CN111192126A - Invoice false-proof method and system based on big data analysis - Google Patents
Invoice false-proof method and system based on big data analysis Download PDFInfo
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- 238000007781 pre-processing Methods 0.000 claims abstract description 8
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- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q30/04—Billing or invoicing
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- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/12—Accounting
- G06Q40/123—Tax preparation or submission
Abstract
The invention relates to an invoice false-open prevention method and system based on big data analysis, which comprises the following steps: preprocessing the acquired tax related data of the enterprise, analyzing the preprocessed tax related data by using a clustering algorithm, and determining abnormal invoicing behaviors of the enterprise with problems; determining the weight corresponding to the abnormal invoicing behavior of the problem enterprise, determining the risk score of the problem enterprise according to the weight corresponding to the abnormal invoicing behavior and the score standard, and sequencing the problem enterprise according to the risk score; according to the risk score and the sequencing result of the problem enterprise, performing risk identification according to a preset risk grade determination strategy to determine a risk enterprise; and authorizing the billing information of the inauguration enterprise and the signature of the invoicing system so as to perform false-open-prevention intervention on the billing behavior of the inauguration enterprise. The invention improves the efficiency of positioning the risk enterprises, accelerates the intervention opportunity of the invoicing risk enterprises, effectively prevents the false invoicing action of the value-added tax special invoice and greatly reduces the tax loss.
Description
Technical Field
The invention relates to the technical field of tax system invoice issuing, in particular to an invoice false-proof issuing method and system based on big data analysis.
Background
The problem of false invoicing of value-added tax special invoices is always a chronic disease in value-added tax management, not only causes national tax loss and disturbs market economic order, but also seriously harms national economic safety and brings great law enforcement risk to tax staff. With the continuous promotion of tax informatization, the management means of the tax authority on the value-added tax special invoices is continuously promoted, the management intensity is continuously strengthened, but the cases of falsely issuing the value-added tax special invoices still occur frequently, the discovery time is delayed, the falsely issuing behavior occurs, and the financial income loss is caused, so the tax authority needs an effective method for effectively preventing the taxpayer from falsely issuing the invoices.
At present, an existing big data analysis system of a tax authority only analyzes tax-related data of an enterprise by using a big data technology, then further analyzes the enterprise with doubtful points, and the abnormity and risk judgment needs manual intervention, and meanwhile, an analysis result cannot be processed in the system. Because the approval system can intervene in the invoicing behavior, the analysis result of the data analysis system cannot be timely sent to the approval system, and the behavior of falsely invoicing the value-added tax special invoice of the enterprise cannot be timely and effectively controlled.
Disclosure of Invention
The invention provides an invoice false invoice prevention method and system based on big data analysis, and aims to solve the problem of how to prevent false invoice opening behaviors of enterprises.
In order to solve the above problems, according to an aspect of the present invention, there is provided an invoice anti-false-open method based on big data analysis, the method including:
preprocessing the acquired tax related data of the enterprise, analyzing the preprocessed tax related data by using a clustering algorithm, and determining abnormal invoicing behaviors of the enterprise with problems;
determining the weight corresponding to the abnormal invoicing behavior of the problem enterprise, determining the risk score of the problem enterprise according to the weight corresponding to the abnormal invoicing behavior and the score standard, and sequencing the problem enterprise according to the risk score;
according to the risk score and the sequencing result of the problem enterprise, performing risk identification according to a preset risk grade determination strategy to determine a risk enterprise;
and authorizing the billing information of the inauguration enterprise and the signature of the invoicing system so as to perform false-open-prevention intervention on the billing behavior of the inauguration enterprise.
Preferably, the preprocessing the acquired tax-related data of the enterprise includes:
and performing data conversion, clear and missing value supplement and dirty data processing on the billing information of the enterprise, the stored enterprise business model data, the enterprise tax data, the tax policy data, the maturity index rule, the enterprise basic information and the enterprise registration information.
