An integrated enterprise finance and tax risk management platform integrating multi-source data

Abstract

The present invention relates to the technical field of enterprise finance and tax risk management and control, and discloses an enterprise finance and tax integrated risk management and control platform with multi-source data fusion. The platform collects multi-source heterogeneous finance and tax data in real time through the finance and tax data collection module, and the data fusion preprocessing module uses federated learning and cross-domain data alignment algorithms to generate fused data cubes. The risk feature modeling module constructs a multi-dimensional risk feature map based on graph convolutional networks and dynamic Bayesian networks, and the risk dynamic assessment module assesses risks in real time through adaptive weighted ensemble learning algorithms and risk conduction models. The risk management and control decision module uses multi-objective optimization algorithms and game theory strategies to generate management and control plans. In addition, the finance and tax data security storage module ensures data security. The platform realizes multi-source data fusion and efficient risk management, can accurately assess risks, provide scientific decisions, ensure the security of enterprise finance and tax data, and improve the level of enterprise finance and tax management.

Description

Technical Field

[0001] This invention relates to the field of enterprise financial and tax risk management technology, specifically to an integrated enterprise financial and tax risk management platform that integrates multi-source data. Background Technology

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

[0003] From a data acquisition perspective, enterprises' financial and tax data come from a wide range of sources and are scattered. Financial statements, tax filing records, supply chain transaction logs, and policy and regulatory updates are among the many heterogeneous data sources stored in different systems and platforms. Significant differences in data formats exist between internal financial systems and external supply chain systems, and the lack of effective integration mechanisms makes centralized data acquisition and unified analysis difficult. This makes it challenging for enterprises to guarantee the completeness and timeliness of data when conducting financial and tax risk assessments, hindering a comprehensive and accurate understanding of the potential risks they face.

[0004] In data processing, traditional methods struggle to handle the complexity of multi-source, heterogeneous data. Due to differences in data format, semantics, and structure, direct comprehensive analysis of this data is extremely difficult. For example, financial statement account settings and accounting methods may differ across regions and industries, and data standards in tax returns also vary, making data fusion and preprocessing a major challenge. Furthermore, traditional data processing techniques are inefficient at removing and supplementing redundant information and missing values ​​in massive datasets, severely impacting the accuracy and reliability of data analysis.

[0005] From the perspective of risk assessment and management, existing risk assessment models are often based on a single data source or a simple combination of data, failing to fully explore the inherent relationships between financial and tax data. These models struggle to accurately capture the propagation path and potential impact of risks, leading to discrepancies between risk assessment results and actual situations. In risk management decision-making, the lack of scientific multi-objective optimization methods and the application of game theory strategies means that when formulating tax compliance recommendations, fund allocation plans, and risk hedging strategies, companies often consider only a single objective, neglecting the interrelationships and balances between objectives, thus failing to achieve optimal overall risk management results.

[0006] Furthermore, with the acceleration of enterprise digital transformation, the security of financial and tax data has become increasingly prominent. Enterprise financial and tax data contains a large amount of sensitive information, such as financial status and tax planning schemes. Once leaked or tampered with, it will cause huge losses to the enterprise. However, traditional financial and tax management systems are inadequate in terms of secure data storage and access control, making it difficult to effectively guarantee the security and integrity of the data. Summary of the Invention

[0007] The purpose of this invention is to provide an integrated enterprise financial and tax risk management platform that integrates multi-source data to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a multi-source data fusion enterprise financial and tax integrated risk management platform, the platform comprising:

[0009] The financial and tax data acquisition module collects multi-source heterogeneous financial and tax data from both inside and outside the enterprise in real time.

[0010] The data fusion preprocessing module uses federated learning combined with cross-domain data alignment algorithms to perform feature cleaning, format standardization and correlation matching on multi-source data, generating a fused data cube.

[0011] The risk feature modeling module, based on graph convolutional networks and dynamic Bayesian networks, extracts the financial and tax entity association features and risk propagation paths from the fused data cube to construct a multi-dimensional risk feature map.

[0012] The risk dynamic assessment module uses an adaptive weighted ensemble learning algorithm combined with a pre-built risk transmission model to perform real-time risk assessment on a multi-dimensional risk feature map, generating risk probability distribution and level classification results.

