CN115293540A

CN115293540A - Finance and tax risk management method and system based on big data and cloud computing

Info

Publication number: CN115293540A
Application number: CN202210877675.9A
Authority: CN
Inventors: 王珍; 徐俊杰
Original assignee: Nanchang Institute of Technology
Current assignee: Nanchang Institute of Technology
Priority date: 2022-07-25
Filing date: 2022-07-25
Publication date: 2022-11-04

Abstract

The invention discloses a finance and tax risk management method and a system thereof based on big data and cloud computing, which comprises a background big data storage platform and a risk control platform, wherein the overall architecture of the risk control platform consists of three architectures, namely a data layer, a computing layer and a display layer, the computing layer consists of a grading management module and a grading management module, and the display layer consists of a risk evaluation and processing module; the finance and tax risk management method and the finance and tax risk management system based on big data and cloud computing comprise a data extraction module, an index management module, a grading management module and a risk evaluation and processing module, cloud computing is carried out through background big data, information is extracted regularly based on the same data source to update risk evaluation, the reality, the dynamic state and the timeliness of risks are guaranteed, and on the basis of providing comprehensive evaluation modes of grading and grading, each risk evaluation mode comprises three calculation modes respectively, different tax service scenes are flexibly coped with, and page-based quick configuration is provided for different tax departments.

Description

Finance and tax risk management method and system based on big data and cloud computing

Technical Field

The invention relates to a risk evaluation system, in particular to a finance and tax risk management method and system based on big data and cloud computing, and belongs to the technical field of risk evaluation.

Background

With the development of internationalization and diversification of enterprises, the types and tax environments of the enterprises face increasingly complex, the tax-related cost of the enterprises is increasingly high, and the tax-related risk is continuously increased, so that the enterprises face not only the operation risk, but also the greater risk comes from the tax risk. The enterprise is bigger, and the tax risk is higher, and the development and the operation safety of enterprise are threatened directly to the tax risk, and enterprise tax risk mainly includes two aspects: on one hand, the tax payment behaviors of enterprises do not accord with the regulations of tax collection laws and regulations, and the enterprises need to pay taxes but not pay taxes or pay taxes less, so that the enterprises face risks of tax compensation, fine, late collection, penalty punishment, reputation damage and the like; on the other hand, the enterprise operation behavior is inaccurate in applicable tax law, no policy with relevant preferential benefit is used, more taxes are paid, unnecessary tax burden is borne, and at present, some tax risk monitoring platforms are designed and developed for helping governments to know and master tax information related to tax payers, tax payment declaration, bill management and the like according to the requirements of government tax collection and administration.

At present, various tax risk evaluation technologies appear in the prior art, but the accuracy of a statistical high-price evaluation model is more based in the prior art, and a specific risk evaluation model cannot be constructed for specific tax business requirements, so that improvement is needed.

Disclosure of Invention

The invention aims to provide a finance and tax risk management method and a finance and tax risk management system based on big data and cloud computing, and solve the problems that in the prior art, the accuracy of an evaluation model is improved more based on statistics and big data, and a specific risk evaluation model cannot be constructed according to specific business requirements.

In order to achieve the purpose, the invention provides the following technical scheme: the finance and taxation risk management method and system based on big data and cloud computing comprise a background big data storage platform and a risk control platform, wherein the overall architecture of the risk control platform comprises three architectures of a data layer, a computing layer and a display layer, the data layer comprises a data extraction module and an index management module, the computing layer comprises a grading management module and a grading management module, and the display layer comprises a risk evaluation and processing module.

As a preferred technical scheme of the invention, the risk management system further comprises a background big data storage platform, and the data layer, the calculation layer and the display layer extract data from the background big data storage platform.

As a preferred technical solution of the present invention, the specific functions of the resource extraction module are as follows:

the problem that risk data are massive, multi-source and asynchronous is solved, a data table related to a newly-built evaluation model of a legal taxation system is configured to serve as a data base of a taxpayer evaluation system, data support is provided for risk evaluation application through configuration of a public database, useful data are extracted, and information accuracy is improved.

As a preferred technical solution of the present invention, the index management module includes a tax payment system, and specifically operates as follows:

the index management module enters a tax payment system, and sets a grading index for the risk subject through the functions of adding, modifying and deleting, wherein the calculation modes of the index comprise two modes: calculating according to the index value, recording the number of the items and customizing the sql statement.

