# Evaluation of Enterprise Financial Risk Level under Digital Transformation with Artificial Neural Network

Authors: Dijie Yang
Journal: Security and Communication Networks
 (2022)

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## Abstract

With the continuous development of the national economy, enterprises have gradually entered the digital age from the industrial age, and digital transformation has become a must for enterprise survival. Digital transformation empowers the high-quality development of enterprises and puts forward higher requirements for financial risk management. The problem of enterprise financial risk is one that every business must deal with at some point during its operations. The digital age has ushered in a new era for enterprise financial risk management. Digital transformation is also a weather vane, which is beneficial to comprehensively avoid financial risks, do a good job in system defense and multiparty coordination, and can quickly and accurately manage corporate finances and solve risks in a timely manner. Starting from the background of digital transformation, this article clarifies the significance of preventing corporate financial risks in the new era. Combining it with artificial neural networks, this work proposes an intelligent method for assessing the financial risk level of enterprises in this context. This work proposes an EFRL-ResNet neural network, which is improved on the basis of ResNet. At the same time, the depth-wise separable convolution (DSConv) structure in Mobile-Net is combined with the ResNet network to build a lightweight deep neural network. Through the case of enterprise financial risk, it is verified that the method can reduce the training time of the model without losing the accuracy of grade evaluation. At the same time, this paper improves the loss function in the network model for the problem of an unbalanced number of data samples in the financial risk level assessment model.


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## Body

## 1. Introduction

Enterprise management involves all aspects, among which financial risk management is the top priority in enterprise management. Because the operation and operation of the enterprise must have financial support, it needs to have a stable and organized management system. When companies face market and even global economic risks, corporate financial risk management is very necessary. It can minimize the loss of the enterprise and make the operation of the enterprise beneficially guaranteed. In addition, the good operation of the enterprise is also a kind of guarantee for the employees, which is related to the national economy and the people’s livelihood. From a broader perspective, the financial stability of an enterprise will make related enterprises in its financial chain more stable. It has a good effect on the economic stability of a field or even the entire country. Especially in today’s economic globalization, trade between countries is frequent, and the stability of a company will provide a small boost to the stability of the global economy. Therefore, it is of special significance in the new era to take advantage of the opportunity of digital transformation to do a good job in enterprise risk management planning and innovation [1–5].Nowadays, the economy is developing rapidly, but there are many unstable factors, which will indirectly affect the operation of enterprises and generate a series of potential or explicit financial risks. The normal operation of any enterprise will be based on the initial stage of the capital chain; that is, there will be stable and multichannel financing channels. However, in the market economy, there are often broken financing channels or insufficient funds for enterprises, resulting in the broken capital chain of enterprises and the inability to operate normally. Another risk is that corporate financing is smooth, but due to the instability of economic factors, it is unable to repay debts on time and in quantity. Interest rolling or untrustworthiness will bring a crisis to the company. Financing is an important aspect of corporate risk management. There are often many risks in the investment output of enterprises. The most basic one is that when there is no market for overstocked goods, it is easy to produce the risk of slow sales, deterioration, or damage. There are also some hidden investments that are also at risk and may lead to job-hopping or other unrecoverable investments. This makes the investment disproportionate to the return and falls short of expectations. Whether the cost of production or operation of the enterprise can be converted into settlement assets in a timely manner and whether the settlement amount can be turned into monetary funds is an important process of enterprise capital recovery. If there is too much investment and no corresponding return on capital and appreciation, it will cause great risks in capital recovery, and business operations will be in a dangerous state. Enterprise income is the result of financing and investment recovery, and the distribution of income is also the last link of the enterprise capital chain. How to distribute revenue well is a problem that enterprises need to explore in-depth, and different distribution methods will produce different effects. In addition to the distribution of employees, the retention of funds and the distribution of dividends should also be considered [6–10].Financial management needs to collect, store, analyze, evaluate, and manage the basic financial data of the enterprise, provide support for the business decision-making of the enterprise, and realize the continuous improvement of enterprise value. The improvement of enterprise value requires the continuous realization of the value-added value of various business activities of the enterprise. The root cause comes from the efficient development of each economic activity and the effective support and cooperation between each other. Big data empower corporate governance and financial transformation to become the general trend. The digital transformation of enterprises provides a large amount of data support for enterprise financial management. In turn, the financial management supported by the big data support formed in the digital transformation has created a digital infrastructure for financial risk control and will also lead enterprises to make timely, fast, accurate, and reliable scientific decision-making [11–15].Based on this, the assessment of corporate financial risk levels in the context of digital transformation has become an important topic. Financial risks generally exist in all stages of enterprise development. Only by having a better and more accurate understanding and control of risks can an enterprise avoid risks to a certain extent and make the development of the enterprise more stable and long term. This work combines the evaluation of enterprise financial risk levels under the background of digital transformation with neural networks and proposes an intelligent enterprise financial risk level evaluation method.The paper arrangements are as follows: Section2 describes the related work; Section 3 defines the methods. Section 4 discusses the experiment and discussion. In Section 5, the article comes to a close.


