Multi-source data fusion enterprise finance and tax integrated risk management and control platform

Abstract

The invention relates to the technical field of enterprise finance and taxation risk management and control, and discloses an enterprise finance and taxation integrated risk management and control platform based on multi-source data fusion. The platform collects multi-source heterogeneous finance and taxation data in real time through a finance and taxation data collection module, and a data fusion preprocessing module generates a fusion data cube by using federal learning and a cross-domain data alignment algorithm. The risk feature modeling module constructs a multi-dimensional risk feature map based on a graph convolutional network and a dynamic Bayesian network, and the risk dynamic assessment module assesses risks in real time through an adaptive weighted ensemble learning algorithm and a risk conduction model. And the risk management and control decision module generates a management and control scheme by adopting a multi-objective optimization algorithm and a game theory strategy. In addition, the finance and tax data security storage module ensures data security. According to the platform, multi-source data fusion and efficient risk management and control are realized, risks can be accurately evaluated, scientific decisions are provided, enterprise finance and taxation data security is guaranteed, and enterprise finance and taxation management level is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise finance and tax risk management and control, and specifically to an enterprise finance and tax integrated risk management and control platform that integrates multi-source data. Background Art

[0002] In today's complex and ever-changing business environment, the financial and tax risks faced by enterprises are becoming increasingly diverse and complex. Traditional financial and tax management methods have gradually exposed many drawbacks and are unable to meet the needs of enterprises for risk management.

[0003] From the perspective of data acquisition, the sources of corporate financial and tax data are wide and scattered. Multi-source heterogeneous data such as financial statements, tax declaration records, supply chain transaction flows, and policy and regulatory update information are stored in different systems and platforms. The data formats between the internal financial system and the external supply chain system of the enterprise are very different, and there is a lack of effective integration mechanisms, which makes it difficult to centrally acquire and uniformly analyze data. This makes it difficult for enterprises to ensure the integrity and timeliness of data when conducting financial and tax risk assessments, and it is impossible to fully and accurately grasp the potential risks faced by enterprises.

[0004] In terms of data processing, traditional methods have difficulty coping with the complexity of multi-source heterogeneous data. Due to differences in data format, semantics, and structure, it is extremely difficult to conduct a comprehensive analysis of these data directly. For example, the financial statement account settings and accounting methods may differ in different regions and industries, and the data standards in tax declaration records are also different, which makes data fusion and preprocessing a major problem. At the same time, traditional data processing technology cannot efficiently eliminate and supplement redundant information and missing values ​​in massive data, which seriously affects the accuracy and reliability of data analysis.

[0005] From the perspective of risk assessment and control, existing risk assessment models are often based on a single data source or a simple combination of data, and are unable to fully tap into the inherent connections between financial and tax data. These models are unable to accurately capture the transmission path and potential impact of risks, resulting in deviations between risk assessment results and actual conditions. In risk management decisions, there is a lack of scientific multi-objective optimization methods and the application of game theory strategies. When formulating tax compliance recommendations, capital allocation plans, and risk hedging strategies, companies often only consider a single goal, ignoring the mutual influence and balance between the various goals, and are unable to achieve the overall optimal risk management effect.

[0006] In addition, with the acceleration of digital transformation of enterprises, the security of financial and tax data has become increasingly prominent. Corporate financial and tax data contains a large amount of sensitive information, such as financial status, tax planning schemes, etc. Once leaked or tampered with, it will cause huge losses to the company. However, traditional financial and tax management systems have deficiencies in data security storage and access control, making it difficult to effectively ensure the security and integrity of data. Summary of the invention

[0007] The purpose of the present invention is to provide an enterprise finance and taxation integrated risk management and control platform that integrates multi-source data to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above-mentioned purpose, the present invention provides the following technical solution: an enterprise finance and taxation integrated risk management and control platform integrating multi-source data, the platform comprising: The financial and taxation data collection module collects multi-source heterogeneous financial and taxation data from inside and outside the enterprise in real time; The data fusion preprocessing module uses federated learning combined with a cross-domain data alignment algorithm to perform feature cleaning, format standardization, and relationship matching on multi-source data to generate a fused data cube. The risk feature modeling module, based on graph convolutional networks and dynamic Bayesian networks, extracts the association features of financial and tax entities and risk propagation paths from the fusion data cube to construct a multi-dimensional risk feature map; The risk dynamic assessment module uses an adaptive weighted ensemble learning algorithm combined with a pre-built risk transmission model to conduct real-time risk assessment on multi-dimensional risk feature maps and generate risk probability distribution and grade classification results; The risk management and decision-making module adopts multi-objective optimization algorithms and game theory strategies to dynamically generate tax compliance recommendations, capital allocation plans and risk hedging strategies based on risk assessment results.

