# Financial Accounting Information Data Analysis System Based on Internet of Things

Authors: Yulin Bi
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

In order to meet the intelligent demand of modern financial data analysis, this paper proposes a financial accounting information data analysis system based on the Internet of things. Based on the central reinforcement learning architecture, the model uses multiple execution modules to enhance the computing and generalization ability of the single-agent reinforcement learning algorithm. In the selection of reinforcement learning algorithm, the instantaneous time difference algorithm is introduced. The algorithm can synchronize the experience of the previous iteration state in the learning process and does not depend on the final prediction value, which greatly saves the storage cost. In the establishment of the financial data analysis index system, the paper comprehensively considers the enterprise’s operation, development, debt repayment, and other capabilities, ensuring the integrity and rationality of the index system. In order to evaluate the performance of the algorithm, this paper takes the real financial data as the sample and uses BP neural network to conduct a comparative experiment. The experimental results show that the recognition accuracy of the model is better than that of the BP neural network in each experimental scenario, and the recognition accuracy of Experiment 3 is improved by 4.6%.Conclusion. The performance of the distributed reinforcement learning algorithm is better than that of the common back-propagation neural network in the real data set scenario.


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## Body

## 1. Introduction

With the increasing competition, many enterprises begin to pay more and more attention to the fine management reform based on “accurate decision-making, accurate planning, accurate control, and accurate assessment.” How to transform massive data into valuable information in a timely and effective manner, how to give full play to the maximum value of enterprise information, so that managers can have an insight into the huge economic benefits brought by data management solutions and effectively help enterprises realize information strategies have become the most concerned issues of many enterprise managers [1, 2]. Business intelligence is another important application in the field of enterprise management software after ERP (enterprise resource planning). After years of development, business intelligence covers a wider range of contents, including financial intelligence, sales intelligence, procurement intelligence, and production intelligence. We need to think deeply and study the new intelligent three-dimensional dynamic accounting information platform [3]. Through this system, we can realize various functions including shortening the response time of complex problems, tentatively sending out new views and insights, trying the effects of different strategies, enhancing management control, reducing costs, and making objective decisions, so as to meet the needs of accounting data mining [4]. Under the guidance of this trend, the intelligent three-dimensional dynamic accounting information platform will constitute the main content of accounting informatization in the twenty-first century.


## 2. Literature Review

The accounting information of an enterprise is mainly used to analyze the operation of a certain period of time. It is an important way for the outside world to understand the operation of an enterprise. Therefore, the research on accounting information has always been the focus of the economic industry. Zheng and others put forward through the investigation of the actual situation that the quality of accounting information is relatively low when managers hold low shares in the company. With the continuous increase of their shares, the quality of accounting information is also increasing. However, when the shareholding level of managers is high, the relationship between the two is opposite [5]. Ah and others also conducted research on the quality of accounting information. Through a large number of studies, they believe that the quality of accounting information of an enterprise has a great relationship with the company shares held by the managers. The lower the shareholding ratio, the higher the quality of accounting information [6]. Lin and others have the same research results on accounting information and corporate governance. They believe that there are many determinants of the quality of accounting information, of which corporate governance behavior is a very important aspect [7]. Through empirical research, Zhang and others proposed that the higher the ownership concentration of an enterprise, the lower the quality of accounting information of the enterprise, and the two are negatively correlated [8]. Modalavalasa and others put forward through a large number of investigations that with the increase of the number of external directors, the probability of fraud in the enterprise will be lower [9]. Zhou and others studied from the perspective of the size of corporate directors and believed that there was a negative correlation between earnings management and the size of corporate directors [10]. Dominic and others believe that if the enterprise’s earnings management fails to achieve the corresponding objectives, the company’s directors will take certain measures to curb earnings management [11].At present, with the development of artificial intelligence technology, building an intelligent financial data analysis model with the help of computer technology to realize the identification and alarm of abnormal financial data is one of the important manifestations of the “Finance + computer” integration trend. However, the existing analysis algorithms have the disadvantages of low efficiency and poor accuracy. In order to overcome these shortcomings, this paper studies the distributed reinforcement learning algorithm. By establishing a reasonable financial data analysis index system, the identification of abnormal financial data is realized.