## 2. Related Work

Reference [16] is the first to put forward the theory of corporate bankruptcy. When the financial case breaks through a certain limit, the company may bear more financial risks. After comparing the actual financial data of the bankrupt company, it is found that there are two indicators that can well predict whether the company has a bankruptcy crisis. Supported by the previous theoretical foundation, the literature [17] proposes a univariate early warning model, which is calculated by collecting financial data from 158 companies under different operating conditions and through certain data. It analyzes financial risks with the help of the model and believes that there is a close relationship between the two indicators of asset-liability ratio and cash protection ratio and the development of corporate financial risks. Reference [18] adopted a new method based on the original univariate discriminant analysis. The author summarizes various financial data of 33 nonbankrupt companies and learns some valuable data for analysis. These data are used to assess the company’s overall financial risk factors, and some achievements have been made in the process. In the later stage of development, in order to make more effective use of the existing indicators, five relevant financial data were selected to evaluate the company’s overall financial risk. The basic characteristics of the development of financial risk assessment include, first, the assessment accuracy rate is getting higher and higher, and the judgment error is less. Second, it has a strong correlation with the actual situation and can well reflect the actual problems faced by enterprises. Reference [19] created a logistic discriminant model, which believed that different industries have different levels of development. Especially in the development process of the construction industry and the food manufacturing industry, there are obvious differences in the corresponding financial risk indicators. And because the actual development scale of enterprises is different, the actual financing risk assessment indicators of enterprises are bound to be different.Reference [20] uses quantitative indicators to evaluate the rationality of risk development in the insurance industry. It believes that a sound risk index system for the insurance industry should be constructed, and appropriate risk evaluation indicators should be determined. Then, logistic regression analysis for quantitative analysis is chosen, and finally, the validity of other models in testing logistic regression analysis in constructing a financial risk model is compared. Literature [21] was not satisfied with the accuracy of the above method after research in the financial field. They selected 282 companies with a 2 : 1 ratio of good companies to bankrupt companies. These financial data are summarized and organized, and these data are used to build a neural network model, which can be used to understand various financial risk factors that the company actually faces in the process of development with high accuracy. Compared to the previous Z model, the evaluation accuracy factor is higher. Reference [22] used the preliminary option term model to analyze the financial development status of 78 companies and to understand the relevant financial data. It concludes that the nominal value of maturing debt and the current market price of the asset can provide an understanding of the company’s financial risk in this way, valuing it higher than it actually is. Literature [23] believes that the governance level is one of the important factors affecting financial risk. Value at Risk (VaR) is the culmination of previous research. Compared with the existing model, the VaR model is not only easier to learn but also has realistic operability. It can be applied on a large scale in the field of financial risk research.Reference [24] uses different models to conduct a comprehensive evaluation of Serbian insolvency proceedings, and in this way, it is possible to understand the actual development status of enterprises. At the same time, five companies in Serbia are taken as the main research objects, and various financial indicators are selected that can show the company’s overall financial development status, and the overall development characteristics of the company are integrated for a comprehensive analysis. Its use of the Z-axis movement model Kralicek can improve the efficiency of the actual situation estimation. Through a series of analyses and research, it can be known that the Kralicek rapid test can more comprehensively display the company’s overall financial development status [25]. Reference [26] takes public services as an example, assuming that product changes related to the production of public services are uncertain variables. And five financial risk indicators are selected that are highly affected by market volatility and simulated Monte Carlo. This operation obtains a discrete distribution of uncertain variables. At the same time, with the help of the objective optimization model, it is considered that each index represents the subfunction of the annual total cost, and the final research conclusion is drawn. Reference [27] used empirical research to assess portfolio risk under the revised copula model. Since the entire return shows an error distribution, risk metrics should be chosen to test the validity of this model analysis. The results were comprehensively compared with the conclusions obtained from the unmodified Copula model.


## 3. Method

In this section, we defined the theoretical basis of convolutional neural networks, ResNet block, and depth-wise separable convolution to improve loss for data imbalance and EFRL-ResNet architecture.Deep learning can fit some complex nonlinear models with their deep network layers and can find the mapping relationship between input and output data through data-driven methods. Usually, as the depth of the network increases, the features extracted by the network are more refined, and the original input data can be more accurately represented. However, one problem brought about by the increase in network depth is the increase in the number of network parameters. The enterprise financial risk level assessment method based on EFRL-ResNet proposed in this paper combines residual network and depth-wise separable convolution, which can not only extract effective features of different scales but also reduce the number of parameters in the network. This section first gives an overview of the basic model concepts and network structure of deep learning and briefly outlines the network structure of residual convolution and depth-wise separable convolution. Then, it focuses on the structure of the EFRL-ResNet lightweight risk level classification network and the principle that this structure can reduce network parameters. The network structure and parameter quantity before and after the improvement are compared, the problem of data imbalance is considered, and the loss function is improved by the idea of focal loss in the network.


