CN112016843A - Organizational finance and tax data risk analysis method and related device - Google Patents
Organizational finance and tax data risk analysis method and related device Download PDFInfo
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Abstract
The application discloses a risk analysis method for organization property and tax data, which comprises the following steps: acquiring industry data and a plurality of corresponding organization fiscal data through a preset path; performing index calculation on the industry number and the plurality of organization fiscal data according to a target calculation dimension to obtain a risk index; and analyzing the acquired target organization fiscal data according to the risk indexes to obtain a risk analysis result. According to the method, risk indexes are calculated from the industry data according to different dimensions, and finally corresponding risk analysis is carried out on the target organization fiscal data according to the risk indexes, so that the accuracy of analyzing the risk of the organization fiscal data is improved. The application also discloses a risk analysis device for the organization finance and tax data, a server and a computer readable storage medium, which have the beneficial effects.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a risk analysis method for organizing fiscal data, a risk analysis device for organizing fiscal data, a server, and a computer-readable storage medium.
Background
With the continuous development of information technology, enterprises can generate and relate to a lot of organization financial and tax data in the operation process, the more complex the business is, the higher the compounding degree of the data is, and the difficulty of manual analysis is very high. At present, black swan thunderstorm time frequently appears in the society, including cash flow breakage, service qualification failure, operation flow failure, tax treatment violation and the like, and in order to achieve stable operation, enterprises need to know and avoid own risk of organizing financial and tax data in the daily operation process.
In the prior art, after a two-year financial statement, an obtained tax return statement, an obtained tax attached table and an obtained tax advance payment table of an enterprise are obtained, data in the two-year financial statement, the obtained tax attached table and the obtained tax advance payment table are identified, and a financial and tax risk is detected according to a logic relationship built in a system. However, the technical scheme cannot analyze the cross comparison with external data to obtain a relatively objective comparison result risk, in addition, the value-added tax risk is also ignored, and meanwhile, the used data hierarchy is too high, so that the accuracy of analyzing the organization fiscal data risk is low, and the organization fiscal data risk cannot be accurately analyzed.
Therefore, how to improve the accuracy of the analysis of the risk of organizing fiscal data is a major concern for those skilled in the art.
Disclosure of Invention
The application aims to provide a risk analysis method for organizing financial and tax data, a risk analysis device for organizing financial and tax data, a server and a computer readable storage medium, and the accuracy of analyzing the risk of organizing financial and tax data is improved by carrying out corresponding risk analysis on risk indexes calculated according to different dimensions from industrial data.
In order to solve the technical problem, the present application provides a risk analysis method for organization tax data, including:
acquiring industry data and a plurality of corresponding organization fiscal data through a preset path;
performing index calculation on the industry number and the plurality of organization fiscal data according to a target calculation dimension to obtain a risk index;
and analyzing the acquired target organization fiscal data according to the risk indexes to obtain a risk analysis result.
Optionally, the method further includes:
processing the acquired target organization fiscal data through a risk judgment model to obtain a model judgment result;
adding the model decision result to the risk analysis result.
Optionally, the method further includes:
acquiring primary financial data and actual balance table data from the acquired target organization fiscal data;
comparing the primary financial data with the actual balance table data to obtain a balance table analysis result;
adding the balance sheet analysis result to the risk analysis result.
Optionally, performing index calculation on the industry number and the plurality of organization tax data according to a target calculation dimension to obtain a risk index, including:
determining a plurality of index algorithms according to the target calculation dimension;
calculating the industry data according to the index algorithms to obtain an industry risk index; and taking the industry risk index as the risk index.
The application also provides a risk analysis device for organizing fiscal data, including:
the industry data acquisition module is used for acquiring industry data and a plurality of corresponding organization fiscal data through a preset path;
the risk index calculation module is used for performing index calculation on the industry number and the plurality of organization fiscal data according to a target calculation dimension to obtain a risk index;
and the risk analysis module is used for analyzing the acquired target organization fiscal data according to the risk indexes to obtain a risk analysis result.
Optionally, the method further includes:
the model judgment module is used for processing the acquired target organization finance and tax data through a risk judgment model to obtain a model judgment result;
a first risk adding module for adding the model determination result to the risk analysis result.
Optionally, the method further includes:
the financial data acquisition module is used for acquiring primary financial data and actual balance table data from the acquired target organization fiscal data;
the balance table comparison module is used for comparing the primary financial data with the actual balance table data to obtain a balance table analysis result;
a second risk adding module for adding the balance sheet analysis result to the risk analysis result.