[0009] Preferably, the multi-source heterogeneous financial and taxation data includes financial statements, tax declaration records, supply chain transaction flow and policy and regulation update information; the operation process of the financial and taxation data acquisition module includes: Obtain structured data of financial statements and time series data of supply chain transaction flows through API interfaces and blockchain nodes, collect verification codes of tax declaration records and semantic labels of policies and regulations; Map structured data to accounts according to accounting standards and generate standard accounting account codes; Perform timestamp alignment and missing value interpolation on time series data, and use wavelet transform to extract periodic transaction features; If the conflict rate of standard accounting subject codes or the noise ratio of periodic transaction characteristics exceeds the preset collection tolerance, the data source verification process is triggered.

[0010] Preferably, the specific process of the data fusion preprocessing module includes: The distributed stored tax data is trained locally through the federated learning framework, and the field similarity matrix is ​​calculated using the cross-domain data alignment algorithm to complete the semantic alignment of heterogeneous data. Multiple interpolation of missing fields was performed, and redundant features were eliminated based on principal component analysis to generate a preliminary fused data set; The association rules between financial and tax entities are established through knowledge graph technology, and the preliminary fused data set is mapped into a weighted fused data cube.

[0011] Preferably, the modeling process of the risk feature modeling module includes: The fused data cube is input into the graph convolutional network to extract the topological correlation characteristics and capital flow intensity between financial and tax entities; Model the risk transmission path based on dynamic Bayesian network and calculate the conditional probability of risk event triggering; The topological correlation features and conditional probabilities are tensor-joined to generate a multi-dimensional risk feature map; Divide the graph into communities, mark high-risk communities and extract their core node attributes.

[0012] Preferably, the assessment process of the risk dynamic assessment module includes: Adopting adaptive weighted ensemble learning algorithm, integrating the output results of logistic regression, random forest and gradient boosting tree models, to generate the initial value of risk probability; The risk transmission model is used to simulate the diffusion path of risks between entities, and the Monte Carlo method is used to calculate the risk level distribution; If the deviation between the initial value of the risk probability and the conduction simulation result exceeds the preset assessment threshold, the model weight redistribution process is triggered.

[0013] Preferably, the specific implementation of the risk transmission model includes: Construct a financial and tax entity relationship network, where nodes represent enterprises or transaction entities, and edge weights represent the scale of capital flow; Improve the risk propagation dynamics equation based on the SEIR infectious disease model and define the risk infection rate, recovery rate and immune attenuation coefficient; Based on the relationship network of financial and tax entities, the equations are solved by numerical differentiation methods to predict the spread and impact intensity of risks within a specified time window.

[0014] Preferably, the multi-objective optimization algorithm of the risk management and control decision module includes: The decision variables are defined as the tax compliance adjustment range, capital allocation ratio and risk hedging cost, and the objective function is risk reduction rate, compliance cost minimization and capital utilization efficiency maximization; The NSGA-II algorithm is used to solve the Pareto optimal solution set, and the equilibrium solution is screened through game theory strategies; If the conflict rate of each objective in the solution exceeds the preset decision threshold, the fuzzy comprehensive evaluation mechanism is introduced to re-rank them.

[0015] Preferably, the specific implementation of the game theory strategy includes: Construct a three-party game model among tax authorities, enterprises and financial institutions, and define the strategy space as audit intensity, compliance investment and risk-taking ratio; Calculate the Nash equilibrium point and distribute the benefits of multi-party collaboration based on the Shapley value; If the equilibrium point is not unique, the particle swarm algorithm is used to iteratively optimize the strategy combination until convergence.

[0016] Preferably, the platform further comprises: The financial and tax data security storage module is used to encrypt the fused data cube and risk feature map in slices, and manage data permissions using attribute-based access control strategies.