Preferably, the determining a weight corresponding to the abnormal invoicing behavior of the problem enterprise, and determining a risk score of the problem enterprise according to the weight corresponding to the abnormal invoicing behavior and a scoring standard, includes:
matching the determined abnormal invoicing of the problem enterprise with data in a preset abnormal invoicing behavior library to determine the weight corresponding to the abnormal invoicing behavior;
and determining an abnormal behavior score corresponding to each abnormal invoicing behavior according to the weight and the scoring standard corresponding to each abnormal invoicing behavior, and summing the abnormal behavior scores to determine the risk score of the problem enterprise.
Preferably, the determining risk identification according to the risk scoring and the ranking result of the problem enterprise and the preset risk level determination strategy to determine the risk enterprise includes:
according to the corresponding relation between the risk score of the problem enterprise and the preset risk level, carrying out risk identification and determining the risk enterprise; and/or
And selecting the problem enterprises with high risk scores with preset number threshold values to carry out risk identification according to the sorting results of the problem enterprises, and determining the risk enterprises.
Preferably, the approving the billing information of the inauguration enterprise and the signature of the invoicing system to perform the false-proof intervention on the billing behavior of the inauguration enterprise comprises the following steps:
and checking the invoicing information of the risk enterprise and the signature of the invoice issuing system, distributing a unique identification code to the invoice after all checks are passed, and returning to the invoice issuing system to perform invoice issuing operation.
According to another aspect of the present invention, there is provided an invoice anti-false-open system based on big data analysis, the system including:
the abnormal invoicing behavior determining unit is used for preprocessing the acquired tax related data of the enterprise, analyzing the preprocessed tax related data by using a clustering algorithm and determining the abnormal invoicing behavior of the enterprise with the problem;
the risk score determining unit is used for determining the weight corresponding to the abnormal invoicing behavior of the problem enterprise, determining the risk score of the problem enterprise according to the weight corresponding to the abnormal invoicing behavior and the score standard, and sequencing the problem enterprise according to the risk score;
the risk enterprise determining unit is used for performing risk identification according to a preset risk grade determining strategy according to the risk score and the sequencing result of the problem enterprise and determining the risk enterprise;
and the false opening prevention intervention unit is used for approving the invoicing information of the risk enterprise and the signature of the invoicing system so as to perform false opening prevention intervention on the invoicing behavior of the risk enterprise.
Preferably, the abnormal invoicing behavior determining unit preprocesses the acquired tax-related data of the enterprise, and includes:
and performing data conversion, clear and missing value supplement and dirty data processing on the billing information of the enterprise, the stored enterprise business model data, the enterprise tax data, the tax policy data, the maturity index rule, the enterprise basic information and the enterprise registration information.
Preferably, the risk score determining unit determines a weight corresponding to the abnormal billing behavior of the problem enterprise, and determines the risk score of the problem enterprise according to the weight corresponding to the abnormal billing behavior and the scoring standard, including:
matching the determined abnormal invoicing of the problem enterprise with data in a preset abnormal invoicing behavior library to determine the weight corresponding to the abnormal invoicing behavior;
and determining an abnormal behavior score corresponding to each abnormal invoicing behavior according to the weight and the scoring standard corresponding to each abnormal invoicing behavior, and summing the abnormal behavior scores to determine the risk score of the problem enterprise.
Preferably, the determining unit of the inauguration enterprise, according to the risk score and the ranking result of the problem enterprise, performs risk identification according to a preset risk level determination policy, and determines the inauguration enterprise, includes:
according to the corresponding relation between the risk score of the problem enterprise and the preset risk level, carrying out risk identification and determining the risk enterprise; and/or
And selecting the problem enterprises with high risk scores with preset number threshold values to carry out risk identification according to the sorting results of the problem enterprises, and determining the risk enterprises.