Optionally, the risk indicator calculating module includes:
the algorithm determining unit is used for determining a plurality of index algorithms according to the target calculation dimension;
the industry index calculation unit is used for calculating the industry data according to the index algorithms to obtain an industry risk index; and taking the industry risk index as the risk index.
The present application further provides a server, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the organizational fiscal data risk analysis method as described above when executing the computer program.
The present application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of organizing the risk analysis of fiscal data as described above.
The application provides a risk analysis method for organization property tax data, which comprises the following steps: acquiring industry data and a plurality of corresponding organization fiscal data through a preset path; performing index calculation on the industry number and the plurality of organization fiscal data according to a target calculation dimension to obtain a risk index; and analyzing the acquired target organization fiscal data according to the risk indexes to obtain a risk analysis result.
According to the method, risk indexes are calculated from the industry data according to different dimensions, and finally corresponding risk analysis is carried out on the target organization fiscal data according to the risk indexes, so that the accuracy of analyzing the risk of the organization fiscal data is improved.
The application also provides a risk analysis device, a server and a computer readable storage medium for organizing the fiscal data, which have the beneficial effects, and are not repeated herein.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a risk analysis method for organizing tax data according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a risk analysis device for organizing fiscal data according to an embodiment of the present application.
Detailed Description
The core of the application is to provide a risk analysis method for organizing the finance and tax data, a risk analysis device for organizing the finance and tax data, a server and a computer readable storage medium, and the accuracy of analyzing the risk of organizing the finance and tax data is improved by carrying out corresponding risk analysis on risk indexes calculated according to different dimensions from industry data.
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the prior art, after a two-year financial statement, an obtained tax return statement, an obtained tax attached table and an obtained tax advance payment table of an enterprise are obtained, data in the two-year financial statement, the obtained tax attached table and the obtained tax advance payment table are identified, and a financial and tax risk is detected according to a logic relationship built in a system. However, the technical scheme cannot analyze the cross comparison with external data to obtain a relatively objective comparison result risk, in addition, the value-added tax risk is also ignored, and meanwhile, the used data hierarchy is too high, so that the accuracy of analyzing the organization fiscal data risk is low, and the organization fiscal data risk cannot be accurately analyzed.
Therefore, the risk analysis method for the organization property and tax data is provided, risk indexes are calculated from industry data according to different dimensions, and finally corresponding risk analysis is carried out on the target organization property and tax data according to the risk indexes, so that the accuracy of analyzing the risk of the organization property and tax data is improved.
The following describes a risk analysis method for organization tax data provided by the present application, by way of an example.
Referring to fig. 1, fig. 1 is a flowchart illustrating a risk analysis method for organizing tax data according to an embodiment of the present disclosure.
In this embodiment, the method may include:
s101, acquiring industry data and a plurality of corresponding organization fiscal data through a preset path;
the step aims to acquire industry data and a plurality of corresponding organization finance and tax data through a preset path. That is, reference data for analysis in the present embodiment is acquired.
In the embodiment, the financial and tax data of the organization is mainly analyzed and processed correspondingly, so that whether the organization has risks or not can be determined by acquiring the industry data of the organization. The industry data may be obtained from an industry website or a financial system database, and is not specifically limited herein.
The preset path may be obtained by a crawler tool, or may be directly downloaded from a corresponding website, which is not specifically limited herein.
S102, performing index calculation on the industry number and the multiple organization property and tax data according to the target calculation dimension to obtain a risk index;
on the basis of S101, the step aims to perform index calculation on the industry number and the multiple organization property and tax data according to the target calculation dimension to obtain a risk index.
Namely, corresponding calculation operation is carried out on the industry data and the corresponding plurality of organization fiscal data to obtain a risk index reflecting the industry risk. Due to the fact that the types of industry data are more, different calculation modes exist in different calculation dimensions. Therefore, in this embodiment, the dimension for calculation needs to be determined by the target calculation dimension, so as to accurately obtain the risk indicator for analyzing the risk.
Optionally, this step may include:
step 1, determining a plurality of index algorithms according to target calculation dimensions;
step 2, calculating industry data according to a plurality of index algorithms to obtain an industry risk index; and taking the industry risk index as a risk index.