### 3.1. Theoretical Basis of Convolutional Neural Networks

In the CNN training process, the gradient back-propagation method is used to pass forward in turn, and finally, the weights that optimize the entire network are obtained. The main idea of the convolutional neural network’s parameter updating approach can be stated as local connection and weight sharing. In image processing tasks, speech recognition, object detection, and other domains, a number of network models with CNN as the basic network framework have been widely employed, and CNN plays an essential role in the feature extraction phase of the algorithm. Convolutional layers, pooling layers, fully connected layers, and nonlinear activation functions constitute the backbone structure of convolutional neural networks. These structures take useful features from the input data and transform the signal into representations of various sizes to represent the data.The original data are input into the neural network, and each pixel value on the input feature layer is convolved with the same weight by sliding the convolution kernel to achieve weight sharing. In the convolutional neural network, the number of channels of the convolution kernel should be the same as the number of channels of the input feature layer. At this time, there can be multiple convolution kernels to learn different features. The extracted feature layer is input to the pooling layer for dimensionality reduction processing, and the nonlinearity between layers is increased through the nonlinear activation function, thereby improving the robustness and generalization ability of the network. The output feature layer is expanded into a column vector, and finally, the sigmoid activation function is used to output the final classification result. The convolution layer is the core of the convolutional neural network. The convolution operation is to complete the feature extraction by sliding the convolution kernel on the feature layer and convolution with each value on the feature layer.Each input layer and hidden layer in classic deep neural networks are fully connected. Each input neuron interacts with each hidden layer neuron, resulting in a vast number of parameters for each layer of network feature extraction, making training deeper networks harder. The shallower network extracts local features that include a lot of detailed information in a deep neural network. As the network layer gradually deepens, the extracted information is more and more likely to reflect the global information in the feature layer. Therefore, in the shallow stage where the convolution operation extracts local detail information, it is not necessary to use the fully connected method for feature extraction. In a convolutional neural network, shallow features can be extracted through local connections. The number of neurons in the hidden layer was established throughout the process of creating the network model.In a convolutional neural network, the same convolution kernel is used to scan the input feature layer. Each position of the feature layer is convolved with the same weight, so the convolution operation has the characteristic of weight sharing. In a shallow feature extraction network, the feature extracted by the network is independent of the location of the feature in the original image. Whether the feature is distributed at the edge of the image or the center of the image, it can be obtained through the convolution operation. Therefore, in the convolutional neural network structure, the convolution kernel of the same weight can be used to extract global features in the shallower network. As the depth of the network increases, the features extracted by the convolution operation will become more and more detailed, and the detailed features are related to the position of the feature. In the last few layers of the network, the fully connected layer needs to be used to extract finer features.The receptive field is a key concept in convolutional neural networks, and it reflects the ability of pixels on the output feature layer to convey features. The size of the mapping range on the input image is reflected in each pixel on the output feature map, which is a measure of how much the output feature layer mirrors the area of the input feature layer. The receptive field can characterize the feature extraction of each layer, and the abstraction degree of the obtained feature layer on the original input feature layer. Generally, the larger the receptive field, the larger the range of the original image that the feature layer can touch, and the more the extracted features tend to be global features. The extracted features are increasingly likely to be local detail features as the receptive field decreases.The pooling layer is an important structure of the convolutional neural network. The pooling layer can reduce the overfitting phenomenon of the network and reduce the dimension of the extracted feature map through the downsampling operation, which plays a role in compressing the number of network parameters and improves the fault tolerance of the model. Two common pooling methods are max pooling and average pooling.The fully connected layer flattens the input into a column vector and fuses the features extracted by the entire network. The neural network is a process of increasing dimension. It converts the input feature layer into a higher dimension and finds feature associations between data.The activation function is a structure that is put between two layers of neurons in a neural network to boost the network’s nonlinear features. In the structure of the convolutional neural network, the combination of convolution, normalization operation, and nonlinear activation function is often used to build the framework of the convolution operation. The activation function in the network’s role is to improve the model’s generalization ability by enhancing the network’s nonlinear properties. Because deep neural networks are frequently used to fit more sophisticated nonlinear models, using nonlinear activation functions can help to avoid overfitting.(1)Sigmoidx=11+exp−x,ReLUx=xx≥00x<0,Tanhx=1−exp−2x1+exp−2x.


