, as shown in the following formula:(12)qx,A=ix,A+F⋅ibest,A−ix,A+F⋅ir1,A−ir2,A.Crossover: the experimental individual is obtained by crossover operation between the parent individual and the mutant individual.(13)pxy,A=qxy,A,if rand0,1≤CR ory=yrand,ixy,A,otherwise.Selection refers to the selection of experimental individuals and paternal individuals based on the principle of greed, and the excellent individuals will enter the next generation.


#### 2.3.2. Adaptive Mechanism

The difference vector is the disturbance of the variable-dimensional decision variables ofirx and A. And F controls the degree of disturbance. If the generated difference vectors are relatively close apart in the search space, F should take a larger value; otherwise the perturbation quantity is too small to be conducive to global search in the early stage of evolution. If the generated difference vectors are far apart in the search space, F should take a smaller value; otherwise the disturbance is too large, which is not conducive to local search in the early stage of evolution. The value of F should be adjusted adaptively according to the relative position of the two individual vectors generating difference vectors in space to balance the contradiction between local search and global search.(14)Fx,A+1=Fmin+randFmax−Fminiffpx,A>fix,AFx,A,otherwise,where Fx,A+1 represents the scaling factor of the xth individual of the A + 1 generation, Fx,A represents the scaling factor of the ith individual of the A generation, and an appropriate scaling factor is conducive to the generation of offspring with high survival rate. Therefore, a new Cri can be generated according to formula (15) by recording the factor parameters of the most recent history.(15)Crx,A+1=randtxv,εifpx,A>fix,ACRx,Aotherwise,where Crx follows a Gaussian distribution and represents the crossover rate of the xth individual in the A generation, while randtxv,ε is the Gaussian distribution of mean v and standard deviation ε.


#### 2.3.3. Two-Dimensional Rotation Crossover Algorithm

With the deepening of evolutionary algebra, the standard DE algorithm cannot cope with the adaptive changes of population diversity and the global optimal solution cannot guarantee convergence in the feasible solution space. By introducing a two-dimensional rotational crossover algorithm, the offspring of the target and the mutant are generated around the target. Suppose that the rotation radius of the population containing NP d-dimension individuals is shown in the following formula:(16)r⟶x,A=θq⟶x,A−i⟶x,A,R⟶x,A=12r⟶x,AR⟶x.The direction control parameterθ is set to ensure that the offspring can be evenly distributed near the mutant and the target. When θ = 1, the direction of the cross vector is from q⟶x,A to i⟶x,A, and when θ = −1, the vector is reversed. R⟶x,A is the distance between the target individual and the target individual, R⟶x,A is the radius of rotation distributed near the new offspring, ω is the vector R⟶x,A rotation angle of A, A is the iteration number, R⟶ is the vector controlling rotation, and its value is shown in the following formula:(17)R⟶=C1A+1−1/2R′.C1 is a universal constant, and the modulus of R⟶ decreases with the increase of A, which is conducive to increasing the search accuracy and accelerating the convergence of TRC-DE algorithm. The adjusted control vector factor R′ follows the Cauchy distribution as shown in the following formula:(18)R′=rand Cauchy.R′ is a matrix of NP × D, whose individuals satisfy the Cauchy distribution. Because the Cauchy distribution has a long tail, the selection range of progeny produced by controlling the Cauchy distribution increases.According to the above analysis, with parenti⟶x,A and variant q⟶x,A as the center, and with rotation control vector R⟶ and direction control vector θ acting on the mode of ΔR=θ⋅R⋅r as the rotation radius, the cross vector K⟶x,A is obtained. The rotation crossover operation is shown in the following equation:(19)K⟶x,A=θR⟶x,Aq⟶x,A−i⟶x,A+q⟶x,Arandxy≤Cr ory=yrand,θR⟶x,Aq⟶x,A−i⟶x,A+i⟶x,Aotherwise.The diversity of progeny can be increased by incorporating two-dimensional rotational crossover.In this paper, an example of rotating vectors in two-dimensional space is given to prove how to improve the population diversity and convergence of standard DE algorithm by introducing two-dimensional rotating crossover algorithm. AssumingR⟶x=r1,r2r1⩽r2, i⟶x,A=i1,i2, q⟶x,A=q1,q2, θ = 1, substitute into the following formula:(20)q⟶x,A−i⟶x,A2=Δi1,Δi2R⟶x,A=q⟶x,A−i⟶x,AR⟶x,where Δi1=q1−i1, Δi2=q2−i2, the rotation vector can be expressed as R⟶x,A=r1Δi1,r2Δi2, and the modulus of the rotation vector can be expressed as(21)R⟶x,A=r1Δi12+r2Δi22=r2r1/r2Δi12+Δi22.Letr1≤r2, then r1≤R⟶x,A≤r2, the modulus R⟶x,A of R⟶x,A can vary in size with different coordinate values of R⟶x, and the rotation angle ω of R⟶x,A can be obtained by the following formula:(22)ωR⟶x,A=grcngtr1r2⋅Δi1Δi2.In formula (22), there are three possible situations: if r1=r2, the vector R⟶x,A has the same direction as q⟶x,A−i⟶x,A. If r1<r2, then ω will have a greater angle than the vectors q⟶x,A−i⟶x,A. If r1>r2 then the angle ω will be smaller than the vectors q⟶x,A−i⟶x,A, and the magnitude and ω of the rotation vector vary with the coordinates of the vector R⟶i.