[0017] Preferably, the specific implementation of the financial and tax data security storage module includes: The data is sharded and stored in distributed nodes, with each shard attached with a hash-based timestamp and digital signature.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The enterprise finance and taxation integrated risk management and control platform with multi-source data fusion proposed in the present invention has significant beneficial effects in many aspects.

[0019] In terms of data collection and integration, the financial and taxation data collection module can collect multi-source heterogeneous financial and taxation data from inside and outside the enterprise in real time, covering financial statements, tax declaration records, supply chain transaction flows, and policy and regulatory update information. Data is obtained through API interfaces and blockchain nodes to ensure the authenticity and timeliness of data, and account mapping is performed according to accounting standards, and time series data is processed by timestamp alignment and missing value interpolation. The data fusion preprocessing module uses federated learning combined with a cross-domain data alignment algorithm to complete the semantic alignment of heterogeneous data, eliminate redundant features, and generate a fusion data cube, achieving efficient data integration and cleaning, and providing a high-quality data foundation for subsequent analysis. This process enables enterprises to fully and accurately grasp various types of financial and taxation data, avoid risk misjudgment due to missing or incorrect data, and greatly improve the availability and reliability of data.

[0020] At the risk feature modeling and assessment level, the risk feature modeling module is based on graph convolutional networks and dynamic Bayesian networks. It extracts the correlation characteristics of financial and tax entities and risk propagation paths from the fused data cube, constructs a multi-dimensional risk feature map, and can deeply explore the complex correlation relationships between data and accurately locate potential risk points. The risk dynamic assessment module uses an adaptive weighted integrated learning algorithm combined with a risk transmission model to assess risks in real time and generate risk probability distribution and grade classification results. If the assessment results are biased, the model weight redistribution process can also be triggered to ensure the accuracy of the assessment. This advanced modeling and assessment method can timely and accurately discover the financial and tax risks faced by enterprises, provide strong support for enterprises to take preventive measures in advance, and effectively reduce the probability of risk occurrence and possible losses.

[0021] In terms of risk management and control decisions, the risk management and control decision module adopts multi-objective optimization algorithms and game theory strategies to dynamically generate tax compliance recommendations, fund allocation plans and risk hedging strategies based on risk assessment results. By defining reasonable decision variables and objective functions, the NSGA-II algorithm is used to solve the Pareto optimal solution set, and the game theory is combined to screen the equilibrium solution to achieve a balance between multiple objectives. If the target conflict rate in the solution set is too high, a fuzzy comprehensive evaluation mechanism is introduced to re-rank them, providing enterprises with a scientific and reasonable decision-making basis. This enables enterprises to comprehensively consider multiple factors when facing financial and tax risks and formulate the best response strategy, which not only reduces risks, but also takes into account compliance costs and capital utilization efficiency, and enhances the overall competitiveness of enterprises.

[0022] In terms of data security, the financial and tax data security storage module encrypts the fused data cube and risk feature map in slices, uses attribute-based access control strategies to manage data permissions, and stores data slices in distributed nodes with hash timestamps and digital signatures. This measure effectively prevents data leakage and tampering, ensures the security and integrity of corporate financial and tax data, and allows companies to have no worries about data security during digital operations, providing solid guarantees for the stable development of companies. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a working principle diagram of the enterprise finance and taxation integrated risk management and control platform with multi-source data fusion according to the present invention; Figure 2 This is the working principle diagram of the financial and taxation data collection module; Figure 3 A diagram showing the working principle of the risk signature modeling module; Figure 4 This is a working principle diagram of the risk dynamic assessment module. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] See also Figure 1-Figure 4 The present invention relates to an enterprise finance and taxation integrated risk management and control platform that integrates multi-source data, aiming to achieve comprehensive management and control of enterprise finance and taxation risks by integrating multi-source heterogeneous finance and taxation data inside and outside the enterprise. The specific implementation scheme is as follows: Financial and tax data collection module: This module is responsible for real-time collection of multi-source heterogeneous financial and tax data inside and outside the enterprise, providing a data basis for subsequent analysis. These data come from a wide range of sources, covering financial statements, tax declaration records, supply chain transaction flows, and policy and regulatory update information. During the collection process, the structured data of financial statements and the time series data of supply chain transaction flows are obtained through the API interface and blockchain nodes, and the verification code of the tax declaration record and the semantic labels of policies and regulations are collected at the same time. After the structured data is collected, the account mapping will be carried out according to the accounting standards to generate standard accounting account codes; for time series data, timestamp alignment and missing value interpolation will be performed, and wavelet transform will be used to extract periodic transaction features. If the conflict rate of the standard accounting account code or the noise ratio of the periodic transaction feature exceeds the preset collection tolerance, the system will trigger the data source verification process to ensure the accuracy and reliability of the data.

