Financial risk analysis method and system based on big data analysis model

Abstract

The invention relates to a financial risk analysis method and system based on a big data analysis model, and the method comprises the steps: obtaining enterprise finance and tax data and external industry data of an enterprise in a certain time period, and carrying out the data preprocessing, and obtaining the preprocessed enterprise internal data and enterprise external data, and randomly screening the enterprise internal data and the enterprise external data to perform association relationship analysis, constructing a big data analysis model of enterprise finance and taxation, obtaining finance and taxation risk data exceeding a preset finance and taxation project index in the enterprise internal data, and inputting the finance and taxation risk data into the big data analysis model. And performing risk decision processing on the finance and taxation risk data, performing classification iteration processing on the finance and taxation risk data according to a risk decision result, calculating a finance and taxation risk decision deviation coefficient according to an iteration result, and correcting the risk decision result to obtain finance risk analysis data. The method has the effect of improving the accuracy of financial risk analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of financial and tax analysis, and in particular to a financial risk analysis method and system based on a big data analysis model. Background Art

[0002] At present, with the continuous development of the global economy, financial risk has become an important consideration in corporate management and investment decisions. Both companies and investors need to conduct in-depth analysis and evaluation of potential financial risks in order to make correct decisions. Therefore, accurate analysis of financial risks is required.

[0003] Existing financial risk analysis methods usually pre-set financial indicator discrimination standards for corresponding projects based on financial types. When the actual financial data of the corresponding items reaches or exceeds the corresponding discrimination standards, it means that the current financial project is at risk. However, in large enterprises, there are many types of finances and there are close connections between financial indicators. Only qualitative assumptions are used to indicate the risks of corporate finances in certain aspects, which can easily lead to unstable financial risk verification results. In addition, when the correlation between indicators is ignored, the risk verification conclusions of different financial projects may be contradictory, resulting in the inability to obtain risk judgment results that are consistent with the actual operating conditions of the enterprise. Therefore, the financial risk analysis method needs to be further optimized. Summary of the invention

[0004] In order to improve the accuracy of financial risk analysis, the present application provides a financial risk analysis method and system based on a big data analysis model.

[0005] In the first aspect, the above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0006] A financial risk analysis method based on a big data analysis model, comprising:

[0007] Obtain the enterprise's financial and tax data and external industry data within a certain period of time and perform data preprocessing to obtain the preprocessed internal enterprise data and external enterprise data;

[0008] Randomly screening the internal data of the enterprise and the external data of the enterprise respectively, analyzing the correlation between the screened internal data of the enterprise and the external data of the enterprise, and building a big data analysis model for enterprise finance and taxation;

[0009] Acquire the financial and tax risk data exceeding the preset financial and tax item indicators in the internal data of the enterprise, input the financial and tax risk data into the big data analysis model, and perform risk decision processing on the financial and tax risk data;

[0010] According to the risk decision results, the financial and tax risk data are classified and iteratively processed, and the financial and tax risk decision deviation coefficient is calculated based on the iterative results and the risk decision results are corrected to obtain financial risk analysis data.

[0011] By adopting the above technical solution, the corporate financial and taxation data and external industry data within the selected time period are pre-processed, which helps to filter out low-quality or duplicate and invalid data and retain high-value data with commercial value. The randomness of data selection is increased by randomly screening data, and the correlation between internal and external data of the enterprise is combined to build a big data model of corporate finance and taxation, which helps to improve the correlation between internal and external data of the enterprise. With external data as analysis indicators, the stability of model demonstration is improved from multiple dimensions. By screening financial and taxation risk data that exceeds the preset financial and taxation project indicators, targeted directional risk analysis is conducted on financial and taxation risk data to improve the accuracy of risk decisions. Through classification iteration and deviation correction of financial and taxation risk data, the results of financial and taxation risk analysis are further corrected, and the results of financial and taxation risk analysis are multi-verified from multiple angles, thereby improving the accuracy of financial risk analysis.

