Enterprise tax risk identification method, equipment and medium
By analyzing invoice data to identify core goods and applying statistical methods, the method addresses tax risk misclassification and underclassification in group enterprises, enhancing accuracy and efficiency.
Patent Information
- Application Number
- CN202510493269.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-18
- Publication Date
- 2025-07-15
AI Technical Summary
Enterprise tax risk identification is prone to mislabeling and missing labels, and is time-consuming, especially in multiple independent enterprises under the group enterprise, which is difficult to effectively monitor.
By obtaining the company's input invoice data and sales invoice data, conducting product category focus analysis, identifying the main purchase and main sale goods, building a differentiation model of the purchase and sales items, and using big data algorithms to identify risk to the target enterprise.
It has achieved the accuracy and efficiency of the identification of tax risks of the group's subordinate enterprises, and has automated the analysis of invoice data to assist the group in making decisions, reduce manual intervention, and improve work efficiency.
Smart Images
Figure CN120317682A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of tax technologies, and in particular, to a method, device, and medium for identifying enterprise tax risks. Background Art
[0002] With the popularization of digital electronic invoices, the informatization of enterprise taxation has been continuously developed, and more and more enterprises are applying tax digital management systems. How to use the gradually improved invoice data to help enterprises identify their own tax risks, monitor the risk behaviors of taxpaying entities, and then measure and control risks in advance.
[0003] Currently, especially for multiple independent enterprises under a group enterprise, it is necessary to conduct taxpaying entity analysis and then give risk warnings to the group enterprise. However, due to manually judging the risk identification and monitoring of different independent enterprises, the identification of enterprise tax risks is prone to mislabeling and missing labeling, and it takes a long time. Summary of the Invention
[0004] Embodiments of this application provide a method, device, and medium for identifying enterprise tax risks, which are used to solve the problems that the identification of enterprise tax risks is prone to mislabeling and missing labeling, and it takes a long time.
[0005] Embodiments of this application adopt the following technical solutions:
[0006] On the one hand, embodiments of this application provide a method for identifying enterprise tax risks. The method includes: obtaining the input invoice data and output invoice data of each enterprise, and counting the input amount and output amount of each type of commodity; performing commodity category focus analysis on the input amount and output amount of each type of commodity to obtain the main purchased commodities and main sold commodities of each enterprise; selecting target enterprises with the same main sold commodities from the set of enterprises with the same main purchased commodities to obtain an enterprise risk identification set; determining the input-output difference degree of a single target enterprise relative to other target enterprises according to the input amount and output amount of each target enterprise in the enterprise risk identification set; and identifying the tax risk of the single target enterprise according to the input-output difference degree.
[0007] In an example, the determining the input-output difference degree of a single target enterprise relative to other target enterprises according to the input amount and output amount of each target enterprise in the enterprise risk identification set specifically includes: calculating the ratio between the output amount and input amount of each target enterprise in the enterprise risk identification set; obtaining the mean and standard deviation of multiple ratios; and determining the input-output difference degree of the single target enterprise relative to other target enterprises according to the ratio of the single target enterprise, the mean, and the standard deviation.
[0008] In one example, determining the input-output item difference degree of a single target enterprise relative to other target enterprises based on the ratio of the single target enterprise, the mean value, and the standard deviation specifically includes: obtaining the absolute value of the difference between the ratio of the single target enterprise and the mean value; determining the ratio between the absolute value of the difference and the standard deviation as the input-output item difference degree of the single target enterprise relative to other target enterprises.
[0009] In one example, identifying the tax risk of the single target enterprise based on the input-output item difference degree specifically includes: when the input-output item difference degree is greater than or equal to a preset difference threshold, determining the single target enterprise as a risky enterprise, and determining the risk level of the single target enterprise according to the difference between the input-output item difference degree and the preset difference threshold; when the input-output item difference degree is less than the preset difference threshold, determining the single target enterprise as a non-risky enterprise, or identifying the tax risk of the single target enterprise according to the number of enterprises in the enterprise set and the number of enterprises in the enterprise risk identification set.