Therefore, how to obtain the risk indicator in the alternative is described. In the alternative scheme, a plurality of index algorithms are determined according to target calculation dimensions; then, calculating the industry data according to a plurality of index algorithms to obtain an industry risk index; and taking the industry risk index as a risk index. Namely, the index algorithm to be used is determined first, and then calculation is directly performed to obtain the risk index.
And S103, analyzing the acquired target organization fiscal data according to the risk indexes to obtain a risk analysis result.
On the basis of the step S102, the step aims to analyze the acquired target organization tax data through the risk indicator to obtain a risk analysis result.
Specifically, the risk index is compared with the corresponding target organization property and tax data, and whether the difference is larger than the preset difference is judged. If so, it indicates that there may be a certain risk and attention needs to be paid. If not, no risk is indicated.
Optionally, this embodiment may further include:
step 1, processing the acquired target organization fiscal data through a risk judgment model to obtain a model judgment result;
and 2, adding the model judgment result to the risk analysis result.
It can be seen that this alternative scheme mainly illustrates that the corresponding judgment operation is performed by the risk judgment model in this embodiment. Specifically, in the alternative scheme, firstly, the acquired target organization fiscal data is processed through a risk judgment model to obtain a model judgment result; then, the model decision result is added to the risk analysis result.
Optionally, this embodiment may further include:
step 1, acquiring primary financial data and actual balance table data from acquired target organization fiscal data;
step 2, comparing the primary financial data with the actual balance table data to obtain a balance table analysis result;
and 3, adding the balance table analysis result to the risk analysis result.
It can be seen that this alternative scheme mainly illustrates that the corresponding determination operation is performed through balance table data in this embodiment. Specifically, in the alternative, first, primary financial data and actual balance table data are acquired from acquired target organization fiscal data; then, comparing the primary financial data with the actual balance table data to obtain a balance table analysis result; and finally, adding the balance table analysis result to the risk analysis result.
In summary, according to the embodiment, the risk indexes are calculated from the industry data according to different dimensions, and finally, corresponding risk analysis is performed on the target organization fiscal data according to the risk indexes, so that the accuracy of analyzing the risk of the organization fiscal data is improved.
The risk analysis method for organizing the fiscal data provided by the present application is further described below by another specific embodiment.
In this embodiment, the analysis is mainly performed by fiscal data, and the method may include:
step 1, collecting industry big data;
step 101, collecting data of various industry marketing companies and industry general data.
Public marketing company data is obtained through a Wandwind financial data terminal, and fields comprise 'name of the affiliated detail industry, code of the affiliated detail industry, year of the affiliated country, income of business, cost of business, sales cost, management cost, financial cost, tax due to payment, income tax cost, tax and additional and partial remarks' and the like.
Step 102: industry data published each year by various industry associations is collected.
2017, 2018 and 2019 industry data are obtained through a Wandwind financial data terminal, and fields comprise' the industry, the industry overall profitability, the return on investment, the cost rate and the like
Step 103: collecting industry data published by statistical departments
Collecting annual statistical yearbook and monthly rose industry data of the statistical department, wherein the field comprises 'the industry, the whole output value of the industry, the growth condition of the industry' and the like
Step 2, obtaining the financial and tax data of the enterprise after obtaining the tax authorization of the enterprise
Step 3, identifying the electronic balance list provided by the enterprise
Step 4, identifying the finance and tax risks of the industry big data
Step 401: obtaining industry name and standard code related to enterprise
Step 402: calculating key indexes of each dimension of the enterprise, specifically comprising:
the value-added tax rate is equal to the current tax amount to be added/current (tax sales according to applicable tax rate + tax sales according to simple method + export sales without reimbursement method + tax sales without reimbursement);
the income tax contribution rate is the income tax amount/business income;
the rate of increase of the obtained tax adjustment is equal to the rate of increase of the obtained tax adjustment/business income;
the income tax adjustment reduction rate is equal to income tax adjustment reduction amount/business income;
the gross profit rate is (business income-business cost)/business income;
the operation profit rate is the operation profit/the whole operation income;
the financial rate is the financial cost/revenue;
the management fee rate is management fee/business income;
the sales charge rate is sales charge/major business income;
403: calculating key indexes of all dimensions of the industry, which comprises the following steps:
after the industry data are collected, calculating the average proportion of the industry (such as average gross interest rate which is 1-overall cost/overall income) by using the overall industry data (such as overall income and overall cost);
defining each detail industry (modified version according to national economic industry classification-2019, GB/T4754- & gt 2017) and years (each different year) by using the obtained industry data, and then calculating the average level (such as the profit level) of each aspect of the industry through a lower calculation formula after summarizing;
the average value-added tax rate is summarized as the current time to be summed/summarized according to the tax amount (the tax sales amount is calculated according to the applicable tax rate, the tax sales amount is calculated according to a simple method, the export sales amount is calculated according to a non-return method and the non-tax sales amount is calculated);
the average income tax contribution rate is the sum of income tax amount and business income;
the average gain tax adjustment increase rate is summarized as the aggregated gain tax adjustment increase amount/aggregated operating income;
the average income tax adjustment reduction rate is the aggregated income tax adjustment reduction amount/aggregated business income;
average gross profit rate (summary operating income-summary operating cost)/summary operating income;
the average operating profit rate is summarized operating profit/total operating income;
average financial rate is summarized financial cost/summarized revenue;
the average management charge rate is the aggregate management charge/the aggregate business income;
the average sales cost rate is summarized sales cost/summarized major business income;
step 404: and comparing the enterprise key indexes with the industry key indexes to obtain the positions of the enterprise data in the industry and know the risks in the industry positioning.
