Tax risk intelligent early warning and compliance optimization system

Abstract

The invention discloses a tax risk intelligent early warning and compliance optimization system, which integrates enterprise internal financial tax data, industry benchmark and external supervision information, and adopts an improved Monte Carlo simulation and in-risk value risk assessment model to predict and analyze potential tax violation and possible loss thereof. And abnormal tax-related behaviors can be found in time and early warning can be given out in advance. Meanwhile, the system regards various selectable compliance improvement measures as'investment portfolio ', an optimal allocation scheme of each compliance resource is automatically calculated by establishing a mathematical optimization model, and scientific compliance management guidance is provided for enterprises. The system comprises a data acquisition module, a risk analysis module, a compliance optimization module, a result output module and the like, and a risk identification model and a decision algorithm are autonomously constructed. Compared with the prior art, the tax risk can be accurately early warned in real time, a quantitative compliance improvement scheme is given, and the perspectiveness and effectiveness of enterprise tax risk management are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of enterprise tax risk management and intelligent decision support technology, and in particular to a tax risk intelligent early warning and compliance optimization system. Background Art

[0002] Currently, when it comes to tax risk management, many businesses and tax authorities rely primarily on manual audits and simple rule-based verification to identify tax compliance issues. This traditional approach has numerous shortcomings: First, manual verification, faced with massive amounts of transaction and declaration data, is labor-intensive and difficult to promptly identify potential risks, often resulting in lags. Second, existing tax risk early warning systems typically monitor based on pre-set thresholds or fixed indicator systems, lacking intelligence and adaptability, and may miss new and unusual patterns. Third, even when risks are identified, traditional systems rarely provide targeted compliance improvement recommendations, leaving businesses unsure how to allocate resources to mitigate them. Therefore, there is an urgent need for a new system that can integrate multi-source data in real time, intelligently identify tax risks, and provide optimized decision support to enhance the foresight and effectiveness of tax risk management. Summary of the Invention

[0003] Technical Purpose: To address the shortcomings of existing technologies, the present invention discloses an intelligent tax risk early warning and compliance optimization system. By introducing simulation and optimization methods from the field of quantitative finance, it conducts in-depth analysis of corporate tax-related behaviors, achieves timely early warning of tax risks and intelligently optimizes compliance management strategies.

[0004] Technical solution: To achieve the above technical objectives, the present invention adopts the following technical solution:

[0005] A tax risk intelligent early warning and compliance optimization system, including a data acquisition module, a data processing module, a risk analysis module, a compliance optimization module and an output module;

[0006] The data collection module is used to extract tax-related data from multiple sources;

[0007] The data processing module receives tax-related data from the data acquisition module, cleans, standardizes, and extracts features from it, generating key indicators and characteristic variables that can reflect tax risk status;

[0008] The risk analysis module uses quantitative financial simulation algorithms to identify and predict risks in acquired tax-related data, constructing a tax risk assessment model to generate risk warning signals;

[0009] The compliance optimization module uses portfolio optimization theory to perform decision calculations on multiple alternative compliance measures and outputs the optimal compliance resource allocation plan to reduce tax risks;

[0010] The output module provides the risk warning signal generated by the risk analysis module and the optimal compliance resource allocation plan generated by the compliance optimization module to the user through a graphical interface, automatic report or application programming interface.

[0011] Preferably, the risk analysis module includes an anomaly detection unit and a risk simulation unit, wherein the anomaly detection unit uses a machine learning model to identify abnormal patterns in tax data, and the risk simulation unit uses Monte Carlo simulation to conduct a large number of scenario deductions on possible future tax behaviors, and calculates the risk index of value at risk to evaluate the probability distribution of potential tax losses.

[0012] Preferably, the formula for calculating the value at risk is as follows:

[0013] VaR α =inf{x|P(L≤x)≥α}

[0014] Where L is the potential tax loss random variable, P(L≤x) is the probability that the loss does not exceed x, α is the confidence level, and 0<α<1; the risk simulation unit further calculates the modified value at risk As an early warning indicator, its expression is:

[0015]

[0016] Among them, β is the compliance factor, which represents the level of internal control and compliance management of the enterprise, and its value range is 0≤β≤1.