Preferably, the false opening prevention unit approves the billing information of the inauguration enterprise and the signature of the invoicing system to perform false opening prevention on the billing behavior of the inauguration enterprise, and includes:
and checking the invoicing information of the risk enterprise and the signature of the invoice issuing system, distributing a unique identification code to the invoice after all checks are passed, and returning to the invoice issuing system to perform invoice issuing operation.
The invention provides a big data analysis-based invoice false-open prevention method and system, wherein an automatic false-open prevention linkage mechanism is adopted to build a linkage mechanism for studying and judging abnormal behaviors, identifying risks, processing and approving, data analysis is automatically carried out by using indexes and corresponding thresholds, abnormal invoice making behaviors of enterprises are judged, risk grades of the enterprises are identified, the enterprise risk grades are directly linked with an approving system for determining whether invoicing can be successfully carried out, the risk enterprises are sent to the approving system at the first time, and the false-open invoice making behaviors of taxpayers are intercepted in advance and in the process; the analysis of abnormal behaviors of enterprises and the full automation of risk level evaluation are realized, the efficiency of positioning risk enterprises is improved, the intervention opportunity of invoicing risk enterprises is quickened, the false invoicing behavior of value-added tax special invoices is effectively prevented, and the tax loss is greatly reduced; the whole process is performed automatically without manual intervention.
Drawings
A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
FIG. 1 is a flow diagram of a big data analysis based invoice anti-false positive method 100, according to an embodiment of the invention;
FIG. 2 is an architecture diagram of a big data analysis based invoice anti-false-open platform according to an embodiment of the present invention; and
fig. 3 is a schematic structural diagram of an invoice anti-false-open system 300 based on big data analysis according to an embodiment of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
FIG. 1 is a flow diagram of a big data analysis-based invoice anti-false positive method 100, according to an embodiment of the invention. As shown in the drawings, the invoice false-open prevention method based on big data analysis provided by the embodiment of the invention establishes a linkage mechanism by an automatic false-open prevention linkage mechanism through abnormal behavior study and judgment, risk identification processing and approval processing, automatically performs data analysis by using indexes and corresponding threshold values, judges abnormal billing behaviors of enterprises, identifies the risk level of the enterprises, directly links with an approval system for determining whether the invoicing can be successful, and sends the risk enterprises to the approval system for intercepting the false-open billing behaviors of taxpayers in advance and in the process at the first time; the analysis of abnormal behaviors of enterprises and the full automation of risk level evaluation are realized, the efficiency of positioning risk enterprises is improved, the intervention opportunity of invoicing risk enterprises is quickened, the false invoicing behavior of value-added tax special invoices is effectively prevented, and the tax loss is greatly reduced; the whole process is performed automatically without manual intervention. The invoice false-open prevention method 100 based on big data analysis provided by the embodiment of the invention starts from step 101, and preprocesses the acquired tax related data of the enterprise in step 101, and analyzes the preprocessed tax related data by using a clustering algorithm to determine abnormal invoice-making behaviors of the enterprise with problems.
Preferably, the preprocessing the acquired tax-related data of the enterprise includes:
and performing data conversion, clear and missing value supplement and dirty data processing on the billing information of the enterprise, the stored enterprise business model data, the enterprise tax data, the tax policy data, the maturity index rule, the enterprise basic information and the enterprise registration information.
FIG. 2 is an architecture diagram of a big data analysis based invoice anti-false-open platform according to an embodiment of the present invention. As shown in fig. 2, in the embodiment of the present invention, the false invoice issuing behavior can be intercepted in advance and in advance through the real-time linkage between the two systems. For the tax office, the tax administration management and control platform of the tax office including three linkages, respectively: the abnormal behavior studying and judging platform is used for acquiring business model data, analyzing tax-related data of new model indexes and generating abnormal behavior marks of problem enterprises; the risk identification platform is used for grading the abnormal behaviors of the problem enterprises, determining risk scores, determining risk identification according to the risk scores, determining risk grades and determining the processing actions of the abnormal behavior enterprises according to different risk settings; and the approval platform is used for supervising and managing a plurality of indexes such as an opening subject, an opening qualification, an opening quota, certificate authenticity, an opening record and the like, and transmitting the processing action of the risk identification platform to the opening platform.