[0013] The risk management decision-making module employs 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.

[0014] Preferably, the multi-source heterogeneous financial and tax data includes financial statements, tax declaration records, supply chain transaction records, and policy and regulatory updates; the operation process of the financial and tax data acquisition module includes:

[0015] By using API interfaces to connect with blockchain nodes, we can obtain structured data from financial statements and time-series data from supply chain transactions, as well as collect verification codes from tax declaration records and semantic tags from policies and regulations.

[0016] Structured data is mapped to accounts according to accounting standards to generate standard accounting account codes;

[0017] The time series data is timestamped and missing values ​​are imputed. Wavelet transform is used to extract periodic transaction features.

[0018] 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.

[0019] Preferably, the specific process of the data fusion preprocessing module includes:

[0020] The federated learning framework is used to train local models on distributed tax data, and a cross-domain data alignment algorithm is used to calculate the field similarity matrix to complete the semantic alignment of heterogeneous data.

[0021] The missing fields are imputed multiple times, and redundant features are removed based on principal component analysis to generate a preliminary fused dataset.

[0022] By establishing association rules between financial and tax entities through knowledge graph technology, the preliminary fused dataset is mapped into a weighted fused data cube.

[0023] Preferably, the modeling process of the risk feature modeling module includes:

[0024] By inputting the fused data cube into the graph convolutional network, the topological relationship features and capital flow intensity between financial and tax entities are extracted;

[0025] The risk transmission path is modeled based on dynamic Bayesian network, and the conditional probability of risk event triggering is calculated.

[0026] Tensor concatenation of topological correlation features and conditional probabilities is used to generate a multidimensional risk feature map.

[0027] The graph is divided into communities, high-risk communities are marked, and their core node attributes are extracted.

[0028] Preferably, the assessment process of the dynamic risk assessment module includes:

[0029] An adaptive weighted ensemble learning algorithm is used to combine the outputs of logistic regression, random forest, and gradient boosting tree models to generate initial risk probability values.

[0030] The risk transmission model is used to simulate the diffusion path of risk between entities, and the risk level distribution is calculated by combining the Monte Carlo method.

[0031] If the deviation between the initial risk probability and the transmission simulation result exceeds the preset evaluation threshold, the model weight reassignment process will be triggered.

[0032] Preferably, the specific implementation of the risk transmission model includes:

[0033] Construct a network of financial and tax entity relationships, where nodes represent enterprises or transaction entities and edge weights represent the scale of capital flows;

[0034] The risk transmission dynamics equation is improved based on the SEIR infectious disease model, and the risk infection rate, recovery rate and immune attenuation coefficient are defined.

[0035] Based on the financial and tax entity relationship network, the equations are solved using numerical differentiation methods to predict the spread and intensity of risk within a specified time window.

[0036] Preferably, the multi-objective optimization algorithm of the risk management decision module includes:

[0037] The decision variables are defined as the tax compliance adjustment range, the fund allocation ratio, and the risk hedging cost. The objective function is the risk reduction rate, the minimization of compliance costs, and the maximization of fund utilization efficiency.

[0038] The NSGA-II algorithm is used to solve for the Pareto optimal solution set, and the equilibrium solution is selected through game theory strategy.

[0039] If the conflict rate of each objective in the solution set exceeds the preset decision threshold, a fuzzy comprehensive evaluation mechanism is introduced to reorder them.

[0040] Preferably, the specific implementation of the game theory strategy includes:

[0041] Construct a tripartite game model among tax authorities, enterprises, and financial institutions, defining the strategy space as audit intensity, compliance investment, and risk-bearing ratio;

[0042] Calculate the Nash equilibrium point and allocate the multi-party collaborative benefits by combining the Shapley value;

[0043] If the equilibrium point is not unique, the strategy combination is iteratively optimized using the particle swarm optimization algorithm until convergence.

[0044] Preferably, the platform further includes:

[0045] The financial and tax data security storage module is used to encrypt the fused data cube and risk feature map in segments, and manage data permissions using attribute-based access control policies.