#### 2.3.4. IDE-DBN Algorithm

IDE-DBN is a combination of TRC-DE algorithm and DBN algorithm. It treats DBN weights and bias values as populations and uses them to perform mutation, crossover, and selection operations.


## 2.1. Data Preprocessing

In order to continue to forecast and analyze effectively, it is necessary to analyze and extract various forms and statements data from the financial system of the enterprise [11]. Data cleaning is used to generate comma-separated values (CSV) data as required.


### 2.1.1. Data Cleaning

Aiming at the problem of low integrity and much overlap of expenditure funds in corporate financial institutions, the data cleaning adopted in this paper is divided into four steps.(1)
Missing value cleaning: Set the threshold value of miss judgment to 80%, and select the original data according to the standard, remove the feature columns exceeding the threshold value, and fill the missing value in the area with “0” value(2)
Format content cleaning: Set the imported data to a unified storage format, for example, 2019-02-21(3)
Repeated content cleaning: Then, the data is filtered again, and multiple feature columns with high content repetition are deleted to keep only one of them, which is conducive to dimensionality reduction(4)
Nonrequired data cleaning: The irrelevant data that are not in the predicted time span are deleted, and only the sample data with the minimum time span of 1 month are retainedAfter the above four steps, all the processed data is saved in the required CSV format


### 2.1.2. Feature Selection

The characteristics of each column in the data sample need to be reasonably selected, so as to reflect the required relational mapping and avoid overfitting as much as possible and to strengthen the ability of multiple model generalization.In this paper, L1 norm regularization method is used for feature selection, which can be effectively applied to nonlinear scenes [12]. The L1 norm scores of all statistical features were calculated. 0.6 was set as the selection threshold in this paper, and features with scores less than 0.6 were deleted to complete the feature selection process.


### 2.1.3. Normalization

After cleaning the financial data, it is also necessary to unify the value range of the actual sample values in order to unify the scale of the sample characteristics. This paper adopts mean variance normalization to process all data samples, which are uniformly expressed as numbers between [0, 1], as shown in the following equation:(1)Iscale=i−minmax−min,where min represents the minimum eigenvalue and max represents the maximum eigenvalue.


### 2.1.4. Generation of Sliding Samples

Since financial forecasting is a periodic work, time span needs to be set, similar to the window frame in the graphic image processing mechanism. In this paper, 2 years is set as the time span for sliding selection of data samples. Too short or too long time span will have a certain impact on the performance of the prediction, and 2 years is the empirical value of multiple experiments.


## 2.1.1. Data Cleaning

Aiming at the problem of low integrity and much overlap of expenditure funds in corporate financial institutions, the data cleaning adopted in this paper is divided into four steps.(1)
Missing value cleaning: Set the threshold value of miss judgment to 80%, and select the original data according to the standard, remove the feature columns exceeding the threshold value, and fill the missing value in the area with “0” value(2)
Format content cleaning: Set the imported data to a unified storage format, for example, 2019-02-21(3)
Repeated content cleaning: Then, the data is filtered again, and multiple feature columns with high content repetition are deleted to keep only one of them, which is conducive to dimensionality reduction(4)
Nonrequired data cleaning: The irrelevant data that are not in the predicted time span are deleted, and only the sample data with the minimum time span of 1 month are retainedAfter the above four steps, all the processed data is saved in the required CSV format


## 2.1.2. Feature Selection

The characteristics of each column in the data sample need to be reasonably selected, so as to reflect the required relational mapping and avoid overfitting as much as possible and to strengthen the ability of multiple model generalization.In this paper, L1 norm regularization method is used for feature selection, which can be effectively applied to nonlinear scenes [12]. The L1 norm scores of all statistical features were calculated. 0.6 was set as the selection threshold in this paper, and features with scores less than 0.6 were deleted to complete the feature selection process.


## 2.1.3. Normalization

After cleaning the financial data, it is also necessary to unify the value range of the actual sample values in order to unify the scale of the sample characteristics. This paper adopts mean variance normalization to process all data samples, which are uniformly expressed as numbers between [0, 1], as shown in the following equation:(1)Iscale=i−minmax−min,where min represents the minimum eigenvalue and max represents the maximum eigenvalue.


## 2.1.4. Generation of Sliding Samples

Since financial forecasting is a periodic work, time span needs to be set, similar to the window frame in the graphic image processing mechanism. In this paper, 2 years is set as the time span for sliding selection of data samples. Too short or too long time span will have a certain impact on the performance of the prediction, and 2 years is the empirical value of multiple experiments.