[0017] Preferably, the compliance optimization module includes a decision optimization unit, which establishes an objective function based on a portfolio optimization model to strike a balance between maximizing risk reduction effects and minimizing compliance input costs. The objective function comprehensively considers the marginal improvement benefits and implementation costs of various compliance measures on risk indicators to obtain the optimal implementation ratio or priority of each compliance measure.

[0018] Preferably, the decision optimization unit obtains the optimal compliance measure implementation intensity vector x=[x1, x2,…, x n ] T :

[0019]

[0020] Where f(x) represents the objective function established by the decision optimization unit, i and j represent the compliance measure indexes, n represents the total number of optional compliance measures, and x i and x j Represents the implementation intensity or proportion of the i-th and j-th compliance measures, respectively, 0≤x i ≤1, r iThe expected risk reduction of the i-th compliance measure is usually expressed as a risk reduction percentage or a monetary amount. ij represents the overlap coefficient between compliance measures i and j, reflecting the expected risk reduction when the two compliance measures are implemented simultaneously;

[0021] The constraints are:

[0022]

[0023] Among them, c i represents the resource cost required to implement the i-th measure, C max Indicates the total budget or resource cap that can be used for compliance optimization.

[0024] Preferably, the data acquisition module is used to obtain multi-source data from internal and external sources, including corporate financial and tax declaration system data, corporate business transaction data, industry average tax level data, tax laws, regulations and policy change information, as well as media public opinion and tax violation case data disclosed by regulatory agencies, and transmit the data to the data processing module in real time.

[0025] Preferably, the output module includes a visual user interface and a report generator. The visual user interface dynamically displays the company's current tax risk level, main risk indicators and their trends in the form of a dashboard, and highlights early warning prompts when the risk exceeds the threshold; the report generator regularly outputs detailed tax risk analysis reports and corresponding compliance optimization suggestions, and the reports can be called by third-party risk control systems through the API interface.

[0026] Beneficial effects: The tax risk intelligent early warning and compliance optimization system provided by the present invention has the following beneficial effects:

[0027] 1. The risk analysis module of the present system utilizes improved Monte Carlo simulation technology to conduct extensive simulations of a company's potential future tax behavior and external environmental changes. Based on this simulation, it calculates innovative tax risk indicators such as Value at Risk. Compared to traditional early warning methods based on static thresholds, this approach can quantify the maximum potential loss in extreme situations and provide early detection of high-risk scenarios. Combined with a machine learning anomaly detection model, the system can identify the degree to which a company's "tax behavior fingerprint" deviates from normal patterns, further enhancing the sensitivity of risk identification.

[0028] 2. The compliance optimization module of the present system treats multiple optional compliance measures (such as internal audits, personnel training, and adjustments to tax filing strategies) as elements of an investment portfolio, establishes an objective function and constraint model, and automatically calculates the optimal implementation strength of each measure. The system uses mathematical programming to determine the investment ratio of different measures within a limited compliance management budget to maximize the expected risk reduction. This design of the present invention overcomes the lack of targeted guidance in the existing technology, enabling enterprises to optimize compliance resource allocation based on evidence.

[0029] 3. The system consists of five modules: data collection, data processing, risk analysis, compliance optimization, and output presentation. Each module has clear functions and is connected through a unified data interface, forming a closed-loop control system. The system has established an independent risk assessment model and decision-making algorithm library, which automatically updates model parameters as data accumulates, allowing for continuous learning and optimization. The entire system can be deployed and operated independently, without relying on existing third-party artificial intelligence or big data analysis platforms, thus ensuring autonomy and confidentiality. Furthermore, through an output format that combines a visual interface with automatic reporting, the system can present complex risk analysis results and optimization recommendations to users in an intuitive and easy-to-understand manner, achieving user-friendly human-computer interaction.