## 3. Research Methods

### 3.1. Distributed Reinforcement Learning

The inspiration of reinforcement learning comes from human observation of animal learning behavior. Reinforcement learning is a typical artificial intelligence system [12, 13]. The system takes exploratory actions by sensing changes in the environment. At the same time, the system perceives the feedback result of this action to judge the fitness of the state. The system repeats this feedback process continuously to obtain the optimal response behavior in this environment. The traditional reinforcement learning method usually has only one learning carrier. In recent years, with the enhancement of computer computing ability, the distributed reinforcement learning system with multiple learning units has become one of the research hotspots. The distributed reinforcement learning system includes central reinforcement learning, independent reinforcement learning, and group reinforcement learning. The central reinforcement learning system is used in this paper.Figure1 shows the architecture of central reinforcement learning. The central reinforcement learning method can be expressed in mathematical form as the following formula:(1)RLC=L,E,W,where W represents the set of RLC system environment variables, L is the set of learning units, and E is the set of execution units. W, E is defined in the following formula:(2)E=A1,A2,…,An,W=S,Δ,T,R.Figure 1
Central reinforcement learning system structure.In the definition of environment variable setW, S is all possible different states in this environment; Δ is composed of several transition vectors, representing the transition probabilities of different states in S; T is the transition mapping set of state environment. According to the definition of variables in W, the relationship shown in formula (3) can be obtained:(3)T:S×A^−>Δ,where A^ is the set of all possible actions of the execution unit in E, as shown in the following formula (4):(4)A^=An.W contains the environment enhancement module R. The module maps the real number excitation as shown in the following equation (5) through the instruction pair of < environment, action >:(5)R:S×A^−>Γ.InRLC system, L is the learning unit of the system, and its definition is shown in the following formula:(6)L=X,I,A^,P,where X=x1,x2,…,xn is the set of learning unit inputs, I is the mapping from the environment state S to the learning unit, and P is the learning rate of L. According to these definitions, (7) can be obtained:(7)I:S−>X,P:X×A^×Γ−>A^.ForRLC system, its learning module does not have the ability of active learning, so it can passively perform the obtained tasks and optimize the parameters of the strategy module through relevant learning algorithms.


### 3.2. Instantaneous Time Difference Algorithm

For the strengthened system, there is a delay in a certain excitation to the system, so a certain response of the system may be caused by a very early action [14]. In order to solve this delay problem, the paper introduces the temporary difference algorithm, which can synchronize the previous state experience in learning. For the specific TD algorithm, the state si at m+1 different times, the observed data x¯i, and the predicted value Vi of each state are defined, as follows:(8)si=s1,s2,…,sm,sm+1,x¯i=x1,x2,…,xm,Vi=Vs1,Vs2,…,Vsm,Vsm+1.In the learning process ofTD algorithm, for time t, it is not necessary to wait until the final predicted value y is obtained to correct the state, but it can be updated at time t+1, that is, the following formula:(9)Vst<−Vst+αrt+1+γVst+1−Vst.When implementingTD algorithm, it is necessary to introduce a neural network structure to record Vst. At this time, the learning process in the TD algorithm can use the w¯ rule, as shown in the following formula:(10)w¯,w¯+m∑t=1mΔw¯t.The correction ofw¯ needs to be based on the back propagation of the “predicted value actual value” error. First, define the error function, as shown in the following formula:(11)Et=12y−Vst2.Calculate the partial derivative ofΔw¯t as follows:(12)Δw¯t=−η∂Et∂Δw¯t=ηy−Vst∇wVst.Usually,Vsts is linear with x¯t and w¯. In this case, the above equation can be written as(13)Δw⟶t=ηy−w⟶Tx⟶tx⟶t.According to equation (5), equation (12) can be written as follows:(14)Δw¯t=ηVst+1−Vst∑k=1t∇wVsk.The advantage of (14) is that the value of Δw¯t is only determined by the sum of the prediction difference and ∇wVsk. In the implementation, there is no need to store the value of ∇wVst at each time in the past, which greatly saves the storage overhead.


