# Genetic Algorithm Application on the Risk Assessment for Accounting Resource Sharing Management

Authors: Rui Chen; Yuanling Lu; Yan Wang
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

A risk assessment model of accounting resource sharing management based on genetic algorithm is proposed. By selecting the risk assessment indicators of accounting resource sharing management, the weight of risk assessment indicators is determined by the Delphi method, and the quantitative model of risk assessment of accounting resource sharing management is constructed. On this foundation, a genetic algorithm is utilised to compute the best solution of parameters, design risk degree assessment criteria to evaluate risk degree, and implement risk evaluation of accounting resource sharing management using a genetic algorithm. The experimental results show that compared with the traditional accounting resource sharing management risk evaluation model, the constructed accounting resource sharing management risk evaluation model greatly reduces the evaluation time and can accurately evaluate the accounting resource sharing management risk, which fully shows that the model has better evaluation performance.


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## Body

## 1. Introduction

The continuous development of information technology has greatly promoted the reform of all walks of life and prompted great changes in people’s lifestyle. At the same time, it has also had a great impact on the traditional accounting work. Nowadays, informatization has been gradually integrated into the accounting industry. From the perspective of social development, enterprises are based on informatization, and the key of enterprise informatization is accounting informatization. Accounting informatization is essentially the realization of accounting computerization, which can realize the sharing and management of accounting resources and then improve the informatization degree of enterprises. Accounting resources refer to the information after the original transaction data are processed by the accounting processing method. Its function is to meet the economic information required by accounting and economic management [1–3]. Accounting resource sharing refers to the process that the same accounting resource is used by different users for different purposes. Accounting computerization can not only realize the sharing of accounting resources but also manage them accordingly. Accounting computerization facilitates the sharing and administration of accounting resources by allowing different users to access the same accounting resource’s electronic data without difficulty [4]. While accounting resource sharing management makes life easier for employees, it also poses certain security threats. As a result, studying the risk assessment of accounting resource sharing management is critical. This paper proposed a logistics based financial risk early warning evaluation model for manufacturing enterprises, and financial data from some manufacturing listed enterprises in Shanghai and Shenzhen were the research objects. The existing financial risk early warning evaluation model was used to screen the samples and variable parameters, and factor analysis and principal component analysis were used to analyze the data. The logistic financial risk early warning model is built for China’s listed manufacturing businesses. The test shows that the model has good prediction effect in practical application, but the evaluation time of the model is long. Literature [5–7] the proposed the financial risk evaluation model of small and medium-sized enterprises based on fuzzy analytic hierarchy process. Taking enterprise S as the research object, 12 financial indicators were selected. And fuzzy analytic hierarchy process (FAHP) was used to construct the financial risk evaluation structure model of small and medium-sized enterprises, while the financial risks of enterprise S from 2015 to 2017 were evaluated and analyzed. Finally, it put forward reasonable suggestions for enterprise S’s financial risk control strategy. However, the evaluation accuracy of the model was low, resulting in poor evaluation effect.Genetic algorithm has been applied to many measurement industries and has achieved very successful applications in the fields of mathematical function optimization, machine learning, parameter estimation, large-scale integrated circuit design, pattern clustering, and so on. Hollstien was the first to use genetic algorithm to solve the function optimization problem. He studied the performance of genetic algorithm under different selection and crossover strategies and achieved good results [8]. Xinlei employed a genetic algorithm to find the best prediction approach for a central network-based bank bankruptcy prediction model [9]. In contrast to the gradient technique and the simulated annealing algorithm, the genetic algorithm may continually seek optimization from the population rather than relying on a single design. Goldberg presented a powerful genetic algorithm that can handle complicated problems rapidly, precisely, and consistently without the need for particular issue coding, operation, or other types of user involvement. The shortest path (TSP) issue may be solved using a genetic algorithm, according to references [10–13]. Ahn et al. suggested an elite genetic algorithm with efficient memory that can tackle complicated optimization problems rapidly and effectively. In order to solve the structural engineering optimization problem, Leite and Topping carried out various forms of deep cross operations. Topping also refers to the execution of genetic algorithms in parallel. Saka used a genetic algorithm to address the optimum design challenge of a network system. Wang Hebu discovered how to use genetic algorithm picture presentation technology to structured topology optimization challenges and compare its performance with algorithms Hoogs Kiehl, Lamb, and Senturk (2017) proposed a genetic algorithm to detect financial fraud. Genetic algorithm is also widely used in power system [14–17]. This paper summarizes the application of genetic algorithm in economic operation of power system, power grid planning, optimization of power system controller, and so on.Aiming at the problems of the above methods, this paper proposes a risk assessment model of accounting resource sharing management based on genetic algorithm. Genetic algorithm is proposed according to the law of biological evolution in nature. It is a computer model of biological evolution that mimics Darwin’s biological evolution theory’s natural selection and genetic mechanism. It is an approach that simulates the natural evolution process to get the best answer. The ideal individual is generated by selection, crossover, and mutation processes using a genetic algorithm, and its associated value is the optimal solution of model parameters. The risk degree evaluation rules are formulated according to the optimal solution to determine the risk degree, in order to realize the risk evaluation of accounting resource sharing management based on genetic algorithm, reduce the model’s evaluation time, and improve the model’s evaluation accuracy.


