Enterprise finance and tax data management method and system based on big data

Abstract

The invention relates to the technical field of data management, and discloses an enterprise finance and taxation data management method and system based on big data, and the method comprises the steps: building an enterprise finance and taxation data middle platform, obtaining data, carrying out the preprocessing of the obtained data, and numbering the data to form a finance data set, a taxation data set, a business data set, and a market data set. Calculating a financial health predicted value Cjk, a tax compliance predicted value Shg and a business stability predicted value Ywd by combining an algorithm calculation formula, constructing a risk scoring rule, and performing risk assessment and enterprise risk level assessment according to the financial health predicted value Cjk, the tax compliance predicted value Shg and the business stability predicted value Ywd. According to the method and the system, the risk assessment and enterprise risk level assessment results are output, comprehensive and targeted assessment is carried out on the enterprise risk from different dimensions, the assessment efficiency is high, the assessment effect is accurate, and accurate monitoring and dynamic early warning of enterprise finance and tax data are realized.

Description

Technical Field

[0001] The present invention relates to the field of data management technology, and in particular to a method and system for enterprise financial and tax data management based on big data. Background Art

[0002] Enterprise financial and tax data management refers to the process of collecting, organizing, storing, analyzing, and applying internal financial and tax data using advanced information technology and management concepts. It encompasses the entire lifecycle of data management, from source collection to final application. It aims to ensure the accuracy, completeness, timeliness, and security of financial and tax data, providing strong data support for enterprise decision-making, financial management, tax planning, and risk control. With the rapid development of information technology, emerging technologies such as big data, cloud computing, and artificial intelligence are emerging and widely used in enterprise management. These technologies provide powerful tools and means for enterprise financial and tax data management, enabling enterprises to more efficiently collect, store, process, and analyze massive amounts of financial and tax data, thereby driving the development of enterprise financial and tax data management. In a market economy, enterprises face increasingly fierce competition. To enhance their competitiveness, enterprises need to continuously optimize internal management, reduce costs, and improve efficiency. Financial and tax data, as a key reflection of a company's economic activities, plays a crucial role in decision-making, risk control, and resource allocation. Therefore, strengthening financial and tax data management has become an inevitable choice for enterprises to adapt to market competition.

[0003] Currently, many companies' financial and tax data management still centers around static reporting, primarily serving post-event record-keeping and compliance reporting, while failing to fully leverage the data's predictive, early warning, and decision-making support capabilities. This model has exposed numerous problems in the digital age, hindering companies' financial agility and strategic competitiveness, leading to delayed decision-making and an inability to identify market risks in a timely manner. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides a method and system for enterprise financial and tax data management based on big data, which has the advantages of comprehensively evaluating enterprise risks through multiple data, establishing a dynamic evaluation system, and quickly locating enterprise risks. It guides the priority allocation of resources through risk classification, ensures that management can effectively respond to different levels of risks, and then converts multi-dimensional data into actionable decision-making basis to help enterprises achieve automatic risk prevention, thereby improving financial and tax management efficiency, reducing compliance costs, and enhancing business stability.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for managing enterprise financial and tax data based on big data, comprising the following steps:

[0008] Step 1: Build a corporate financial and tax data platform and obtain financial data, tax data, business data, and market data respectively;

[0009] Step 2: Preprocess the acquired data and number the preprocessed data to form financial data sets, tax data sets, business data sets, and market data sets;

[0010] Step 3: Calculate the financial health prediction value Cjk based on the financial data set and the market data set in combination with the algorithm calculation formula;

[0011] Step 4: Calculate the tax compliance forecast value Shg based on the tax dataset and market dataset combined with the algorithm calculation formula;

[0012] Step 5: Calculate the business stability forecast value Ywd based on the business data set and the market data set in combination with the algorithm calculation formula;

[0013] Step 6: Construct risk scoring rules and conduct risk assessment and enterprise risk level assessment based on the financial health prediction value Cjk, tax compliance prediction value Shg, and business stability prediction value Ywd;

[0014] Step 7: Output the risk assessment and enterprise risk level assessment results.

[0015] Preferably, the numbering expression of the financial data set is: [Cs1, Cs2, Cs3, ..., Cs n ], in the expression, Cs1 represents the first financial data, Cs n represents the nth financial data, including cash flow, profitability, debt-paying ability, operating efficiency, cost, return on equity, interest coverage ratio, R&D expense ratio, capital expenditure, and net profit ratio;

[0016] The numbering expression of the tax data set is: [Ss1, Ss2, Ss3, . . . , Ss n ], in the expression, Ss1 represents the first tax data, Ss n Represents the nth tax data, including value-added tax, income tax, invoice risk, declaration compliance, tax incentives, stamp duty, loss carryforward amount, and tax credit rating;

[0017] The numbering expression of the business data set is: [Ys1, Ys2, Ys3, . . . , Ys n ], in the expression, Ys1 represents the first business data, Ys nRepresents the nth business data, including inventory turnover rate, customer concentration, accounts receivable turnover rate, bad debt provision ratio, production efficiency, operational efficiency, order execution rate, logistics cost ratio, production delivery cycle, and supplier payment period.