[0010] In one example, identifying the tax risk of the single target enterprise based on the input-output item difference degree, the number of enterprises in the enterprise set, and the number of enterprises in the enterprise risk identification set specifically includes: calculating the quantity ratio between the number of enterprises in the enterprise set and the number of enterprises in the enterprise risk identification set; obtaining the difference between 1 and the quantity ratio to get the set input-output item difference degree of the enterprise risk identification set relative to the enterprise set; when the set input-output item difference degree is less than a preset set difference threshold, determining the single target enterprise as a non-risky enterprise; when the set input-output item difference degree is greater than or equal to the preset set difference threshold, identifying the tax risk of the single target enterprise according to the input-output item difference degree and the preset difference threshold.
[0011] In one example, identifying the tax risk of the single target enterprise based on the input-output item difference degree and the preset difference threshold specifically includes: when the difference between the input-output item difference degree and the preset difference threshold is greater than a preset difference threshold, determining the single target enterprise as a non-risky enterprise; when the difference between the input-output item difference degree and the preset difference threshold is less than or equal to the preset difference threshold, determining the single target enterprise as a risky enterprise.
[0012] In one example, the merchandise category focused analysis of the input amount and output amount of each category of merchandise to obtain the main purchased merchandise and main sold merchandise of each enterprise specifically includes: sorting the input amount and output amount of each category of merchandise in descending order respectively, and sequentially accumulating the ratios of the input amount to the total input amount according to the sorting order until the accumulated value exceeds a preset input threshold, and determining the merchandise category participating in the input amount accumulation calculation as the main purchased merchandise; and sequentially accumulating the ratios of the output amount to the total output amount until the accumulated value exceeds a preset output threshold, and determining the merchandise category participating in the output amount accumulation calculation as the main sold merchandise.
[0013] In one example, the obtaining of the input invoice data and output invoice data of each enterprise specifically includes: obtaining the original input invoice data and original output invoice data of each enterprise; extracting the original input invoice data according to preset input fields, and extracting the original output invoice data according to preset output fields; summarizing the extracted input invoice data and the extracted output invoice data to obtain an initial virtual table for each enterprise; in the initial virtual table of each enterprise, summarizing the input amount and output amount of each category of merchandise according to the month of the invoice date to obtain a virtual table for each enterprise; and obtaining the input invoice data and output invoice data of each enterprise from the virtual table of each enterprise.
[0014] On the other hand, an embodiment of the present application provides an enterprise tax risk identification device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an enterprise tax risk identification method as described in any one of the above.
[0015] On the other hand, an embodiment of the present application provides a non-volatile computer storage medium for enterprise tax risk identification, storing computer-executable instructions that can execute an enterprise tax risk identification method as described in any one of the above.
[0016] The above at least one technical solution adopted in the embodiments of the present application can achieve the following beneficial effects:
[0017] By utilizing the inherent data advantages of large group enterprises using tax ERP products, applying big data technology to the ERP industry, applying big data algorithms, constructing an abnormal risk identification model for enterprise input and output merchandise data, analyzing and mining the input and output invoice data generated by users in production and operation, thereby analyzing the business conditions of the enterprises under the group, and further assisting the group in making the next decision. Description of the Drawings
[0018] To more clearly illustrate the technical solutions of the present application, the following will describe some embodiments of the present application in detail with reference to the accompanying drawings. In the drawings:
[0019] Figure 1 It is a schematic flowchart of a method for identifying enterprise tax risks provided by an embodiment of the present application;
[0020] Figure 2 It is a schematic structural diagram of a device for identifying enterprise tax risks provided by an embodiment of the present application. Detailed implementation manners
[0021] To make the objectives, technical solutions and advantages of the present application clearer, the following will clearly and completely describe the technical solutions of the present application in conjunction with specific embodiments and the corresponding drawings. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
[0022] The following will describe some embodiments of the present application in detail with reference to the accompanying drawings.