[0026] Data fusion preprocessing module: Federated learning combined with cross-domain data alignment algorithm is used to process the collected multi-source data. First, the local model training of distributed stored fiscal and tax data is carried out through the federated learning framework, and the field similarity matrix is ​​calculated using the cross-domain data alignment algorithm to complete the semantic alignment of heterogeneous data. Then, multiple interpolation is performed on the missing fields, and redundant features are eliminated based on principal component analysis to generate a preliminary fused data set. Finally, the association rules between fiscal and tax entities are established with the help of knowledge graph technology, and the preliminary fused data set is mapped into a weighted fused data cube to facilitate subsequent analysis and processing.

[0027] Risk feature modeling module: Based on graph convolutional networks and dynamic Bayesian networks, the association features of fiscal and tax entities and risk propagation paths are extracted from the fused data cube. The fused data cube is input into the graph convolutional network to extract the topological association features and capital flow intensity between fiscal and tax entities; the dynamic Bayesian network is used to model the risk transmission path and calculate the conditional probability of risk event triggering; the topological association features and conditional probabilities are then tensor-joined to generate a multi-dimensional risk feature map. The map is divided into communities, high-risk communities are marked, and their core node attributes are extracted to provide strong support for risk assessment and control.

[0028] Dynamic risk assessment module: Through the adaptive weighted ensemble learning algorithm combined with the pre-built risk conduction model, real-time risk assessment is performed on the multi-dimensional risk feature map. Adopting the adaptive weighted ensemble learning algorithm, the output results of the logistic regression, random forest and gradient boosting tree models are integrated to generate the initial value of risk probability; the diffusion path of risk between entities is simulated through the risk conduction model, and the risk level distribution is calculated in combination with the Monte Carlo method. If the deviation between the initial value of the risk probability and the conduction simulation result exceeds the preset assessment threshold, the model weight redistribution process is triggered to ensure the accuracy and timeliness of the assessment results.

[0029] Risk management decision module: adopts multi-objective optimization algorithm and game theory strategy to dynamically generate tax compliance suggestions, fund allocation plan and risk hedging strategy according to risk assessment results. Define the decision variables as tax compliance adjustment range, fund allocation ratio and risk hedging cost, and the objective function is risk reduction rate, compliance cost minimization and fund utilization efficiency maximization; use NSGA-II algorithm to solve Pareto optimal solution set, and select equilibrium solution through game theory strategy; if the conflict rate of each target in the solution set exceeds the preset decision threshold, introduce fuzzy comprehensive evaluation mechanism to re-rank, and provide scientific and reasonable decision-making basis for enterprises.

[0030] The implementation of the present invention is further described below in conjunction with Examples 1 to 6.

[0031] Embodiment 1: In this embodiment, the collection process of multi-source heterogeneous financial and tax data is described in detail. Multi-source heterogeneous financial and tax data covers financial statements, tax declaration records, supply chain transaction flows, and policy and regulatory update information. When collecting financial statement structured data and supply chain transaction flow time series data, use the API interface and blockchain nodes. The API interface can be connected to the company's internal financial system, supply chain management system, etc. to obtain data stably; blockchain nodes can ensure the security and non-tamperability of the data. For tax declaration records, collect their verification codes. The verification codes are used to verify the integrity and accuracy of the declared data and prevent the data from being tampered with during transmission or storage. Collect semantic tags for policy and regulatory update information. Semantic tags can accurately mark the key content of the regulations, which is convenient for subsequent data association and analysis based on semantic understanding.

[0032] After collecting structured data, account mapping is performed according to accounting standards. Accounting standards provide unified specifications for data processing. For example, the customized accounting accounts in the enterprise financial system are mapped to standard accounting account codes according to accounting standards. If the enterprise records "office equipment purchase expenses" as a special account, it can be mapped to the standard code of "management expenses-office expenses".