## 2. Design of Risk Assessment Model for Accounting Resource Sharing Management

### 2.1. Selection of Evaluation Indicators

In the process of accounting resource sharing management, it will be affected by many factors, mainly including three aspects: human factors, technical factors, and management factors. It is tough to separate the three components of human, technology, and management. The three are a unity of opposites in terms of accounting resource sharing management security, and they have a mutual limitation, mutual influence, and mutual growth connection [18]. The relationship between influencing factors of accounting resource sharing management is shown in Figure 1.Figure 1
Relationship between influencing factors of accounting resource sharing management.According to the above relationship of influencing factors of accounting resource sharing management, the risk assessment indicators of accounting resource sharing management are selected, as shown in Table1.Table 1
Risk assessment indicators of accounting resource sharing management.
Primary evaluation indexCodeSecondary evaluation indexHuman factorsA1Human needsA2Human qualityA3Human technologyTechnical factorsB1Technical safetyB2Technical advanceB3Technical applicabilityManagement factorsC1Resource safety awarenessC2Safety rules and regulationsC3Division of safety responsibilitiesC4Risk response systemIt can be seen from Table1 that the primary evaluation indicators in the risk assessment of accounting resource sharing management include human factors, technical factors, and management factors. Among them, human factors include human needs, human quality, human technology, and other secondary indicators. Secondary indications such as technical safety, technological development, and technical applicability are included in technical considerations. Secondary indicators such as resource safety awareness, safety laws and regulations, safety responsibility division, and risk response system are examples of management factors. Through the above process, the selection of risk assessment indicators for accounting resource sharing management is completed to prepare for the following risk assessment.


### 2.2. Determination of Evaluation Index Weight

Based on the selected risk assessment indicators of accounting resource sharing management, the weight of risk assessment indicators is determined by the Delphi method. The specific weight calculation process is described below. The Delphi method is essentially an expert consultation method, which is widely used in risk assessment. This method has the advantage of high accuracy, but the process is cumbersome [19, 20]. The flow of the Delphi method is shown in Figure 2.Figure 2
Delphi process.As can be seen from the flowchart above, the process of the Delphi method is first selected and organized by the moderator. Based on its knowledge and experience, the expert group formulates the consultation form and then carries out the first round of consultation. Based on the outcomes of the first round of consultation, determine the weight of risk indicators. Then, start a fresh round of consultations to assess the risk indicators’ weight once again. Assess if the views are focused based on the importance of risk indicators obtained in the second consultation. The weight value of the evaluation index must be established if the views are somewhat concentrated. Otherwise, an individual survey shall be carried out to solicit the opinions of the minority, and the first round of consultation shall be carried out again.Execute the process shown in Figure2, and the obtained weight matrices A, V, and t represent the human factor weight value, technical factor weight value, and management factor weight value, respectively.