[0018] Preferably, the numbering expression of the market data set is: [Js1, Js2, Js3, . . . , Js n ], in the expression, Js1 represents the first market data, Js n Represents the nth market data, including industry PMI index, industry average gross profit margin, market share of leading companies, competitor dynamics, changes in tax preferential policies, market size, regional economic activity, and commodity price fluctuations.

[0019] Preferably, the sum of the differences between the industry PMI index, industry average gross profit margin, market share of leading enterprises, competitor dynamics, changes in tax preferential policies, market size, regional economic activity, and commodity price fluctuations in the market data and the historical industry average PMI index, industry average gross profit margin, market share of leading enterprises, competitor dynamics, changes in tax preferential policies, market size, regional economic activity, and commodity price fluctuations is extracted and marked as Cjz. The calculation formula of the financial health prediction value Cjk is:

[0020]

[0021] In the calculation formula, Cjk represents the calculation result of the financial health prediction value, Ci represents the actual value of the i-th indicator in the financial data set, Ci0 represents the weight of the i-th indicator, and Cf i Represents the industry average of the i-th indicator, Cv i represents the industry standard deviation of the i-th indicator, X represents the cash flow adjustment term, p represents the risk penalty term, and β represents the weight of the risk penalty term;

[0022] Represents the standardized score of each financial indicator in the financial data set relative to its industry average, Ci-Cf i Represents the deviation between the actual value of the i-th indicator and the industry average value, represents the standardized score of the i-th indicator;

[0023] represents the weight of adjusting the standardized score, e represents the base of the natural logarithm, and a represents the adjustment parameter.

[0024] Preferably, the tax compliance prediction value Shg is calculated as follows:

[0025]

[0026] In the calculation formula, Shg represents the calculation result of the tax compliance prediction value, Si represents the actual value of the i-th indicator in the tax data set, and Si min Represents the minimum value of the i-th indicator, Si max represents the maximum value of the i-th indicator, Si0 represents the weight of the i-th indicator, R represents the risk value, which reflects the risk level of the enterprise, and R max represents the maximum risk value, which is used to standardize the risk value; H represents the policy reward item, which reflects the additional support provided by the policy to the enterprise;

[0027] Represents the normalized score of each tax data indicator in the tax data set, Represents the ratio between the difference between the actual value and the minimum value of the i-th indicator and the difference between the maximum value and the minimum value of the i-th indicator;

[0028] It represents the risk adjustment factor, which reduces the compliance prediction value of high-risk enterprises and reflects the impact of risk on compliance.

[0029] Preferably, the calculation formula of the business stability prediction value Ywd is:

[0030]

[0031] In the calculation formula, Ywd represents the calculation result of the business stability forecast value, Yi represents the actual value of the i-th indicator in the business data set, and Yi min Represents the minimum value of the i-th indicator, Yi max represents the maximum value of the i-th indicator, Yi0 represents the weight, and M represents the adjustment coefficient, which is used to fine-tune the final result;

[0032] Represents the normalized score of each business indicator in the business data set, Represents the ratio of the difference between the actual value and the minimum value of the i-th indicator to the difference between the maximum value and the minimum value of the i-th indicator.

[0033] Preferably, the risk assessment includes financial risk, tax risk, and business risk. The risk assessment compares the financial health prediction value Cjk, the tax compliance prediction value Shg, and the business stability prediction value Ywd with the financial risk threshold, the tax risk threshold, and the business risk threshold, respectively. When any one of them exceeds the corresponding threshold, it is judged that there is a primary risk. When any two of them exceed the corresponding threshold, it is judged that there is an intermediate risk. When all three of them exceed the corresponding threshold, it is judged that there is a high-level risk.

[0034] Preferably, the enterprise risk level assessment is performed by an enterprise risk index Qfx, and the enterprise risk level includes enterprise level one risk, enterprise level two risk, and enterprise level three risk. When the calculated value of the enterprise risk index Qfx exceeds the enterprise level three risk threshold and is lower than the enterprise level two risk threshold, the enterprise is determined to be at a level three risk level.

[0035] When the calculated value of the enterprise risk index Qfx exceeds the enterprise secondary risk threshold but is lower than the enterprise primary risk threshold, the enterprise is determined to be at the secondary risk level;

[0036] When the calculated value of the enterprise risk index Qfx exceeds the enterprise level 1 risk threshold, the enterprise is judged to be level 1 risk;

[0037] When the enterprise risk index Qfx is lower than the third-level risk threshold, the enterprise is determined to be risk-free.