## 2.2. Deep Belief Networks

DBN is a neural network classification, recognition, and prediction model with multiple hidden layers. Compared with shallow machine learning and traditional neural network, DBN with a large number of hidden layers has good feature extraction and recognition ability for abnormal data flow of enterprise finance. Through multilayer nonlinear transformation, deep abstract features are trained from complex enterprise financial data, and the internal correlation of data is described. The layer-by-layer training method overcomes the defect that feedforward neural network is easy to fall into local minimum value and obtains better prediction results and faster convergence speed.


### 2.2.1. Sample Input

This paper adopts Support Vector Classification (SVC) model to collect four kinds of historical sample data sets and obtains the current enterprise financial situation assessment grade setV=vx according to the evaluation method of existing literature.vx is the situation assessment grade value of enterprise finance at moment x, and then the assessment set V is converted into the input data set N of DBN network. As shown in formula (2) ia is the input feature vector, ja is the label value, a is the serial number of the sliding window, t is the size of the sliding window, the sliding step of the window is 1, t ≤ T, a ≤ T − t.(2)N=ia,ja⇒lia,…,lia+t−2,lja,…,lja+t−1.


### 2.2.2. Training Phase

DBN uses two steps of unsupervised pretraining and supervised global fine-tuning to adjust the weight of neural network. In the pretraining, Restricted Boltzmann machine (RBM) was trained separately, layer by layer from low level to high level. In fine-tuning stage, BP neural network is used to fine-tune the weight and bias of DBN. The DBN structure is shown in Figure1.Figure 1
Structure of DBN.RBM obtains the weights of the generated model through pretraining in an unsupervised, layer-by-layer greedy manner. During the neural network training, the visual value is mapped to the hidden layer node, and the hidden layer node is reconstructed as the visible node. The value of each node is in the set {0, 1}; that is, there exists anyx, y such that qx∈0,1, by∈0,1, mx,y are the weights between the visible node and the hidden node, and the offset of the visible node c=c1,c2,…,cx, and the offset of hidden layer node h=h1,h2,…,hy. For RBM with x visible nodes and y hidden layer nodes, b and q represent the states of hidden layer nodes and visible nodes, respectively. Set a set of states (b, q), and the energy function of RBM is defined as the following formula:(3)Eq,b|ϕ=−∑cxqx−∑hyby−∑∑mxyqxby,where ϕ=cx,hy,mxy represents the joint distribution law of (b, q) obtained by formula (3) for RBM parameters, as shown in the following formula:(4)uq,b|ϕ=Fϕ−1expq,b,where E is the expected value and Fϕ is the normalized factor, as shown in the following formula:(5)Fϕ=∑∑exp−Eq,b|ϕ.When the visible layer is known, all nodes of the hidden layer are independent of each other, and the probability distribution of they-th node in the hidden layer is shown in the following formula:(6)uQ|B=∏xuby|q,uby=1|q=fhx+∑mxyqx,uby=0|q=1−uby=1|q,where u is the probability and f is the sigmoid activation function. Similarly, if the hidden layer is known, the probability distribution of the xth node in the visible layer is obtained. Because the data in the sample training set has the label of enterprise financial situation data source, in the RBM training at the top level, in addition to the dominant neurons, there should also be neurons representing classification labels in the visible layer. Here, for each group of training data, the corresponding label neurons are opened as 1, while the others are closed and set as 0. The algorithm of DBN training process is obtained.


## 2.2.1. Sample Input

This paper adopts Support Vector Classification (SVC) model to collect four kinds of historical sample data sets and obtains the current enterprise financial situation assessment grade setV=vx according to the evaluation method of existing literature.vx is the situation assessment grade value of enterprise finance at moment x, and then the assessment set V is converted into the input data set N of DBN network. As shown in formula (2) ia is the input feature vector, ja is the label value, a is the serial number of the sliding window, t is the size of the sliding window, the sliding step of the window is 1, t ≤ T, a ≤ T − t.(2)N=ia,ja⇒lia,…,lia+t−2,lja,…,lja+t−1.