### 3.3. Intelligent Accounting Information Platform

In order to realize the new accounting function, the accounting information system must be reconstructed, but how to reconstruct it is a topic worth pondering. The traditional accounting information system has the characteristics of integration, integrity, uniqueness, and personalization. Integration is the restriction of practical conditions, which reflects the integration of accounting and business. Integrity is the high-level requirement of information users for information. Uniqueness is the requirement of the accounting business and the significance of accounting information system. Personalization is the preference of information users and the vitality of accounting information system. However, in the new accounting functions, information resource integration requires the provision of multidimensional accounting information, value creation management requires the provision of intelligent decision-making support, process supervision and control require the provision of intelligent, three-dimensional, and dynamic control, and three-dimensional dynamic reflection directly emphasizes the two characteristics of three-dimensional and dynamic. It can be seen that the principles of accounting information system reconstruction are intellectualization, three-dimensional, and dynamic.


## 3.1. Distributed Reinforcement Learning

The inspiration of reinforcement learning comes from human observation of animal learning behavior. Reinforcement learning is a typical artificial intelligence system [12, 13]. The system takes exploratory actions by sensing changes in the environment. At the same time, the system perceives the feedback result of this action to judge the fitness of the state. The system repeats this feedback process continuously to obtain the optimal response behavior in this environment. The traditional reinforcement learning method usually has only one learning carrier. In recent years, with the enhancement of computer computing ability, the distributed reinforcement learning system with multiple learning units has become one of the research hotspots. The distributed reinforcement learning system includes central reinforcement learning, independent reinforcement learning, and group reinforcement learning. The central reinforcement learning system is used in this paper.Figure1 shows the architecture of central reinforcement learning. The central reinforcement learning method can be expressed in mathematical form as the following formula:(1)RLC=L,E,W,where W represents the set of RLC system environment variables, L is the set of learning units, and E is the set of execution units. W, E is defined in the following formula:(2)E=A1,A2,…,An,W=S,Δ,T,R.Figure 1
Central reinforcement learning system structure.In the definition of environment variable setW, S is all possible different states in this environment; Δ is composed of several transition vectors, representing the transition probabilities of different states in S; T is the transition mapping set of state environment. According to the definition of variables in W, the relationship shown in formula (3) can be obtained:(3)T:S×A^−>Δ,where A^ is the set of all possible actions of the execution unit in E, as shown in the following formula (4):(4)A^=An.W contains the environment enhancement module R. The module maps the real number excitation as shown in the following equation (5) through the instruction pair of < environment, action >:(5)R:S×A^−>Γ.InRLC system, L is the learning unit of the system, and its definition is shown in the following formula:(6)L=X,I,A^,P,where X=x1,x2,…,xn is the set of learning unit inputs, I is the mapping from the environment state S to the learning unit, and P is the learning rate of L. According to these definitions, (7) can be obtained:(7)I:S−>X,P:X×A^×Γ−>A^.ForRLC system, its learning module does not have the ability of active learning, so it can passively perform the obtained tasks and optimize the parameters of the strategy module through relevant learning algorithms.