[0046] Preferably, the specific implementation of the financial and tax data secure storage module includes:

[0047] Data is sharded and stored on distributed nodes, with each shard appended with a hash-based timestamp and digital signature.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] The enterprise financial and tax integrated risk management platform proposed in this invention, which integrates multi-source data fusion, has many significant beneficial effects.

[0050] In terms of data collection and integration, the financial and tax data collection module can collect multi-source heterogeneous financial and tax data from both internal and external sources in real time, covering financial statements, tax declaration records, supply chain transaction flows, and policy and regulatory updates. Data is acquired through API interfaces and blockchain nodes to ensure its authenticity and timeliness. Account mapping is performed according to accounting standards, and time-series data is processed with timestamp alignment and missing value imputation. The data fusion preprocessing module uses federated learning combined with cross-domain data alignment algorithms to complete semantic alignment of heterogeneous data, remove redundant features, and generate a fused data cube. This achieves efficient data integration and cleaning, providing a high-quality data foundation for subsequent analysis. This process enables enterprises to comprehensively and accurately grasp various types of financial and tax data, avoiding risk misjudgments due to missing or incorrect data, and greatly improving data availability and reliability.

[0051] At the risk feature modeling and assessment level, the risk feature modeling module, based on graph convolutional networks and dynamic Bayesian networks, extracts the correlation features of financial and tax entities and risk propagation paths from the fused data cube, constructing a multi-dimensional risk feature map. This allows for in-depth mining of complex relationships between data and precise location of potential risk points. The dynamic risk assessment module, through an adaptive weighted ensemble learning algorithm combined with a risk transmission model, assesses risks in real time, generating risk probability distributions and level classifications. If the assessment results deviate, it can trigger a model weight reassignment process to ensure the accuracy of the assessment. This advanced modeling and assessment method can promptly and accurately identify the financial and tax risks faced by enterprises, providing strong support for enterprises to take preventative measures in advance and effectively reducing the probability of risk occurrence and potential losses.

[0052] In terms of risk management decision-making, the risk management decision-making module employs 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 for the Pareto optimal solution set, and game theory is combined to screen equilibrium solutions, achieving balance among multiple objectives. If the conflict rate of objectives in the solution set is too high, a fuzzy comprehensive evaluation mechanism is introduced to re-rank the solutions, providing enterprises with a scientific and reasonable basis for decision-making. This enables enterprises to comprehensively consider multiple factors when facing financial and tax risks, formulate optimal response strategies, reduce risks, balance compliance costs and capital utilization efficiency, and enhance the overall competitiveness of the enterprise.

[0053] In terms of data security, the financial and tax data security storage module encrypts and segments the fused data cube and risk feature map, employs attribute-based access control policies to manage data permissions, and stores data segments on distributed nodes with hash timestamps and digital signatures. This measure effectively prevents data leakage and tampering, ensuring the security and integrity of corporate financial and tax data, allowing enterprises to operate digitally without data security concerns, and providing a solid guarantee for the stable development of enterprises. Attached Figure Description

[0054] Figure 1 This is a schematic diagram illustrating the working principle of the enterprise financial and tax integrated risk management platform based on multi-source data fusion as described in this invention.

[0055] Figure 2 This is a schematic diagram illustrating the working principle of the financial and tax data acquisition module.

[0056] Figure 3 A schematic diagram illustrating the working principle of the risk feature modeling module;

[0057] Figure 4 This is a diagram illustrating the working principle of the dynamic risk assessment module. Detailed Implementation

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0059] Please see Figures 1-4 This invention relates to an integrated enterprise financial and tax risk management platform that integrates multi-source data, aiming to achieve comprehensive management of enterprise financial and tax risks by integrating heterogeneous financial and tax data from multiple sources both internally and externally. The specific implementation scheme is as follows:

[0060] Tax and Financial Data Acquisition Module: This module is responsible for real-time acquisition of multi-source heterogeneous tax and financial data from both internal and external sources, providing a data foundation for subsequent analysis. These data sources are extensive, covering financial statements, tax declaration records, supply chain transaction flows, and policy and regulatory updates. During the acquisition process, structured data from financial statements and time-series data from supply chain transactions are obtained from blockchain nodes via API interfaces. Simultaneously, verification codes from tax declaration records and semantic tags from policies and regulations are collected. After acquiring the structured data, account mapping is performed according to accounting standards to generate standard accounting subject codes. For time-series data, timestamp alignment and missing value imputation are performed, and wavelet transform is used to extract periodic transaction features. If the conflict rate of the standard accounting subject codes or the noise ratio of the periodic transaction features exceeds the preset acquisition tolerance, the system will trigger a data source verification process to ensure the accuracy and reliability of the data.