As a preferred technical solution of the present invention, in the score management module, a specific method for constructing a score model is as follows:

the scoring management module respectively establishes a scoring model of the taxpayer body through functions of adding, editing and deleting, and the scoring model is divided into three scoring modes: additive calculations, analytic hierarchy processes and direct weighting.

As a preferred technical solution of the present invention, in the rating management module, a specific method for a user to construct a rating model is as follows:

the rating model of the taxpayer main body is respectively established through the adding, editing and deleting functions of the rating management module, and the rating model is divided into three rating modes: ranking by key indicator, ranking by score, and ranking by object fraction, wherein,

grading according to key indexes only: directly defining an evaluation level according to the key indexes;

grading by score: setting evaluation grades according to the scores from high to low;

rating by object proportion: setting an evaluation grade according to the object ratios of the sequence of the subject scores from top to bottom;

both the ranking by score and the ranking by object ratio may be combined with index prioritization for overall ranking.

As a preferred technical solution of the present invention, the risk evaluation and processing module includes an evaluation record query unit, a risk processing unit and a risk feedback unit, wherein,

evaluation record query unit: the system can carry out grading or evaluation sequencing according to the information of taxpayers, generates a risk grade list after risk sequencing, analyzes and displays the risk grade list, and displays the content displayed by the whole module more vividly and easily understood through a technology of combining a basic chart and visual analysis, and grasps the whole risk condition of a risk subject and the analysis data of each index;

a risk processing unit: after the risk grade list is generated, a risk handling link is subsequently entered, the risk handling is carried out in a task handling mode, in the risk handling link, according to a task generation rule configured in advance, a corresponding task is generated according to risks, according to a task distribution rule configured in advance, the task is automatically distributed to relevant tax staff, and the task distribution can also be carried out in a manual distribution mode.

A risk feedback unit: the risk feedback is that after the risk coping process is finished, the processing result of the risk coping process is fed back to the risk system by each risk coping link, and because a feedback table designed by the system mainly reflects the tax reimbursement condition, the core collection and management cannot distinguish the tax reimbursement under which condition (the conditions of tax reimbursement are many, such as illegal violation, inspection legal, tax owed management, overdue declaration and the like), so that the automatic feedback of the system cannot be realized, and tax managers are required to fill in the tax reimbursement condition manually.

8. The fiscal risk management method and system based on big data and cloud computing according to claim 1, characterized in that: the specific functions of the big data calculation module are as follows:

the method has the advantages that the situations of low calculation speed and storage load pressure of the traditional sql are relieved, massive risk information is integrated and obtained, massive storage and real-time processing are achieved, the modeling result is applied to the distributed big data cluster for parallel calculation, finally model output is generated, and the model output is synchronized to a local database in an asynchronous mode for use by a display layer.

Compared with the prior art, the invention has the beneficial effects that:

the finance and tax risk management method and the finance and tax risk management system based on big data and cloud computing comprise a data extraction module, an index management module, a grading management module and a risk evaluation and processing module, cloud computing is carried out through background big data, information is extracted regularly based on the same data source to update risk evaluation, the reality, the dynamic state and the timeliness of risks are guaranteed, and on the basis of providing comprehensive evaluation modes of grading and grading, each risk evaluation mode comprises three calculation modes respectively, different tax service scenes are flexibly coped with, and page-based quick configuration is provided for different tax departments.

Drawings

FIG. 1 is a schematic view of the structure of the present invention;

FIG. 2 is a diagram illustrating an operation procedure of the index management model according to the present invention;

FIG. 3 is a schematic diagram of an operating program structure of a score management module according to the present invention;

FIG. 4 is a diagram illustrating an operating program of the rating management module of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.

Referring to fig. 1-4, the invention provides a finance and taxation risk management method and system based on big data and cloud computing, which comprises a background big data storage platform and a risk control platform, wherein the overall architecture of the risk control platform comprises three architectures of a data layer, a computing layer and a display layer, the data layer comprises a data extraction module and an index management module, the computing layer comprises a grading management module and a grading management module, and the display layer comprises a risk evaluation and processing module.