And comparing the key index value of the enterprise with the industry key index value to obtain a range of 70-130% of the industry key index, if the corresponding index of the enterprise is in the range, judging the enterprise is normal, and if not, judging the enterprise is abnormal.
For example: the gross profit rate of an enterprise is 3 percent, the subordinate industry is 1711 cotton spinning processing, the industry interval is 7 to 13 percent, and the enterprise value is lower than the interval lower limit, which indicates that the gross profit is positioned too low and has risks.
And 5, identifying the finance and tax risks of the enterprises.
Step 501: and comparing the self-tax data of the enterprise with the tax risk judgment model.
And substituting the data into the model, and obtaining two results of early warning or no early warning by the model according to the numerical condition.
Examples are as follows:
the model name: receiving tax returns without recording income;
the required data are: earning outside business, returning cash after receiving tax;
and (4) judging the standard: the income outside the operation is less than the cash returned after receiving the tax, and the cash returned after receiving the tax is not equal to 0;
and (3) early warning explanation: the received tax refund generally should be counted into the external income of the business, if the amount of the received tax refund in the cash flow table is larger than the amount of the external income of the business, the received tax refund does not count into the income, and the profit of the current year is influenced;
the early warning scheme is as follows: the method mainly checks the due payment or delayed income of the special item of the balance sheet, checks specific items to see whether the condition of delaying the return of the tax is met or not, and also needs to check the corresponding regulation or asset of the return of the tax and know the return property.
Step 502: and obtaining the detection result of the self fiscal risk.
And 6, carrying out cross comparison on the balance tables to identify the fiscal risk.
Step 601: acquiring balance table data of an enterprise;
step 602: identifying to obtain each balance table field;
step 603: and performing cross comparison on the primary financial data of the enterprise and the balance table data to obtain the balance table cross comparison risk.
Step 7, summarizing risk detection results of the three modules
Step 701: and unifying the risk detection results of the three aspects, and displaying the risk detection results to the user, wherein the risk detection results comprise subject abnormity, income abnormity, expense abnormity, industry risk abnormity and the like.
Therefore, according to the embodiment, the risk indexes are calculated from the industry data according to different dimensions, and finally, corresponding risk analysis is carried out on the target organization property and tax data according to the risk indexes, so that the accuracy of analyzing the risk of the organization property and tax data is improved.
In the following, the risk analysis device for organizing the fiscal data provided in the embodiment of the present application is introduced, and the risk analysis device for organizing the fiscal data described below and the risk analysis method for organizing the fiscal data described above may be referred to in correspondence.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a risk analysis device for organizing fiscal data according to an embodiment of the present disclosure.
In this embodiment, the apparatus may include:
the industry data acquisition module 100 is used for acquiring industry data and a plurality of corresponding organization fiscal data through a preset path;
the risk index calculation module 200 is used for performing index calculation on the industry number and the multiple organization property and tax data according to the target calculation dimension to obtain a risk index;
and the risk analysis module 300 is configured to analyze the acquired target organization fiscal data according to the risk indicator to obtain a risk analysis result.
Optionally, the apparatus further comprises:
the model judgment module is used for processing the acquired target organization fiscal data through the risk judgment model to obtain a model judgment result;
and the first risk adding module is used for adding the model judgment result to the risk analysis result.