## 2.2.2. Training Phase

DBN uses two steps of unsupervised pretraining and supervised global fine-tuning to adjust the weight of neural network. In the pretraining, Restricted Boltzmann machine (RBM) was trained separately, layer by layer from low level to high level. In fine-tuning stage, BP neural network is used to fine-tune the weight and bias of DBN. The DBN structure is shown in Figure1.Figure 1
Structure of DBN.RBM obtains the weights of the generated model through pretraining in an unsupervised, layer-by-layer greedy manner. During the neural network training, the visual value is mapped to the hidden layer node, and the hidden layer node is reconstructed as the visible node. The value of each node is in the set {0, 1}; that is, there exists anyx, y such that qx∈0,1, by∈0,1, mx,y are the weights between the visible node and the hidden node, and the offset of the visible node c=c1,c2,…,cx, and the offset of hidden layer node h=h1,h2,…,hy. For RBM with x visible nodes and y hidden layer nodes, b and q represent the states of hidden layer nodes and visible nodes, respectively. Set a set of states (b, q), and the energy function of RBM is defined as the following formula:(3)Eq,b|ϕ=−∑cxqx−∑hyby−∑∑mxyqxby,where ϕ=cx,hy,mxy represents the joint distribution law of (b, q) obtained by formula (3) for RBM parameters, as shown in the following formula:(4)uq,b|ϕ=Fϕ−1expq,b,where E is the expected value and Fϕ is the normalized factor, as shown in the following formula:(5)Fϕ=∑∑exp−Eq,b|ϕ.When the visible layer is known, all nodes of the hidden layer are independent of each other, and the probability distribution of they-th node in the hidden layer is shown in the following formula:(6)uQ|B=∏xuby|q,uby=1|q=fhx+∑mxyqx,uby=0|q=1−uby=1|q,where u is the probability and f is the sigmoid activation function. Similarly, if the hidden layer is known, the probability distribution of the xth node in the visible layer is obtained. Because the data in the sample training set has the label of enterprise financial situation data source, in the RBM training at the top level, in addition to the dominant neurons, there should also be neurons representing classification labels in the visible layer. Here, for each group of training data, the corresponding label neurons are opened as 1, while the others are closed and set as 0. The algorithm of DBN training process is obtained.


## 2.3. Prediction Model

Because the layer number of hidden layer unit of DBN network is mostly set by experience, and the related parameters of hidden layer are sensitive to this, improper selection may lead to serious decline in accuracy of prediction results or too long training time. In order to solve these problems, Differential Evolution (DE) algorithm is integrated into deep belief network to simplify network structure and build a good deep learning model [13]. The core problem of the enterprise financial situation prediction model based on IDE-DBN is to determine the number of hidden node layers and the weight and bias between layers. The improved DE algorithm enhanced the global search performance and further reduced the local extremum [14]. The basic idea is to map the parameters of hidden layer nodes of DBN network used for enterprise financial situation prediction to the initial target individuals of differential vector space evolution [15]. By judging the fitness level of the new individuals and the old individuals, the weak ones are selected and the strong ones are retained. After several iterations, the target search is guided to the optimal solution with low error. The mathematical description of DE algorithm is shown in the following formula:(7)DE=a0,w0,l0,v0,f0,h0,c0,where a0 is the number of nodes in hidden layer of DBN network, w0 is the parameter of hidden layer of DBN, l0 is the population size, v0 is the fitness function of individual, and three operations are defined: f0 is the replication operation, h0 is the crossover operation, and c0 is the mutation operation.


### 2.3.1. Standard DE Algorithm

The main process of standard DE algorithm is shown in Figure2.Figure 2
Standard DE algorithm flow.In the standard DE algorithm, the difference vector between the parent individuals is generated, and it is superimposed on a random target individual. The optimal individual is retained and added to the next generation population through the crossover and selection operation between the new offspring and the parent. Algorithm optimization is a process of constant transformation in search space. However, it is easy to fall into local optimality, so the crossover process is improved in this paper. Population initialization: Set TU to represent population size andD to represent individual dimension and randomly generate the initial population in the problem decision space according to the following formula:(8)ix,0=ir1,0,ir2,0,…,irD,0x=1,2,…,TU,where ix,0 is the ith individual of the population of the 0th generation, and the individual is valued in each dimension according to the following formula:(9)ixy,0=Ly+randBy−Ly,y=1,2,…,D,where By,Ly represents the value range of the yth dimension, ixy,0 represents the yth dimension component of the xth individual of the initial generation, and RAND represents the random number evenly distributed on the interval (0, 1).Mutation means that, after initialization, DE algorithm generates a population composed of NP experimental vectors through mutation and recombination operation of the population and produces mutant individuals by perturbation of target individuals. Common mutation strategies are as follows: the DE/RAND/1 differential mutation operation adds a scalable and randomly selected vector to a third-party vector, ss shown in the following formula:(10)vi,G=xr1,G+F⋅xr2,G−xr3,G.In DE/ Best/1 strategy, the differential variation operation with a scalable and randomly selected vector is added to the vector with the best performance of the basis vector in the current population, as shown in the following formula:(11)qx,A=ibest,A+F⋅ir1,A−ir2,A.In DE/current-to-best/1 strategy, the differential mutation operation and two randomly selected scalable vectors are added to the third-party vector, and the vector difference contains the vector with the best performance of the basis vector in the current population, as shown in the following formula:(12)qx,A=ix,A+F⋅ibest,A−ix,A+F⋅ir1,A−ir2,A.Crossover: the experimental individual is obtained by crossover operation between the parent individual and the mutant individual.(13)pxy,A=qxy,A,if rand0,1≤CR ory=yrand,ixy,A,otherwise.Selection refers to the selection of experimental individuals and paternal individuals based on the principle of greed, and the excellent individuals will enter the next generation.