### 3.2. ResNet Block and Depth-Wise Separable Convolution

In classification tasks, more representative features can be obtained by increasing the network depth. However, the number of network parameters will also increase with the deepening of the network, which will also lead to overfitting and network degradation. But these problems cannot be eliminated by initializing network parameters, normalizing, etc. The proposal of ResNet solves the phenomenon of network degradation in the model as the network depth increases. The typical structural shortcut in the residual network directly superimposes the input feature layer with the output feature layer after feature extraction, which can reduce the phenomenon of gradient disappearance and gradient explosion. The phenomenon of network degradation refers to the phenomenon that when the network depth is increased, the classification accuracy of the model does not continue to increase, but the accuracy decreases. In a traditional convolutional neural network, the data source of neurons in each layer is the output of neurons in the previous layer. However, the convolution kernel in each convolutional layer will down-sample the data to obtain smaller size features. When the loss of down-sampling reaches a certain level, the data will not be able to be identified. As the number of network layers continues to deepen, the classification accuracy of the network no longer increases with the increase of the number of network layers, but instead saturates or declines, which is the degradation problem. The residual network can significantly alleviate the phenomenon of gradient disappearance and gradient explosion and can reduce the problem of network degradation.The residual network introduces the shortcut connection method so that the deep network can be effectively trained, and this structure is beneficial to solve the problem of network degradation. The shortcut structure in the residual network adds an identity map to the feature extraction network, which brings the input layer straight to the output layer and superimposes it with a succession of feature extraction network layers. The network containing the shortcut structure is also called the residual block, and the shortcut structure is shown in Figure1.Figure 1
The residue block structure.Wherex is the input of the network, Fx is the output before the activation function of the second layer, and the activation function is the ReLU function. The shortcut connection can be regarded as passing the input layer over the feature extraction network and directly superimposing it with the output feature layer. This process is an identity mapping that does not increase the amount of network parameters.(2)y=x+Fx,Wi.The residual network’s shortcut structure can train a very deep network and overcome network deterioration caused by increased network depth. Gradient vanishing and gradient explosion phenomena are minimized at the same time.A bottleneck structure is included in the residual network, and the bottleneck structure is realized by 1 × 1 convolution. The 1 × 1 convolution can change the number of channels of the feature without changing the size of the feature layer. This cross-channel aggregation can reduce or increase the dimension of the feature. The bottleneck structure in the residual network refers to the use of 1 × 1 convolution to reduce or restore the number of channels in the network and the use of 3 × 3 convolution kernels to extract network features. A comparison of bottleneck structure and calculation amount of standard convolution operation is illustrated in Table1.Table 1
Comparison of bottleneck structure and standard convolution.
StructureParameterBottleneck structure896Standard convolution2304The convolution operation of designing bottleneck structure feature extraction in a residual network not only increases the depth of the network but also reduces the number of parameters of the network.Depth-wise separable convolutions in Mobile-Net networks are a key structure for enabling lightweight feature extraction. Different from the original convolution calculation method, for the input feature layer with channel numberN, each convolution operation uses N convolution kernel with channel number 1 to traverse all channels of the input feature layer. It obtains N feature layers, adjusts the number of channels through 1 × 1 convolution, and then features fusion.The depth-wise separable convolution operation performs feature extraction layer by layer according to the number of channels in the input feature layer and then uses 1 × 1 convolution to adjust the number of channels in the output feature layer. The structure diagram of the depth-wise separable convolution operation is shown in Figure2.Figure 2
The structure of depth-wise separable convolution.The depth-wise separable convolution changes the calculation method of the original standard convolution operation and reduces the number of parameters calculated by the convolution operation. When the input feature layer size is 38 × 38 × 3, the convolution operation is performed with a 3 × 3 convolution kernel, and the number of channels of the output feature layer is 16. A comparison of the parameters of a standard convolution operation and a depth-wise separable convolution is shown in Table2.Table 2
Comparison of standard convolution and depth-wise separable convolution.
StructureParameterStandard convolution432Depth-wise separable75According to the comparison of the parameters of a convolution operation, it can be seen that the depth-wise separable convolution can greatly reduce the parameter calculation amount of the convolution operation. The deep classification network part of this paper applies the depth-wise separable convolutional structure to the deep residual network structure to construct a lightweight deep classification network.


### 3.3. Improve Loss for Data Imbalance

When the number of positive samples is much smaller than the number of negative samples, the recognition accuracy will be affected. Focal loss solves the problem of data imbalance by controlling the training of a certain class of samples and the training of hard-to-classify samples and easy-to-classify samples. The cross-entropy loss function is simplified to get(3)CEp,y=−logpt.The training on positive and negative samples is controlled by prepending a weight coefficient to the loss function. The loss function after adding the weight parameter is(4)CEp,y=−αtlogpt.In addition, the focal loss function also controls the weight of hard-to-classify and easy-to-classify samples by adding a modulation factor. The modulation coefficient expression and the loss function expression after adding the modulation coefficient are(5)FLpt=−αt1−ptγlogpt,where pt represents the probability that the sample belongs to a certain category. The larger the pt value, the easier the classification of the sample. The smaller the pt value, the harder the sample is to classify. The training of hard-to-classify and classified samples is controlled by increasing the modulation coefficient, that is, to strengthen the training of hard-to-classify samples and reduce the training of easy-to-classify samples.