[0033] For time series data, timestamp alignment is first performed. Since there may be differences in the recording time of supply chain transaction flow data, timestamp alignment can ensure that data from different sources are comparable in the time dimension. Missing value interpolation uses methods such as mean interpolation and linear interpolation, and the appropriate method is selected according to the characteristics of the data. After that, wavelet transform is used to extract periodic transaction features. The wavelet transform formula is: in, is the wavelet transform result, is the original time series data, is the scale parameter, which determines the expansion and contraction of the wavelet function. is the translation parameter, which controls the translation of the wavelet function. It is the conjugate of the wavelet basis function. Through this transformation, periodic features such as monthly fixed purchases and quarterly settlements can be extracted.

[0034] If the conflict rate of the standard accounting subject code or the noise ratio of the periodic transaction characteristics exceeds the preset collection tolerance, the preset collection tolerance is set according to the actual data quality requirements of the enterprise. For example, if the conflict rate exceeds 5% and the noise ratio exceeds 10%, the data source verification process is triggered. Recheck the data source and check whether there are any problems in the data interface and data entry process to ensure the quality of data collection.

[0035] Embodiment 2: This embodiment focuses on the specific process of the data fusion preprocessing module. In the data fusion preprocessing process, the federated learning framework plays a key role. In the actual enterprise scenario, financial and tax data may be distributed and stored in different departments or partners. Through the federated learning framework, each participant performs local model training on local data and does not directly share the original data to ensure data privacy and security. For example, the financial department and tax department of an enterprise each hold part of the financial and tax data, and each uses the federated learning algorithm to train models locally, such as logistic regression models to predict financial risks or tax risks.

[0036] The cross-domain data alignment algorithm calculates the field similarity matrix to complete the semantic alignment of heterogeneous data. For fields from different data sources, such as the "income" field in financial statements and the "taxable income" field in tax declaration records, their semantic correspondence is determined by calculating their similarity. Similarity calculation can be done using methods such as edit distance and cosine similarity. Edit distance calculates the minimum number of operations between two strings through insertion, deletion, and replacement of characters. The smaller the number of operations, the higher the similarity.

[0037] Perform multiple imputations on missing fields, such as using the multiple imputation method (MICE). The MICE algorithm is based on chain equations and uses other relevant variables to impute missing values ​​multiple times to generate multiple complete data sets. The results of these data sets are combined to reduce the bias caused by a single imputation method.

[0038] Redundant features are eliminated based on principal component analysis. The principal component analysis formula is: ,in is the transformed data matrix, is the eigenvector matrix, is the original data matrix. Through this analysis, multiple related features are converted into a few unrelated principal components, redundant information is removed, and data processing efficiency is improved. For example, multiple cost-related features in corporate financial data may be highly correlated. After principal component analysis, the main cost components can be extracted to simplify the data structure.

[0039] Use knowledge graph technology to establish association rules between financial and tax entities. For example, based on the company's financial data and tax data, determine the transaction relationship between the company and its suppliers and customers, as well as the company's associated information in tax declarations. Map the initial fusion data set into a weighted fusion data cube, and the weight can be determined based on factors such as the importance and credibility of the data. For example, the weight of the transaction data of the company's core business can be set higher, which has a greater impact on risk assessment.

[0040] Embodiment 3: This embodiment introduces the modeling process of the risk feature modeling module in depth. The fused data cube is input into the graph convolutional network. The graph convolutional network (GCN) extracts the topological association characteristics and capital flow intensity between financial and tax entities by performing convolution operations on the graph structure data. In the graph structure, nodes represent financial and tax entities, such as enterprises, suppliers, customers, etc., and edges represent the relationships between them, such as transaction relationships and capital flow relationships. The formula of GCN is: in It is The node feature matrix of the layer, It is The weight matrix of the layer, is the adjacency matrix with self-loops added, is the original adjacency matrix, is the identity matrix, yes The degree matrix of is the activation function. Through this formula, GCN can aggregate the features of neighbor nodes, learn the topological association features between entities, and extract the intensity of capital flow from the edge weight information of capital transactions.