[0030] In summary, the system of the present invention can timely and accurately identify the possible risk points of enterprises in the process of tax declaration and payment, and significantly reduce the probability of fines and losses caused by omissions, misreporting or violations of laws and regulations; through quantitative simulation, the early warning signals of this system are more credible and have a lower false alarm rate, helping tax regulatory departments and corporate financial personnel to focus on the truly important risks; at the same time, the measures and suggestions given by the compliance optimization module enable enterprises to achieve the greatest risk reduction with the least investment, thereby improving the economy and effectiveness of compliance management. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for describing the embodiments or the prior art.

[0032] Figure 1 This is a schematic diagram of the architecture of the tax risk intelligent early warning and compliance optimization system of the present invention;

[0033] Figure 2 A schematic diagram of the process flow of the method provided by the present invention;

[0034] Figure 3 This is a simulation diagram of risk loss distribution of the present invention;

[0035] Figure 4 This is a comparison chart of the compliance optimization results of the present invention. DETAILED DESCRIPTION

[0036] The present invention will be described more clearly and completely below by way of a preferred embodiment in conjunction with the accompanying drawings, but the present invention is not limited to the scope of the embodiment.

[0037] like Figure 1 As shown, the present invention provides a tax risk intelligent early warning and compliance optimization system, including a data acquisition module, a data processing module, a risk analysis module, a compliance optimization module and an output module;

[0038] The data collection module is used to extract tax-related data from multiple sources;

[0039] Data sources may include, but are not limited to: internal enterprise financial system data (such as general ledgers, financial statements, and tax return records), invoice and transaction data, and internal audit records; external industry average financial indicators, industry tax benchmarks, policies, regulations, and tax incentives published by tax authorities; and publicly available online databases of corporate tax-related public opinion, news reports, and historical violation cases. The data acquisition module, through pre-configured interfaces and crawler algorithms, acquires data from these sources periodically or in real time, performs preliminary format conversion, and stores it. This module ensures comprehensive and up-to-date data support for subsequent analysis.

[0040] The data processing module receives tax-related data from the data acquisition module, cleans, standardizes, and extracts features from it, generating key indicators and characteristic variables that can reflect tax risk status;

[0041] The output processing module processes tax-related data to generate key indicators and characteristic variables that reflect a company's tax risk profile. This module removes outliers, fills in missing data, and calculates standardized basic indicators such as sales revenue, total tax paid, profit margin, and the accuracy of tax declarations. Furthermore, it incorporates industry comparisons to calculate the degree of deviation between a company and its industry average, as well as derivative indicators such as tax burden volatility. The processed data and extracted features are stored in a knowledge base / model repository and serve as input to the risk analysis module. This process transforms raw, chaotic data into standardized risk assessment input, enhancing the reliability of risk analysis.

[0042] The risk analysis module uses quantitative financial simulation algorithms to identify and predict risks in acquired tax-related data, constructing a tax risk assessment model to generate risk warning signals;

[0043] The risk analysis module is the core of the system, used to intelligently assess and warn of a company's current and future tax risks. This module includes an anomaly detection unit and a risk simulation unit, which combine to achieve accurate risk identification:

[0044] Anomaly Detection Unit: This unit utilizes machine learning and statistical analysis techniques to establish a baseline model of corporate tax behavior. For example, algorithms such as cluster analysis or one-class support vector machines (SVMs) can be used to learn from historical data the normal tax filing patterns and financial ratio characteristics of each company, thereby forming a unique "tax compliance fingerprint" for each company. When new data arrives, the anomaly detection unit calculates the degree of deviation from the baseline model. If a significant deviation (exceeding a set threshold) is detected, it is considered potential abnormal behavior and anomalies are identified by matching the company's behavioral characteristics. The unit outputs an anomaly score A, which quantifies the difference between the current behavior and the normal pattern. A larger value indicates a more abnormal behavior.