The method for determining the abnormal billing behavior of the problem enterprise by the abnormal behavior studying and judging platform comprises the following steps: collecting tax core expropriation and management and invoice electronic bottom account system data; establishing a virtual open prevention model, cleaning the acquired data, and bringing the data into the model for training and preference selection; bringing the data into a training and preferred model for data analysis, and finding out abnormal billing behaviors of enterprises; and sending the abnormal billing behavior of the enterprise to a risk identification platform.
Specifically, the abnormal behavior studying and judging platform determines the abnormal billing behavior of the problem enterprise through the data storage module, the ETL module, the data mining module, the data analysis module and the data display module. The data storage module is used for storing enterprise business model data; enterprise tax-related data; tax policy data; a maturity index rule; basic information of an enterprise; and (4) enterprise registration information. The ETL module is used for completing the functions of data conversion, cleaning, missing value supplement, dirty data processing and the like on the data of the data storage module. The data mining module is used for model training, model optimization, model verification and pushing the verified model to the system to serve as a new data analysis model. And the data analysis module is used for completing real-time data analysis and automatic pushing of analysis results by using mature rules and data mining training rules according to the data analysis model. The data display module provides an online display analysis result and an external data interface.
In step 102, determining the weight corresponding to the abnormal invoicing behavior of the problem enterprise, determining the risk score of the problem enterprise according to the weight corresponding to the abnormal invoicing behavior and the score standard, and sequencing the problem enterprise according to the risk score.
Preferably, the determining a weight corresponding to the abnormal invoicing behavior of the problem enterprise, and determining a risk score of the problem enterprise according to the weight corresponding to the abnormal invoicing behavior and a scoring standard, includes:
matching the determined abnormal invoicing of the problem enterprise with data in a preset abnormal invoicing behavior library to determine the weight corresponding to the abnormal invoicing behavior;
and determining an abnormal behavior score corresponding to each abnormal invoicing behavior according to the weight and the scoring standard corresponding to each abnormal invoicing behavior, and summing the abnormal behavior scores to determine the risk score of the problem enterprise.
In step 103, risk identification is carried out according to a preset risk level determination strategy according to the risk score and the ranking result of the problem enterprise, and the risk enterprise is determined.
Preferably, the determining risk identification according to the risk scoring and the ranking result of the problem enterprise and the preset risk level determination strategy to determine the risk enterprise includes:
according to the corresponding relation between the risk score of the problem enterprise and the preset risk level, carrying out risk identification and determining the risk enterprise; and/or
And selecting the problem enterprises with high risk scores with preset number threshold values to carry out risk identification according to the sorting results of the problem enterprises, and determining the risk enterprises.
In the implementation mode of the invention, after the abnormal invoicing behavior of an enterprise is determined, the abnormal invoicing behavior of the enterprise is determined, the abnormal behavior score corresponding to each abnormal invoicing behavior is determined according to the set weight ratio and the score standard, the abnormal behavior scores are summed and converted, the risk score of the enterprise is determined, and the abnormal behavior of the enterprise is ranked according to the risk score from high to low. Then, according to the set risk level ratio, carrying out risk identification on the abnormal enterprise, wherein the risk identification comprises the following steps: according to the corresponding relation between the risk score of the problem enterprise and the preset risk level, carrying out risk identification and determining the risk enterprise; and/or selecting the problem enterprises with high risk scores with preset number threshold values to carry out risk identification according to the sorting results of the problem enterprises, and determining the risk enterprises.