[0041] Modeling risk transmission paths based on dynamic Bayesian networks. Dynamic Bayesian networks (DBNs) can describe system states and causal relationships that change over time. In fiscal and tax risk modeling, the nodes of DBN represent different risk events or fiscal and tax states, and the edges represent the transmission relationships of risks. By calculating the conditional probability of risk events triggering, determine how risks propagate between different entities and events. For example, if a company's supplier has financial problems (risk event A), based on historical data and business logic, calculate the conditional probability that the event will lead to a disruption in the company's raw material supply (risk event B). .

[0042] The topological correlation features and conditional probabilities are tensor spliced ​​to generate a multi-dimensional risk feature map. Tensor splicing can integrate information from different dimensions to form a map that comprehensively reflects the characteristics of financial and tax risks. The nodes and edges in the map carry rich information and intuitively display the correlation of corporate financial and tax risks.

[0043] The Louvain algorithm can be used to divide the graph into communities. The Louvain algorithm divides the graph into different communities by optimizing the modularity function. The modularity function formula is: in is the total number of edges in the graph, is the adjacency matrix element, and The nodes are and The degree, and Is a node and The community you belong to, is the Kronecker function, when When the risk is high, it is 1, otherwise it is 0. After the division, the high-risk communities are marked and their core node attributes are extracted. The core node attributes, such as enterprise scale and financial indicators, provide a basis for key attention and risk prevention and control.

[0044] Embodiment 4: In this embodiment, the evaluation process of the risk dynamic evaluation module and the specific implementation of the risk transmission model are described in detail. In the risk dynamic evaluation module, an adaptive weighted ensemble learning algorithm is used to integrate the output results of the logistic regression, random forest and gradient boosting tree models to generate the initial value of the risk probability. The logistic regression model fits the historical data to obtain the probability prediction formula of the risk occurrence: in is a binary variable for risk occurrence (1 for occurrence, 0 for non-occurrence), is the input feature vector, is a model parameter. The random forest model constructs multiple decision trees and integrates the prediction results of the decision trees to conduct risk assessment; the gradient boosting tree model gradually reduces the prediction error and improves the accuracy of risk prediction through iterative training. The adaptive weighted ensemble learning algorithm dynamically adjusts the weight according to the performance of each model on different data subsets. The formula is: in is the final prediction result, It is The prediction results of the model are It is The weight of the model, is the number of models, Dynamically adjust based on model performance.

[0045] The risk transmission model is used to simulate the diffusion path of risks between entities, and the Monte Carlo method is used to calculate the risk level distribution. The risk transmission model constructs a financial and tax entity relationship network, where nodes represent enterprises or transaction entities, and edge weights represent the scale of capital flow. Based on the SEIR infectious disease model, the risk transmission dynamics equation is improved to define the risk infection rate. , which represents the probability that an entity with infection risk infects other susceptible entities within a unit time; recovery rate , which indicates the probability of an entity with infection risk returning to normal within a unit time; the immune decay coefficient , which indicates the degree of attenuation of the entity's immune ability over time. The improved risk propagation dynamics equation is: in represents the number of susceptible entities, Indicates the number of latent infected entities, Indicates the number of infected entities, Indicates the number of restored immune entities, is the total number of entities, It is the rate at which latent infected entities are transformed into infected entities. Based on the relationship network of fiscal and tax entities, numerical differential methods such as the Euler method are used to solve equations to predict the spread and impact intensity of risks within a specified time window. The Monte Carlo method randomly simulates the risk propagation process multiple times, counts the frequency of occurrence of different risk levels, and obtains the risk level distribution.

[0046] If the deviation between the initial value of the risk probability and the result of the conduction simulation exceeds the preset assessment threshold, which is set according to the enterprise's requirements for the accuracy of risk assessment. If the deviation exceeds 10%, the model weight redistribution process is triggered. The weights of each model are readjusted to improve the accuracy of risk assessment.

[0047] Embodiment 5: This embodiment elaborates on the specific implementation of the multi-objective optimization algorithm and game theory strategy of the risk management and control decision module. In the multi-objective optimization algorithm of the risk management and control decision module, the decision variables are defined as the tax compliance adjustment range, the capital allocation ratio and the risk hedging cost. The tax compliance adjustment range affects the tax compliance of the enterprise, the capital allocation ratio determines the efficiency of the use of the enterprise's funds, and the risk hedging cost is related to the investment of the enterprise in taking risk hedging measures. The objective functions are risk reduction rate, compliance cost minimization and capital utilization efficiency maximization. The risk reduction rate reflects the effect of risk management measures on reducing the financial and tax risks of the enterprise; minimizing compliance costs aims to reduce the investment of the enterprise to meet tax compliance requirements; maximizing capital utilization efficiency ensures that the enterprise's funds are reasonably allocated.