### 2.3. Establishment of Risk Assessment Quantitative Model

Based on the above evaluation index weights, a quantitative model for risk assessment of accounting resource sharing management is established to provide model support for risk assessment [21].Build the basic risk assessment model of accounting resource sharing management, and its expression is(1)R=FV,T,C,where R represents the risk of accounting resource sharing management; C represents the existing risk control measures; V represents the efficiency of accounting resource sharing management; and T represents the risk control cycle. According to the risk defined by ISO/IEC, it can be expressed by the vulnerability of the threat, the severity of the possibility, etc., and then formula (1) can be expressed as(2)R=Fc,Pv,V,where Pt represents the probability of threat occurrence and Pv indicates the severity of vulnerability. Set the value range of threat probability Pt as [0, 1], reflecting the possibility of risk events. The risk event is more likely to occur if the probability of threat occurrence is near to one; on the other hand, the risk event is less likely to occur if the likelihood of threat occurrence is close to one. Vulnerability severity Pv exists objectively, but it will only pose a danger to accounting resource sharing management if it is used. In other words, the more serious the vulnerability, the higher the danger of accounting resource sharing management. The accuracy of risk assessment is influenced by the efficacy of risk management mechanisms, which impacts the likelihood of risk occurrences. The greater the effectiveness of risk control measures, the smaller the risk of accounting resource sharing management. The calculation formula for the effectiveness of risk control measures is(3)Sm=1−NvNR,where Nv refers to the number of risks of accounting resource sharing management platform and NR indicates the total number of threats to the accounting resource sharing platform. According to the actual situation, the occurrence of risk events of accounting resource sharing management platform has randomness and statistical regularity. Therefore, Poisson distribution is used to quantify the risk assessment index. Poisson distribution sets the random variable x, whose value is 0,+∞. The calculation formula is expressed as(4)Px=k=e−λxSm.Among them,λ is a constant, indicating the average occurrence rate of random events in unit time, with the range of 0,+∞, and the random variable x obeys the parameter λ. The position distribution of is abbreviated as X∗ Π (λ). When the maximum value of λ is reached, Poisson distribution formula can be converted into normal distribution formula, which is expressed as follows:(5)Px=n=12πλen−λ2/zλ.According to the experimental results, whenλ equals to 10, the Poisson distribution curve is close to the normal distribution curve.To sum up, Poisson distribution is used to quantify the risk assessment indicators. Combined with formula (5), the quantitative model of accounting resource sharing management risk assessment is as follows:(6)Q=∑i=1kV∗e−λxSm∗PV,k<10,∑i=1kV∗12πλen−λ2/zλ∗PV,k>10.