[0061] The data fusion preprocessing module employs federated learning combined with cross-domain data alignment algorithms to process the collected multi-source data. First, a local model is trained on the distributed-stored financial and tax data using a federated learning framework. Then, a field similarity matrix is ​​calculated using the cross-domain data alignment algorithm to achieve semantic alignment of the heterogeneous data. Next, multiple imputation is performed on missing fields, and redundant features are removed based on principal component analysis to generate a preliminary fused dataset. Finally, knowledge graph technology is used to establish association rules between financial and tax entities, mapping the preliminary fused dataset into a weighted fused data cube for easier subsequent analysis and processing.

[0062] Risk Feature Modeling Module: Based on graph convolutional networks and dynamic Bayesian networks, this module extracts the correlation features of financial and tax entities and risk propagation paths from a fused data cube. The fused data cube is input into a graph convolutional network to extract topological correlation features and capital flow intensity between financial and tax entities. A dynamic Bayesian network is used to model risk transmission paths and calculate the conditional probability of risk events. The topological correlation features and conditional probabilities are then concatenated using tensors to generate a multidimensional risk feature map. The map is then divided into communities, high-risk communities are marked, and their core node attributes are extracted, providing strong support for risk assessment and control.

[0063] The dynamic risk assessment module performs real-time risk assessment on a multi-dimensional risk feature map using an adaptive weighted ensemble learning algorithm combined with a pre-built risk transmission model. The algorithm integrates the outputs of logistic regression, random forest, and gradient boosting tree models to generate initial risk probabilities. It simulates the risk diffusion path between entities using the risk transmission model and calculates the risk level distribution using the Monte Carlo method. If the deviation between the initial risk probability and the transmission simulation results exceeds a preset assessment threshold, a model weight reassignment process is triggered to ensure the accuracy and timeliness of the assessment results.

[0064] Risk management decision-making module: Employing multi-objective optimization algorithms and game theory strategies, it dynamically generates tax compliance recommendations, fund allocation plans, and risk hedging strategies based on risk assessment results. Decision variables are defined as the adjustment range for tax compliance, the proportion of fund allocation, and the cost of risk hedging. The objective functions are the risk reduction rate, minimizing compliance costs, and maximizing fund utilization efficiency. The NSGA-II algorithm is used to solve for the Pareto optimal solution set, and game theory strategies are used to select equilibrium solutions. If the conflict rate of objectives in the solution set exceeds a preset decision threshold, a fuzzy comprehensive evaluation mechanism is introduced to re-rank the solutions, providing enterprises with a scientific and reasonable basis for decision-making.

[0065] The implementation of the present invention will be further described below with reference to Examples 1 to 6.

[0066] Example 1:

[0067] This embodiment details the process of collecting multi-source heterogeneous financial and tax data. This data encompasses financial statements, tax return records, supply chain transaction logs, and policy and regulatory updates. When collecting structured financial statement data and time-series supply chain transaction log data, API interfaces and blockchain nodes are used. The API interfaces can connect with internal enterprise financial systems and supply chain management systems to reliably acquire data; the blockchain nodes ensure data security and immutability. For tax return records, verification codes are collected. These codes are used to verify the integrity and accuracy of the data, preventing tampering during transmission or storage. Semantic tags are collected from policy and regulatory updates. These tags accurately mark key content in regulations, facilitating subsequent data association and analysis based on semantic understanding.

[0068] After collecting structured data, account mapping is performed according to accounting standards. Accounting standards provide unified specifications for data processing. For example, custom accounting accounts in a company's financial system are mapped to standard accounting account codes according to accounting standards. If a company records "office equipment purchase expenses" as a special account, it can be mapped to the standard code of "administrative expenses - office expenses".