Optionally, the apparatus further comprises:
the financial data acquisition module is used for acquiring primary financial data and actual balance table data from the acquired target organization fiscal data;
the balance table comparison module is used for comparing the primary financial data with the actual balance table data to obtain a balance table analysis result;
and the second risk adding module is used for adding the balance table analysis result to the risk analysis result.
Optionally, the risk indicator calculating module 200 may include:
the algorithm determining unit is used for determining a plurality of index algorithms according to the target calculation dimension;
the industry index calculation unit is used for calculating industry data according to a plurality of index algorithms to obtain an industry risk index; and taking the industry risk index as a risk index.
An embodiment of the present application further provides a server, including:
a memory for storing a computer program;
a processor for implementing the steps of the organizational fiscal data risk analysis method as described in the above embodiments when executing the computer program.
Embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the risk analysis method for organizing fiscal data according to the above embodiments.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The risk analysis method for organizing fiscal data, the risk analysis device for organizing fiscal data, the server and the computer-readable storage medium provided by the application are described in detail above. The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
Claims (10)
1. A risk analysis method for organization tax data is characterized by comprising the following steps:
acquiring industry data and a plurality of corresponding organization fiscal data through a preset path;
performing index calculation on the industry number and the plurality of organization fiscal data according to a target calculation dimension to obtain a risk index;
and analyzing the acquired target organization fiscal data according to the risk indexes to obtain a risk analysis result.
2. The organizational tax data risk analysis method of claim 1, further comprising:
processing the acquired target organization fiscal data through a risk judgment model to obtain a model judgment result;
adding the model decision result to the risk analysis result.
3. The organizational tax data risk analysis method of claim 1, further comprising:
acquiring primary financial data and actual balance table data from the acquired target organization fiscal data;
comparing the primary financial data with the actual balance table data to obtain a balance table analysis result;
adding the balance sheet analysis result to the risk analysis result.
4. The method for risk analysis of organizational fiscal data according to claim 1, wherein performing an index calculation on the industry number and the plurality of organizational fiscal data according to a target calculation dimension to obtain a risk index comprises:
determining a plurality of index algorithms according to the target calculation dimension;
calculating the industry data according to the index algorithms to obtain an industry risk index; and taking the industry risk index as the risk index.
5. A kind of organization property tax data risk analytical equipment, characterized by that, comprising:
the industry data acquisition module is used for acquiring industry data and a plurality of corresponding organization fiscal data through a preset path;
the risk index calculation module is used for performing index calculation on the industry number and the plurality of organization fiscal data according to a target calculation dimension to obtain a risk index;
and the risk analysis module is used for analyzing the acquired target organization fiscal data according to the risk indexes to obtain a risk analysis result.
6. The organizational tax data risk analysis device of claim 5, further comprising:
the model judgment module is used for processing the acquired target organization finance and tax data through a risk judgment model to obtain a model judgment result;
a first risk adding module for adding the model determination result to the risk analysis result.
7. The organizational tax data risk analysis device of claim 5, further comprising:
the financial data acquisition module is used for acquiring primary financial data and actual balance table data from the acquired target organization fiscal data;
the balance table comparison module is used for comparing the primary financial data with the actual balance table data to obtain a balance table analysis result;
a second risk adding module for adding the balance sheet analysis result to the risk analysis result.
8. The organizational fiscal data risk analysis device of claim 5 wherein the risk indicator calculation module comprises:
the algorithm determining unit is used for determining a plurality of index algorithms according to the target calculation dimension;
the industry index calculation unit is used for calculating the industry data according to the index algorithms to obtain an industry risk index; and taking the industry risk index as the risk index.
9. A server, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the method of risk analysis of organizational fiscal data according to any one of claims 1 to 4 when executing said computer program.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of risk analysis of organizational fiscal data according to any one of claims 1 to 4.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113902533A (en) * | 2021-10-11 | 2022-01-07 | 税安科技(杭州)有限公司 | Application suitable for index customization and automatic operation in finance and tax field |
| CN114331663A (en) * | 2022-03-15 | 2022-04-12 | 国网浙江省电力有限公司金华供电公司 | Multidimensional path comparison type data processing method and device suitable for financial information |
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| CN113902533B (en) * | 2021-10-11 | 2023-08-25 | 税安科技(杭州)有限公司 | Application method suitable for finance and tax field index self-definition and automatic operation |
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