[0038] Preferably, the enterprise risk index Qfx is calculated as follows:

[0039] Qfx=θ1*Cjk+θ2*Shg+θ3*Ywd

[0040] In the calculation formula, Qfx represents the calculation result of the enterprise risk index, and θ1, θ2, and θ3 represent the weights of the financial health forecast value, tax compliance forecast value, and business stability forecast value, respectively.

[0041] A big data-based enterprise finance and taxation data management system is applied to a big data-based enterprise finance and taxation data management method, including a data acquisition module, a data preprocessing module, a data calculation module, a data analysis module and an output module;

[0042] The data acquisition module is used to obtain financial data, tax data, business data and market data;

[0043] The data preprocessing module is used to preprocess the acquired data and number the preprocessed data to form a financial data set, a tax data set, a business data set and a market data set;

[0044] The data calculation module calculates the financial health prediction value Cjk, the tax compliance prediction value Shg, the business stability prediction value Ywd and the enterprise risk index Qfx based on the financial data set, the tax data set, the business data set and the market data set;

[0045] The data analysis module performs risk assessment and enterprise risk level assessment based on the financial health prediction value Cjk, the tax compliance prediction value Shg, the business stability prediction value Ywd and the enterprise risk index Qfx;

[0046] The output module is used to output risk assessment and enterprise risk level assessment results.

[0047] Compared with the existing technology, the present invention provides a method and system for enterprise financial and tax data management based on big data, which has the following beneficial effects:

[0048] 1. The present invention ensures data quality by eliminating duplicate values, erroneous values ​​and missing data, reduces data redundancy, provides efficient and reliable input for algorithm models, ensures the accuracy, completeness and consistency of corporate financial and tax data, avoids repeated calculations, erroneous analysis and invalid decisions, and retains the latest and most reliable data assets, providing a high-quality and reliable data foundation for subsequent financial health forecasts, tax compliance assessments and business stability analysis, thereby improving corporate management efficiency, reducing risks and supporting scientific decision-making.

[0049] 2. The present invention combines multiple indicators to form financial data sets, tax data sets, business data sets and market data sets, and calculates the financial health prediction value Cjk, tax compliance prediction value Shg, business stability prediction value Ywd and enterprise risk index Qfx respectively. The calculated value is then compared with the risk threshold and the risk level is determined by classification, thereby realizing accurate monitoring and dynamic early warning of corporate financial and tax data. It can not only quickly locate specific risk points through threshold comparison, but also guide resource priority allocation through risk classification, ensuring that management can effectively respond to risks of different levels, and then convert multi-dimensional data into actionable decision-making basis to help enterprises achieve automatic risk prevention, thereby improving financial and tax management efficiency, reducing compliance costs, and enhancing business stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Schematic diagram of the steps of the method of the present invention;

[0051] Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0052] 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.

[0053] See also Figure 1-2 , a method for enterprise financial and tax data management based on big data, comprising the following steps:

[0054] Step 1: Build a middle platform for enterprise finance and taxation data, and obtain financial data, tax data, business data, and market data separately. Through the middle platform, integrate the data scattered in the finance, tax, business, market and other departments, realize cross-system and cross-domain data interoperability, and centrally manage multi-source data, such as financial statements, tax declaration records, sales orders, industry policies, etc., to provide standardized transmission for subsequent analysis. Through the middle platform, realize real-time data collection and updating, and avoid the lag of relying on static reports in the traditional model;

[0055] Step 2: Preprocess the acquired data and number the preprocessed data to form financial data sets, tax data sets, business data sets, and market data sets. Preprocessing includes removing duplicate values, erroneous values, and missing data.

[0056] The specific method to remove duplicate values ​​is:

[0057] Set a unique identifier based on the data indicator, perform deletion based on the unique identifier, automatically delete data with duplicate unique identifiers, and retain the latest data as a reference value;

[0058] The specific method of removing error values ​​is:

[0059] Set data logic and data format according to data indicators, select data, extract data with errors in logic or data format, and correct the data;

[0060] The specific method to remove missing data is:

[0061] Set keywords based on data indicators, select data based on data indicators, and eliminate data indicators with missing keywords;

[0062] By eliminating duplicate values, erroneous values, and missing data, data quality is ensured, data redundancy is reduced, and efficient and reliable input is provided for algorithm models. This ensures the accuracy, completeness, and consistency of corporate financial and tax data, avoids duplicate calculations, erroneous analysis, and ineffective decision-making, while retaining the latest and most reliable data assets. This provides a high-quality, reliable data foundation for subsequent financial health forecasts, tax compliance assessments, and business stability analysis, thereby improving corporate management efficiency, reducing risks, and supporting scientific decision-making.