As shown in fig. 2, in an embodiment of the present invention, the risk enterprise is identified by a risk identification platform, and the risk identification platform includes: the risk behavior identification and processing module and the statistic query module. And the risk behavior identification and processing module is used for performing score accounting according to the weight proportion of the abnormal behavior index item, sequencing the abnormal behavior index item from high to low according to the score, and performing risk identification and action corresponding operation on the sequenced enterprises according to the risk grade percentage. And the statistical query module is used for displaying the risk description, the risk grade, the processing opinion and the like of each abnormal behavior on line.
At step 104, the billing information of the inauguration enterprise and the signature of the invoicing system are approved to perform false-proof intervention on the billing behavior of the inauguration enterprise.
Preferably, the approving the billing information of the inauguration enterprise and the signature of the invoicing system to perform the false-proof intervention on the billing behavior of the inauguration enterprise comprises the following steps:
and checking the invoicing information of the risk enterprise and the signature of the invoice issuing system, distributing a unique identification code to the invoice after all checks are passed, and returning to the invoice issuing system to perform invoice issuing operation.
In the embodiment of the invention, after the inauguration enterprise is determined, the inauguration enterprise is sent to an approval platform, and the approval platform performs false opening prevention intervention on the invoicing behavior of the enterprise according to the risk level. Specifically, as shown in fig. 2, the approval platform includes: the tax payer system comprises a taxpayer file module, an approval module, a ticket source management module and a risk identification synchronization module. And the taxpayer file module is used for storing basic information of the taxpayer, tax information of the taxpayer, ticket information of the taxpayer and special enterprise information. And the ticket source management module is used for storing the ticket source information of various vouchers. And the risk identification synchronization module is used for storing risk setting information and synchronizing the risk setting information to the approval system in real time. The approval module is used for receiving invoicing of the inauguration enterprise, namely information to be approved, checking the invoicing information of the inauguration enterprise and the signature of the invoice issuing system, distributing a unique identification code to the invoice after all checks are passed, and returning the unique identification code to the invoice issuing system to issue the invoice.
The invoice false-open prevention method provided by the embodiment of the invention establishes a linkage mechanism for abnormal behavior study and judgment, risk identification processing and approval, sends the first time of a risk enterprise to an approval platform, and intervenes the invoicing behavior of a risk taxpayer in time, and the linkage mechanism of the abnormal behavior study and judgment platform, the risk identification processing platform and the approval platform is full-automatic, does not need manual intervention and processing, realizes the interception of the false-open invoicing behavior of the taxpayer in advance and in the process, accelerates the interception opportunity of the false-open behavior, and effectively prevents the false-open behavior of the special invoice for value-added tax.
Fig. 3 is a schematic structural diagram of an invoice anti-false-open system 300 based on big data analysis according to an embodiment of the present invention. As shown in fig. 3, an invoice anti-false-open system 300 based on big data analysis according to an embodiment of the present invention includes: an abnormal billing behavior determination unit 301, a risk score determination unit 302, an inauguration enterprise determination unit 303 and an anti-false open intervention unit 304.
Preferably, the abnormal invoicing behavior determining unit 301 is configured to preprocess the obtained tax related data of the enterprise, analyze the preprocessed tax related data by using a clustering algorithm, and determine an abnormal invoicing behavior of the enterprise with the problem.
Preferably, the abnormal invoicing behavior determining unit 301 preprocesses the acquired tax related data of the enterprise, and includes:
and performing data conversion, clear and missing value supplement and dirty data processing on the billing information of the enterprise, the stored enterprise business model data, the enterprise tax data, the tax policy data, the maturity index rule, the enterprise basic information and the enterprise registration information.
Preferably, the risk score determining unit 302 is configured to determine a weight corresponding to the abnormal invoicing behavior of the problem enterprise, determine a risk score of the problem enterprise according to the weight corresponding to the abnormal invoicing behavior and a scoring standard, and rank the problem enterprise according to the risk score.