[0023] Figure 1 It is a schematic flowchart of a method for identifying enterprise tax risks provided by an embodiment of the present application. This method can be applied to different business fields, such as the Internet finance business field, the e-commerce business field, the instant messaging business field, the game business field, the official business field, etc. Some input parameters or intermediate results in this process allow manual intervention and adjustment to help improve accuracy.
[0024] The implementation of the analysis method involved in the embodiments of the present application can be a terminal device or a server, and the present application does not make special limitations on this. For the convenience of understanding and description, the following embodiments will be described in detail taking the server as an example.
[0025] It should be noted that this server can be a single device or a system composed of multiple devices, that is, a distributed server, and the present application does not make specific limitations on this.
[0026] Figure 1 The process in includes the following steps:
[0027] S101: Obtain the input invoice data and output invoice data of each enterprise, and count the input amount and output amount of each type of commodity.
[0028] In some embodiments of the present application, since some information on the invoice is related to the input amount and output amount, it is necessary to screen the invoice information to more conveniently count the input amount and output amount of different commodities.
[0029] Based on this, first, obtain the original input invoice data and original output invoice data of each enterprise. Then, extract the original input invoice data according to the preset input fields, and extract the original output invoice data according to the preset output fields. Then, summarize the extracted input invoice data and the extracted output invoice data to obtain the initial virtual table of each enterprise. Then, in the initial virtual table of each enterprise, summarize the input amount and output amount of each type of commodity according to the month of the invoice date to obtain the virtual table of each enterprise. Finally, obtain the input invoice data and output invoice data of each enterprise from the virtual table of each enterprise.
[0030] Among them, the invoice data can be the input and output invoice data of the enterprise in the past year, which can be set according to actual needs. Data cleaning can also be performed on the original input invoice data and the original output invoice data. When cleaning the data, attention should be paid to the differences between digital electronic invoices and value-added tax invoices. Invoices with red-ink cancellation, voiding, and other abnormal statuses should all be regarded as invalid invoices.
[0031] Among them, when performing classification summary, the sorting can be done according to the priority of the buyer-seller flag, tax organization ID, invoice month, invoice status, and commodity name.
[0032] It should be noted that since the input invoice data only has the commodity name, while the output invoice data has the commodity name and the tax classification code (the unique identifier of the commodity), when the commodity names of the two are inconsistent, they cannot be corresponded to the commodity information in the system, that is, they cannot be completely corresponded to the commodity information in the output invoice data. Therefore, users need to pay attention to the standardization of the input data.
[0033] For example, if the invoice dates are March 4, 2023 and March 10, 2023, and the summary is done according to the month, it will be summarized as March 2023. In addition, the field names of the virtual table can be the tax organization ID, seller tax number, buyer tax number, amount, tax amount, invoice status, invoice month, buyer-seller flag, and commodity name.
[0034] It should be noted that the input invoice includes the input invoice main table, the input invoice commodity sub-table, the output invoice main table, and the output invoice commodity sub-table.
[0035] For example, the input invoice main table is shown in Table 1.
[0036] Table 1
[0037]
[0038] In addition, the input invoice commodity sub-table is shown in Table 2.
[0039] Table 2
[0040] Field Name Field Meaning Data Type Remarks ID ID String Internal Code PARENTID Master Table ID String Corresponding Invoice ID GOODSORSERVICES Goods Name String GOODSAMOUNT_AMOUNT Amount Decimal GOODSAMOUNT_TAXAMOUNT Tax Amount Decimal GOODSAMOUNTPRICEPLUSTAX Total Amount of Price and Tax Decimal
[0041] In addition, the main table of output invoices is shown in Table 3 below.
