CN111192121A - ANN-based automatic risk taxpayer early warning method and system - Google Patents
ANN-based automatic risk taxpayer early warning method and system Download PDFInfo
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Abstract
The invention discloses an ANN-based automatic risk taxpayer early warning method and system, which are characterized by comprising the following steps: receiving service data; identifying the taxpayer according to the service data, and extracting other service information of the taxpayer in a database in a correlation manner according to the taxpayer to generate taxpayer information; inputting the taxpayer information into a preset ANN judgment model to obtain risk parameters aiming at the taxpayer; comparing the risk parameter to a pre-obtained risk threshold; if the risk parameter meets the requirement of a risk threshold, storing the taxpayer information into a database; if the risk parameter does not meet the requirement of a risk threshold, marking the taxpayer as a risk taxpayer, and feeding back the risk taxpayer to a preset service processing position; the method and the system effectively save corresponding human resource waste, can conveniently and quickly obtain more accurate risk taxpayer early warning, and provide powerful guarantee for tax risk control.
Description
Technical Field
The invention relates to the technical field of tax control, in particular to an ANN-based automatic risk taxpayer early warning method and system.
Background
The risk taxpayer and the risk tax handling behavior are very difficult to control in the tax system, an enterprise is often found after the enterprise is turned into a walk-away enterprise, the tax work is seriously influenced, in order to avoid the problem, the enterprise account or data is mostly screened in a manual mode in the past, the mode is time-consuming and labor-consuming, the result is not accurate enough, and the forecast time of the risk taxpayer is also late.
Disclosure of Invention
In order to solve the problems of time and labor consumption and inaccurate results of screening of risk taxpayers in the background art, the invention provides an ANN-based automatic early warning method and system for the risk taxpayers, wherein the method and the system screen tax payment behavior data through a pre-trained artificial neural network, judge whether the tax payment behavior of the taxpayers has risks, and realize automatic early warning; the automatic risk taxpayer early warning method based on the ANN comprises the following steps:
receiving service data;
identifying the taxpayer according to the service data, and extracting other service information of the taxpayer in a database in a correlation manner according to the taxpayer to generate taxpayer information;
inputting the taxpayer information into a preset ANN judgment model to obtain risk parameters aiming at the taxpayer;
comparing the risk parameter to a pre-obtained risk threshold;
if the risk parameter meets the requirement of a risk threshold, storing the taxpayer information into a database;
and if the risk parameter does not meet the requirement of the risk threshold, marking the taxpayer as a risk taxpayer, and feeding back the risk taxpayer to a preset service processing position.
Further, the other service information includes, before receiving the service data of this time, the information stored in the database for the taxpayer: enterprise registration information, enterprise ticketing information, enterprise invoicing information, enterprise credit registration, enterprise declaration information, enterprise debt information, enterprise overdue information, enterprise authentication information, and enterprise violation information.
Further, the method for obtaining the pre-obtained risk threshold includes:
acquiring a plurality of groups of taxpayer information marked as risk taxpayers in the past from the database;
taking the multiple groups of taxpayer information as learning sample data, and learning in a preset artificial neural network in a supervised learning mode;
and taking the output prediction result as a risk threshold value.
Further, the preset training method of the ANN judgment model includes:
acquiring a plurality of groups of taxpayer information marked as risk taxpayers in the past from the database;
and taking the multiple groups of taxpayer information as learning sample data, and learning in a preset artificial neural network in a reinforcement learning mode to obtain an ANN judgment model.
Further, the artificial neural network adopts sigmoid neurons, and the structure of the artificial neural network is a feedback type network model.
The ANN-based automatic risk taxpayer early warning system comprises:
the service acquisition unit is used for receiving service data;
the information association unit is used for identifying the taxpayer according to the service data, extracting other service information of the taxpayer in a database according to the correlation of the taxpayer, and generating taxpayer information;
the risk parameter calculation unit is used for inputting the taxpayer information into a preset ANN judgment model to obtain a risk parameter aiming at the taxpayer;
a risk judgment unit for comparing the risk parameter with a risk threshold value obtained in advance; if the risk parameter meets the requirement of a risk threshold, storing the taxpayer information into a database; and if the risk parameter does not meet the requirement of the risk threshold, marking the taxpayer as a risk taxpayer, and feeding back the risk taxpayer to a preset service processing position.