## 3.2. Instantaneous Time Difference Algorithm

For the strengthened system, there is a delay in a certain excitation to the system, so a certain response of the system may be caused by a very early action [14]. In order to solve this delay problem, the paper introduces the temporary difference algorithm, which can synchronize the previous state experience in learning. For the specific TD algorithm, the state si at m+1 different times, the observed data x¯i, and the predicted value Vi of each state are defined, as follows:(8)si=s1,s2,…,sm,sm+1,x¯i=x1,x2,…,xm,Vi=Vs1,Vs2,…,Vsm,Vsm+1.In the learning process ofTD algorithm, for time t, it is not necessary to wait until the final predicted value y is obtained to correct the state, but it can be updated at time t+1, that is, the following formula:(9)Vst<−Vst+αrt+1+γVst+1−Vst.When implementingTD algorithm, it is necessary to introduce a neural network structure to record Vst. At this time, the learning process in the TD algorithm can use the w¯ rule, as shown in the following formula:(10)w¯,w¯+m∑t=1mΔw¯t.The correction ofw¯ needs to be based on the back propagation of the “predicted value actual value” error. First, define the error function, as shown in the following formula:(11)Et=12y−Vst2.Calculate the partial derivative ofΔw¯t as follows:(12)Δw¯t=−η∂Et∂Δw¯t=ηy−Vst∇wVst.Usually,Vsts is linear with x¯t and w¯. In this case, the above equation can be written as(13)Δw⟶t=ηy−w⟶Tx⟶tx⟶t.According to equation (5), equation (12) can be written as follows:(14)Δw¯t=ηVst+1−Vst∑k=1t∇wVsk.The advantage of (14) is that the value of Δw¯t is only determined by the sum of the prediction difference and ∇wVsk. In the implementation, there is no need to store the value of ∇wVst at each time in the past, which greatly saves the storage overhead.


## 3.3. Intelligent Accounting Information Platform

In order to realize the new accounting function, the accounting information system must be reconstructed, but how to reconstruct it is a topic worth pondering. The traditional accounting information system has the characteristics of integration, integrity, uniqueness, and personalization. Integration is the restriction of practical conditions, which reflects the integration of accounting and business. Integrity is the high-level requirement of information users for information. Uniqueness is the requirement of the accounting business and the significance of accounting information system. Personalization is the preference of information users and the vitality of accounting information system. However, in the new accounting functions, information resource integration requires the provision of multidimensional accounting information, value creation management requires the provision of intelligent decision-making support, process supervision and control require the provision of intelligent, three-dimensional, and dynamic control, and three-dimensional dynamic reflection directly emphasizes the two characteristics of three-dimensional and dynamic. It can be seen that the principles of accounting information system reconstruction are intellectualization, three-dimensional, and dynamic.


## 4. Result Analysis

### 4.1. Establishment of Index System

It is one of the demands of the capital market to conduct real-time analysis of the enterprise’s finance, timely find abnormal financial data, and give an early warning [15]. Therefore, it is necessary to follow the principles of authenticity, systematization, scientificity, and feasibility when constructing the financial index system. Therefore, the selection of indicators needs to comprehensively reflect the company’s debt repayment, operation, profitability, growth, and other aspects. In addition, the operating data of an enterprise do not only include financial indicators, and nonfinancial indicators can also reflect the financial status of the company. Therefore, it can be properly introduced when establishing the index system.The indicator system includes financial and nonfinancial indicators [16]. Financial indicators can not only reflect the solvency, operation, profitability, development, and other capabilities of enterprises but also reflect the cash flow of enterprises. In addition, financial indicators also introduce the per share index of listed enterprises. Nonfinancial indicators mainly reflect the corporate governance structure, equity structure, and financial audit opinions and can reflect the financial status of the company from the side [17].In the process of data testing and analysis, on the data collection of the algorithm, the paper screened the real financial data of 300 companies from 2018 to 2021. In these data, there are 150ST companies and 150 non-ST companies in 2021, and the ratio of ST to non-ST is 1 : 1. For each enterprise, these data include its T, T−1, T−2, T−3 years.As shown in Table1, since each company became an ST company in different years due to abnormal financial data, the data attributes of each company in this data set are also different. If a company does not become an ST company until 2021, there will be sufficient data to analyze its T, T−1, T−2, T−3 change status. According to Table 1, 20 companies can analyze the data changes in 3 years.Table 1
Simulation data structure.
Particular yearNumber of ST companies20183220197820201302021150