[0063] The numbering expression of the financial data set is: [Cs1, Cs2, Cs3, ···, Cs n ], in the expression, Cs1 represents the first financial data, Cs n Represents the nth financial data, including cash flow, profitability, debt repayment ability, operating efficiency, cost, return on equity, interest coverage ratio, R&D expense ratio, capital expenditure, and net profit ratio;

[0064] Cash flow collection can clarify cash flow status to assess payment and reinvestment capabilities and prevent the risk of capital chain rupture. Profitability indicators can reflect the company's profitability level and sustainability, and reveal the quality of business profits. Debt-paying ability analysis helps determine the degree of debt repayment security and avoid debt repayment crises. Operational efficiency data can demonstrate the effectiveness of asset and resource utilization and identify blockages and waste in operational links. Cost-related indicators can measure the level of cost control and identify opportunities for cost reduction and efficiency improvement. Return on equity can measure shareholder equity return and assess the company's overall value creation ability. Interest coverage ratio is related to the company's interest payment ability and financial stability. The proportion of R&D expenses reflects the intensity of innovation investment and future competitiveness potential. Capital expenditures reflect the company's expansion and upgrading plans and funding needs. Net profit ratio can analyze the profit structure and quality from multiple dimensions. Taken together, these business data can provide comprehensive and in-depth insights into the company's operating status, accurately identify potential risk points, and provide a strong basis for risk warning, decision optimization, resource allocation, etc., helping companies achieve stable and sustainable development.

[0065] The number expression of the tax data set is: [Ss1, Ss2, Ss3, ···, Ss n ], in the expression, Ss1 represents the first tax data, Ss n Represents the nth tax data, including VAT, income tax, invoice risk, declaration compliance, tax incentives, stamp duty, loss carryforward amount, and tax credit rating;

[0066] Collecting VAT data can accurately grasp the tax burden and circulation of the enterprise's transaction chain. Income tax data can clearly show the tax burden level and tax adjustment status corresponding to profits. Invoice risk information helps to promptly discover potential violations such as false invoices and abnormal deductions. Reporting compliance can ensure that enterprises comply with tax laws and regulations and avoid penalties and reputation loss. Tax incentive-related data can measure the degree of policy benefits and compliance of enterprises. Stamp tax data collection can complete the tax cost puzzle and prevent underpayment. The loss carryforward amount reflects the enterprise's tax planning space and potential tax deduction resources. Tax credit rating data is related to the enterprise's market image, financing convenience and access to tax preferential policies. Comprehensive analysis of these tax data can comprehensively assess the tax health of the enterprise, effectively identify tax risk points, and provide strong support for optimizing tax planning, ensuring tax compliance, and rationally utilizing tax policies, ensuring that enterprises maximize economic benefits and maintain a good market reputation within the tax compliance framework.

[0067] The numbering expression of the business data set is: [Ys1, Ys2, Ys3, ···, Ys n ], in the expression, Ys1 represents the first business data, Ys nRepresents the nth business data, including inventory turnover rate, customer concentration, accounts receivable turnover rate, bad debt provision ratio, production efficiency, operational efficiency, order execution rate, logistics cost ratio, production delivery cycle, and supplier payment period;

[0068] Inventory turnover reflects inventory management efficiency and capital utilization, helping companies optimize inventory levels and avoid backlogs or stockouts. Customer concentration reveals a company's market dependence and sales stability, guiding the expansion or adjustment of its customer base. Accounts receivable turnover measures the speed of collection and the efficiency of capital recovery, which is crucial to a company's cash flow health. The bad debt provision ratio demonstrates the effectiveness of credit risk management and the estimation of potential losses. Production efficiency reflects the speed and efficiency of resource conversion into products and is key to cost control and capacity improvement. Operational efficiency comprehensively reflects the coordination and fluidity of a company's overall business processes. Order execution rate influences customer satisfaction and corporate reputation and is a barometer of production and supply chain collaboration. The proportion of logistics costs is related to cost control and competitiveness in the product distribution process. The production delivery cycle determines the speed of response to customer needs and the ability to adapt to the market. Supplier payment terms reflect the financial game between a company and its upstream and downstream partners and the stability of its supply chain. Comprehensive analysis of these data can provide comprehensive insights into a company's operating conditions, accurately identify the strengths and bottlenecks of business links, and provide a favorable basis for optimizing operational strategies, strengthening risk prevention and control, and improving market competitiveness, thereby promoting efficient, stable, and sustainable development of the company.