### 2.4. Risk Degree Determination

Based on the above quantitative risk assessment model of accounting resource sharing management, genetic algorithm is used to calculate the optimal solution of parameters, formulate the risk assessment interval, and determine the risk degree, so as to realize the risk assessment of accounting resource sharing management based on genetic algorithm [22–24]. Formula (6) needs to calculate the values of Q and K. The specific solution process is described below. Set the initial population as chrome = {(RT1, K1), …, (RTI, Ki), …, (rt20, K20)}, where RTI and Ki, respectively, represent the real value within the value range of I gene individuals RT and locus K, and the value range is [0, 1]. Calculate the individual fitness of the population, and the expression is(7)fitness=Vj−Vi,where Vj represents the individual fitness value of the population and Vi represents the expert evaluation value. The smaller the fitness value is, the greater the chance that the individual will be retained in the new generation population. The optimum individual is produced by selection, crossover, and mutation processes, and its corresponding value is the optimal solution of model parameters, according to the calculation findings of formula (7). The ideal quantitative model of accounting resource sharing management for risk assessment can be calculated by substituting into formula (6). Input the sample data into the model to obtain the risk assessment value of accounting resource sharing management, which is taken as the basis. Determine the risk degree, as shown in Table 2.Table 2
Risk degree judgment rules.
Degree of riskRisk assessment valueExplanationNo risk[0, 60)The accounting resource sharing platform operates normally and there is no possibility of riskLow risk[60, 75)The accounting resource sharing management platform is basically stable, and there is possibility of riskMedium risk[75, 90)The operation of accounting resource sharing management platform fluctuates, and there is an obvious possibility of riskHigh risk[90, 100]The operation status of accounting resource sharing management platform deteriorates and risks may occurAccording to the risk degree judgment rules in Table2, this research divides the sharing degree into four levels, namely, no risk, low risk, medium risk, and high risk. Different risk levels have different risk assessment values. Among them, if the risk assessment value is less than 60, it is considered as no risk, that is, the accounting resource sharing platform operates normally and there is no possibility of risk; if the risk assessment value is greater than 60 and less than 75, it is considered as low risk, that is, the operation of accounting resource sharing management platform is basically stable and there is possibility of risk; it is deemed medium risk if the risk assessment value is more than 75 but less than 90, indicating that the functioning state of the accounting resource sharing management platform varies and there is an evident danger; if the risk assessment value is more than 90 but less than 100, it is termed high risk, meaning that the accounting resource sharing management platform’s functioning state has deteriorated, and a risk has arisen. Through the above process, the design and operation of the risk assessment model of accounting resource sharing management based on genetic algorithm are completed, which provides a more effective guarantee for the security of accounting resource sharing management.


## 2.1. Selection of Evaluation Indicators

In the process of accounting resource sharing management, it will be affected by many factors, mainly including three aspects: human factors, technical factors, and management factors. It is tough to separate the three components of human, technology, and management. The three are a unity of opposites in terms of accounting resource sharing management security, and they have a mutual limitation, mutual influence, and mutual growth connection [18]. The relationship between influencing factors of accounting resource sharing management is shown in Figure 1.Figure 1
Relationship between influencing factors of accounting resource sharing management.According to the above relationship of influencing factors of accounting resource sharing management, the risk assessment indicators of accounting resource sharing management are selected, as shown in Table1.Table 1
Risk assessment indicators of accounting resource sharing management.
Primary evaluation indexCodeSecondary evaluation indexHuman factorsA1Human needsA2Human qualityA3Human technologyTechnical factorsB1Technical safetyB2Technical advanceB3Technical applicabilityManagement factorsC1Resource safety awarenessC2Safety rules and regulationsC3Division of safety responsibilitiesC4Risk response systemIt can be seen from Table1 that the primary evaluation indicators in the risk assessment of accounting resource sharing management include human factors, technical factors, and management factors. Among them, human factors include human needs, human quality, human technology, and other secondary indicators. Secondary indications such as technical safety, technological development, and technical applicability are included in technical considerations. Secondary indicators such as resource safety awareness, safety laws and regulations, safety responsibility division, and risk response system are examples of management factors. Through the above process, the selection of risk assessment indicators for accounting resource sharing management is completed to prepare for the following risk assessment.


## 2.2. Determination of Evaluation Index Weight

Based on the selected risk assessment indicators of accounting resource sharing management, the weight of risk assessment indicators is determined by the Delphi method. The specific weight calculation process is described below. The Delphi method is essentially an expert consultation method, which is widely used in risk assessment. This method has the advantage of high accuracy, but the process is cumbersome [19, 20]. The flow of the Delphi method is shown in Figure 2.Figure 2
Delphi process.As can be seen from the flowchart above, the process of the Delphi method is first selected and organized by the moderator. Based on its knowledge and experience, the expert group formulates the consultation form and then carries out the first round of consultation. Based on the outcomes of the first round of consultation, determine the weight of risk indicators. Then, start a fresh round of consultations to assess the risk indicators’ weight once again. Assess if the views are focused based on the importance of risk indicators obtained in the second consultation. The weight value of the evaluation index must be established if the views are somewhat concentrated. Otherwise, an individual survey shall be carried out to solicit the opinions of the minority, and the first round of consultation shall be carried out again.Execute the process shown in Figure2, and the obtained weight matrices A, V, and t represent the human factor weight value, technical factor weight value, and management factor weight value, respectively.