### 3.4. EFRL-ResNet Architecture

The classification network in the EFRL-ResNet enterprise financial risk level assessment method is improved on the basis of the residual network, and a depth-wise separable convolution structure is added. On the premise of ensuring network depth and model classification accuracy, the deep classification network in this method can reduce model parameters and shorten training time.The classification network used in the EFRL-ResNet method in this paper is improved on the basis of the residual network. It applies depth-wise separable convolution to the feature extraction network and replaces the convolution operation with a convolution kernel size of 3 × 3 in the bottleneck structure with depth-wise separable convolution. The improved network structure in this paper still includes the identity_block and conv_block structures, which are DS_identity_block and DS_conv_block, respectively. The structure is illustrated in Figures3 and 4.Figure 3
The structure of DS_identity_block.Figure 4
The structure of DS_conv_block.The EFRL-ResNet method is composed of several DS_conv_block and DS_identity_block structures, and Figure5 depicts the overall structure of the improved lightweight network.Figure 5
The structure of EFRL-ResNet.


## 3.1. Theoretical Basis of Convolutional Neural Networks

In the CNN training process, the gradient back-propagation method is used to pass forward in turn, and finally, the weights that optimize the entire network are obtained. The main idea of the convolutional neural network’s parameter updating approach can be stated as local connection and weight sharing. In image processing tasks, speech recognition, object detection, and other domains, a number of network models with CNN as the basic network framework have been widely employed, and CNN plays an essential role in the feature extraction phase of the algorithm. Convolutional layers, pooling layers, fully connected layers, and nonlinear activation functions constitute the backbone structure of convolutional neural networks. These structures take useful features from the input data and transform the signal into representations of various sizes to represent the data.The original data are input into the neural network, and each pixel value on the input feature layer is convolved with the same weight by sliding the convolution kernel to achieve weight sharing. In the convolutional neural network, the number of channels of the convolution kernel should be the same as the number of channels of the input feature layer. At this time, there can be multiple convolution kernels to learn different features. The extracted feature layer is input to the pooling layer for dimensionality reduction processing, and the nonlinearity between layers is increased through the nonlinear activation function, thereby improving the robustness and generalization ability of the network. The output feature layer is expanded into a column vector, and finally, the sigmoid activation function is used to output the final classification result. The convolution layer is the core of the convolutional neural network. The convolution operation is to complete the feature extraction by sliding the convolution kernel on the feature layer and convolution with each value on the feature layer.Each input layer and hidden layer in classic deep neural networks are fully connected. Each input neuron interacts with each hidden layer neuron, resulting in a vast number of parameters for each layer of network feature extraction, making training deeper networks harder. The shallower network extracts local features that include a lot of detailed information in a deep neural network. As the network layer gradually deepens, the extracted information is more and more likely to reflect the global information in the feature layer. Therefore, in the shallow stage where the convolution operation extracts local detail information, it is not necessary to use the fully connected method for feature extraction. In a convolutional neural network, shallow features can be extracted through local connections. The number of neurons in the hidden layer was established throughout the process of creating the network model.In a convolutional neural network, the same convolution kernel is used to scan the input feature layer. Each position of the feature layer is convolved with the same weight, so the convolution operation has the characteristic of weight sharing. In a shallow feature extraction network, the feature extracted by the network is independent of the location of the feature in the original image. Whether the feature is distributed at the edge of the image or the center of the image, it can be obtained through the convolution operation. Therefore, in the convolutional neural network structure, the convolution kernel of the same weight can be used to extract global features in the shallower network. As the depth of the network increases, the features extracted by the convolution operation will become more and more detailed, and the detailed features are related to the position of the feature. In the last few layers of the network, the fully connected layer needs to be used to extract finer features.The receptive field is a key concept in convolutional neural networks, and it reflects the ability of pixels on the output feature layer to convey features. The size of the mapping range on the input image is reflected in each pixel on the output feature map, which is a measure of how much the output feature layer mirrors the area of the input feature layer. The receptive field can characterize the feature extraction of each layer, and the abstraction degree of the obtained feature layer on the original input feature layer. Generally, the larger the receptive field, the larger the range of the original image that the feature layer can touch, and the more the extracted features tend to be global features. The extracted features are increasingly likely to be local detail features as the receptive field decreases.The pooling layer is an important structure of the convolutional neural network. The pooling layer can reduce the overfitting phenomenon of the network and reduce the dimension of the extracted feature map through the downsampling operation, which plays a role in compressing the number of network parameters and improves the fault tolerance of the model. Two common pooling methods are max pooling and average pooling.The fully connected layer flattens the input into a column vector and fuses the features extracted by the entire network. The neural network is a process of increasing dimension. It converts the input feature layer into a higher dimension and finds feature associations between data.The activation function is a structure that is put between two layers of neurons in a neural network to boost the network’s nonlinear features. In the structure of the convolutional neural network, the combination of convolution, normalization operation, and nonlinear activation function is often used to build the framework of the convolution operation. The activation function in the network’s role is to improve the model’s generalization ability by enhancing the network’s nonlinear properties. Because deep neural networks are frequently used to fit more sophisticated nonlinear models, using nonlinear activation functions can help to avoid overfitting.(1)Sigmoidx=11+exp−x,ReLUx=xx≥00x<0,Tanhx=1−exp−2x1+exp−2x.