### 2.3.2. Adaptive Mechanism

The difference vector is the disturbance of the variable-dimensional decision variables ofirx and A. And F controls the degree of disturbance. If the generated difference vectors are relatively close apart in the search space, F should take a larger value; otherwise the perturbation quantity is too small to be conducive to global search in the early stage of evolution. If the generated difference vectors are far apart in the search space, F should take a smaller value; otherwise the disturbance is too large, which is not conducive to local search in the early stage of evolution. The value of F should be adjusted adaptively according to the relative position of the two individual vectors generating difference vectors in space to balance the contradiction between local search and global search.(14)Fx,A+1=Fmin+randFmax−Fminiffpx,A>fix,AFx,A,otherwise,where Fx,A+1 represents the scaling factor of the xth individual of the A + 1 generation, Fx,A represents the scaling factor of the ith individual of the A generation, and an appropriate scaling factor is conducive to the generation of offspring with high survival rate. Therefore, a new Cri can be generated according to formula (15) by recording the factor parameters of the most recent history.(15)Crx,A+1=randtxv,εifpx,A>fix,ACRx,Aotherwise,where Crx follows a Gaussian distribution and represents the crossover rate of the xth individual in the A generation, while randtxv,ε is the Gaussian distribution of mean v and standard deviation ε.


### 2.3.3. Two-Dimensional Rotation Crossover Algorithm

With the deepening of evolutionary algebra, the standard DE algorithm cannot cope with the adaptive changes of population diversity and the global optimal solution cannot guarantee convergence in the feasible solution space. By introducing a two-dimensional rotational crossover algorithm, the offspring of the target and the mutant are generated around the target. Suppose that the rotation radius of the population containing NP d-dimension individuals is shown in the following formula:(16)r⟶x,A=θq⟶x,A−i⟶x,A,R⟶x,A=12r⟶x,AR⟶x.The direction control parameterθ is set to ensure that the offspring can be evenly distributed near the mutant and the target. When θ = 1, the direction of the cross vector is from q⟶x,A to i⟶x,A, and when θ = −1, the vector is reversed. R⟶x,A is the distance between the target individual and the target individual, R⟶x,A is the radius of rotation distributed near the new offspring, ω is the vector R⟶x,A rotation angle of A, A is the iteration number, R⟶ is the vector controlling rotation, and its value is shown in the following formula:(17)R⟶=C1A+1−1/2R′.C1 is a universal constant, and the modulus of R⟶ decreases with the increase of A, which is conducive to increasing the search accuracy and accelerating the convergence of TRC-DE algorithm. The adjusted control vector factor R′ follows the Cauchy distribution as shown in the following formula:(18)R′=rand Cauchy.R′ is a matrix of NP × D, whose individuals satisfy the Cauchy distribution. Because the Cauchy distribution has a long tail, the selection range of progeny produced by controlling the Cauchy distribution increases.According to the above analysis, with parenti⟶x,A and variant q⟶x,A as the center, and with rotation control vector R⟶ and direction control vector θ acting on the mode of ΔR=θ⋅R⋅r as the rotation radius, the cross vector K⟶x,A is obtained. The rotation crossover operation is shown in the following equation:(19)K⟶x,A=θR⟶x,Aq⟶x,A−i⟶x,A+q⟶x,Arandxy≤Cr ory=yrand,θR⟶x,Aq⟶x,A−i⟶x,A+i⟶x,Aotherwise.The diversity of progeny can be increased by incorporating two-dimensional rotational crossover.In this paper, an example of rotating vectors in two-dimensional space is given to prove how to improve the population diversity and convergence of standard DE algorithm by introducing two-dimensional rotating crossover algorithm. AssumingR⟶x=r1,r2r1⩽r2, i⟶x,A=i1,i2, q⟶x,A=q1,q2, θ = 1, substitute into the following formula:(20)q⟶x,A−i⟶x,A2=Δi1,Δi2R⟶x,A=q⟶x,A−i⟶x,AR⟶x,where Δi1=q1−i1, Δi2=q2−i2, the rotation vector can be expressed as R⟶x,A=r1Δi1,r2Δi2, and the modulus of the rotation vector can be expressed as(21)R⟶x,A=r1Δi12+r2Δi22=r2r1/r2Δi12+Δi22.Letr1≤r2, then r1≤R⟶x,A≤r2, the modulus R⟶x,A of R⟶x,A can vary in size with different coordinate values of R⟶x, and the rotation angle ω of R⟶x,A can be obtained by the following formula:(22)ωR⟶x,A=grcngtr1r2⋅Δi1Δi2.In formula (22), there are three possible situations: if r1=r2, the vector R⟶x,A has the same direction as q⟶x,A−i⟶x,A. If r1<r2, then ω will have a greater angle than the vectors q⟶x,A−i⟶x,A. If r1>r2 then the angle ω will be smaller than the vectors q⟶x,A−i⟶x,A, and the magnitude and ω of the rotation vector vary with the coordinates of the vector R⟶i.