## 2.3. Establishment of Risk Assessment Quantitative Model

Based on the above evaluation index weights, a quantitative model for risk assessment of accounting resource sharing management is established to provide model support for risk assessment [21].Build the basic risk assessment model of accounting resource sharing management, and its expression is(1)R=FV,T,C,where R represents the risk of accounting resource sharing management; C represents the existing risk control measures; V represents the efficiency of accounting resource sharing management; and T represents the risk control cycle. According to the risk defined by ISO/IEC, it can be expressed by the vulnerability of the threat, the severity of the possibility, etc., and then formula (1) can be expressed as(2)R=Fc,Pv,V,where Pt represents the probability of threat occurrence and Pv indicates the severity of vulnerability. Set the value range of threat probability Pt as [0, 1], reflecting the possibility of risk events. The risk event is more likely to occur if the probability of threat occurrence is near to one; on the other hand, the risk event is less likely to occur if the likelihood of threat occurrence is close to one. Vulnerability severity Pv exists objectively, but it will only pose a danger to accounting resource sharing management if it is used. In other words, the more serious the vulnerability, the higher the danger of accounting resource sharing management. The accuracy of risk assessment is influenced by the efficacy of risk management mechanisms, which impacts the likelihood of risk occurrences. The greater the effectiveness of risk control measures, the smaller the risk of accounting resource sharing management. The calculation formula for the effectiveness of risk control measures is(3)Sm=1−NvNR,where Nv refers to the number of risks of accounting resource sharing management platform and NR indicates the total number of threats to the accounting resource sharing platform. According to the actual situation, the occurrence of risk events of accounting resource sharing management platform has randomness and statistical regularity. Therefore, Poisson distribution is used to quantify the risk assessment index. Poisson distribution sets the random variable x, whose value is 0,+∞. The calculation formula is expressed as(4)Px=k=e−λxSm.Among them,λ is a constant, indicating the average occurrence rate of random events in unit time, with the range of 0,+∞, and the random variable x obeys the parameter λ. The position distribution of is abbreviated as X∗ Π (λ). When the maximum value of λ is reached, Poisson distribution formula can be converted into normal distribution formula, which is expressed as follows:(5)Px=n=12πλen−λ2/zλ.According to the experimental results, whenλ equals to 10, the Poisson distribution curve is close to the normal distribution curve.To sum up, Poisson distribution is used to quantify the risk assessment indicators. Combined with formula (5), the quantitative model of accounting resource sharing management risk assessment is as follows:(6)Q=∑i=1kV∗e−λxSm∗PV,k<10,∑i=1kV∗12πλen−λ2/zλ∗PV,k>10.