[0048] The NSGA-II algorithm is used to solve the Pareto optimal solution set. The NSGA-II algorithm finds a set of optimal solutions among multiple objectives through fast non-dominated sorting and congestion calculation. If the conflict rate of each objective in the solution set exceeds the preset decision threshold, the preset decision threshold is set according to the actual decision-making needs of the enterprise. For example, when the conflict rate exceeds 30%, a fuzzy comprehensive evaluation mechanism is introduced to re-sort. The fuzzy comprehensive evaluation mechanism establishes a fuzzy relationship matrix and combines the weights of each objective to comprehensively evaluate and sort the solutions in the solution set.

[0049] The specific implementation of the game theory strategy includes building a three-party game model among tax authorities, enterprises and financial institutions. The strategy space is defined as audit intensity, compliance investment and risk-bearing ratio. Audit intensity reflects the strictness of tax inspections by tax authorities on enterprises; compliance investment is the investment made by enterprises to comply with tax laws and regulations; and risk-bearing ratio determines the proportion of losses borne by enterprises and financial institutions when facing financial and tax risks. Calculate the Nash equilibrium point, which is a strategy combination under which none of the parties will change their strategies individually to obtain better benefits. Combine the Shapley value to distribute the benefits of multi-party collaboration. The Shapley value considers the marginal contribution of each participant to the alliance and distributes the benefits fairly. If the equilibrium point is not unique, the particle swarm algorithm is used to iteratively optimize the strategy combination until convergence. The particle swarm algorithm simulates the foraging behavior of bird flocks and finds the optimal strategy combination by updating the position and velocity of particles.

[0050] Embodiment 6: This embodiment focuses on the specific implementation of the financial and tax data security storage module. The financial and tax data security storage module is used to encrypt the fused data cube and risk feature map in slices, and manage data permissions using attribute-based access control strategies. The data slices are stored in distributed nodes, and each slice is attached with a hash-based timestamp and digital signature. The hash function can be selected from SHA-256, etc. The hash formula is: in is the hash value, It is the data to be hashed. The hashed timestamp can record the storage time of the data shard, ensuring the timeliness and traceability of the data. The digital signature uses asymmetric encryption technology, such as the RSA algorithm. The sender uses the private key to sign the data shard, and the receiver uses the sender's public key to verify it, ensuring the integrity and authenticity of the data.

[0051] Attribute-based access control policies determine the access rights to data based on the user's attributes, such as position, department, business role, etc. For example, senior managers in the corporate finance department can access detailed financial and tax data, while ordinary employees can only access partial summary data. In this way, the security of corporate financial and tax data is effectively guaranteed to prevent data leakage and illegal access.

[0052] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0053] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An enterprise finance and taxation integrated risk management and control platform with multi-source data fusion, characterized by: include: The financial and taxation data collection module collects multi-source heterogeneous financial and taxation data from inside and outside the enterprise in real time; The data fusion preprocessing module uses federated learning combined with a cross-domain data alignment algorithm to perform feature cleaning, format standardization, and relationship matching on multi-source data to generate a fused data cube. The risk feature modeling module, based on graph convolutional networks and dynamic Bayesian networks, extracts the association features of financial and tax entities and risk propagation paths from the fusion data cube to construct a multi-dimensional risk feature map; The risk dynamic assessment module uses an adaptive weighted ensemble learning algorithm combined with a pre-built risk transmission model to conduct real-time risk assessment on multi-dimensional risk feature maps and generate risk probability distribution and grade classification results; The risk management and decision-making module adopts multi-objective optimization algorithms and game theory strategies to dynamically generate tax compliance recommendations, capital allocation plans and risk hedging strategies based on risk assessment results.