## 3.2. ResNet Block and Depth-Wise Separable Convolution

In classification tasks, more representative features can be obtained by increasing the network depth. However, the number of network parameters will also increase with the deepening of the network, which will also lead to overfitting and network degradation. But these problems cannot be eliminated by initializing network parameters, normalizing, etc. The proposal of ResNet solves the phenomenon of network degradation in the model as the network depth increases. The typical structural shortcut in the residual network directly superimposes the input feature layer with the output feature layer after feature extraction, which can reduce the phenomenon of gradient disappearance and gradient explosion. The phenomenon of network degradation refers to the phenomenon that when the network depth is increased, the classification accuracy of the model does not continue to increase, but the accuracy decreases. In a traditional convolutional neural network, the data source of neurons in each layer is the output of neurons in the previous layer. However, the convolution kernel in each convolutional layer will down-sample the data to obtain smaller size features. When the loss of down-sampling reaches a certain level, the data will not be able to be identified. As the number of network layers continues to deepen, the classification accuracy of the network no longer increases with the increase of the number of network layers, but instead saturates or declines, which is the degradation problem. The residual network can significantly alleviate the phenomenon of gradient disappearance and gradient explosion and can reduce the problem of network degradation.The residual network introduces the shortcut connection method so that the deep network can be effectively trained, and this structure is beneficial to solve the problem of network degradation. The shortcut structure in the residual network adds an identity map to the feature extraction network, which brings the input layer straight to the output layer and superimposes it with a succession of feature extraction network layers. The network containing the shortcut structure is also called the residual block, and the shortcut structure is shown in Figure1.Figure 1
The residue block structure.Wherex is the input of the network, Fx is the output before the activation function of the second layer, and the activation function is the ReLU function. The shortcut connection can be regarded as passing the input layer over the feature extraction network and directly superimposing it with the output feature layer. This process is an identity mapping that does not increase the amount of network parameters.(2)y=x+Fx,Wi.The residual network’s shortcut structure can train a very deep network and overcome network deterioration caused by increased network depth. Gradient vanishing and gradient explosion phenomena are minimized at the same time.A bottleneck structure is included in the residual network, and the bottleneck structure is realized by 1 × 1 convolution. The 1 × 1 convolution can change the number of channels of the feature without changing the size of the feature layer. This cross-channel aggregation can reduce or increase the dimension of the feature. The bottleneck structure in the residual network refers to the use of 1 × 1 convolution to reduce or restore the number of channels in the network and the use of 3 × 3 convolution kernels to extract network features. A comparison of bottleneck structure and calculation amount of standard convolution operation is illustrated in Table1.Table 1
Comparison of bottleneck structure and standard convolution.
StructureParameterBottleneck structure896Standard convolution2304The convolution operation of designing bottleneck structure feature extraction in a residual network not only increases the depth of the network but also reduces the number of parameters of the network.Depth-wise separable convolutions in Mobile-Net networks are a key structure for enabling lightweight feature extraction. Different from the original convolution calculation method, for the input feature layer with channel numberN, each convolution operation uses N convolution kernel with channel number 1 to traverse all channels of the input feature layer. It obtains N feature layers, adjusts the number of channels through 1 × 1 convolution, and then features fusion.The depth-wise separable convolution operation performs feature extraction layer by layer according to the number of channels in the input feature layer and then uses 1 × 1 convolution to adjust the number of channels in the output feature layer. The structure diagram of the depth-wise separable convolution operation is shown in Figure2.Figure 2
The structure of depth-wise separable convolution.The depth-wise separable convolution changes the calculation method of the original standard convolution operation and reduces the number of parameters calculated by the convolution operation. When the input feature layer size is 38 × 38 × 3, the convolution operation is performed with a 3 × 3 convolution kernel, and the number of channels of the output feature layer is 16. A comparison of the parameters of a standard convolution operation and a depth-wise separable convolution is shown in Table2.Table 2
Comparison of standard convolution and depth-wise separable convolution.
StructureParameterStandard convolution432Depth-wise separable75According to the comparison of the parameters of a convolution operation, it can be seen that the depth-wise separable convolution can greatly reduce the parameter calculation amount of the convolution operation. The deep classification network part of this paper applies the depth-wise separable convolutional structure to the deep residual network structure to construct a lightweight deep classification network.