## 2.4. Risk Degree Determination

Based on the above quantitative risk assessment model of accounting resource sharing management, genetic algorithm is used to calculate the optimal solution of parameters, formulate the risk assessment interval, and determine the risk degree, so as to realize the risk assessment of accounting resource sharing management based on genetic algorithm [22–24]. Formula (6) needs to calculate the values of Q and K. The specific solution process is described below. Set the initial population as chrome = {(RT1, K1), …, (RTI, Ki), …, (rt20, K20)}, where RTI and Ki, respectively, represent the real value within the value range of I gene individuals RT and locus K, and the value range is [0, 1]. Calculate the individual fitness of the population, and the expression is(7)fitness=Vj−Vi,where Vj represents the individual fitness value of the population and Vi represents the expert evaluation value. The smaller the fitness value is, the greater the chance that the individual will be retained in the new generation population. The optimum individual is produced by selection, crossover, and mutation processes, and its corresponding value is the optimal solution of model parameters, according to the calculation findings of formula (7). The ideal quantitative model of accounting resource sharing management for risk assessment can be calculated by substituting into formula (6). Input the sample data into the model to obtain the risk assessment value of accounting resource sharing management, which is taken as the basis. Determine the risk degree, as shown in Table 2.Table 2
Risk degree judgment rules.
Degree of riskRisk assessment valueExplanationNo risk[0, 60)The accounting resource sharing platform operates normally and there is no possibility of riskLow risk[60, 75)The accounting resource sharing management platform is basically stable, and there is possibility of riskMedium risk[75, 90)The operation of accounting resource sharing management platform fluctuates, and there is an obvious possibility of riskHigh risk[90, 100]The operation status of accounting resource sharing management platform deteriorates and risks may occurAccording to the risk degree judgment rules in Table2, this research divides the sharing degree into four levels, namely, no risk, low risk, medium risk, and high risk. Different risk levels have different risk assessment values. Among them, if the risk assessment value is less than 60, it is considered as no risk, that is, the accounting resource sharing platform operates normally and there is no possibility of risk; if the risk assessment value is greater than 60 and less than 75, it is considered as low risk, that is, the operation of accounting resource sharing management platform is basically stable and there is possibility of risk; it is deemed medium risk if the risk assessment value is more than 75 but less than 90, indicating that the functioning state of the accounting resource sharing management platform varies and there is an evident danger; if the risk assessment value is more than 90 but less than 100, it is termed high risk, meaning that the accounting resource sharing management platform’s functioning state has deteriorated, and a risk has arisen. Through the above process, the design and operation of the risk assessment model of accounting resource sharing management based on genetic algorithm are completed, which provides a more effective guarantee for the security of accounting resource sharing management.


## 3. Experimental Results and Analysis

In order to verify the evaluation performance of the accounting resource sharing management risk evaluation model in this paper, a simulation comparison experiment is designed. The models participating in the experiment are the manufacturing enterprise financial risk early warning evaluation model based on logistic, the small and medium-sized enterprise financial risk evaluation model based on the FAHP method, and the evaluation model in this paper. The evaluation performance of the model is reflected by the evaluation time and accuracy. The following describes the specific experimental process.


### 3.1. Experimental Preparation

Accounting resource sharing management needs to be based on a good experimental environment. Therefore, VS2010, opencv 2.4.13, windows10 operating system and Intel (R) Xeon (R) cpu E5-2603v4 are used. The simulation comparison experiment is carried out under the environment of 20 GHz and 32 GB memory. The network parameters set are shown in Table3.Table 3
Network parameter setting.
Parameter nameNumerical valueMain processorIntel(R) Core(TM) i5-10400FInfrared parameters (M)20SNR (db)>50Network interface (M)10/100Protocol supportTCP/IPOperating temperature (°C)−10∼+55Operating humidity (%)0∼85/WEquipment power (V)10Power supply12Based on the above parameter settings, select the financial statement data of a company from 2019 to 2020, a total of 1000 sample datasets, and randomly divide the sample data into two categories: training set and test set, which are, respectively, used in the training and testing processes of accounting resource sharing management risk assessment model. The specific proportion of the number of samples of training set and test set is 400 : 600.