### 2.3.4. IDE-DBN Algorithm

IDE-DBN is a combination of TRC-DE algorithm and DBN algorithm. It treats DBN weights and bias values as populations and uses them to perform mutation, crossover, and selection operations.


## 2.3.1. Standard DE Algorithm

The main process of standard DE algorithm is shown in Figure2.Figure 2
Standard DE algorithm flow.In the standard DE algorithm, the difference vector between the parent individuals is generated, and it is superimposed on a random target individual. The optimal individual is retained and added to the next generation population through the crossover and selection operation between the new offspring and the parent. Algorithm optimization is a process of constant transformation in search space. However, it is easy to fall into local optimality, so the crossover process is improved in this paper. Population initialization: Set TU to represent population size andD to represent individual dimension and randomly generate the initial population in the problem decision space according to the following formula:(8)ix,0=ir1,0,ir2,0,…,irD,0x=1,2,…,TU,where ix,0 is the ith individual of the population of the 0th generation, and the individual is valued in each dimension according to the following formula:(9)ixy,0=Ly+randBy−Ly,y=1,2,…,D,where By,Ly represents the value range of the yth dimension, ixy,0 represents the yth dimension component of the xth individual of the initial generation, and RAND represents the random number evenly distributed on the interval (0, 1).Mutation means that, after initialization, DE algorithm generates a population composed of NP experimental vectors through mutation and recombination operation of the population and produces mutant individuals by perturbation of target individuals. Common mutation strategies are as follows: the DE/RAND/1 differential mutation operation adds a scalable and randomly selected vector to a third-party vector, ss shown in the following formula:(10)vi,G=xr1,G+F⋅xr2,G−xr3,G.In DE/ Best/1 strategy, the differential variation operation with a scalable and randomly selected vector is added to the vector with the best performance of the basis vector in the current population, as shown in the following formula:(11)qx,A=ibest,A+F⋅ir1,A−ir2,A.In DE/current-to-best/1 strategy, the differential mutation operation and two randomly selected scalable vectors are added to the third-party vector, and the vector difference contains the vector with the best performance of the basis vector in the current population, as shown in the following formula:(12)qx,A=ix,A+F⋅ibest,A−ix,A+F⋅ir1,A−ir2,A.Crossover: the experimental individual is obtained by crossover operation between the parent individual and the mutant individual.(13)pxy,A=qxy,A,if rand0,1≤CR ory=yrand,ixy,A,otherwise.Selection refers to the selection of experimental individuals and paternal individuals based on the principle of greed, and the excellent individuals will enter the next generation.


## 2.3.2. Adaptive Mechanism

The difference vector is the disturbance of the variable-dimensional decision variables ofirx and A. And F controls the degree of disturbance. If the generated difference vectors are relatively close apart in the search space, F should take a larger value; otherwise the perturbation quantity is too small to be conducive to global search in the early stage of evolution. If the generated difference vectors are far apart in the search space, F should take a smaller value; otherwise the disturbance is too large, which is not conducive to local search in the early stage of evolution. The value of F should be adjusted adaptively according to the relative position of the two individual vectors generating difference vectors in space to balance the contradiction between local search and global search.(14)Fx,A+1=Fmin+randFmax−Fminiffpx,A>fix,AFx,A,otherwise,where Fx,A+1 represents the scaling factor of the xth individual of the A + 1 generation, Fx,A represents the scaling factor of the ith individual of the A generation, and an appropriate scaling factor is conducive to the generation of offspring with high survival rate. Therefore, a new Cri can be generated according to formula (15) by recording the factor parameters of the most recent history.(15)Crx,A+1=randtxv,εifpx,A>fix,ACRx,Aotherwise,where Crx follows a Gaussian distribution and represents the crossover rate of the xth individual in the A generation, while randtxv,ε is the Gaussian distribution of mean v and standard deviation ε.