[0069] For time-series data, timestamp alignment is performed first. Because the recording times of supply chain transaction data may differ, timestamp alignment ensures that data from different sources are comparable in the time dimension. Missing value imputation uses methods such as mean imputation and linear interpolation, selecting the appropriate method based on the data characteristics. Then, wavelet transform is used to extract periodic transaction features. The wavelet transform formula is:

[0070]

[0071] in, It is the result of wavelet transform. It is raw time-series data. It is the scaling parameter that determines the scaling of the wavelet function. These are translation parameters that control the translation of the wavelet function. It is the conjugate of the wavelet basis functions. Through this transformation, periodic features such as monthly fixed purchases and quarterly settlements can be extracted.

[0072] If the conflict rate of standard accounting subject codes or the noise ratio of periodic transaction characteristics exceeds the preset data collection tolerance (set according to the company's actual data quality requirements, e.g., a conflict rate exceeding 5% or a noise ratio exceeding 10%), the data source verification process is triggered. The data source is then re-verified, and the data interface and data entry processes are checked for any issues to ensure data collection quality.

[0073] Example 2:

[0074] This embodiment focuses on the specific process of the data fusion preprocessing module. In this process, the federated learning framework plays a crucial role. In real-world enterprise scenarios, financial and tax data may be distributed and stored across different departments or partners. Through the federated learning framework, each participant trains its local model on its local data, without directly sharing the original data, thus ensuring data privacy and security. For example, the enterprise's finance department and tax department may each hold a portion of the financial and tax data, and each trains its own model locally using federated learning algorithms, such as logistic regression models, to predict financial or tax risks.

[0075] Cross-domain data alignment algorithms calculate field similarity matrices to achieve semantic alignment of heterogeneous data. For fields from different data sources, such as the "Revenue" field in financial statements and the "Taxable Income" field in tax returns, their semantic correspondence is determined by calculating their similarity. Similarity calculation can use methods such as edit distance and cosine similarity. Edit distance calculates the minimum number of operations required to transform two strings through character insertion, deletion, and replacement; the smaller the number of operations, the higher the similarity.

[0076] For missing fields, multiple imputation can be performed, such as using the Multiple Imputation and Filling (MICE) algorithm. The MICE algorithm is based on a chain equation and uses other relevant variables to impute missing values ​​multiple times, generating multiple complete datasets. By combining the results of these datasets, the bias caused by a single imputation method can be reduced.

[0077] Redundant features are removed based on principal component analysis. The formula for principal component analysis is: ,in It is the transformed data matrix. It is the eigenvector matrix. This is the original data matrix. Through this analysis, multiple related features are transformed into a few uncorrelated principal components, removing redundant information and improving data processing efficiency. For example, multiple cost-related features in corporate financial data may be highly correlated; principal component analysis can extract the main cost components, simplifying the data structure.

[0078] Knowledge graph technology is used to establish rules governing relationships between financial and tax entities. For example, based on a company's financial and tax data, transaction relationships between the company and its suppliers and customers can be determined, as well as related information in tax returns. The initial fused dataset is then mapped into a weighted fused data cube, with weights determined based on factors such as data importance and reliability. For instance, transaction data related to a company's core business can be assigned higher weights, having a greater impact on risk assessment.

[0079] Example 3:

[0080] This embodiment provides a detailed introduction to the modeling process of the risk feature modeling module. The fused data cube is input into a graph convolutional network (GCN). The GCN extracts the topological relationships and capital flow strength between financial entities by performing convolution operations on the graph-structured data. In the graph structure, nodes represent financial entities, such as enterprises, suppliers, and customers, and edges represent the relationships between them, such as transaction relationships and capital flow relationships. The formula for GCN is:

[0081]

[0082] in It is The node feature matrix of the layer It is The weight matrix of the layer, It is an adjacency matrix with self-loops added. It is the original adjacency matrix. It is the identity matrix. yes The degree matrix, It is the activation function. Through this formula, GCN can aggregate the features of neighboring nodes, learn the topological association features between entities, and extract the intensity of capital flow from the edge weight information of capital transactions.