[0045] Risk simulation unit: While capturing anomalies, the risk simulation unit conducts forward-looking scenario deductions for possible risk events. The present invention adopts an improved Monte Carlo simulation method to conduct a large number of random simulations of the tax-related situations of enterprises in a future evaluation cycle (such as the next quarter or the next fiscal year). Specifically, considering the various uncertain factors that affect tax compliance, such as sales fluctuations, cost fluctuations, policy changes, and potential fraudulent behaviors, thousands to tens of thousands of possible financial scenarios are generated by randomly sampling the changes in these factors. For each scenario, the possible tax amount, declaration error rate, and the potential loss L (such as the amount of tax or fine) caused by the enterprise are calculated. Thus, a probability distribution of potential tax losses is obtained. Based on this distribution, risk indicators such as value at risk VaR are calculated: for example, the value at risk VaR at a 95% confidence level is calculated. 0.95 , that is, to find the loss amount from the loss distribution so that there is a 95% probability that the loss does not exceed this value. As shown in the following formula:

[0046] VaR 0.95 =inf{x|P(L≤x)≥0.95}

[0047] Where L represents the potential tax loss random variable obtained by simulation, and P(L≤x) is the probability that the loss does not exceed x. The VaR value can be used to understand the loss level in the worst case. In addition, based on the traditional VaR indicator, this invention innovatively introduces the compliance factor β, incorporating the soundness of the company's daily compliance management into the risk assessment model. The modified value at risk can be defined as Among them, β (between 0 and 1) reflects the level of internal control and compliance management of the enterprise. The higher β is, the more perfect the compliance management is. By introducing β, the system can reflect the contribution of the company's proactive compliance behavior to reducing extreme risks, thereby more comprehensively assessing the risk level. The above-mentioned abnormal score A and multiple risk indicators (including VaR, The risk analysis module compares these results with pre-set risk thresholds. If a key risk indicator exceeds the warning threshold, a risk warning signal is generated, marking the company as high risk. Otherwise, the risk is assessed as medium or low based on the indicator level and continuously monitored.

[0048] like Figure 3 As shown in the figure, the histogram distribution of potential tax losses L generated by Monte Carlo simulation is shown. The horizontal axis loss L (yuan) shows the amount range of the simulated sample, the vertical axis probability shows the frequency of occurrence of each loss interval, and the dotted line shows the VaR at the 95% confidence level. 0.95 Threshold, used to quantify the maximum expected loss under extreme scenarios.

[0049] The compliance optimization module uses portfolio optimization theory to perform decision calculations on multiple alternative compliance measures and outputs the optimal compliance resource allocation plan to reduce tax risks;

[0050] The compliance optimization module is activated after the risk analysis module completes its assessment and issues an early warning based on the situation. Its main function is to provide specific compliance improvement plans for the detected risk points to help companies reduce future risks. This module rationally allocates limited compliance management resources to different measures to minimize the overall risk. In terms of specific implementation, first define a set of optional compliance measures, such as: M = {strengthening invoice review, employee tax law training, introducing external tax consultants, upgrading internal audits, adjusting tax filing processes}, etc. For each compliance measure i, establish the following parameters: the amount of risk it is expected to reduce r i (which can be estimated through historical data and expert experience, indicating the extent of reduction of certain risk indicators after the implementation of the measure), the cost or resources required i and possible overlapping effects between measures. Then an optimization model is established, for example, the objective function can be set to maximize the overall risk reduction:

[0051]

[0052] Where f(x) represents the objective function established by the decision optimization unit, i and j represent the compliance measure indexes, n represents the total number of optional compliance measures, and x i and x j Represents the implementation intensity or proportion of the i-th and j-th compliance measures, respectively, 0≤x i ≤1, when x i =1 means that the measure is fully implemented, x i =0 means not implemented, r i The expected risk reduction of the i-th compliance measure is usually expressed as a risk reduction percentage or a monetary amount. ijrepresents the overlap coefficient between compliance measures i and j, reflecting the expected risk reduction when the two compliance measures are implemented simultaneously. If the compliance measures are independent, γ ij =0;

[0053] Meeting resource budget constraints 0≤x i ≤1,i=1,2,…,n, where c i represents the resource cost required to implement the i-th measure, C max Indicates the total budget or resource cap that can be used for compliance optimization.