[0069] The numbering expression of the market data set is: [Js1, Js2, Js3, ···, Js n ], in the expression, Js1 represents the first market data, Js n Represents the nth market data, including industry PMI index, industry average gross profit margin, market share of leading companies, competitor dynamics, changes in tax preferential policies, market size, regional economic activity, and commodity price fluctuations;

[0070] Step 3: Calculate the predicted financial health value Cjk based on the financial and market data sets and the algorithm calculation formula. By analyzing financial indicators such as profitability, cash flow, assets and liabilities, and combining them with the market environment, the company's financial health is predicted, and early warnings of risks such as capital chain rupture and insufficient debt repayment ability are issued, providing a basis for financing and investment decisions.

[0071] The industry PMI index reflects economic conditions and trends, helping to determine expansion and contraction; average gross profit margin measures profitability and promotes reasonable cost pricing; market share of leading companies indicates market concentration, helping to identify gaps; dynamic monitoring of competitors allows for flexible response to competition; tax incentives affect costs and profits, providing a reference for strategic adjustments; market size sets production capacity targets and assesses potential; regional activity helps allocate resources; price fluctuations affect procurement profits, supply and demand, and optimize supply chain pricing. Comprehensive analysis allows for accurate market understanding, insight into trends, enhanced competitiveness, and the pursuit of opportunities through steady development.

[0072] The sum of the differences between the industry PMI index, industry average gross profit margin, market share of leading enterprises, competitor dynamics, changes in tax preferential policies, market size, regional economic activity, and commodity price fluctuations extracted from the market data set and the historical industry average PMI index, industry average gross profit margin, market share of leading enterprises, competitor dynamics, changes in tax preferential policies, market size, regional economic activity, and commodity price fluctuations is marked as Cjz. The calculation formula for the financial health forecast value Cjk is:

[0073]

[0074] In the calculation formula, Cjk represents the calculation result of the financial health prediction value, Ci represents the actual value of the i-th indicator in the financial data set, Ci0 represents the weight of the i-th indicator, and Cf i Represents the industry average of the i-th indicator, Cv i represents the industry standard deviation of the i-th indicator, X represents the cash flow adjustment term, p represents the risk penalty term, and β represents the weight of the risk penalty term;

[0075] Represents the standardized score of each financial indicator in the financial data set relative to its industry average, Ci-Cf i Represents the deviation between the actual value of the i-th indicator and the industry average value, represents the standardized score of the i-th indicator;

[0076] represents the weight of adjusting the standardized score, e represents the base of the natural logarithm, and a represents the adjustment parameter;

[0077] By combining various financial indicators in the financial dataset and considering their standardized scores relative to the industry average and cash flow adjustments, a comprehensive and objective assessment of a company's financial health can be achieved. Specifically, a summation formula is used to calculate the standardized scores of the financial data, while a fractional formula maps these scores to a probability interval, allowing financial indicators of different magnitudes to be compared on the same scale. Furthermore, by introducing a risk penalty term and its weight, the formula also considers the impact of potential financial risks on the company's health. Ultimately, all these factors are combined and multiplied by the sum of the differences extracted from the market dataset to obtain a comprehensive financial health forecast. This approach not only considers the company's current financial status but also foresees future risks and changes, providing a more accurate and comprehensive financial health assessment for the company.

[0078] Step 4: Calculate the tax compliance prediction value Shg based on the tax and market data sets combined with the algorithm calculation formula. By analyzing tax data such as tax declarations, invoice management, and tax incentives, combined with policy changes, predict the company's tax compliance, identify potential tax risk points, and explore the utilization space of tax incentive policies to reduce the blindness of manual verification;

[0079] The calculation formula for the tax compliance forecast value Shg is:

[0080]

[0081] In the calculation formula, Shg represents the calculation result of the tax compliance prediction value, Si represents the actual value of the i-th indicator in the tax data set, and Si min Represents the minimum value of the i-th indicator, Si max represents the maximum value of the i-th indicator, Si0 represents the weight of the i-th indicator, R represents the risk value, which reflects the risk level of the enterprise, and R max represents the maximum risk value, which is used to standardize the risk value; H represents the policy reward item, which reflects the additional support provided by the policy to the enterprise;

[0082] Represents the normalized score of each tax data indicator in the tax data set, Represents the ratio between the difference between the actual value and the minimum value of the i-th indicator and the difference between the maximum value and the minimum value of the i-th indicator;

[0083] Represents the risk adjustment factor, which reduces the compliance prediction value of high-risk enterprises and reflects the impact of risk on compliance;

[0084] By weighted normalization of eight tax data sets, the impact of dimensionality is eliminated and the importance of indicators is reflected. Risk adjustment coefficients are used to reflect corporate risk levels. Policy incentives are introduced to support enterprises. Finally, the results are fine-tuned through adjustment factors. This comprehensive, standardized, flexible, and interpretable system can comprehensively assess corporate tax compliance and provide a scientific basis for risk management and policy formulation.