Further, the other service information includes, before receiving the service data of this time, the information stored in the database for the taxpayer: enterprise registration information, enterprise ticketing information, enterprise invoicing information, enterprise credit registration, enterprise declaration information, enterprise debt information, enterprise overdue information, enterprise authentication information, and enterprise violation information.
Further, the system comprises a risk threshold calculation unit;
the risk threshold value calculation unit is used for acquiring a plurality of groups of taxpayer information marked as risk taxpayers in the past in the database;
the risk threshold value calculation unit is used for taking the multiple groups of taxpayer information as learning sample data and learning in a preset artificial neural network in a supervised learning mode; and taking the output prediction result as a risk threshold value.
Further, the system comprises an ANN judgment model training unit;
the ANN judgment model training unit is used for acquiring and obtaining a plurality of groups of taxpayer information marked as risk taxpayers in the past in the database;
and taking the multiple groups of taxpayer information as learning sample data, and learning in a preset artificial neural network in a reinforcement learning mode to obtain an ANN judgment model.
Further, the artificial neural network applied by the risk threshold calculation unit adopts sigmoid neurons, and the structure of the artificial neural network is a feedback type network model;
the ANN judgment model training unit is characterized in that the artificial neural network applied by the ANN judgment model training unit adopts sigmoid neurons, and the structure of the artificial neural network is a feedback type network model.
The invention has the beneficial effects that: the technical scheme of the invention provides an ANN-based automatic risk taxpayer early warning method and system, the method and system screen tax payment behavior data through a pre-trained artificial neural network, judge whether the tax payment behavior of the taxpayer has risks, and realize automatic early warning; with the increase of the number of the learning templates, the neural network can adjust the internal related parameters, so that the accuracy of the prediction result is higher and higher; the method and the system effectively save corresponding human resource waste, can conveniently and quickly obtain more accurate risk taxpayer early warning, and provide powerful guarantee for tax risk control.
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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 flowchart of an ANN-based automatic risk taxpayer warning method according to an embodiment of the present invention;
fig. 2 is a structural diagram of an ANN-based risk taxpayer automatic warning system 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 flowchart of an ANN-based automatic risk taxpayer warning method according to an embodiment of the present invention; as shown in fig. 1, the method includes:
in this embodiment, the service data receiving path includes multiple paths, including all data generating positions for generating the service data of the tax enterprise, such as tax window uploading, tax handling ATM, tax handling APP, tax handling website, and the like;
after the business data are obtained through the way and before the business data reach a background storage system, the method is applied to judge, so that the stored data are clear and have risk prompt;
the method of the embodiment aims to judge whether a taxpayer corresponding to generated service data has risk, so after the service data are received, the taxpayer of the service data is identified and confirmed according to the service data, other service information of the taxpayer is extracted from a database, and the taxpayer information is generated together with the service data;
the other service information comprises the following information which is stored in the database aiming at the taxpayer before the service data is received: enterprise registration information, enterprise ticketing information, enterprise invoicing information, enterprise credit registration, enterprise declaration information, enterprise debt information, enterprise overdue information, enterprise authentication information, and enterprise violation information.
The obtained taxpayer information can be analyzed to obtain whether the taxpayer has risks;
the preset ANN judgment model is obtained by pre-training an ANN (artificial neural network), and specifically,
acquiring a plurality of groups of taxpayer information marked as risk taxpayers in the past from the database;
and taking the multiple groups of taxpayer information as learning sample data, and learning in a preset artificial neural network in a reinforcement learning mode to obtain an ANN judgment model.
The ANN judgment model adopts a reinforcement learning mode, and aims to enable colleagues who judge new service data by using the previously learned results to continuously learn the new service data, so that parameters of each neuron in the neural network are further optimized, and more accurate judgment results can be obtained by continuous use of the ANN judgment model.