The finance and taxation risk management method and system based on big data and cloud computing are characterized in that the risk management system further comprises a background big data storage platform, and the data layer, the computing layer and the display layer extract data from the background big data storage platform.

The invention relates to a finance and tax risk management method and a system based on big data and cloud computing, wherein the resource extraction module has the following specific functions:

the problem of massive, multi-source and asynchronous risk data is solved, a data table configuration related to an evaluation model newly established in a legal taxation system is used as a data base of a taxpayer evaluation system, a common database configuration is used for providing data support for risk evaluation application, useful data are extracted, and information accuracy is improved.

The invention relates to a finance and taxation risk management method and a system thereof based on big data and cloud computing, wherein an index management module comprises a taxation system, and the operation is as follows:

the index management module enters a tax payment system, a grading index is set for a risk subject through newly adding, modifying and deleting functions, and the calculation modes of the index include two types: calculating according to the index value, recording the number of the items and customizing the sql statement.

The invention relates to a finance and taxation risk management method and a system based on big data and cloud computing, wherein in a scoring management module, a specific method for constructing a scoring model is as follows:

Specifically, the steps of the three scoring modes of the addition calculation method, the analytic hierarchy process and the direct weighting method are slightly different, the system skips different model construction steps according to the specific scoring mode, the process is disassembled, the process is performed step by step, but the common steps can be decomposed into the following steps:

step 1, constructing a distributed risk scoring system based on business requirements;

the built-in jsmand plug-in of the system supports free dragging components to form a tree structure, such as: the natural human risk assessment system comprises primary factors: basic information, honor information, penalty information, business information;

step 2, setting the score requirement and the calculation time of the model;

the score comprises a basic score, a model score and a dynamic score, wherein the basic score is an original score of the model, the basic score is subjected to addition and subtraction according to the actual condition of the model on the basis, the model score is an upper limit and a lower limit of the score calculated by the model, if the actual calculated score of the model exceeds the upper limit and the lower limit, the upper limit and the lower limit are final model scores, the dynamic score is the requirement of the government for work, the corresponding dynamic score is automatically added to all subjects every month, and the calculation time is mainly set as the time and the calculation period for the model to start calculation for the first time;

step 3, extracting real data according to the evaluation indexes corresponding to the bottom layer of the scoring system;

if the basic information corresponds to the household register, occupation and academic calendar of the principal responsible person in the department; the honor information corresponds to the quality quantity, the blood donation quantity, the duration of the volunteer and the quantity of the red list; the penalty information corresponds to the administrative penalty quantity, the blacklist quantity and the default quantity; the business information comprises social insurance payment conditions, accumulation fund payment conditions and risk card repayment conditions, the indexes are maintained in the index management module, and the evaluation index data is obtained by intercepting the current latest index information of the corresponding risk subject and analyzing and calculating according to the index definition;

step 4, completing the construction of the system weight;

the step is mainly embodied in a chromatography analysis method and a direct weighting method, the addition calculation method is that all index weights are the same,

the addition calculation method is that the addition and subtraction scores are directly set for each index, and the final result is the model score;

the analytic hierarchy process is to compare the importance of the factors or indexes in each hierarchy level, and calculate the weight of each index by using the principle of the analytic hierarchy process, wherein the total weight is equal to 1;

1) Performing same-level importance comparison on factors and indexes of each layer

Scoring according to an importance scale of 1-9, wherein 1 is equally important, 3 is slightly important, 5 is more important, 7 is very important, 9 is absolutely important, 2, 4, 6 and 8 are intermediate values of adjacent judgment, indexes or factors are clicked, all factors or judgment matrixes formed by the indexes in the same layer are displayed on the right side, and importance scoring is performed;

2) After the scoring is finished, the system carries out weight calculation

A. Generating a judgment matrix Aij according to the importance comparison score, wherein diagonals are all 1,Aji =1/Aij;

B. obtaining Bij according to Aij: bij = Aij/∑ Aij (each element divided by the sum of the columns in which each element is located);

C. and summing rows of Bij to obtain Bj, and finding the sum sigma Bj = n of elements of each row to obtain the weight Wi: wi = Bj/Σ Bj (sum/n of rows of elements);