Preferably, the determining unit 302 of risk score determines a weight corresponding to the abnormal billing behavior of the problem enterprise, and determines the risk score of the problem enterprise according to the weight corresponding to the abnormal billing behavior and the scoring standard, including:
matching the determined abnormal invoicing of the problem enterprise with data in a preset abnormal invoicing behavior library to determine the weight corresponding to the abnormal invoicing behavior;
and determining an abnormal behavior score corresponding to each abnormal invoicing behavior according to the weight and the scoring standard corresponding to each abnormal invoicing behavior, and summing the abnormal behavior scores to determine the risk score of the problem enterprise.
Preferably, the risk enterprise determining unit 303 is configured to perform risk identification according to a preset risk level determining strategy according to the risk score and the ranking result of the problem enterprise, and determine the risk enterprise.
Preferably, the determining unit 303 for risk enterprises, according to the risk score and the ranking result of the problem enterprise and according to a preset risk level determination policy, performs risk identification to determine risk enterprises, and includes:
according to the corresponding relation between the risk score of the problem enterprise and the preset risk level, carrying out risk identification and determining the risk enterprise; and/or selecting the problem enterprises with high risk scores with preset number threshold values to carry out risk identification according to the sorting results of the problem enterprises, and determining the risk enterprises.
Preferably, the false opening prevention unit 304 is configured to approve the billing information of the inauguration enterprise and the signature of the invoicing system, so as to perform false opening prevention on the billing behavior of the inauguration enterprise.
Preferably, the false opening prevention unit 304, which authorizes the billing information of the inauguration enterprise and the signature of the invoicing system to perform false opening prevention on the billing behavior of the inauguration enterprise, includes:
and checking the invoicing information of the risk enterprise and the signature of the invoice issuing system, distributing a unique identification code to the invoice after all checks are passed, and returning to the invoice issuing system to perform invoice issuing operation.
The system 300 for preventing false invoice based on big data analysis according to the embodiment of the present invention corresponds to the method 100 for preventing false invoice based on big data analysis according to another embodiment of the present invention, and will not be described herein again.
The invention has been described with reference to a few embodiments. However, other embodiments of the invention than the one disclosed above are equally possible within the scope of the invention, as would be apparent to a person skilled in the art from the appended patent claims.
Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a/an/the [ device, component, etc ]" are to be interpreted openly as referring to at least one instance of said device, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. An invoice false-open prevention method based on big data analysis is characterized by comprising the following steps:
preprocessing the acquired tax related data of the enterprise, analyzing the preprocessed tax related data by using a clustering algorithm, and determining abnormal invoicing behaviors of the enterprise with problems;
determining the weight corresponding to the abnormal invoicing behavior of the problem enterprise, determining the risk score of the problem enterprise according to the weight corresponding to the abnormal invoicing behavior and the score standard, and sequencing the problem enterprise according to the risk score;
according to the risk score and the sequencing result of the problem enterprise, performing risk identification according to a preset risk grade determination strategy to determine a risk enterprise;
and authorizing the billing information of the inauguration enterprise and the signature of the invoicing system so as to perform false-open-prevention intervention on the billing behavior of the inauguration enterprise.
2. The method of claim 1, wherein preprocessing the obtained tax-related data for the business comprises:
and performing data conversion, clear and missing value supplement and dirty data processing on the billing information of the enterprise, the stored enterprise business model data, the enterprise tax data, the tax policy data, the maturity index rule, the enterprise basic information and the enterprise registration information.
3. The method of claim 1, wherein determining the weight corresponding to the abnormal billing behavior of the problem enterprise, and determining the risk score of the problem enterprise according to the weight corresponding to the abnormal billing behavior and the scoring criteria comprises:
matching the determined abnormal invoicing of the problem enterprise with data in a preset abnormal invoicing behavior library to determine the weight corresponding to the abnormal invoicing behavior;
and determining an abnormal behavior score corresponding to each abnormal invoicing behavior according to the weight and the scoring standard corresponding to each abnormal invoicing behavior, and summing the abnormal behavior scores to determine the risk score of the problem enterprise.