[0085] Step 5: Calculate the business stability forecast value Ywd based on the business and market data sets and the algorithm calculation formula. By analyzing business indicators such as inventory turnover, customer concentration, and order execution rate, combined with market demand, predict the company's business stability, identify bottlenecks such as production delivery cycle and logistics costs, promote supply chain optimization and resource allocation, predict demand changes in advance, adjust production plans or sales strategies, and avoid inventory backlogs or stock-out risks.

[0086] The calculation formula for the business stability forecast value Ywd is:

[0087]

[0088] In the calculation formula, Ywd represents the calculation result of the business stability forecast value, Yi represents the actual value of the i-th indicator in the business data set, and Yi min Represents the minimum value of the i-th indicator, Yi max represents the maximum value of the i-th indicator, Yi0 represents the weight, and M represents the adjustment coefficient, which is used to fine-tune the final result;

[0089] Represents the normalized score of each business indicator in the business data set, Represents the ratio between the difference between the actual value and the minimum value of the i-th indicator and the difference between the maximum value and the minimum value of the i-th indicator;

[0090] By integrating 10 key indicators, including inventory turnover rate, customer concentration, and accounts receivable turnover rate, and combining weighted normalization and adjustment coefficients, this method comprehensively and scientifically quantifies a company's business stability, achieving multi-dimensional coverage of core areas such as operations, finance, and supply chain. Standardization eliminates dimensional differences, flexible weights adapt to different scenarios, and strong interpretability facilitates decision-making analysis. It not only comprehensively assesses corporate stability but also identifies potential risk points, providing data support for risk management, resource optimization, and strategy formulation. Dynamic adjustments also drive continuous improvement, enhancing risk resistance and operational resilience.

[0091] Step 6: Build risk scoring rules. Conduct risk assessments based on the predicted value of financial health (Cjk), the predicted value of tax compliance (Shg), and the predicted value of business stability (Ywd), as well as the enterprise risk level. Combine the predicted values ​​of the three dimensions of finance, taxation, and business into a unified risk score. This allows management to quickly assess overall risk, categorize risk levels based on the scores, and implement differentiated response measures for different levels. Risk scoring can be used to guide resources to the most critical areas, avoiding "one-size-fits-all" management and improving resource utilization efficiency.

[0092] Risk assessment includes financial risk, tax risk, and business risk. The risk assessment compares the predicted financial health value Cjk, the predicted tax compliance value Shg, and the predicted business stability value Ywd with the financial risk threshold, the tax risk threshold, and the business risk threshold, respectively. If any one of these exceeds the corresponding threshold, it is judged that there is a primary risk. If any two of these exceed the corresponding threshold, it is judged that there is an intermediate risk. If all three of these exceed the corresponding threshold, it is judged that there is a high-level risk.

[0093] By comparing predicted values ​​for financial health, tax compliance, and business stability with risk thresholds and grading risk levels, it enables precise monitoring and dynamic early warning of corporate financial and tax data. It can quickly locate specific risk points through threshold comparison and prioritize resource allocation through risk grading, ensuring that management effectively responds to different levels of risk. It then transforms multi-dimensional data into actionable decision-making basis, helping companies achieve automatic risk prevention, thereby improving financial and tax management efficiency, reducing compliance costs, and enhancing business stability.

[0094] Enterprise risk level assessment is conducted through enterprise risk index Qfx. Enterprise risk levels include enterprise level 1 risk, enterprise level 2 risk, and enterprise level 3 risk.

[0095] The calculation formula of enterprise risk index Qfx is:

[0096] Qfx=θ1*Cjk+θ2*Shg+θ3*Ywd

[0097] In the calculation formula, Qfx represents the calculation result of the enterprise risk index, θ1, θ2, and θ3 represent the weights of the financial health forecast value, tax compliance forecast value, and business stability forecast value, respectively;

[0098] When the calculated value of the enterprise risk index Qfx exceeds the enterprise third-level risk threshold and is lower than the enterprise second-level risk threshold, the enterprise is judged to be at the third-level risk level;

[0099] When the calculated value of the enterprise risk index Qfx exceeds the enterprise's second-level risk threshold but is lower than the enterprise's first-level risk threshold, the enterprise is judged to be at the second-level risk level;

[0100] When the calculated value of the enterprise risk index Qfx exceeds the enterprise level 1 risk threshold, the enterprise is judged to be level 1 risk;

[0101] When the enterprise risk index Qfx is lower than the third-level risk threshold, the enterprise is judged to be risk-free;

[0102] By weightedly calculating the predicted values ​​of financial health, tax compliance, and business stability to generate a risk index, and dividing risk levels based on thresholds, it achieves quantitative assessment and hierarchical management of corporate financial and tax risks, integrating multi-dimensional data into a single index. This not only simplifies the complex risk judgment process, but also highlights the importance of different indicators through weight allocation, ensuring that the assessment results are consistent with the actual situation of the enterprise, clarifying the boundaries of risk levels, avoiding subjective and ambiguous judgments, and optimizing resource allocation. At the same time, as data is updated, the corporate risk index Qfx can reflect changes in corporate risks in real time, assisting in the adjustment of financial and tax strategies and ensuring business stability, thereby improving management efficiency and compliance.