D. the calculation is finished from one level to the top, and the weights of the lower level are all weighted weights, namely, the lower level weight = the weight of the lower level occupying the upper level weight;

3) The background also needs to carry out consistency check

A. The calculation matrix AWi, EXCEL formula = MMULT (Aij, wi), i.e.

AW1＝A11*W1+A12*W2+A13*W3+A14*W4……,

AW2＝A21*W1+A22*W2+A23*W3+A24*W4……,

AW3＝A31*W1+A32*W2+A33*W3+A34*W4……,

AW4＝A41*W1+A42*W2+A43*W3+A44*W4……,

……

B. Calculating the maximum characteristic root lambda = average (AWi/Wi) (dividing each element of Awi by the corresponding row element of the Wi matrix, and then averaging),

C. calculating CR = CI/RI = (lambda-n)/(n-1)/RI.n is the number of the elements of the level, RI finds the corresponding fixed value according to n, CR <0.1 is consistent, and the weight is right,

the direct weighting method is that aiming at a policy file with definite, factors and indexes in each layer are assigned with weights definitely, and the weights can be directly weighted on a page, wherein the total weight is equal to 1;

step 4, determining index score calculation rules including interval score and direct score;

the interval score is, for example: qualification number is in the range of [0, 1) (no qualification), scoring 0; the qualification number is in the range of [1,3 ] (with 1 or 2 qualifications), 3 points are obtained; the qualification number is in the range of [3, more) (with 3 or more qualifications), and 6 points are obtained;

direct scoring is for example: if the qualification number is calculated as 1 score, the index score is equal to the actual number 1;

and 5, displaying the complete risk scoring system and each index calculation rule by using a tree structure, and confirming the model.

In addition, the scoring model also provides a testing function, and the calculation result of the model can be tested quickly.

Specifically, the steps of the three scoring modes are slightly different, the system can jump to different model building steps according to the specific scoring modes, the process is disassembled, the steps are performed step by step, but the common steps can be decomposed as follows:

step 1: creating basic information of the model;

if the rating mode is rated according to the score or the object ratio, an enabled rating model is required to be selected as a rating basis, and the calculation time and period are consistent with the rating model. If the method is a rating method according to key indexes, the method is not needed.

Step 2: selecting indexes required by rating;

the key index algorithm must be selected, and if no index is required, the key index algorithm can be selected if the key index algorithm is graded according to the score or the object ratio;

and 3, step 3: the grade is set.

The invention relates to a finance and taxation risk management method and a system thereof based on big data and cloud computing, wherein in a rating management module, a specific method for a user to construct a rating model is as follows:

the rating model of the taxpayer main body is respectively established through the adding, editing and deleting functions of the rating management module, and the rating model is divided into three rating modes: ranking by key indicator, ranking by fractional score, and ranking by object fraction, wherein,

both the score by score rating and the object by object ratio rating may be combined with index prioritization for overall rating.

The invention relates to a finance and tax risk management method and a system based on big data and cloud computing, wherein a risk evaluation and processing module comprises an evaluation record query unit, a statistical analysis unit and an externally provided query interface unit,

An external query interface unit can be further arranged, and the main functions of the external interface unit are as follows: the risk evaluation result, namely grading/rating, is data which is opened outwards, the platform classifies, integrates and encapsulates all the data which are put into storage again on the basis of a service level on the basis of a logic framework of an original database, provides a relatively specific data class calling interface outwards, and meets the use requirements on specific data, including the lowest score and the highest score of a newly calculated grading model, the grade of the newly calculated grading model and the latest grading record records of all main bodies.

The invention relates to a finance and tax risk management method and a system based on big data and cloud computing, wherein the big data computing module has the following specific functions:

In the invention, on the basis of unchanging the model page configuration thought, the calculation process of the model evaluation result can be smoothly completed by selecting sql calculation for the items with less main bodies, the scheme is regarded as an alternative scheme of the invention,

in the invention, the scoring model and the rating model have no strong association relationship, and only the page configuration of the scoring model or the page configuration of the rating model or the scoring configuration of the rating model by utilizing background calculation is reserved on the basis of unchanging the model paging configuration thought, which is regarded as an alternative scheme of the invention.

In the invention, resource extraction and index management are the screening of evaluation data sources, the data sources are directly determined in a code form, and then the configuration of a model is carried out, which is considered as an alternative scheme of the invention.