[0012] In a preferred example, the present application can be further configured as follows: the financial and tax risk data are classified and iteratively processed according to the risk decision results, the financial and tax risk decision deviation coefficient is calculated according to the iterative results, and the risk decision results are corrected to obtain financial risk analysis data, specifically including:

[0013] According to the risk decision results, the financial and tax risk data are secondary classified, and the node data where the financial and tax data has changed is randomly selected from the financial and tax items of each classified data according to the classification results to obtain the first data set and the second data set;

[0014] According to the number of changes in the financial and tax data, respectively, the first data set and the second data set are iterated and the financial and tax risk decision limits of the corresponding data sets are analyzed to construct the risk decision conditions of the big data analysis model;

[0015] According to the difference between the risk decision conditions and the original risk decision results, the financial and tax risk decision deviation coefficient is calculated, and the original risk decision results are corrected according to the financial and tax risk decision deviation coefficient to obtain financial risk data.

[0016] In a preferred example, the present application can be further configured as follows: the calculation of the financial and tax risk decision deviation coefficient according to the difference between the risk decision condition and the original risk decision result specifically includes:

[0017] The deviation coefficient of financial and tax risk decision is calculated by formula (1), which is as follows:

[0018]

[0019] Among them, ρ represents the deviation coefficient of fiscal and tax risk decision, n and m represent the number of nodes with changed fiscal and tax data and the number of fiscal and tax items with changed fiscal and tax data, respectively, and Δp ij Represents the data change value of the j-th fiscal and taxation node in the i-th fiscal and taxation project where there is a change in fiscal and taxation data.

[0020] By adopting the above technical scheme, the financial and tax risk data are secondary classified according to the risk decision results to select data, and the node data where the financial and tax data has changed is selected by random selection as the training data set of big data, so that the first data set and the second data set are obtained according to the secondary classification results, which is helpful to compare and verify the risk decision results, and the number of changes in the financial and tax data is taken as the number of iterations, and the first data set and the second data set are respectively iterated and the financial and tax risk decision limits of the corresponding data sets are analyzed, so as to construct the risk decision conditions of the big data analysis model, constrain the risk decision of the big data analysis model, and combine the financial and tax risk decision deviation coefficient to correct the original risk decision results, so as to further improve the accuracy of financial risk analysis.

[0021] In a preferred example, the present application can be further configured as follows: the financial and tax risk data exceeding the preset financial and tax item indicators in the internal data of the enterprise is obtained, and the financial and tax risk data is input into the big data analysis model, and risk decision processing is performed on the financial and tax risk data, specifically including:

[0022] Constructing a risk association relationship between the financial and tax risk data and the enterprise's external data, analyzing the financial and tax risk distribution of the financial and tax risk data according to the risk association relationship, and obtaining financial and tax risk analysis data;

[0023] Calculate the risk partial derivative coefficient of the current financial and tax risk according to the financial and tax risk analysis data, and optimize the risk decision-making conditions of the big data analysis model according to the risk partial derivative coefficient;

[0024] According to the optimized risk decision conditions and the number of financial and tax projects with risks, the financial and tax risk data are iteratively converged to obtain risk decision data for risky financial and tax projects.

[0025] In a preferred example, the present application may be further configured as follows: the risk partial derivative coefficient of the current financial and tax risk is calculated according to the financial and tax risk analysis data, and the risk decision-making condition of the big data analysis model is optimized according to the risk partial derivative coefficient, specifically including:

[0026] The risk partial derivative coefficient of the current fiscal and tax risk is calculated by formula (2), which is as follows:

[0027]