## 3.3. Improve Loss for Data Imbalance

When the number of positive samples is much smaller than the number of negative samples, the recognition accuracy will be affected. Focal loss solves the problem of data imbalance by controlling the training of a certain class of samples and the training of hard-to-classify samples and easy-to-classify samples. The cross-entropy loss function is simplified to get(3)CEp,y=−logpt.The training on positive and negative samples is controlled by prepending a weight coefficient to the loss function. The loss function after adding the weight parameter is(4)CEp,y=−αtlogpt.In addition, the focal loss function also controls the weight of hard-to-classify and easy-to-classify samples by adding a modulation factor. The modulation coefficient expression and the loss function expression after adding the modulation coefficient are(5)FLpt=−αt1−ptγlogpt,where pt represents the probability that the sample belongs to a certain category. The larger the pt value, the easier the classification of the sample. The smaller the pt value, the harder the sample is to classify. The training of hard-to-classify and classified samples is controlled by increasing the modulation coefficient, that is, to strengthen the training of hard-to-classify samples and reduce the training of easy-to-classify samples.


## 3.4. EFRL-ResNet Architecture

The classification network in the EFRL-ResNet enterprise financial risk level assessment method is improved on the basis of the residual network, and a depth-wise separable convolution structure is added. On the premise of ensuring network depth and model classification accuracy, the deep classification network in this method can reduce model parameters and shorten training time.The classification network used in the EFRL-ResNet method in this paper is improved on the basis of the residual network. It applies depth-wise separable convolution to the feature extraction network and replaces the convolution operation with a convolution kernel size of 3 × 3 in the bottleneck structure with depth-wise separable convolution. The improved network structure in this paper still includes the identity_block and conv_block structures, which are DS_identity_block and DS_conv_block, respectively. The structure is illustrated in Figures3 and 4.Figure 3
The structure of DS_identity_block.Figure 4
The structure of DS_conv_block.The EFRL-ResNet method is composed of several DS_conv_block and DS_identity_block structures, and Figure5 depicts the overall structure of the improved lightweight network.Figure 5
The structure of EFRL-ResNet.


## 4. Experiment and Discussion

In this part, we described the dataset and metric, method comparison, evaluation on improved ResNet block, evaluation on improved loss, and evaluation on BN structure.


### 4.1. Dataset and Metric

This work uses a self-made dataset to conduct experiments, and the data of the experiments are collected from various corporate finance departments in the context of digital transformation. The dataset contains a total of 25,195 training samples and 10,293 test samples. Each sample is a 12-dimensional feature containing different financial risk indicators, as shown in Table3. It should be noted that the data cannot be directly input into the designed neural network, and it needs to be expanded into 256 × 256 data before feature extraction can be performed. The label of each sample is a financial risk level. In this work, the risk level is divided into 10 types. In essence, this work is a classification task. The precision and recall are evaluation metrics in this work.Table 3
The detailed index.
IndexMeaningx1Operating profit ratiox2Cost-to-income ratiox3Return on equity ratiox4Cash flow debt ratiox5Gearing ratiox6Quick ratiox7Total asset turnover ratiox8Asset cash recovery ratiox9Accounts receivable turnover ratiox10Total assets growth ratiox11Capital preservation and appreciation ratiox12Operating growth ratio


### 4.2. Method Comparison

To demonstrate the effectiveness of the method proposed in this work, it is compared with other methods. The compared methods include BP, SVM, and VGG. The experimental results are shown in Figure6.Figure 6
Comparison with other methods.Compared with other methods, the method designed in this paper can obtain better precision and recall. Compared with the best-performing VGG method in the table, our method can achieve 1.3% and 1.4% improvement in precision and recall. This verifies the validity of this work.


### 4.3. Evaluation on Improved ResNet Block

This work improves the residual block with the expectation of reducing network parameters and shortening network training time. To verify the effectiveness of this strategy, this work conducts comparative experiments to compare the financial risk level evaluation performance without and with the improved residual block, respectively. The experimental results are shown in Table4.Table 4
Evaluation on improved ResNet block.
MethodPrecisionRecallTraining time (h)ResNet94.892.84.5EFRL-ResNet94.692.52.3It can be seen that, compared with the ResNet method without the improved residual block, the precision and recall drop of EFRL-ResNet is very small, which is basically negligible. However, in terms of training time, the training time of our method is reduced by nearly 50% compared with the original network. This further verifies the validity and reliability of the method proposed in this work.


### 4.4. Evaluation on Improved Loss

In this paper, we improve the network’s loss in order to solve the data imbalance problem. This work conducts comparison experiments to examine the performance of risk-level evaluation using the original loss and using the enhanced loss, respectively, to verify the usefulness of this improved technique. Figure7 depicts the experimental outcomes.Figure 7
Evaluation on improved loss.Obviously, the best network performance can be obtained when the improved loss is used. Compared with the loss without improvement, the improved network is able to obtain 1.5% and 1.8% precision and recall hints. This confirms the accuracy and efficacy of the effort done to improve the loss function.