[0083] Modeling risk transmission paths using Dynamic Bayesian Networks (DBNs). Dynamic Bayesian Networks (DBNs) can describe the system state and causal relationships that change over time. In financial and tax risk modeling, DBN nodes represent different risk events or financial and tax states, and edges represent risk transmission relationships. By calculating the conditional probability of a risk event triggering, we can determine how risk propagates between different entities and events. For example, if a company's supplier experiences financial problems (risk event A), based on historical data and business logic, we can calculate the conditional probability that this event will lead to a disruption in the company's raw material supply (risk event B). .

[0084] By tensor concatenating topological correlation features with conditional probabilities, a multidimensional risk feature map is generated. Tensor concatenation integrates information from different dimensions to form a comprehensive map reflecting the characteristics of financial and tax risks. The nodes and edges in the map contain rich information, intuitively demonstrating the correlation of corporate financial and tax risks.

[0085] Community partitioning of a graph can be performed using algorithms such as the Louvain algorithm. The Louvain algorithm divides the graph into different communities by optimizing the modularity function. The formula for the modularity function is:

[0086]

[0087] in It is the total number of edges in the graph. These are adjacency matrix elements. and These are nodes and The degree, and It is a node and The community to which it belongs It is the Kronecker function, when The value is 1 if the risk is high and 0 otherwise. After segmentation, high-risk communities are marked and their core node attributes are extracted. These core node attributes, such as enterprise size and financial indicators, provide a basis for key monitoring and risk control.

[0088] Example 4:

[0089] This embodiment details the evaluation process of the dynamic risk assessment module and the specific implementation of the risk transmission model. In the dynamic risk assessment module, an adaptive weighted ensemble learning algorithm is used to fuse the outputs of logistic regression, random forest, and gradient boosting tree models to generate initial risk probability values. The logistic regression model, by fitting historical data, obtains the probability prediction formula for risk occurrence:

[0090]

[0091] in It is a binary variable for the occurrence of risk (1 indicates occurrence, 0 indicates non-occurrence). It is the input feature vector. These are model parameters. Random forest models construct multiple decision trees and synthesize their predictions for risk assessment; gradient boosting tree models, on the other hand, iteratively train to gradually reduce prediction errors and improve the accuracy of risk prediction. Adaptive weighted ensemble learning algorithms dynamically adjust weights based on the performance of each model on different subsets of data, using the following formula:

[0092]

[0093] in This is the final prediction result. It is The prediction results of each model It is The weights of each model, It is the number of models. Adjust dynamically based on model performance.

[0094] This paper simulates the diffusion path of risk among entities using a risk transmission model and calculates the risk level distribution using the Monte Carlo method. The risk transmission model constructs a network of financial and tax entity relationships, where nodes represent enterprises or transaction entities and edge weights represent the scale of capital flows. Based on the SEIR infectious disease model, the paper improves the risk propagation dynamics equation and defines the risk infection rate. This represents the probability that an entity at risk of infection will infect other susceptible entities within a unit of time; recovery rate. This represents the probability that an entity at risk of infection will return to a normal state within a unit of time; the immune attenuation coefficient. , representing the degree of decay of the body's immunity over time. The improved risk transmission dynamics equation is:

[0095]

[0096]

[0097]

[0098]

[0099] in Indicates the number of susceptible entities. Indicates the number of latent infected entities. Indicates the number of infected entities. Indicates the number of immune entities restored. It is the total number of entities. This refers to the rate at which latent infected entities transform into infected entities. Based on the financial and tax entity relationship network, numerical differentiation methods, such as the Euler method, are used to solve the equations to predict the spread and intensity of risk within a specified time window. The Monte Carlo method obtains the risk level distribution by repeatedly simulating the risk propagation process and statistically analyzing the frequency of different risk levels.

[0100] If the deviation between the initial risk probability value and the transmission simulation result exceeds a preset assessment threshold (set according to the company's requirements for the accuracy of risk assessment), and the deviation exceeds 10%, a model weight reassignment process is triggered. The weights of each model are readjusted to improve the accuracy of the risk assessment.