### 4.2. Financial Data Analysis and Early Warning Simulation

In the data analysis,T−1, T−2, and T−3 data are used to simulate the algorithm model designed above. In order to better evaluate the impact of changes in time, financial data, and other dimensions on the performance of the algorithm, this paper designs several groups of experiments, each of which uses financial data in different years. The design of the experiment is shown in Tables 2–4.Table 2
Experiment I.
Enter data yearYear of output dataT−3T−2T−2T−1T−1TTable 3
Experiment II.
Enter data yearYear of output dataT−3,T−2T−1T−2,T−1TTable 4
Experiment III.
Enter data yearYear of output dataT−3,T−2,T−1TThe software and hardware environment for the simulation experiment in this paper is shown in Table5.Table 5
Algorithm simulation environment.
NameParameterOperating systemCentos 6.0Programming environmentPython2.3CPUIntel Corei7-10700KGraphics cardGIGABYTE GTX 1660Based on the index system, build anRLC system based on TD algorithm [18, 19]. Before the simulation of the algorithm, the number of execution modules in the RLC system should be determined. In the mode of Experiment 1, the algorithm accuracy and running time under different number of execution modules are calculated by traversal, as shown in Figures 2(a) and 2(b), respectively.Figure 2
Effect of the number of execution modules on algorithm accuracy and operation time. (a) The influence of the number of execution modules on the accuracy of the algorithm. (b) The influence of the number of execution modules on the algorithm operation time.
(a)(b)As can be seen from Figure2(a), when the number of modules is less than 8, the accuracy of the algorithm increases rapidly, from about 45% to nearly 80%. When the number of execution modules is greater than 8, the correct rate of the algorithm increases slowly and remains at about 80% [20]. As can be seen from Figure 2(b), when the execution module is less than 7, the running time of the algorithm increases slowly and remains at about 1.8 × 105 s. When the number of execution modules is greater than 7, the operation time increases rapidly. Considering Figures 2(a) and 2(b), the number of execution modules used is finally determined to be 8.Table6 shows different experimental results under distributed reinforcement learning [21]. For comparison, BP neural network is also introduced, as shown in Table 7. From the calculation results, it can be seen that for the analysis of abnormal financial data, the recognition accuracy of a distributed reinforcement learning algorithm for ST company is better than BP neural network in each experimental scenario. The calculation accuracy of Experiment 3 is improved by 4.6%. From the perspective of the algorithm itself, the accuracy of Experiment 3 is better than that of Experiment 2, and the accuracy of Experiment 2 is better than that of Experiment 1 [22, 23]. This result shows that the financial status of an enterprise can be better analyzed through the accumulation of financial data in multiple different years.Table 6
Experimental results based on distributed reinforcement learning.
CategoryExperiment 1 (%)Experiment 2 (%)Experiment 3 (%)Is ST79.193.095.3Non-ST79.186.093.0Population79.189.594.2Table 7
Experimental results based on neural network.
CategoryExperiment 1 (%)Experiment 2 (%)Experiment 3 (%)IsST70.484.789.2Non-ST73.182.190.0Population72.583.389.6


## 4.1. Establishment of Index System

It is one of the demands of the capital market to conduct real-time analysis of the enterprise’s finance, timely find abnormal financial data, and give an early warning [15]. Therefore, it is necessary to follow the principles of authenticity, systematization, scientificity, and feasibility when constructing the financial index system. Therefore, the selection of indicators needs to comprehensively reflect the company’s debt repayment, operation, profitability, growth, and other aspects. In addition, the operating data of an enterprise do not only include financial indicators, and nonfinancial indicators can also reflect the financial status of the company. Therefore, it can be properly introduced when establishing the index system.The indicator system includes financial and nonfinancial indicators [16]. Financial indicators can not only reflect the solvency, operation, profitability, development, and other capabilities of enterprises but also reflect the cash flow of enterprises. In addition, financial indicators also introduce the per share index of listed enterprises. Nonfinancial indicators mainly reflect the corporate governance structure, equity structure, and financial audit opinions and can reflect the financial status of the company from the side [17].In the process of data testing and analysis, on the data collection of the algorithm, the paper screened the real financial data of 300 companies from 2018 to 2021. In these data, there are 150ST companies and 150 non-ST companies in 2021, and the ratio of ST to non-ST is 1 : 1. For each enterprise, these data include its T, T−1, T−2, T−3 years.As shown in Table1, since each company became an ST company in different years due to abnormal financial data, the data attributes of each company in this data set are also different. If a company does not become an ST company until 2021, there will be sufficient data to analyze its T, T−1, T−2, T−3 change status. According to Table 1, 20 companies can analyze the data changes in 3 years.Table 1
Simulation data structure.
Particular yearNumber of ST companies20183220197820201302021150