The method is characterized in that a sigmoid neuron is adopted for the artificial neural network, the input and output of the sigmoid neuron are not binary but approach to continuous linearity, the data processing result can process various unknown data more effectively, and the structure of the artificial neural network is a feedback type network model; all neurons are computational nodes, any two neurons are connected, and data is repeatedly transmitted in the network to achieve a stable state.
if the risk parameter meets the requirement of a risk threshold, storing the taxpayer information into a database;
and if the risk parameter does not meet the requirement of the risk threshold, marking the taxpayer as a risk taxpayer, and feeding back the risk taxpayer to a preset service processing position.
The pre-obtained risk threshold value is a risk prediction result obtained by learning in a supervision mode through a pre-artificial neural network, and if the risk prediction result is exceeded, a risk exists;
specifically, a plurality of groups of taxpayer information marked as risk taxpayers in the past are acquired and obtained in the database;
taking the multiple groups of taxpayer information as learning sample data, and learning in a preset artificial neural network in a supervised learning mode;
and taking the output prediction result as a risk threshold value.
The artificial neural network is the same as the ANN judgment model, a sigmoid neuron is adopted, and the structure of the artificial neural network is a feedback type network model.
Fig. 2 is a structural diagram of an ANN-based risk taxpayer automatic warning system according to an embodiment of the present invention. As shown in fig. 2, the system includes:
a service acquisition unit 210, where the service acquisition unit 210 is configured to receive service data;
an information association unit 220, where the information association unit 220 is configured to identify a taxpayer according to the service data, and extract other service information of the taxpayer in a database according to association of the taxpayer, so as to generate taxpayer information;
a risk parameter calculation unit 230, where the risk parameter calculation unit 230 is configured to input the taxpayer information to a preset ANN judgment model, and obtain a risk parameter for the taxpayer;
a risk judging unit 240, wherein the risk judging unit 240 is configured to compare the risk parameter with a risk threshold value obtained in advance; if the risk parameter meets the requirement of a risk threshold, storing the taxpayer information into a database; and if the risk parameter does not meet the requirement of the risk threshold, marking the taxpayer as a risk taxpayer, and feeding back the risk taxpayer to a preset service processing position.
Further, the other service information includes, before receiving the service data of this time, the information stored in the database for the taxpayer: enterprise registration information, enterprise ticketing information, enterprise invoicing information, enterprise credit registration, enterprise declaration information, enterprise debt information, enterprise overdue information, enterprise authentication information, and enterprise violation information.
Further, the system comprises a risk threshold calculation unit;
the risk threshold value calculation unit is used for acquiring a plurality of groups of taxpayer information marked as risk taxpayers in the past in the database;
the risk threshold value calculation unit is used for taking the multiple groups of taxpayer information as learning sample data and learning in a preset artificial neural network in a supervised learning mode; and taking the output prediction result as a risk threshold value.
Further, the system comprises an ANN judgment model training unit;
the ANN judgment model training unit is used for acquiring and obtaining a plurality of groups of taxpayer information marked as risk taxpayers in the past in the database;
and taking the multiple groups of taxpayer information as learning sample data, and learning in a preset artificial neural network in a reinforcement learning mode to obtain an ANN judgment model.
Further, the artificial neural network applied by the risk threshold calculation unit adopts sigmoid neurons, and the structure of the artificial neural network is a feedback type network model;
the ANN judgment model training unit is characterized in that the artificial neural network applied by the ANN judgment model training unit adopts sigmoid neurons, and the structure of the artificial neural network is a feedback type network model.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the disclosure may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Reference to step numbers in this specification is only for distinguishing between steps and is not intended to limit the temporal or logical relationship between steps, which includes all possible scenarios unless the context clearly dictates otherwise.
Moreover, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the disclosure and form different embodiments. For example, any of the embodiments claimed in the claims can be used in any combination.
Various component embodiments of the disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. The present disclosure may also be embodied as device or system programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present disclosure may be stored on a computer-readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the disclosure, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The disclosure may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several systems, several of these systems may be embodied by one and the same item of hardware.
The foregoing is directed to embodiments of the present disclosure, and it is noted that numerous improvements, modifications, and variations may be made by those skilled in the art without departing from the spirit of the disclosure, and that such improvements, modifications, and variations are considered to be within the scope of the present disclosure.