[0042] Table 3
[0043] Field Name Field Meaning Data Type Remarks ID Invoice ID String Unique Invoice Identifier SELLERTAXPAYERIDNO Seller's Taxpayer Identification Number String TAXORG Tax Organization ID String Internal Code of Seller's Organization AMOUNT Amount Decimal TAXAMOUNT Tax Amount Decimal AMOUNTPLUSTAX Total Amount of Price and Tax Decimal INVOICEDATE Invoice Date Datetime REDFLAG Redemption Flag String INVALIDSIGN Invalidation Flag String Invalid after Digital Electronic Invoice
[0044] In addition, the sub-table of output invoice items is shown in Table 4 below.
[0045] Table 4
[0046] Field Name Field Meaning Data Type Remarks ID ID String Internal Code PARENTID Master Table ID String Corresponding Invoice ID GOODSORSERVICES Goods Name String GOODSCODE Tax Classification Code String AMOUNT Amount Decimal TAXAMOUNT Tax Amount Decimal PRICEPLUSTAX Total Amount of Price and Tax Decimal
[0047] S102: Perform item category focused analysis on the input amount and output amount of each category of goods to obtain the main purchased goods and main sold goods of each enterprise.
[0048] In some embodiments of the present application, the process of focused analysis is as follows:
[0049] First, sort the input amount and output amount of each category of goods in descending order respectively. According to the sorting order, accumulate the ratios of the input amount to the total input amount in sequence until the accumulated value exceeds the preset input threshold, and determine the category of goods participating in the input amount accumulation calculation as the main purchased goods; and accumulate the ratios of the output amount to the total output amount in sequence until the accumulated value exceeds the preset output threshold, and determine the category of goods participating in the output amount accumulation calculation as the main sold goods.
[0050] It should be noted that the main purchased goods refer to the category of goods with a relatively high proportion of the enterprise's purchase amount, representing the main purchase direction of the enterprise, and the main sold goods refer to the category of goods with a relatively high proportion of the enterprise's sales amount, representing the main shipment direction of the enterprise.
[0051] For example, the input and output amounts of a certain enterprise are as follows:
[0052] Goods Category Input Tax Amount (in ten thousand yuan) Output Tax Amount (in ten thousand yuan) A 1500 2000 B 1200 1800 C 800 500 D 500 300 E 300 200
[0053] Therefore, the descending order of the input characteristic values is A - B - C - D - E, and the accumulation calculation process is as follows:
[0054] A: 1500 / 5000 = 30%, A + B: 2700 / 5000 = 54%, A + B + C: 3500 / 5000 = 70%, A + B + C + D: 4000 / 5000 = 80%, the preset input threshold is 75%, so the main purchased goods are A, B, C, D.
[0055] In addition, the descending order of the output characteristic values is A - B - C - D - E, and the accumulation calculation process is as follows:
[0056] A: 2000 / 6000 = 33.3%, B: 3800 / 6000 = 63.3%, A + B + C: 4300 / 6000 = 71%. The preset output threshold is 70%. Therefore, the main sold products are A, B, and C.
[0057] S103: Select target enterprises with the same main sold products from the set of enterprises with the same main purchased products to obtain the enterprise risk identification set.
[0058] S104: Determine the input-output difference degree of each target enterprise in the enterprise risk identification set relative to other target enterprises according to the input amount and output amount of each target enterprise.
[0059] It should be noted that when a group enterprise has multiple independent individual enterprises with similar businesses under it, the main purchased product businesses are similar, and the main sold businesses are similar. Then, theoretically, the relationship between the input and output of each individual enterprise should also be similar. Therefore, the relationship between the input and output of a single enterprise is mined.
[0060] In some embodiments of the present application, the process of determining the input-output difference degree is as follows:
[0061] First, calculate the ratio between the output amount and the input amount of each target enterprise in the enterprise risk identification set. Then, obtain the mean and standard deviation of multiple ratios. Then, determine the input-output difference degree of each target enterprise relative to other target enterprises according to the ratio, mean, and standard deviation of a single target enterprise.