[0054] The first term of the objective function ∑r i x i is the total risk reduction contributed by each measure individually, and the second term ∑γ ij x i x j The objective measures the net risk reduction from implementing the combined measures, net of any overlap to ensure there is no overestimation. The constraint ensures that the total cost does not exceed the budget C. max By solving the optimization problem, we can get the optimal decision variables That is, the optimal implementation level of each compliance measure. Based on the solution results, the compliance optimization module generates a list of measures and resource allocation recommendations, such as recommending high-intensity implementation of certain measures ( close to 1), while other measures with relatively low effectiveness and high costs are less invested in or not implemented ( These recommendations will be given in a quantitative form, such as the percentage of risk reduction expected, the required investment of funds / manpower, etc., to facilitate decision makers' consideration.

[0055] like Figure 4 As shown in the figure, this chart compares the risk reduction rates of individual and comprehensive compliance measures. Invoice authenticity review is to review each purchase and sales invoice individually to reduce invoice-related risks. Employee tax law training is to enhance internal compliance awareness and operational accuracy. External tax consultants conduct third-party review of tax returns. Comprehensive measures are the combined implementation of the above three.

[0056] The output module provides the risk warning signal generated by the risk analysis module and the optimal compliance resource allocation plan generated by the compliance optimization module to the user through a graphical interface, automatic report or application programming interface.

[0057] The output module is responsible for presenting the analysis results and optimization recommendations in a user-friendly format, supporting multiple output methods to suit different usage scenarios. First, a graphical user interface (GUI) can be a dashboard containing multiple visualization components, such as risk heat maps, trend line charts, and indicator dashboards. This provides real-time visualization of an enterprise's risk level (e.g., represented by color or score) and the changing trends of key risk indicators. When the risk analysis module issues a warning signal, a highlighted alert (such as a red mark or a pop-up window) appears on the interface, identifying the specific risk indicator and its abnormal values, and prompting the user to review the details. Second, automated report generation allows the system to generate tax risk analysis and compliance recommendation reports on a pre-set basis (e.g., monthly or quarterly). These reports include the values ​​of each risk indicator for the period, an analysis of the causes of risk changes, and a list of recommended measures from the compliance optimization module. Reports can be exported to formats such as PDF and Excel for easy archiving and sharing. Furthermore, by providing a standard API, the system can push risk warnings and compliance recommendations to an enterprise's existing risk management system or regulatory platform, enabling data integration between systems. For example, if a company is assessed as high-risk by the system, early warning information can be sent to the tax authority's monitoring system via API, allowing timely regulatory action. In summary, the output module transforms complex analysis results into intuitive visual information and action recommendations, helping users to efficiently understand and address tax risks.

[0058] like Figure 2 As shown, the workflow of this system includes the following steps:

[0059] S1. The system initiates a complete risk assessment process at a pre-set interval or upon event triggering. For example, it may be run automatically every night or immediately upon detecting new significant transaction or declaration data.

[0060] S2. The data acquisition module collects the latest data from various data sources, and the data processing module pre-processes and integrates the data to ensure that the data entering the analysis phase is complete, consistent and has the necessary characteristic indicators.

[0061] S3. The risk analysis module conducts an in-depth analysis of the processed data: First, the anomaly detection unit calculates the anomaly score A to determine the difference between the current data and the company's historical normal pattern; then the risk simulation unit performs Monte Carlo simulation to generate a large number of future scenarios and calculate the corresponding risk indicators, including VaR and modified VaR * Etc., combine anomaly scores and risk indicators to comprehensively assess the tax risk status of enterprises.

[0062] S4. Compare the result of step S3 with the predetermined risk level standard. The system can use the classification rules to classify the risk level into "low risk", "medium risk" or "high risk". For example, if VaR * If the indicator is lower than a certain value and the abnormal score A is also within the normal range, it is judged as low risk; if the indicator is close to the threshold, it is medium risk; if VaR * If the score far exceeds the threshold or is significantly higher, it is judged as high risk.

[0063] S5. If the enterprise is determined to be at a high risk level in step S4, the system will immediately generate a real-time early warning notification (such as a pop-up warning or SMS / email notification to the relevant responsible person) through the output module, and simultaneously start the compliance optimization module. The compliance optimization module obtains the current risk indicators and the main identified risk causes, calls the internal optimization algorithm model (such as the aforementioned portfolio optimization model), and calculates a set of recommended risk mitigation measures. During this process, the system may also refer to historical cases stored in the knowledge base, match the requirements taken by the regulatory authorities in similar situations or the company's own rectification measures, and further enrich the rationality of the recommended solutions. When the risk level is medium or low, there is no need to immediately enter the compliance optimization, just keep monitoring and end this round of the process normally.