## 2.3.3. Two-Dimensional Rotation Crossover Algorithm

With the deepening of evolutionary algebra, the standard DE algorithm cannot cope with the adaptive changes of population diversity and the global optimal solution cannot guarantee convergence in the feasible solution space. By introducing a two-dimensional rotational crossover algorithm, the offspring of the target and the mutant are generated around the target. Suppose that the rotation radius of the population containing NP d-dimension individuals is shown in the following formula:(16)r⟶x,A=θq⟶x,A−i⟶x,A,R⟶x,A=12r⟶x,AR⟶x.The direction control parameterθ is set to ensure that the offspring can be evenly distributed near the mutant and the target. When θ = 1, the direction of the cross vector is from q⟶x,A to i⟶x,A, and when θ = −1, the vector is reversed. R⟶x,A is the distance between the target individual and the target individual, R⟶x,A is the radius of rotation distributed near the new offspring, ω is the vector R⟶x,A rotation angle of A, A is the iteration number, R⟶ is the vector controlling rotation, and its value is shown in the following formula:(17)R⟶=C1A+1−1/2R′.C1 is a universal constant, and the modulus of R⟶ decreases with the increase of A, which is conducive to increasing the search accuracy and accelerating the convergence of TRC-DE algorithm. The adjusted control vector factor R′ follows the Cauchy distribution as shown in the following formula:(18)R′=rand Cauchy.R′ is a matrix of NP × D, whose individuals satisfy the Cauchy distribution. Because the Cauchy distribution has a long tail, the selection range of progeny produced by controlling the Cauchy distribution increases.According to the above analysis, with parenti⟶x,A and variant q⟶x,A as the center, and with rotation control vector R⟶ and direction control vector θ acting on the mode of ΔR=θ⋅R⋅r as the rotation radius, the cross vector K⟶x,A is obtained. The rotation crossover operation is shown in the following equation:(19)K⟶x,A=θR⟶x,Aq⟶x,A−i⟶x,A+q⟶x,Arandxy≤Cr ory=yrand,θR⟶x,Aq⟶x,A−i⟶x,A+i⟶x,Aotherwise.The diversity of progeny can be increased by incorporating two-dimensional rotational crossover.In this paper, an example of rotating vectors in two-dimensional space is given to prove how to improve the population diversity and convergence of standard DE algorithm by introducing two-dimensional rotating crossover algorithm. AssumingR⟶x=r1,r2r1⩽r2, i⟶x,A=i1,i2, q⟶x,A=q1,q2, θ = 1, substitute into the following formula:(20)q⟶x,A−i⟶x,A2=Δi1,Δi2R⟶x,A=q⟶x,A−i⟶x,AR⟶x,where Δi1=q1−i1, Δi2=q2−i2, the rotation vector can be expressed as R⟶x,A=r1Δi1,r2Δi2, and the modulus of the rotation vector can be expressed as(21)R⟶x,A=r1Δi12+r2Δi22=r2r1/r2Δi12+Δi22.Letr1≤r2, then r1≤R⟶x,A≤r2, the modulus R⟶x,A of R⟶x,A can vary in size with different coordinate values of R⟶x, and the rotation angle ω of R⟶x,A can be obtained by the following formula:(22)ωR⟶x,A=grcngtr1r2⋅Δi1Δi2.In formula (22), there are three possible situations: if r1=r2, the vector R⟶x,A has the same direction as q⟶x,A−i⟶x,A. If r1<r2, then ω will have a greater angle than the vectors q⟶x,A−i⟶x,A. If r1>r2 then the angle ω will be smaller than the vectors q⟶x,A−i⟶x,A, and the magnitude and ω of the rotation vector vary with the coordinates of the vector R⟶i.


## 2.3.4. IDE-DBN Algorithm

IDE-DBN is a combination of TRC-DE algorithm and DBN algorithm. It treats DBN weights and bias values as populations and uses them to perform mutation, crossover, and selection operations.


## 3. Financial Management System Based on SaaS Model

In the use of cloud computing, there are three service models. In order to ensure that the quoted mode is suitable for the financial risk control system, it is necessary to compare the three service modes (as shown in Table1). According to the comparison results, select the mode suitable for this system (Figure 3).Table 1
Comparison of cloud computing service modes.
Mode codeNameService content1SaaS service 7Software, data, and information2PAAS servicesOperation, support, and development3LAAS service 8Server, database, and storageFigure 3
System software module diagram.Through the comparison of service modes, SaaS service is selected as the service mode of the cloud computing method. This method can effectively reduce the cost of enterprises and reduce the threshold and risk of enterprise informatization. The implementation of cloud computing method is set as three parts: cloud collection of financial data, cloud processing, and financial abnormal data.In the cloud collection part, big data network is used to complete the acquisition of financial information. The financial information is processed by distributed computing method to obtain abnormal data in the financial data. At this point, the cloud computing part of the design is complete.


## 4. Experiments

### 4.1. Test Data Set

In order to analyze the risk identification performance of data mining financial information management system, a risk sample data of financial information management system is selected for simulation test, and the sample data is shown in Figure4.Figure 4
Experimental sample data set used in simulation.As it can be seen from Figure4, the risk changes of the information management system are complicated. It has a variety of characteristics, such as regularity, time variability, randomness, and so on. In order to make the data mining financial information management system risk identification results more convincing, the traditional financial information management system risk identification is a comparative test. They are the financial information management system risk identification method based on [16] and the financial information management system risk identification method based on [17]. The correct rate of risk identification, rejection rate, and modelling time of risk identification of financial information management system are selected as evaluation indicators.