[0028] Among them, ω represents the risk partial derivative coefficient of the current fiscal and taxation risk, n and m represent the number of nodes with changed fiscal and taxation data and the number of fiscal and taxation items with changed fiscal and taxation data, respectively, and λ ij represents the constraint coefficient of the jth taxation item node with taxation data changes in the i-th taxation item, which is related to the importance of the taxation item to the enterprise value target. ij ,y ij Indicates the coordinate value of the jth fiscal and taxation project node with fiscal and taxation data changes in the i-th fiscal and taxation project, in the coordinate axis with fiscal and taxation data as the x-axis and the enterprise value of the corresponding fiscal and taxation project as the y-axis;

[0029] The risk decision-making condition of the big data analysis model is optimized according to the risk partial derivative coefficient, wherein the optimization expression of the risk decision-making condition is represented by formula (3), and formula (3) is as follows:

[0030]

[0031] Among them, min ω L(ω,x,y) represents the minimum constraint factor of the risk decision condition, λ i , j They represent the constraint coefficients of the i-th fiscal and taxation item and the j-th fiscal and taxation data change node, respectively. i ,y i represents the coordinate value of the i-th fiscal and taxation item, x j ,y j Represents the coordinate value of the jth fiscal and taxation item node where fiscal and taxation data has changed.

[0032] By adopting the above technical solution, by constructing the risk correlation relationship between financial and tax risk data and external data of enterprises, and taking external data of enterprises as correlation reference indicators, a joint analysis is conducted on the distribution of financial and tax risks in financial and tax projects, which is helpful to focus on monitoring risky financial and tax projects, and combine the risk partial derivative coefficient of current financial and tax risks to optimize the risk decision conditions of the big data analysis model, improve the fit between risk decision conditions and current financial and tax risks, and use the number of financial and tax projects with risks as the number of iterations to perform data convergence processing on financial and tax risk data, so as to obtain risk decision data that better matches current financial and tax risks, and improve the fit between financial and tax risk decisions and current financial and tax risks.

[0033] In the second aspect, the above invention objective of the present application is achieved through the following technical solutions:

[0034] A financial risk analysis system based on a big data analysis model, the system is applied to the above-mentioned financial risk analysis method based on a big data analysis model, the system comprises:

[0035] The data processing module is used to obtain the enterprise's financial and tax data and external industry data within a certain period of time and perform data preprocessing to obtain the preprocessed internal enterprise data and external enterprise data;

[0036] A model building module, used to randomly screen the internal data of the enterprise and the external data of the enterprise respectively, analyze the correlation between the screened internal data of the enterprise and the external data of the enterprise, and build a big data analysis model for enterprise finance and taxation;

[0037] A risk decision module, used to obtain the financial and tax risk data exceeding the preset financial and tax item indicators in the internal data of the enterprise, and input the financial and tax risk data into the big data analysis model to perform risk decision processing on the financial and tax risk data;

[0038] The risk analysis module is used to classify and iteratively process the financial and tax risk data according to the risk decision results, calculate the financial and tax risk decision deviation coefficient according to the iterative results, and correct the risk decision results to obtain financial risk analysis data.

[0039] By adopting the above technical solution, the corporate financial and taxation data and external industry data within the selected time period are pre-processed, which helps to filter out low-quality or duplicate and invalid data and retain high-value data with commercial value. The randomness of data selection is increased by randomly screening data, and the correlation between internal and external data of the enterprise is combined to build a big data model of corporate finance and taxation, which helps to improve the correlation between internal and external data of the enterprise. With external data as analysis indicators, the stability of model demonstration is improved from multiple dimensions. By screening financial and taxation risk data that exceeds the preset financial and taxation project indicators, targeted directional risk analysis is conducted on financial and taxation risk data to improve the accuracy of risk decisions. Through classification iteration and deviation correction of financial and taxation risk data, the results of financial and taxation risk analysis are further corrected, and the results of financial and taxation risk analysis are multi-verified from multiple angles, thereby improving the accuracy of financial risk analysis.

[0040] On the third aspect, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0041] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned financial risk analysis method based on a big data analysis model when executing the computer program.