[0062] Among them, the formula for the standard deviation is as follows:
[0063]
[0064] Among them, is the ratio between the output amount and the input amount of the i-th enterprise in the enterprise risk identification set, is the mean of n enterprises.
[0065] Furthermore, first, obtain the absolute value of the difference between the ratio of a single target enterprise and the mean. Then, determine the ratio between the absolute value of the difference and the standard deviation as the input-output difference degree of the single target enterprise relative to other target enterprises.
[0066] Among them, the formula is as follows:
[0067]
[0068] Among them, is the input-output difference degree of a single target enterprise relative to other target enterprises.
[0069] S105: Identify the tax risks of the single target enterprise according to the input-output difference degree.
[0070] In some embodiments of the present application, when the input-output difference degree is greater than or equal to a preset difference threshold, determine the single target enterprise as a risky enterprise, and determine the risk level of the single target enterprise according to the difference between the input-output difference degree and the preset difference threshold. Among them, the higher the difference, the higher the risk level.
[0071] When the input-output difference degree is less than the preset difference threshold, determine the single target enterprise as a non-risky enterprise.
[0072] In some embodiments of the present application, considering that when the number of enterprise sets or enterprise risk identification sets is small, the comparison will be inaccurate. Therefore, when the input-output difference degree is less than the preset difference threshold, the number of enterprises in the enterprise set and the number of enterprises in the enterprise risk identification set can be combined for further analysis, so as to improve the accuracy of risk identification.
[0073] Based on this, when the input-output difference degree is less than the preset difference threshold, determine the tax risk identification of the single target enterprise according to the number of enterprises in the enterprise set and the number of enterprises in the enterprise risk identification set.
[0074] The specific process is as follows:
[0075] Calculate the quantity ratio between the number of enterprises in the enterprise set and the number of enterprises in the enterprise risk identification set.
[0076] Obtain the difference between 1 and the quantity ratio to get the set input-output difference degree of the enterprise risk identification set relative to the enterprise set.
[0077] When the set input-output difference degree is less than the preset set difference threshold, determine the single target enterprise as a non-risky enterprise.
[0078] It should be noted that when the set input-output difference degree is less than the preset set difference threshold, it indicates that the input-output modes of multiple enterprises in the enterprise set are similar. Therefore, it can not only indicate that the number of enterprises in the enterprise risk identification set is large, excluding accidental situations, but also indicate that multiple enterprises under the group have similar businesses.
[0079] When the set input-output difference degree is greater than or equal to the preset set difference threshold, and when the difference between the input-output difference degree and the preset difference threshold is greater than the preset difference threshold, determine the single target enterprise as a non-risky enterprise.
[0080] When the difference between the input-output difference degree and the preset difference threshold is less than or equal to the preset difference threshold, determine the single target enterprise as a risky enterprise.
[0081] It should be noted that when the difference between the purchase and sales items of the set is greater than or equal to the preset set difference threshold, it means that the enterprise risk identification set is different from other enterprises, and the number is relatively small. Therefore, the specific situation of the purchase and sales difference corresponding to the single target enterprise in the enterprise risk identification set is further analyzed. The smaller the difference, the closer the purchase and sales difference of the single target enterprise is to the difference threshold, and the more different it is from other target enterprises, so the higher the risk.
[0082] It should be noted that although the embodiments of the present application are based on Figure 1 Steps S101 to S105 are described in sequence, but this does not mean that steps S101 to S105 must be performed in a strict order. Figure 1 The order shown in the figure is to introduce and explain step S101 to step S105 in sequence, in order to facilitate those skilled in the art to understand the technical solution of the embodiment of the present application. In other words, in the embodiment of the present application, the order between step S101 to step S105 can be appropriately adjusted according to actual needs.