### 4.2. Division of Training Samples and Test Samples

In the process of risk identification modelling of financial information management system, training is the first step. Therefore the selection of training samples is very critical. In order to reflect the fairness of the experimental results, each method carries out five simulation experiments of risk identification of financial information management system and adopts different numbers of training samples to train the model. The test samples are mainly used to test the generalization ability of the risk identification method of the financial information management system. The partitioning results of the training samples and test samples of each experiment are shown in Table2.Table 2
Partition results of training and test samples.
NumberNumber of risk identification training samplesNumber of risk identification test samples1903027050380404606055070


### 4.3. Results and Analysis

The correct number identified by the three methods from the identification training samples of the financial information management system is counted, and the risk identification accuracy can be obtained by comparing with the number of test samples. The modelling time is the identification training and testing time of the financial information management system, as shown in Figures5 and 6.Figure 5
Financial information management system risk identification accuracy comparison.Figure 6
Comparison of risk identification modelling time of financial information management system.Analysis of them leads to the following conclusions.(1)
Among all the methods, [16] has the lowest risk identification effect of financial information management system. This is mainly because [16] method is a simple linear modelling technology, which cannot fully describe the rule of system risk change, and the risk identification results of financial information management system are not ideal.(2)
The risk identification effect of financial information management system based on [17] is better than that of [16]. The risk identification error of financial information management system based on [16] is reduced. However, [17] also has defects, such as unstable risk identification results and poor reliability of financial information management system.(3)
The risk identification effect of financial information management system based on data mining is better than that of [16] and [17], which reduces the risk identification error of financial information management system and greatly reduces the risk rejection rate of financial information management system. The results of risk identification of financial information management system are more reliable, which overcomes the shortcomings of traditional methods and verifies the superiority of risk identification method of financial information management system based on data mining.(4)
The risk identification time of financial information management system based on data mining is less than that of [16] and [17], and the risk identification efficiency of financial information management system is significantly improved.


## 4.1. Test Data Set

In order to analyze the risk identification performance of data mining financial information management system, a risk sample data of financial information management system is selected for simulation test, and the sample data is shown in Figure4.Figure 4
Experimental sample data set used in simulation.As it can be seen from Figure4, the risk changes of the information management system are complicated. It has a variety of characteristics, such as regularity, time variability, randomness, and so on. In order to make the data mining financial information management system risk identification results more convincing, the traditional financial information management system risk identification is a comparative test. They are the financial information management system risk identification method based on [16] and the financial information management system risk identification method based on [17]. The correct rate of risk identification, rejection rate, and modelling time of risk identification of financial information management system are selected as evaluation indicators.


## 4.2. Division of Training Samples and Test Samples

In the process of risk identification modelling of financial information management system, training is the first step. Therefore the selection of training samples is very critical. In order to reflect the fairness of the experimental results, each method carries out five simulation experiments of risk identification of financial information management system and adopts different numbers of training samples to train the model. The test samples are mainly used to test the generalization ability of the risk identification method of the financial information management system. The partitioning results of the training samples and test samples of each experiment are shown in Table2.Table 2
Partition results of training and test samples.
NumberNumber of risk identification training samplesNumber of risk identification test samples1903027050380404606055070


## 4.3. Results and Analysis

The correct number identified by the three methods from the identification training samples of the financial information management system is counted, and the risk identification accuracy can be obtained by comparing with the number of test samples. The modelling time is the identification training and testing time of the financial information management system, as shown in Figures5 and 6.Figure 5
Financial information management system risk identification accuracy comparison.Figure 6
Comparison of risk identification modelling time of financial information management system.Analysis of them leads to the following conclusions.(1)
Among all the methods, [16] has the lowest risk identification effect of financial information management system. This is mainly because [16] method is a simple linear modelling technology, which cannot fully describe the rule of system risk change, and the risk identification results of financial information management system are not ideal.(2)
The risk identification effect of financial information management system based on [17] is better than that of [16]. The risk identification error of financial information management system based on [16] is reduced. However, [17] also has defects, such as unstable risk identification results and poor reliability of financial information management system.(3)
The risk identification effect of financial information management system based on data mining is better than that of [16] and [17], which reduces the risk identification error of financial information management system and greatly reduces the risk rejection rate of financial information management system. The results of risk identification of financial information management system are more reliable, which overcomes the shortcomings of traditional methods and verifies the superiority of risk identification method of financial information management system based on data mining.(4)
The risk identification time of financial information management system based on data mining is less than that of [16] and [17], and the risk identification efficiency of financial information management system is significantly improved.


## 5. Conclusion

The research on enterprise financial management and prediction system is of great value. It has become a major subject of current financial research. The traditional method cannot describe the changing situation of financial information management system risk comprehensively and scientifically. They cannot guarantee the security of financial information management systems. In order to obtain the ideal risk identification effect of financial information management system, this paper proposes the research and design of enterprise financial management and prediction system based on SaaS model. Comparing with other risk identification methods of financial information management system, the results show that the financial management system proposed in this paper can objectively track the changes of financial information management system risk. The financial management system proposed in this paper establishes a risk identification model with higher accuracy. It shortens the risk identification time of financial information management system and can provide valuable reference information for financial information managers.



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*Source: 3218903-2022-04-13.xml*