[0103] Step 7: Output the risk assessment and enterprise risk level assessment results, converting them into visual reports to provide management with intuitive decision-making basis. Through the automated output mechanism, the decision-making cycle is shortened and the enterprise's ability to respond quickly to risks is enhanced.

[0104] A big data-based enterprise finance and taxation data management system is applied to a big data-based enterprise finance and taxation data management method, including a data acquisition module, a data preprocessing module, a data calculation module, a data analysis module and an output module;

[0105] The data collection module is used to obtain financial data, tax data, business data and market data;

[0106] The data preprocessing module is used to preprocess the acquired data and number the preprocessed data to form financial data sets, tax data sets, business data sets and market data sets;

[0107] The data calculation module calculates the financial health prediction value Cjk, tax compliance prediction value Shg, business stability prediction value Ywd and enterprise risk index Qfx based on the financial data set, tax data set, business data set and market data set;

[0108] The data analysis module conducts risk assessment and enterprise risk level assessment based on the financial health forecast value Cjk, tax compliance forecast value Shg, business stability forecast value Ywd and enterprise risk index Qfx;

[0109] The output module is used to output the risk assessment and enterprise risk level assessment results.

[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 method for enterprise financial and tax data management based on big data, characterized by: The following steps are involved: Step 1: Build a corporate financial and tax data platform and obtain financial data, tax data, business data, and market data respectively; Step 2: Preprocess the acquired data and number the preprocessed data to form financial data sets, tax data sets, business data sets, and market data sets; Step 3: Calculate the financial health prediction value Cjk based on the financial data set and the market data set in combination with the algorithm calculation formula; Step 4: Calculate the tax compliance forecast value Shg based on the tax dataset and market dataset combined with the algorithm calculation formula; Step 5: Calculate the business stability forecast value Ywd based on the business data set and the market data set in combination with the algorithm calculation formula; Step 6: Construct risk scoring rules and conduct risk assessment and enterprise risk level assessment based on the financial health prediction value Cjk, tax compliance prediction value Shg, and business stability prediction value Ywd; Step 7: Output the risk assessment and enterprise risk level assessment results.

2. A big data-based enterprise finance and taxation data management system, based on the big data-based enterprise finance and taxation data management method according to claim 1, characterized in that: It includes data acquisition module, data preprocessing module, data calculation module, data analysis module and output module; The data acquisition module is used to obtain financial data, tax data, business data and market data; The data preprocessing module is used to preprocess the acquired data and number the preprocessed data to form a financial data set, a tax data set, a business data set and a market data set; The data calculation module calculates the financial health prediction value Cjk, the tax compliance prediction value Shg, the business stability prediction value Ywd and the enterprise risk index Qfx based on the financial data set, the tax data set, the business data set and the market data set; The data analysis module performs risk assessment and enterprise risk level assessment based on the financial health prediction value Cjk, the tax compliance prediction value Shg, the business stability prediction value Ywd and the enterprise risk index Qfx; The output module is used to output risk assessment and enterprise risk level assessment results.

3. The enterprise financial and tax data management method and system based on big data according to claim 2 is characterized by: The numbering expression of the financial data set is: [Cs1, Cs2, Cs3, . . . , Cs n ], in the expression, Cs1 represents the first financial data, Cs n represents the nth financial data, including cash flow, profitability, debt-paying ability, operating efficiency, cost, return on equity, interest coverage ratio, R&D expense ratio, capital expenditure, and net profit ratio; The numbering expression of the tax data set is: [Ss1, Ss2, Ss3, . . . , Ss n ], in the expression, Ss1 represents the first tax data, Ss n Represents the nth tax data, including value-added tax, income tax, invoice risk, declaration compliance, tax incentives, stamp duty, loss carryforward amount, and tax credit rating; The numbering expression of the business data set is: [Ys1, Ys2, Ys3, . . . , Ys n ], in the expression, Ys1 represents the first business data, Ys n Represents the nth business data, including inventory turnover rate, customer concentration, accounts receivable turnover rate, bad debt provision ratio, production efficiency, operational efficiency, order execution rate, logistics cost ratio, production delivery cycle, and supplier payment period.

4. The enterprise financial and tax data management method and system based on big data according to claim 3 is characterized by: The numbering expression of the market data set is: [Js1, Js2, Js3, . . . , Js n ], in the expression, Js1 represents the first market data, Js n Represents the nth market data, including industry PMI index, industry average gross profit margin, market share of leading companies, competitor dynamics, changes in tax preferential policies, market size, regional economic activity, and commodity price fluctuations.