[0101] Example 5:

[0102] This embodiment details the implementation of the multi-objective optimization algorithm and game theory strategy for the risk management decision-making module. In the multi-objective optimization algorithm of the risk management decision-making module, the decision variables are defined as the tax compliance adjustment range, the fund allocation ratio, and the risk hedging cost. The tax compliance adjustment range affects the company's tax compliance, the fund allocation ratio determines the efficiency of the company's fund utilization, and the risk hedging cost is related to the company's investment in taking risk hedging measures. The objective functions are the risk reduction rate, the minimization of compliance costs, and the maximization of fund utilization efficiency. The risk reduction rate reflects the effectiveness of risk management measures in reducing the company's financial and tax risks; minimizing compliance costs aims to reduce the company's investment in meeting tax compliance requirements; and maximizing fund utilization efficiency ensures that the company's funds are allocated rationally.

[0103] The NSGA-II algorithm is used to find the Pareto optimal solution set. The NSGA-II algorithm finds an optimal set of solutions among multiple objectives through fast non-dominated sorting and crowding calculation. If the conflict rate of each objective in the solution set exceeds a preset decision threshold (set according to the actual decision-making needs of the enterprise, e.g., if the conflict rate exceeds 30%), a fuzzy comprehensive evaluation mechanism is introduced to re-rank the solutions. This fuzzy comprehensive evaluation mechanism establishes a fuzzy relation matrix and combines the weights of each objective to comprehensively evaluate and rank the solutions in the solution set.

[0104] The specific implementation of the game theory strategy involves constructing a three-way game model involving 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; compliance investment is the investment by enterprises to comply with tax regulations; and the risk-bearing ratio determines the proportion of losses borne by enterprises and financial institutions when facing financial and tax risks. The Nash equilibrium point is calculated. A Nash equilibrium is a strategy combination in which none of the parties will unilaterally change their strategies to obtain better payoffs. The Shapley value is used to allocate the cooperative payoffs among the parties, considering each participant's marginal contribution to the alliance and distributing payoffs fairly. If the equilibrium point is not unique, the strategy combination is iteratively optimized using a particle swarm optimization algorithm until convergence. The particle swarm optimization algorithm simulates the foraging behavior of a flock of birds, finding the optimal strategy combination by updating the position and velocity of particles.

[0105] Example 6:

[0106] This embodiment focuses on the specific implementation of the financial and tax data secure storage module. This module is used to perform sharded encryption on the fused data cube and risk feature map, and employs attribute-based access control policies to manage data permissions. Data shards are stored on distributed nodes, with each shard appended with a hash-based timestamp and digital signature. Hash functions such as SHA-256 can be used, and the hash formula is as follows:

[0107] in It is a hash value. This is the data to be hashed. The hash timestamp records the storage time of the data fragments, ensuring the timeliness and traceability of the data. Digital signatures use asymmetric encryption techniques, such as the RSA algorithm. The sender uses their private key to sign the data fragment, and the receiver uses the sender's public key to verify it, ensuring the integrity and authenticity of the data.

[0108] Attribute-based access control policies determine a user's data access permissions based on their attributes, such as job title, department, and business role. For example, senior managers in a company's finance department may have access to detailed financial and tax data, while ordinary employees may only have access to some summary data. This approach effectively ensures the security of a company's financial and tax data, preventing data leaks and unauthorized access.