[0064] S6. At the end of the process, the output module integrates the risk assessment results and compliance optimization recommendations into a concise summary and detailed report. For high-risk situations, the report highlights the indicators that triggered the warning, the corresponding specific values ​​and threshold comparisons, and explains the reasons for the high risk. It also provides a list of measures recommended by the compliance optimization module and their expected results. For medium and low-risk situations, the report records the current values ​​of each risk indicator, the changing trends, and general improvement suggestions or precautions. After the report is generated, it can be automatically sent to management or relevant departments for review.

[0065] S7. This round of risk assessment process is completed, and the system enters standby mode, waiting for the next cycle or trigger condition to arrive. At the same time, the system will store the new data and analysis results generated this time into the knowledge base for future model updates and trend analysis. It is worth mentioning that as time goes by, the system continues to accumulate more data and cases, and its built-in models (such as the threshold of the anomaly detection model, the adaptive learning rate, the parameter distribution in the simulation, and the estimated parameters in the optimization model) can be adaptively updated through the historical data in the knowledge base. For example, the system can periodically use the latest 6 months of data to retrain the anomaly detection model to reflect new changes in the company's business; or adjust the r of each measure based on the effectiveness of the compliance measures that have been actually implemented. i and γ ij This self-learning mechanism ensures that the system can keep pace with the times and always maintain high accuracy and applicability.

[0066] Example

[0067] The following uses an example scenario to illustrate the working process and effect of the system of the present invention. Suppose a manufacturing company A, which is involved in multiple taxes such as value-added tax and corporate income tax on a daily basis. Recently, due to market fluctuations, the company's sales revenue and cost structure have changed significantly. During a routine operation, the system obtained the company's latest financial data and the industry's average tax burden level through the data acquisition module. The data processing module calculated and found that: Company A's value-added tax burden rate this quarter is 20% higher than the industry average, and its profit margin is abnormally low. Based on this, the anomaly detection unit of the risk analysis module gave a higher anomaly score A, indicating that the company's tax reporting behavior may be abnormal. Furthermore, the risk simulation unit conducted 10,000 Monte Carlo simulations for the next two quarters. The results showed that in the extreme case of 5%, the company may face large tax supplements and fines, and the estimated VaR 0.95 The company's internal compliance factor β was assessed to be only 0.3 (low, meaning internal risk control measures are weak), and the modified risk value VaR was calculated based on this. * =100×(1-0.3)=700,000 yuan. Although the risk has been reduced after adjustment, it still exceeds the warning threshold (for example, 500,000 yuan). Therefore, the system judges Enterprise A as a high-risk level and triggers an early warning.

[0068] Immediately, the compliance optimization module was activated. The system retrieved risk mitigation cases of similar-sized manufacturing companies from the knowledge base, and based on the specific situation of Company A, listed several possible measures: strengthening the review of invoice authenticity (expected to reduce invoice-related risks by 20%, with medium costs), conducting special tax training (expected to reduce the risk of misreporting by 15%, with low costs), hiring tax consultants to review declarations (expected to reduce the overall risk by 30%, with high costs), etc. The optimization module found through calculation that, under the company's annual compliance improvement budget of 500,000 yuan, the best solution is: 100% investment in invoice review (estimated to cost 200,000 yuan, to achieve a maximum risk reduction of 20%), 100% investment in tax training (cost 100,000 yuan, reduce 15% of the risk), and about 50% investment in tax consultant services (cost 200,000 yuan, reduce about 15% of the risk, and the remaining part will not be invested due to budget constraints). It is expected that the overall tax risk can be reduced by about 50%. These results are generated into a report and pushed to the financial director of Company A. The report intuitively shows that if this combination of measures is taken, the VaR of Company A will be reduced. * It is expected to drop from 700,000 yuan to approximately 350,000 yuan, returning to the safety threshold. The company adopted the suggestions and improved relevant measures. After a system evaluation several months later, both the anomaly score and VaR indicators improved significantly, verifying the effectiveness of the proposed system in practical applications.