2. The enterprise finance and taxation integrated risk management and control platform according to claim 1 is characterized in that: The multi-source heterogeneous financial and tax data include financial statements, tax declaration records, supply chain transaction flows, and policy and regulatory update information; The operation process of the financial and tax data collection module includes: Obtain structured data of financial statements and time series data of supply chain transaction flows through API interfaces and blockchain nodes, collect verification codes of tax declaration records and semantic labels of policies and regulations; Map structured data to accounts according to accounting standards and generate standard accounting account codes; Perform timestamp alignment and missing value interpolation on time series data, and use wavelet transform to extract periodic transaction features; If the conflict rate of standard accounting subject codes or the noise ratio of periodic transaction characteristics exceeds the preset collection tolerance, the data source verification process is triggered.

3. The enterprise finance and taxation integrated risk management and control platform according to claim 1 is characterized in that: The specific process of the data fusion preprocessing module includes: The distributed stored tax data is trained locally through the federated learning framework, and the field similarity matrix is ​​calculated using the cross-domain data alignment algorithm to complete the semantic alignment of heterogeneous data. Multiple interpolation of missing fields was performed, and redundant features were eliminated based on principal component analysis to generate a preliminary fused data set; The association rules between financial and tax entities are established through knowledge graph technology, and the preliminary fused data set is mapped into a weighted fused data cube.

4. The enterprise finance and taxation integrated risk management and control platform according to claim 1 is characterized in that: The modeling process of the risk feature modeling module includes: The fused data cube is input into the graph convolutional network to extract the topological correlation characteristics and capital flow intensity between financial and tax entities; Model the risk transmission path based on dynamic Bayesian network and calculate the conditional probability of risk event triggering; The topological correlation features and conditional probabilities are tensor-joined to generate a multi-dimensional risk feature map; Divide the graph into communities, mark high-risk communities and extract their core node attributes.

5. The enterprise finance and taxation integrated risk management and control platform according to claim 1 is characterized in that: The evaluation process of the risk dynamic evaluation module includes: Adopting adaptive weighted ensemble learning algorithm, integrating the output results of logistic regression, random forest and gradient boosting tree models, to generate the initial value of risk probability; The risk transmission model is used to simulate the diffusion path of risks between entities, and the Monte Carlo method is used to calculate the risk level distribution; If the deviation between the initial value of the risk probability and the conduction simulation result exceeds the preset assessment threshold, the model weight redistribution process is triggered.

6. The enterprise finance and taxation integrated risk management and control platform according to claim 5 is characterized in that: The specific implementation of the risk transmission model includes: Construct a financial and tax entity relationship network, where nodes represent enterprises or transaction entities, and edge weights represent the scale of capital flow; Improve the risk propagation dynamics equation based on the SEIR infectious disease model and define the risk infection rate, recovery rate and immune attenuation coefficient; Based on the relationship network of financial and tax entities, the equations are solved by numerical differentiation methods to predict the spread and impact intensity of risks within a specified time window.

7. The enterprise finance and taxation integrated risk management and control platform according to claim 1 is characterized in that: The multi-objective optimization algorithm of the risk management and control decision module includes: The decision variables are defined as the tax compliance adjustment range, capital allocation ratio and risk hedging cost, and the objective function is risk reduction rate, compliance cost minimization and capital utilization efficiency maximization; The NSGA-II algorithm is used to solve the Pareto optimal solution set, and the equilibrium solution is screened through game theory strategies; If the conflict rate of each objective in the solution exceeds the preset decision threshold, the fuzzy comprehensive evaluation mechanism is introduced to re-rank them.

8. The enterprise finance and taxation integrated risk management and control platform according to claim 7 is characterized in that: The specific implementation of the game theory strategy includes: Construct a three-party game model among tax authorities, enterprises and financial institutions, and define the strategy space as audit intensity, compliance investment and risk-taking ratio; Calculate the Nash equilibrium point and distribute the benefits of multi-party collaboration based on the Shapley value; If the equilibrium point is not unique, the particle swarm algorithm is used to iteratively optimize the strategy combination until convergence.

9. The enterprise finance and taxation integrated risk management and control platform according to claim 1 is characterized in that: Also includes: The financial and tax data security storage module is used to encrypt the fused data cube and risk feature map in slices, and manage data permissions using attribute-based access control strategies.

10. The enterprise finance and taxation integrated risk management and control platform according to claim 9 is characterized in that: The specific implementation of the financial and tax data security storage module includes: The data is sharded and stored in distributed nodes, with each shard attached with a hash-based timestamp and digital signature.

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