Claims (10)
1. An ANN-based automatic risk taxpayer early warning method is characterized by comprising the following steps:
receiving service data;
identifying the taxpayer according to the service data, and extracting other service information of the taxpayer in a database in a correlation manner according to the taxpayer to generate taxpayer information;
inputting the taxpayer information into a preset ANN judgment model to obtain risk parameters aiming at the taxpayer;
comparing the risk parameter to a pre-obtained risk threshold;
if the risk parameter meets the requirement of a risk threshold, storing the taxpayer information into a database;
and if the risk parameter does not meet the requirement of the risk threshold, marking the taxpayer as a risk taxpayer, and feeding back the risk taxpayer to a preset service processing position.
2. The method according to claim 1, wherein the other service information includes, before receiving the service data, stored in the database for the taxpayer: enterprise registration information, enterprise ticketing information, enterprise invoicing information, enterprise credit registration, enterprise declaration information, enterprise debt information, enterprise overdue information, enterprise authentication information, and enterprise violation information.
3. The method according to claim 1, wherein the obtaining of the pre-obtained risk threshold comprises:
acquiring a plurality of groups of taxpayer information marked as risk taxpayers in the past from the database;
taking the multiple groups of taxpayer information as learning sample data, and learning in a preset artificial neural network in a supervised learning mode;
and taking the output prediction result as a risk threshold value.
4. The method of claim 1, wherein: the preset ANN judgment model training method comprises the following steps:
acquiring a plurality of groups of taxpayer information marked as risk taxpayers in the past from the database;
and taking the multiple groups of taxpayer information as learning sample data, and learning in a preset artificial neural network in a reinforcement learning mode to obtain an ANN judgment model.
5. The system according to claim 3 or 4, characterized in that:
the artificial neural network adopts sigmoid neurons, and the structure of the artificial neural network is a feedback type network model.
6. An ANN-based automatic risk taxpayer warning system, characterized in that the system comprises:
the service acquisition unit is used for receiving service data;
the information association unit is used for identifying the taxpayer according to the service data, extracting other service information of the taxpayer in a database according to the correlation of the taxpayer, and generating taxpayer information;
the risk parameter calculation unit is used for inputting the taxpayer information into a preset ANN judgment model to obtain a risk parameter aiming at the taxpayer;
a risk judgment unit for comparing the risk parameter with a risk threshold value obtained in advance; if the risk parameter meets the requirement of a risk threshold, storing the taxpayer information into a database; and if the risk parameter does not meet the requirement of the risk threshold, marking the taxpayer as a risk taxpayer, and feeding back the risk taxpayer to a preset service processing position.
7. The system of claim 6, wherein the system comprises: the other service information comprises the following information which is stored in the database aiming at the taxpayer before the service data is received: enterprise registration information, enterprise ticketing information, enterprise invoicing information, enterprise credit registration, enterprise declaration information, enterprise debt information, enterprise overdue information, enterprise authentication information, and enterprise violation information.
8. The system of claim 6, wherein: the system comprises a risk threshold calculation unit;
the risk threshold value calculation unit is used for acquiring a plurality of groups of taxpayer information marked as risk taxpayers in the past in the database;
the risk threshold value calculation unit is used for taking the multiple groups of taxpayer information as learning sample data and learning in a preset artificial neural network in a supervised learning mode; and taking the output prediction result as a risk threshold value.
9. The system of claim 6, wherein: the system comprises an ANN judgment model training unit;
the ANN judgment model training unit is used for acquiring and obtaining a plurality of groups of taxpayer information marked as risk taxpayers in the past in the database;
and taking the multiple groups of taxpayer information as learning sample data, and learning in a preset artificial neural network in a reinforcement learning mode to obtain an ANN judgment model.
10. The system according to claim 8 or 9, characterized in that:
the artificial neural network applied by the risk threshold calculation unit adopts sigmoid neurons, and the structure of the artificial neural network is a feedback type network model;
the ANN judgment model training unit is characterized in that the artificial neural network applied by the ANN judgment model training unit adopts sigmoid neurons, and the structure of the artificial neural network is a feedback type network model.
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