5. The enterprise financial and tax data management method and system based on big data according to claim 4 is characterized by: The sum of the differences between the industry PMI index, industry average gross profit margin, market share of leading enterprises, competitor dynamics, changes in tax preferential policies, market size, regional economic activity, and commodity price fluctuations in the market data and the historical industry average PMI index, industry average gross profit margin, market share of leading enterprises, competitor dynamics, changes in tax preferential policies, market size, regional economic activity, and commodity price fluctuations is extracted and marked as Cjz. The calculation formula of the financial health prediction value Cjk is: In the calculation formula, Cjk represents the calculation result of the financial health prediction value; Ci represents the actual value of the i-th indicator in the financial data set; Ci0 represents the weight of the i-th indicator; Cf i Represents the industry average of the i-th indicator; Cv i represents the industry standard deviation of the i-th indicator; X represents the cash flow adjustment item; p represents the risk penalty item; β represents the weight of the risk penalty term; Represents the standardized score of each financial indicator in the financial data set relative to its industry average; Ci-Cf i Represents the deviation between the actual value of the i-th indicator and the industry average; represents the standardized score of the i-th indicator; represents the weight of adjusting the standardized score, e represents the base of the natural logarithm, and a represents the adjustment parameter.

6. The enterprise financial and tax data management method and system based on big data according to claim 5 is characterized by: The calculation formula for the tax compliance forecast value Shg is: In the calculation formula, Shg represents the calculation result of the tax compliance prediction value; Si represents the actual value of the i-th indicator in the tax data set; Si min Represents the minimum value of the i-th indicator; Si max Represents the maximum value of the i-th indicator; Si0 represents the weight of the i-th indicator; R represents the risk value, which reflects the risk level of the enterprise; max Represents the maximum risk value and is used to standardize the risk value; H represents the policy incentive item, reflecting the additional support provided by the policy to the enterprise; Represents the normalized score of each tax data indicator in the tax data set, Represents the ratio between the difference between the actual value and the minimum value of the i-th indicator and the difference between the maximum value and the minimum value of the i-th indicator; It represents the risk adjustment factor, which reduces the compliance prediction value of high-risk enterprises and reflects the impact of risk on compliance.

7. The enterprise financial and tax data management method and system based on big data according to claim 6 is characterized by: The calculation formula of the business stability prediction value Ywd is: In the calculation formula, Ywd represents the calculation result of the business stability forecast value; Yi represents the actual value of the i-th indicator in the business data set; Yi min Represents the minimum value of the u-th indicator; Yi max represents the maximum value of the i-th indicator; Yi0 represents the weight; M represents the adjustment coefficient, which is used to fine-tune the final result; Represents the normalized score of each business indicator in the business data set; Represents the ratio of the difference between the actual value and the minimum value of the i-th indicator to the difference between the maximum value and the minimum value of the i-th indicator.

8. The enterprise finance and taxation data management method and system based on big data according to claim 7 is characterized by: The risk assessment includes financial risk, tax risk, and business risk. The risk assessment compares the financial health prediction value Cjk, the tax compliance prediction value Shg, and the business stability prediction value Ywd with the financial risk threshold, the tax risk threshold, and the business risk threshold respectively. When any one of them exceeds the corresponding threshold, it is judged that there is a primary risk. When any two of them exceed the corresponding threshold, it is judged that there is an intermediate risk. When all three of them exceed the corresponding threshold, it is judged that there is a high-level risk.

9. The enterprise financial and tax data management method and system based on big data according to claim 8 is characterized by: The enterprise risk level assessment is performed using the enterprise risk index Qfx. The enterprise risk levels include level 1 risk, level 2 risk, and level 3 risk. When the calculated value of the enterprise risk index Qfx exceeds the level 3 risk threshold but is lower than the level 2 risk threshold, the enterprise is determined to be at level 3 risk. When the calculated value of the enterprise risk index Qfx exceeds the enterprise secondary risk threshold but is lower than the enterprise primary risk threshold, the enterprise is determined to be at the secondary risk level; When the calculated value of the enterprise risk index Qfx exceeds the enterprise level 1 risk threshold, the enterprise is judged to be level 1 risk; When the enterprise risk index Qfx is lower than the third-level risk threshold, the enterprise is determined to be risk-free.

10. The enterprise financial and tax data management method and system based on big data according to claim 9 is characterized by: The calculation formula of the enterprise risk index Qfx is: Qfx=θ1*Cjk+θ2*Shg+θ3*Ywd In the calculation formula, Qfx represents the calculation result of the enterprise risk index, and θ1, θ2, and θ3 represent the weights of the financial health forecast value, tax compliance forecast value, and business stability forecast value, respectively.

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