### 3.2. Analysis of Experimental Results

The simulation comparison experiment is carried out in the above experimental environment, and the sample data are processed by MATLAB software. Figure3 shows the risk assessment indicators of accounting resource sharing management, and the gray part refers to the high-risk assessment indicators.Figure 3
Risk assessment indicators of accounting resource sharing management.Human technology, technological applicability, and risk response system, as shown in Figure3, are regarded high-risk assessment indicators among the seven risk assessment indicators of resource sharing management.To compare the evaluation time of high-risk indicators of accounting resource sharing management, the financial risk early warning evaluation model of manufacturing enterprises based on logistic and the financial risk evaluation model of small and medium-sized enterprises based on FAHP method are used in this paper. Table4 shows the findings.Table 4
Comparison results of evaluation time (unit: s).
Number of iterationsLogisticFAHPThe model proposed in this paper2023.0030.2010.204026.0232.5010.006025.1233.609.158020.1035.618.2310020.4136.788.0012019.5837.228.4014023.5838.127.1216018.7739.146.4518017.4640.155.0020020.1542.856.14It can be seen from Table4 that the evaluation time range of the financial risk early warning evaluation model of manufacturing enterprises based on logistic is 17.46 s∼26.02 s. The financial risk assessment model of small and medium-sized enterprises based on the FAHP method takes 30.20 s∼42.85 s. The evaluation time range of the evaluation model in this paper is 5.00 s∼10.20 s. According to the comparison analysis, the assessment model in this research significantly decreases the evaluation time of high-risk indicators of accounting resource sharing management and has higher evaluation performance. The accuracy and error rate of accounting resource sharing management risk assessment of this evaluation model, manufacturing enterprise financial risk early warning evaluation model based on logistic, and small and medium-sized enterprise financial risk evaluation model based on the FAHP method are compared to verify the effectiveness of this method. Table 5 shows the findings.Table 5
Evaluation results of three models.
ModelNumber of test samplesNumber of miscalculationsEvaluation error rate (%)Logistic200948FAHP2001752The model proposed in this paper20012The number of miscalculations of accounting resource sharing management risk in the evaluation model is the highest among the three models, while the number of miscalculations of accounting resource sharing management risk in the evaluation model in this paper is 1, and the evaluation error rate is only 2%. In order to further verify the effectiveness of this method, the accuracy of accounting resource sharing management risk assessment of this assessment model, the financial risk early warning assessment model of manufacturing enterprises based on logistic, and the financial risk assessment model of small and medium-sized enterprises based on the FAHP method are compared and analyzed. The comparison results are shown in Figure4.Figure 4
Comparison results of risk assessment accuracy of three models.Figure4 shows that the accuracy of the accounting resource sharing management risk assessment model in this paper gradually increases with the increase of iteration times, up to 98%, whereas the accuracy of the manufacturing enterprise financial risk early warning assessment model based on logistic is unstable, up to 52%. The accuracy of accounting resource sharing management risk assessment in the financial risk assessment model for small and medium-sized firms based on the FAHP approach decreases as the number of iterations increases, which is the lowest among the three models. To sum up, the evaluation model in this paper can accurately evaluate the risk of accounting resource sharing management and shorten the evaluation time.


## 3.1. Experimental Preparation

Accounting resource sharing management needs to be based on a good experimental environment. Therefore, VS2010, opencv 2.4.13, windows10 operating system and Intel (R) Xeon (R) cpu E5-2603v4 are used. The simulation comparison experiment is carried out under the environment of 20 GHz and 32 GB memory. The network parameters set are shown in Table3.Table 3
Network parameter setting.
Parameter nameNumerical valueMain processorIntel(R) Core(TM) i5-10400FInfrared parameters (M)20SNR (db)>50Network interface (M)10/100Protocol supportTCP/IPOperating temperature (°C)−10∼+55Operating humidity (%)0∼85/WEquipment power (V)10Power supply12Based on the above parameter settings, select the financial statement data of a company from 2019 to 2020, a total of 1000 sample datasets, and randomly divide the sample data into two categories: training set and test set, which are, respectively, used in the training and testing processes of accounting resource sharing management risk assessment model. The specific proportion of the number of samples of training set and test set is 400 : 600.