[0042] Fourthly, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0043] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned financial risk analysis method based on a big data analysis model.

[0044] In summary, the present application includes at least one of the following beneficial technical effects:

[0045] 1. Preprocessing the enterprise's financial and taxation data and external industry data within the selected time period helps to filter out low-quality or duplicate and invalid data, retain high-value data with commercial value, increase the randomness of data selection by randomly screening data, and build a big data model of enterprise finance and taxation by combining the correlation between internal and external data of the enterprise, which helps to improve the correlation between internal and external data of the enterprise. Using external data as analysis indicators, the stability of model demonstration is improved from multiple dimensions. By screening financial and taxation risk data that exceeds the preset financial and taxation project indicators, targeted and directional risk analysis is conducted on financial and taxation risk data to improve the accuracy of risk decision-making. Through the classification iteration and deviation correction of financial and taxation risk data, the results of financial and taxation risk analysis are further corrected, and the results of financial and taxation risk analysis are multi-verified from multiple angles, thereby improving the accuracy of financial risk analysis;

[0046] 2. According to the risk decision results, the financial and tax risk data is secondary classified for data selection, and the node data where the financial and tax data has changed is selected by random selection as the training data set of big data, so as to obtain the first data set and the second data set according to the secondary classification results, which is helpful for comparing and verifying the risk decision results, and the number of changes in the financial and tax data is the number of iterations, and the first data set and the second data set are respectively iterated and the financial and tax risk decision limits of the corresponding data sets are analyzed, so as to construct the risk decision conditions of the big data analysis model, constrain the risk decision of the big data analysis model, and combine the financial and tax risk decision deviation coefficient to correct the original risk decision results, so as to further improve the accuracy of financial risk analysis;

[0047] 3. By constructing the risk correlation relationship between financial and tax risk data and external data of enterprises, and taking external data of enterprises as correlation reference indicators, a joint analysis is conducted on the distribution of financial and tax risks in financial and tax projects, which is helpful to focus on monitoring risky financial and tax projects, and combined with the risk derivative coefficient of current financial and tax risks, the risk decision-making conditions of the big data analysis model are optimized to improve the fit between risk decision-making conditions and current financial and tax risks. The number of financial and tax projects with risks is used as the number of iterations, and data convergence processing is performed on the financial and tax risk data to obtain risk decision data that better matches the current financial and tax risks, thereby improving the fit between financial and tax risk decisions and current financial and tax risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of an implementation of a financial risk analysis method based on a big data analysis model in this embodiment.

[0049] Figure 2 It is a flowchart for implementing the decision-making condition optimization of the financial risk analysis method of this embodiment.

[0050] Figure 3 This is a flowchart for implementing step S40 of the financial risk analysis method of this embodiment.

[0051] Figure 4 This is a structural block diagram of a financial risk analysis system based on a big data analysis model in this embodiment.

[0052] Figure 5 It is a schematic diagram of the internal structure of a computer device used to implement the financial risk analysis method. DETAILED DESCRIPTION

[0053] The present application is further described in detail below in conjunction with the accompanying drawings.

[0054] In one embodiment, if Figure 1 As shown, the present application discloses a financial risk analysis method based on a big data analysis model, which specifically includes the following steps:

[0055] S10: Obtain the enterprise's financial and taxation data and external industry data within a certain period of time and perform data preprocessing to obtain preprocessed enterprise internal data and enterprise external data.

[0056] Specifically, according to the time period selected by the enterprise, such as annual, quarterly or monthly, the enterprise's financial and tax data within the selected time period is extracted, and the external industry data of its peers is extracted according to the nature of the enterprise. The extracted enterprise financial and tax data and the low-value data in the enterprise's external data are analyzed and filtered to retain the financial and tax data with commercial value, thereby obtaining the pre-processed enterprise internal data and enterprise external data.