[0083] pass Figure 1 This method uses the inherent data advantages of large group companies in using tax ERP products, applies big data technology to the ERP industry, applies big data algorithms, builds an abnormal risk identification model for enterprise purchase and sales commodity data, analyzes and mines the purchase and sales invoice data generated by users in production and operation, and analyzes the business conditions of the group's subsidiaries, thereby assisting the group in making the next decision.
[0084] Taking advantage of the inherent advantage of enterprises in collecting large amounts of invoice data through enterprise management software during the process of informatization, complex data from different sources are uniformly collected, organized and analyzed, and the extracted data are effectively analyzed to form a risk identification data chain and risk analysis report, which are displayed to users for review and for them to make the next decision.
[0085] Based on this, the data of a single invoice cannot reflect whether the taxpayer has any abnormal commodity behavior, but by concentrating a large amount of invoice information, it is possible to observe to a certain extent whether the purchase and sales items of multiple companies with similar businesses are abnormal, and then find out whether there are problems in production and operation. It improves the previous method of mining corporate tax risks through manual calculations. The purchase and sales invoice data generated by the company can be used to automatically identify whether there are abnormalities in the company's operations, realize real-time supervision of the company, and conduct self-inspection of risks in advance, which greatly liberates the productivity of operators, improves work efficiency, and avoids potential risks.
[0086] Based on this, 1. Focus on core business accurately: screen the main purchased / sold commodities, filter out the interference of secondary commodities, and focus on the core business activities of the enterprise.
[0087] 2. Enhance the pertinence of risk identification: Analyze the differences in main sales only within the enterprise set with the same main purchaser to ensure the business similarity of the comparison objects.
[0088] Based on the same idea, some embodiments of the present application also provide devices and non-volatile computer storage media corresponding to the above method.
[0089] Figure 2 The structure diagram of an enterprise tax risk identification device provided by an embodiment of the present application includes:
[0090] At least one processor; and,
[0091] A memory communicatively connected to the at least one processor; wherein,
[0092] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an enterprise tax risk identification method described in any one of the above.
[0093] A non-volatile computer storage medium for enterprise tax risk identification provided by some embodiments of the present application stores computer-executable instructions, and the computer-executable instructions can execute an enterprise tax risk identification method described in any one of the above.
[0094] Each embodiment in the present application is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For related parts, reference can be made to the partial description of the method embodiments.
[0095] The devices and media provided by the embodiments of the present application correspond one-to-one with the methods. Therefore, the devices and media also have beneficial technical effects similar to those of the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be elaborated here.
[0096] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can adopt the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.
[0097] The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowchart illustrations and / or block diagrams, and combinations of flows and / or blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processors of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing device create means for implementing the functions specified in the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.
[0098] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or one block or multiple blocks.
[0099] These computer program instructions may also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or one block or multiple blocks.
[0100] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.
[0101] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.
[0102] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media that can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information accessible by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.
[0103] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.
[0104] The above description is only for the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the technical principle of the present application shall fall within the protection scope of the present application.
Claims
1. A method for identifying enterprise tax risks, characterized in that, The method includes: Obtaining the input invoice data and output invoice data of each enterprise, and counting the input amount and output amount of each type of commodity; Conducting commodity category focus analysis on the input amount and output amount of each type of commodity to obtain the main purchased commodities and main sold commodities of each enterprise; Selecting target enterprises with the same main sold commodities from the set of enterprises with the same main purchased commodities to obtain an enterprise risk identification set; Determining the input-output difference degree of a single target enterprise relative to other target enterprises according to the input amount and output amount of each target enterprise in the enterprise risk identification set; Conducting tax risk identification on the single target enterprise according to the input-output difference degree.
2. The method according to claim 1, characterized in that, The determining the input-output difference degree of a single target enterprise relative to other target enterprises according to the input amount and output amount of each target enterprise in the enterprise risk identification set specifically includes: Calculating the ratio between the output amount and input amount of each target enterprise in the enterprise risk identification set; Obtaining the mean and standard deviation among multiple ratios; Determining the input-output difference degree of a single target enterprise relative to other target enterprises according to the ratio of the single target enterprise, the mean, and the standard deviation.