## 3.2. Analysis of Experimental Results

The simulation comparison experiment is carried out in the above experimental environment, and the sample data are processed by MATLAB software. Figure3 shows the risk assessment indicators of accounting resource sharing management, and the gray part refers to the high-risk assessment indicators.Figure 3
Risk assessment indicators of accounting resource sharing management.Human technology, technological applicability, and risk response system, as shown in Figure3, are regarded high-risk assessment indicators among the seven risk assessment indicators of resource sharing management.To compare the evaluation time of high-risk indicators of accounting resource sharing management, the financial risk early warning evaluation model of manufacturing enterprises based on logistic and the financial risk evaluation model of small and medium-sized enterprises based on FAHP method are used in this paper. Table4 shows the findings.Table 4
Comparison results of evaluation time (unit: s).
Number of iterationsLogisticFAHPThe model proposed in this paper2023.0030.2010.204026.0232.5010.006025.1233.609.158020.1035.618.2310020.4136.788.0012019.5837.228.4014023.5838.127.1216018.7739.146.4518017.4640.155.0020020.1542.856.14It can be seen from Table4 that the evaluation time range of the financial risk early warning evaluation model of manufacturing enterprises based on logistic is 17.46 s∼26.02 s. The financial risk assessment model of small and medium-sized enterprises based on the FAHP method takes 30.20 s∼42.85 s. The evaluation time range of the evaluation model in this paper is 5.00 s∼10.20 s. According to the comparison analysis, the assessment model in this research significantly decreases the evaluation time of high-risk indicators of accounting resource sharing management and has higher evaluation performance. The accuracy and error rate of accounting resource sharing management risk assessment of this evaluation model, manufacturing enterprise financial risk early warning evaluation model based on logistic, and small and medium-sized enterprise financial risk evaluation model based on the FAHP method are compared to verify the effectiveness of this method. Table 5 shows the findings.Table 5
Evaluation results of three models.
ModelNumber of test samplesNumber of miscalculationsEvaluation error rate (%)Logistic200948FAHP2001752The model proposed in this paper20012The number of miscalculations of accounting resource sharing management risk in the evaluation model is the highest among the three models, while the number of miscalculations of accounting resource sharing management risk in the evaluation model in this paper is 1, and the evaluation error rate is only 2%. In order to further verify the effectiveness of this method, the accuracy of accounting resource sharing management risk assessment of this assessment model, the financial risk early warning assessment model of manufacturing enterprises based on logistic, and the financial risk assessment model of small and medium-sized enterprises based on the FAHP method are compared and analyzed. The comparison results are shown in Figure4.Figure 4
Comparison results of risk assessment accuracy of three models.Figure4 shows that the accuracy of the accounting resource sharing management risk assessment model in this paper gradually increases with the increase of iteration times, up to 98%, whereas the accuracy of the manufacturing enterprise financial risk early warning assessment model based on logistic is unstable, up to 52%. The accuracy of accounting resource sharing management risk assessment in the financial risk assessment model for small and medium-sized firms based on the FAHP approach decreases as the number of iterations increases, which is the lowest among the three models. To sum up, the evaluation model in this paper can accurately evaluate the risk of accounting resource sharing management and shorten the evaluation time.


## 4. Conclusion

This paper proposes a risk assessment model of accounting resource sharing management based on genetic algorithm. Genetic algorithm is proposed according to the law of biological evolution in nature. It is a computational model of the biological evolution process that simulates Darwin’s biological evolution theory’s natural selection and genetic mechanism. It is a method to search the optimal solution by simulating the natural evolution process. Through the application of genetic algorithm, the optimal individual is obtained through selection, crossover, and mutation operations, and its corresponding value is the optimal solution of model parameters. The risk degree evaluation rules are formulated according to the optimal solution to determine the risk degree, in order to realize the risk evaluation of accounting resource sharing management based on genetic algorithm, reduce the model’s evaluation time, and improve the model’s evaluation accuracy.Because the traditional risk assessment model of accounting resource sharing management has problems with long assessment times and low assessment accuracy, this paper constructs a risk assessment model of accounting resource sharing management based on genetic algorithm, which greatly reduces assessment time and has a good risk assessment effect. It can provide a more effective guarantee for the security of accounting resource sharing management. There are certain discrepancies in the model’s experimental data due to the difference between the simulated environment and the real scenario. Further optimization study on the building model is required in order to acquire more precise experimental findings.



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