[0109] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0110] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multi-source data fusion-based integrated enterprise financial and tax risk management platform, characterized in that, include: The financial and tax data acquisition module collects multi-source heterogeneous financial and tax data from both inside and outside the enterprise in real time. The data fusion preprocessing module uses federated learning combined with cross-domain data alignment algorithms to perform feature cleaning, format standardization and correlation matching on multi-source data, generating a fused data cube. The risk feature modeling module, based on graph convolutional networks and dynamic Bayesian networks, extracts the financial and tax entity association features and risk propagation paths from the fused 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 perform real-time risk assessment on a multi-dimensional risk feature map, generating risk probability distribution and level classification results. The risk management decision-making module uses multi-objective optimization algorithms and game theory strategies to dynamically generate tax compliance suggestions, fund allocation plans and risk hedging strategies based on risk assessment results. The assessment process of the dynamic risk assessment module includes: An adaptive weighted ensemble learning algorithm is used to combine the outputs of logistic regression, random forest, and gradient boosting tree models to generate initial risk probability values. The risk transmission model is used to simulate the diffusion path of risk between entities, and the risk level distribution is calculated by combining the Monte Carlo method. If the deviation between the initial risk probability and the transmission simulation result exceeds the preset evaluation threshold, the model weight reassignment process will be triggered. The modeling process of the risk characteristic modeling module includes: By inputting the fused data cube into the graph convolutional network, the topological relationship features and capital flow intensity between financial and tax entities are extracted; The risk transmission path is modeled based on dynamic Bayesian network, and the conditional probability of risk event triggering is calculated. Tensor concatenation of topological correlation features and conditional probabilities is used to generate a multidimensional risk feature map. The graph is divided into communities, high-risk communities are marked, and their core node attributes are extracted.

2. The enterprise financial and tax integrated risk management platform according to claim 1, characterized in that, The multi-source heterogeneous financial and tax data includes financial statements, tax declaration records, supply chain transaction records, and policy and regulatory updates. The operation process of the financial and tax data acquisition module includes: By using API interfaces to connect with blockchain nodes, we can obtain structured data from financial statements and time-series data from supply chain transactions, as well as collect verification codes from tax declaration records and semantic tags from policies and regulations. Structured data is mapped to accounts according to accounting standards to generate standard accounting account codes; The time series data is timestamped and missing values ​​are imputed. Wavelet transform is used 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 financial and tax integrated risk management platform according to claim 1, characterized in that, The specific process of the data fusion preprocessing module includes: The federated learning framework is used to train local models on distributed tax data, and a cross-domain data alignment algorithm is used to calculate the field similarity matrix to complete the semantic alignment of heterogeneous data. The missing fields are imputed multiple times, and redundant features are removed based on principal component analysis to generate a preliminary fused dataset. By establishing association rules between financial and tax entities through knowledge graph technology, the preliminary fused dataset is mapped into a weighted fused data cube.

4. The enterprise financial and tax integrated risk management platform according to claim 1, characterized in that, The specific implementation of the risk transmission model includes: Construct a network of financial and tax entity relationships, where nodes represent enterprises or transaction entities and edge weights represent the scale of capital flows; The risk transmission dynamics equation is improved based on the SEIR infectious disease model, and the risk infection rate, recovery rate and immune attenuation coefficient are defined. Based on the financial and tax entity relationship network, the equations are solved using numerical differentiation methods to predict the spread and intensity of risk within a specified time window.

5. The enterprise financial and tax integrated risk management platform according to claim 1, characterized in that, The multi-objective optimization algorithm of the risk management decision module includes: The decision variables are defined as the tax compliance adjustment range, the fund allocation ratio, and the risk hedging cost. The objective function is the risk reduction rate, the minimization of compliance costs, and the maximization of fund utilization efficiency. The NSGA-II algorithm is used to solve for the Pareto optimal solution set, and the equilibrium solution is selected through game theory strategy. If the conflict rate of each objective in the solution set exceeds the preset decision threshold, a fuzzy comprehensive evaluation mechanism is introduced to reorder them.

6. The enterprise financial and tax integrated risk management platform according to claim 5, characterized in that, The specific implementation of the game theory strategy includes: Construct a tripartite game model among tax authorities, enterprises, and financial institutions, defining the strategy space as audit intensity, compliance investment, and risk-bearing ratio; Calculate the Nash equilibrium point and allocate the multi-party collaborative benefits by combining the Shapley value; If the equilibrium point is not unique, the strategy combination is iteratively optimized using the particle swarm optimization algorithm until convergence.

7. The enterprise financial and tax integrated risk management platform according to claim 1, 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 segments, and manage data permissions using attribute-based access control policies.

8. The enterprise financial and tax integrated risk management platform according to claim 7, characterized in that, The specific implementation of the secure storage module for financial and tax data includes: Data is sharded and stored on distributed nodes, with each shard appended with a hash-based timestamp and digital signature.

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