3. The method according to claim 2, characterized in that, The determining the input-output difference degree of a single target enterprise relative to other target enterprises according to the ratio of the single target enterprise, the mean, and the standard deviation specifically includes: Obtaining the absolute value of the difference between the ratio of the single target enterprise and the mean; Determining the ratio between the absolute value of the difference and the standard deviation as the input-output difference degree of the single target enterprise relative to other target enterprises.
4. The method according to claim 1, characterized in that The conducting tax risk identification on the single target enterprise according to the input-output difference degree specifically includes: When the input-output difference degree is greater than or equal to a preset difference threshold, determining the single target enterprise as a risky enterprise, and determining the risk level of the single target enterprise according to the difference between the input-output difference degree and the preset difference threshold; When the input-output difference degree is less than the preset difference threshold, determining the single target enterprise as a non-risky enterprise, or conducting tax risk identification on the single target enterprise according to the number of enterprises in the enterprise set and the number of enterprises in the enterprise risk identification set.
5. The method according to claim 4, wherein The conducting tax risk identification on the single target enterprise according to the input-output difference degree, the number of enterprises in the enterprise set, and the number of enterprises in the enterprise risk identification set specifically includes: Calculating the quantity ratio between the number of enterprises in the enterprise set and the number of enterprises in the enterprise risk identification set; Obtaining the difference between 1 and the quantity ratio to get the set input-output difference degree of the enterprise risk identification set relative to the enterprise set; When the set input-output difference degree is less than a preset set difference threshold, determining the single target enterprise as a non-risky enterprise; When the set input-output difference degree is greater than or equal to the preset set difference threshold, conducting tax risk identification on the single target enterprise according to the input-output difference degree and the preset difference threshold.
6. The method according to claim 5, characterized in that, The conducting tax risk identification on the single target enterprise according to the input-output difference degree and the preset difference threshold specifically includes: When the difference between the input-output item difference degree and the preset difference threshold is greater than the preset difference threshold, determine that the single target enterprise is a non-risk enterprise; When the difference between the input-output item difference degree and the preset difference threshold is less than or equal to the preset difference threshold, determine that the single target enterprise is a risk enterprise.
7. The method according to claim 1, characterized in that The commodity category focused analysis of the input amount and output amount of each category of commodities to obtain the main purchased commodities and main sold commodities of each enterprise specifically includes: Sort the input amount and output amount of each category of commodities in descending order respectively. According to the sorting order, accumulate the ratios of the input amount to the total input amount in sequence until the accumulated value exceeds the preset input threshold, and determine the commodity category participating in the input amount accumulation calculation as the main purchased commodity; and accumulate the ratios of the output amount to the total output amount in sequence until the accumulated value exceeds the preset output threshold, and determine the commodity category participating in the output amount accumulation calculation as the main sold commodity.
8. The method according to claim 1, characterized in that, The obtaining of the input invoice data and output invoice data of each enterprise specifically includes: Obtain the original input invoice data and original output invoice data of each enterprise; Extract the original input invoice data according to the preset input fields, and extract the original output invoice data according to the preset output fields; Summarize the extracted input invoice data and the extracted output invoice data to obtain an initial virtual table for each enterprise; In the initial virtual table of each enterprise, summarize the input amount and output amount of each category of commodities according to the month of the invoice date to obtain a virtual table for each enterprise; From the virtual table of each enterprise, obtain the input invoice data and output invoice data of each enterprise.
9. An enterprise tax risk identification device, characterized in that, It includes: At least one processor; And, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for identifying enterprise tax risks according to any one of claims 1-8 above.
10. A non-volatile computer storage medium for enterprise tax risk identification, storing computer-executable instructions, characterized in that, The computer-executable instructions can execute a method for identifying enterprise tax risks according to any one of claims 1-8 above.