### 4.5. Evaluation on BN Structure

As shown in the network structure diagram, this work uses a large number of BN structures. To verify the effectiveness of this structure, comparative experiments are conducted in this work to compare the network performance without and with the BN structure, respectively. The experimental results are shown in Figure8.Figure 8
Evaluation on BN structure.Obviously, the best network performance can be obtained when the BN structure is used. Compared with not using the BN layer, the network with this structure is able to obtain 1.9% and 2.4% precision and recall hints. This verifies the correctness and effectiveness of this work to use the BN structure.


## 4.1. Dataset and Metric

This work uses a self-made dataset to conduct experiments, and the data of the experiments are collected from various corporate finance departments in the context of digital transformation. The dataset contains a total of 25,195 training samples and 10,293 test samples. Each sample is a 12-dimensional feature containing different financial risk indicators, as shown in Table3. It should be noted that the data cannot be directly input into the designed neural network, and it needs to be expanded into 256 × 256 data before feature extraction can be performed. The label of each sample is a financial risk level. In this work, the risk level is divided into 10 types. In essence, this work is a classification task. The precision and recall are evaluation metrics in this work.Table 3
The detailed index.
IndexMeaningx1Operating profit ratiox2Cost-to-income ratiox3Return on equity ratiox4Cash flow debt ratiox5Gearing ratiox6Quick ratiox7Total asset turnover ratiox8Asset cash recovery ratiox9Accounts receivable turnover ratiox10Total assets growth ratiox11Capital preservation and appreciation ratiox12Operating growth ratio


## 4.2. Method Comparison

To demonstrate the effectiveness of the method proposed in this work, it is compared with other methods. The compared methods include BP, SVM, and VGG. The experimental results are shown in Figure6.Figure 6
Comparison with other methods.Compared with other methods, the method designed in this paper can obtain better precision and recall. Compared with the best-performing VGG method in the table, our method can achieve 1.3% and 1.4% improvement in precision and recall. This verifies the validity of this work.


## 4.3. Evaluation on Improved ResNet Block

This work improves the residual block with the expectation of reducing network parameters and shortening network training time. To verify the effectiveness of this strategy, this work conducts comparative experiments to compare the financial risk level evaluation performance without and with the improved residual block, respectively. The experimental results are shown in Table4.Table 4
Evaluation on improved ResNet block.
MethodPrecisionRecallTraining time (h)ResNet94.892.84.5EFRL-ResNet94.692.52.3It can be seen that, compared with the ResNet method without the improved residual block, the precision and recall drop of EFRL-ResNet is very small, which is basically negligible. However, in terms of training time, the training time of our method is reduced by nearly 50% compared with the original network. This further verifies the validity and reliability of the method proposed in this work.


## 4.4. Evaluation on Improved Loss

In this paper, we improve the network’s loss in order to solve the data imbalance problem. This work conducts comparison experiments to examine the performance of risk-level evaluation using the original loss and using the enhanced loss, respectively, to verify the usefulness of this improved technique. Figure7 depicts the experimental outcomes.Figure 7
Evaluation on improved loss.Obviously, the best network performance can be obtained when the improved loss is used. Compared with the loss without improvement, the improved network is able to obtain 1.5% and 1.8% precision and recall hints. This confirms the accuracy and efficacy of the effort done to improve the loss function.


## 4.5. Evaluation on BN Structure

As shown in the network structure diagram, this work uses a large number of BN structures. To verify the effectiveness of this structure, comparative experiments are conducted in this work to compare the network performance without and with the BN structure, respectively. The experimental results are shown in Figure8.Figure 8
Evaluation on BN structure.Obviously, the best network performance can be obtained when the BN structure is used. Compared with not using the BN layer, the network with this structure is able to obtain 1.9% and 2.4% precision and recall hints. This verifies the correctness and effectiveness of this work to use the BN structure.


## 5. Conclusion

It provides a crucial data basis for corporate financial risk management in the context of enterprise digital transformation. Enterprises should seize the opportunities brought by digital transformation to enterprise financial risk management, actively explore innovative management strategies, improve the awareness of financial risk prevention, and enhance the financial risk digital management innovation ability of financial personnel. There is still much to learn about business financial risk management in the age of digital transformation. A realistic financial risk avoidance strategy necessitates efforts at multiple levels. This is a current trend and a challenge that every business should address. In this context, how to effectively assess the financial risk level of enterprises in the context of digital transformation has become an important topic. This work relies on an artificial neural network to design an EFRL-ResNet network for evaluating the financial risk level of enterprises. The method suggested in this paper incorporates the concept of depth-wise separable convolution into the residual network structure, replacing some of the convolution operations used for feature extraction with depth-wise separable convolution operations. Furthermore, the lightweight risk level evaluation method described in this paper’s network model design has advantages in terms of evaluation accuracy and model training speed. And for the problem of an unbalanced number of different types of data samples, modulation coefficients are used in the improved hierarchical classification network to control the training of hard-to-classify samples and easy-to-classify samples, so that the model can obtain better classification results. Extensive and rigorous tests confirm the validity and accuracy of this work.



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*Source: 1882100-2022-05-02.xml*