4. The method of claim 1, wherein the determining risk enterprises by performing risk identification according to a preset risk level determination strategy according to the risk score and the ranking result of the problem enterprises comprises:
according to the corresponding relation between the risk score of the problem enterprise and the preset risk level, carrying out risk identification and determining the risk enterprise; and/or
And selecting the problem enterprises with high risk scores with preset number threshold values to carry out risk identification according to the sorting results of the problem enterprises, and determining the risk enterprises.
5. The method of claim 1, wherein approving the billing information of the inauguration enterprise and the signature of the invoicing system for anti-fraud intervention of the billing behavior of the inauguration enterprise comprises:
and checking the invoicing information of the risk enterprise and the signature of the invoice issuing system, distributing a unique identification code to the invoice after all checks are passed, and returning to the invoice issuing system to perform invoice issuing operation.
6. An invoice anti-false-open system based on big data analysis, the system comprising:
the abnormal invoicing behavior determining unit is used for preprocessing the acquired tax related data of the enterprise, analyzing the preprocessed tax related data by using a clustering algorithm and determining the abnormal invoicing behavior of the enterprise with the problem;
the risk score determining unit is used for determining the weight corresponding to the abnormal invoicing behavior of the problem enterprise, determining the risk score of the problem enterprise according to the weight corresponding to the abnormal invoicing behavior and the score standard, and sequencing the problem enterprise according to the risk score;
the risk enterprise determining unit is used for performing risk identification according to a preset risk grade determining strategy according to the risk score and the sequencing result of the problem enterprise and determining the risk enterprise;
and the false opening prevention intervention unit is used for approving the invoicing information of the risk enterprise and the signature of the invoicing system so as to perform false opening prevention intervention on the invoicing behavior of the risk enterprise.
7. The system of claim 6, wherein the abnormal invoicing behavior determination unit preprocesses the acquired tax-related data of the enterprise, and comprises:
and performing data conversion, clear and missing value supplement and dirty data processing on the billing information of the enterprise, the stored enterprise business model data, the enterprise tax data, the tax policy data, the maturity index rule, the enterprise basic information and the enterprise registration information.
8. The system of claim 6, wherein the risk score determining unit determines a weight corresponding to the abnormal billing behavior of the problem enterprise, and determines the risk score of the problem enterprise according to the weight corresponding to the abnormal billing behavior and the scoring criteria, and comprises:
matching the determined abnormal invoicing of the problem enterprise with data in a preset abnormal invoicing behavior library to determine the weight corresponding to the abnormal invoicing behavior;
and determining an abnormal behavior score corresponding to each abnormal invoicing behavior according to the weight and the scoring standard corresponding to each abnormal invoicing behavior, and summing the abnormal behavior scores to determine the risk score of the problem enterprise.
9. The system of claim 6, wherein the inauguration enterprise determining unit identifies risks according to a preset risk level determination strategy according to the risk scores and the ranking results of the problem enterprises, and determines the inauguration enterprises, including:
according to the corresponding relation between the risk score of the problem enterprise and the preset risk level, carrying out risk identification and determining the risk enterprise; and/or
And selecting the problem enterprises with high risk scores with preset number threshold values to carry out risk identification according to the sorting results of the problem enterprises, and determining the risk enterprises.
10. The system of claim 6, wherein the false opening prevention unit is used for approving the invoicing information of the inauguration enterprise and the signature of the invoicing system so as to perform false opening prevention on the invoicing behavior of the inauguration enterprise, and comprises:
and checking the invoicing information of the risk enterprise and the signature of the invoice issuing system, distributing a unique identification code to the invoice after all checks are passed, and returning to the invoice issuing system to perform invoice issuing operation.
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