[0069] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A tax risk intelligent early warning and compliance optimization system, characterized by: It includes data acquisition module, data processing module, risk analysis module, compliance optimization module and output module; The data collection module is used to extract tax-related data from multiple sources; The data processing module receives tax-related data from the data acquisition module, cleans, standardizes, and extracts features from it, generating key indicators and characteristic variables that can reflect tax risk status; The risk analysis module uses quantitative financial simulation algorithms to identify and predict risks in acquired tax-related data, constructing a tax risk assessment model to generate risk warning signals; The compliance optimization module uses portfolio optimization theory to perform decision calculations on multiple alternative compliance measures and outputs the optimal compliance resource allocation plan to reduce tax risks; The output module provides the risk warning signal generated by the risk analysis module and the optimal compliance resource allocation plan generated by the compliance optimization module to the user through a graphical interface, automatic report or application programming interface.

2. The tax risk intelligent early warning and compliance optimization system according to claim 1 is characterized by: The risk analysis module includes an anomaly detection unit and a risk simulation unit. The anomaly detection unit uses a machine learning model to identify abnormal patterns in tax data. The risk simulation unit uses Monte Carlo simulation to conduct a large number of scenario deductions on possible future tax behaviors and calculates the risk index of value at risk to assess the probability distribution of potential tax losses.

3. The tax risk intelligent early warning and compliance optimization system according to claim 2 is characterized by: The formula for calculating value at risk is as follows: VaR α =inf{x∣P(L≤x)≥α} Where L is the potential tax loss random variable, P(L≤x) is the probability that the loss does not exceed x, α is the confidence level, and 0<α<1; the risk simulation unit further calculates the modified value at risk As an early warning indicator, its expression is: Among them, β is the compliance factor, which represents the level of internal control and compliance management of the enterprise, and its value range is 0≤β≤1.

4. The tax risk intelligent early warning and compliance optimization system according to claim 1 is characterized in that: The compliance optimization module includes a decision optimization unit, which establishes an objective function based on a portfolio optimization model to strike a balance between maximizing risk reduction effects and minimizing compliance input costs. The objective function comprehensively considers the marginal improvement benefits and implementation costs of various compliance measures on risk indicators to obtain the optimal implementation ratio or priority of each compliance measure.

5. The tax risk intelligent early warning and compliance optimization system according to claim 4 is characterized in that: The decision optimization unit obtains the optimal compliance measure implementation intensity vector x=[x1,x2,…,x n ] T : Where f(x) represents the objective function established by the decision optimization unit, i and j represent the compliance measure indexes, n represents the total number of optional compliance measures, and x i and x j Represents the implementation intensity or proportion of the i-th and j-th compliance measures, respectively, 0≤x i ≤1, r i The expected risk reduction of the i-th compliance measure is usually expressed as a risk reduction percentage or a monetary amount. ij represents the overlap coefficient between compliance measures i and j, reflecting the expected risk reduction when the two compliance measures are implemented simultaneously; The constraints are: Among them, c i represents the resource cost required to implement the i-th measure, C max Indicates the total budget or resource cap that can be used for compliance optimization.

6. The tax risk intelligent early warning and compliance optimization system according to claim 1 is characterized by: The data acquisition module is used to obtain multi-source data from internal and external sources, including corporate financial and tax declaration system data, corporate business transaction data, industry average tax level data, tax laws, regulations and policy change information, as well as media public opinion and tax violation case data disclosed by regulatory agencies, and transmit the data to the data processing module in real time.

7. The tax risk intelligent early warning and compliance optimization system according to claim 1 is characterized by: The output module includes a visual user interface and a report generator. The visual user interface dynamically displays the company's current tax risk level, key risk indicators and their trends in the form of a dashboard, and highlights warning prompts when the risk exceeds the threshold; The report generator regularly outputs detailed tax risk analysis reports and corresponding compliance optimization suggestions, and the reports can be called by a third-party risk control system through the API interface.

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