## 4.2. Financial Data Analysis and Early Warning Simulation

In the data analysis,T−1, T−2, and T−3 data are used to simulate the algorithm model designed above. In order to better evaluate the impact of changes in time, financial data, and other dimensions on the performance of the algorithm, this paper designs several groups of experiments, each of which uses financial data in different years. The design of the experiment is shown in Tables 2–4.Table 2
Experiment I.
Enter data yearYear of output dataT−3T−2T−2T−1T−1TTable 3
Experiment II.
Enter data yearYear of output dataT−3,T−2T−1T−2,T−1TTable 4
Experiment III.
Enter data yearYear of output dataT−3,T−2,T−1TThe software and hardware environment for the simulation experiment in this paper is shown in Table5.Table 5
Algorithm simulation environment.
NameParameterOperating systemCentos 6.0Programming environmentPython2.3CPUIntel Corei7-10700KGraphics cardGIGABYTE GTX 1660Based on the index system, build anRLC system based on TD algorithm [18, 19]. Before the simulation of the algorithm, the number of execution modules in the RLC system should be determined. In the mode of Experiment 1, the algorithm accuracy and running time under different number of execution modules are calculated by traversal, as shown in Figures 2(a) and 2(b), respectively.Figure 2
Effect of the number of execution modules on algorithm accuracy and operation time. (a) The influence of the number of execution modules on the accuracy of the algorithm. (b) The influence of the number of execution modules on the algorithm operation time.
(a)(b)As can be seen from Figure2(a), when the number of modules is less than 8, the accuracy of the algorithm increases rapidly, from about 45% to nearly 80%. When the number of execution modules is greater than 8, the correct rate of the algorithm increases slowly and remains at about 80% [20]. As can be seen from Figure 2(b), when the execution module is less than 7, the running time of the algorithm increases slowly and remains at about 1.8 × 105 s. When the number of execution modules is greater than 7, the operation time increases rapidly. Considering Figures 2(a) and 2(b), the number of execution modules used is finally determined to be 8.Table6 shows different experimental results under distributed reinforcement learning [21]. For comparison, BP neural network is also introduced, as shown in Table 7. From the calculation results, it can be seen that for the analysis of abnormal financial data, the recognition accuracy of a distributed reinforcement learning algorithm for ST company is better than BP neural network in each experimental scenario. The calculation accuracy of Experiment 3 is improved by 4.6%. From the perspective of the algorithm itself, the accuracy of Experiment 3 is better than that of Experiment 2, and the accuracy of Experiment 2 is better than that of Experiment 1 [22, 23]. This result shows that the financial status of an enterprise can be better analyzed through the accumulation of financial data in multiple different years.Table 6
Experimental results based on distributed reinforcement learning.
CategoryExperiment 1 (%)Experiment 2 (%)Experiment 3 (%)Is ST79.193.095.3Non-ST79.186.093.0Population79.189.594.2Table 7
Experimental results based on neural network.
CategoryExperiment 1 (%)Experiment 2 (%)Experiment 3 (%)IsST70.484.789.2Non-ST73.182.190.0Population72.583.389.6


## 5. Conclusion

The financial data analysis of an enterprise is a complex system engineering. In this paper, the distributed reinforcement learning method is introduced into the enterprise’s financial data analysis, and the accurate evaluation of the enterprise’s operating status is achieved by constructing a reasonable financial evaluation index system. The data test and simulation results using real data sets show that the performance of distributed reinforcement learning algorithm is better than that of an ordinary back-propagation neural network in this scenario.



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