In the invention, the classification of the index calculation mode, the classification of the score model calculation mode, the related step dismantling and the classification of the rating model calculation mode and the related step dismantling are integrated based on the requirements of actual projects, and new classifications are directly applied and dismantled or combined or added, so that the method is regarded as an alternative scheme of the invention.

In the description of the present invention, it is to be understood that the indicated orientations or positional relationships are based on the orientations or positional relationships shown in the drawings and are only for convenience in describing the present invention and simplifying the description, but are not intended to indicate or imply that the indicated devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are not to be construed as limiting the present invention.

In the present invention, unless otherwise explicitly specified or limited, for example, it may be fixedly attached, detachably attached, or integrated; can be mechanically or electrically connected; the terms may be directly connected or indirectly connected through an intermediate, and may be communication between two elements or interaction relationship between two elements, unless otherwise specifically limited, and the specific meaning of the terms in the present invention will be understood by those skilled in the art according to specific situations.

Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims

1. The finance and taxation risk management system based on big data and cloud computing is characterized by comprising a background big data storage platform and a risk control platform, wherein the overall architecture of the risk control platform comprises three layers of architectures including a data layer, a computing layer and a display layer, the data layer comprises a data extraction module and an index management module, the computing layer comprises a grading management module and a grading management module, and the display layer comprises a risk evaluation and processing module.

2. The big data and cloud computing based tax risk management system according to claim 1, wherein: the risk management system further comprises a background big data storage platform, and the data layer, the calculation layer and the display layer extract data from the background big data storage platform.

3. The big data and cloud computing based tax risk management system according to claim 1, wherein: the specific functions of the resource extraction module are as follows:

4. The big data and cloud computing based tax risk management system according to claim 1, wherein: the index management module comprises a tax payment system and specifically comprises the following operations:

the index management module enters a tax payment system, and sets a grading index for the risk subject through the functions of adding, modifying and deleting, wherein the calculation modes of the index comprise two modes: calculating according to the index value, recording the number of the items and self-defining the sql statement.

5. The big data and cloud computing based tax risk management system according to claim 1, wherein: in the scoring management module, a specific method for constructing a scoring model is as follows:

6. The big data and cloud computing based tax and finance risk management system according to claim 1, wherein: in the rating management module, a specific method for a user to construct a rating model is as follows:

a rating model of a taxpayer main body is respectively established through the functions of adding, editing and deleting of a rating management module, and the rating model is divided into three rating modes: ranking by key indicator, ranking by fractional score, and ranking by object fraction, wherein,

7. The big data and cloud computing based tax and finance risk management system according to claim 1, wherein: the risk evaluation and processing module comprises an evaluation record query unit, a risk processing unit and a risk feedback unit, wherein,

evaluation record query unit: the system can perform scoring or evaluation sequencing according to the information of taxpayers, generates a risk grade inventory after risk sequencing, analyzes and displays the risk grade inventory, and displays the content displayed by the whole module through a technology of combining a basic chart and visual analysis, so that the content displayed by the whole module is more vivid and easier to understand, and the whole risk condition of a risk subject and the analysis data of each index are mastered;

a risk processing unit: after the risk level list is generated, a risk handling link is subsequently entered, risk handling is carried out in a task handling mode, in the risk handling link, according to a task generation rule configured in advance, a corresponding task is generated according to risks, according to a task distribution rule configured in advance, the task is automatically distributed to relevant tax staffs, and task distribution can also be carried out in a manual distribution mode.

8. The big data and cloud computing based tax risk management system according to claim 1, wherein: the big data calculation module has the following specific functions:

9. A fiscal and taxation risk management method based on big data and cloud computing, said management method being based on the risk management system of any of the preceding claims 1-8, characterized in that the specific steps of said management method are as follows:

the tax data of each department of the bill to be processed and recorded are uploaded to a background big data storage platform, the situations of low calculation speed and load pressure storage of the traditional sql are relieved, massive risk information is integrated and obtained, massive storage and real-time processing are achieved, remote processing is mainly conducted on a calculation layer through cloud computing, after a risk main body is scored and graded through a scoring management module and a grading management module, and then the risk is processed through a risk evaluating and processing module.