[0057] S20: Randomly screen the internal data and external data of the enterprise respectively, analyze the correlation between the screened internal data and external data of the enterprise, and build a big data analysis model for enterprise finance and taxation.

[0058] Specifically, data samples are screened from internal and external data of enterprises through random sampling, and the correlation between financial and tax items in enterprise business transactions is combined to analyze the correlation between the screened internal and external data of enterprises, including the correlation between business transactions between enterprises and the correlation between the same financial and tax items in peer enterprises, so as to construct a big data analysis model for enterprise finance and taxation.

[0059] S30: Obtain the financial and tax risk data that exceeds the preset financial and tax project indicators in the internal data of the enterprise, input the financial and tax risk data into the big data analysis model, and perform risk decision-making on the financial and tax risk data.

[0060] Specifically, the financial and taxation project indicators of each financial and taxation project are set according to the enterprise revenue indicators, and the financial and taxation risk data exceeding the preset financial and taxation project indicators in the enterprise internal data are extracted, and the financial and taxation risk data are input into the big data analysis model to make risk decisions on the financial and taxation risk data, such as Figure 2 As shown, specifically including:

[0061] S301: Construct a risk association relationship between financial and tax risk data and enterprise external data, analyze the financial and tax risk distribution of the financial and tax risk data based on the risk association relationship, and obtain financial and tax risk analysis data.

[0062] Specifically, based on the correlation between the same type of fiscal and taxation projects among peer companies and between fiscal and taxation projects in corporate business dealings, a risk correlation relationship between fiscal and taxation risk data and corporate external data is constructed. Based on the risk correlation relationship, the distribution of fiscal and taxation risk data in fiscal and taxation projects is analyzed, that is, the distribution of fiscal and taxation projects with fiscal and taxation risks in business dealings and among peer companies, as well as the specific distribution location of fiscal and taxation risk data in fiscal and taxation projects, thereby obtaining fiscal and taxation risk analysis data.

[0063] S302: Calculate the risk derivative coefficient of the current financial and tax risk based on the financial and tax risk analysis data, and optimize the risk decision-making conditions of the big data analysis model based on the risk derivative coefficient.

[0064] Specifically, the risk partial derivative coefficient of the current fiscal and tax risk is calculated by formula (2), which is as follows:

[0065]

[0066] Among them, ω represents the risk partial derivative coefficient of the current fiscal and taxation risk, n and m represent the number of nodes with changed fiscal and taxation data and the number of fiscal and taxation items with changed fiscal and taxation data, respectively, and λ ij represents the constraint coefficient of the jth taxation item node with taxation data changes in the i-th taxation item, which is related to the importance of the taxation item to the enterprise value target. ij ,y ij It represents the coordinate value of the j-th fiscal and taxation project node of the ith fiscal and taxation project with changes in fiscal and taxation data in the coordinate axis with fiscal and taxation data as the x-axis and the enterprise value of the corresponding fiscal and taxation project as the y-axis.

[0067] The risk decision-making conditions of the big data analysis model are optimized according to the risk partial derivative coefficient, wherein the optimization expression of the risk decision-making conditions is expressed by formula (3), which is as follows:

[0068]

[0069] Among them, min ω L(ω,x,y) represents the minimum constraint factor of the risk decision condition, λ i , j They represent the constraint coefficients of the i-th fiscal and taxation item and the j-th fiscal and taxation data change node, respectively. i ,y i represents the coordinate value of the i-th fiscal and taxation item, x j ,y j Represents the coordinate value of the jth fiscal and taxation item node where fiscal and taxation data has changed.

[0070] S303: According to the optimized risk decision conditions and the number of financial and tax projects with risks, the financial and tax risk data is iteratively converged to obtain the risk decision data of the risky financial and tax projects.

[0071] Specifically, risk decision processing is performed on the financial and tax risk data according to the optimized risk decision conditions, and the number of financial and tax projects with risks is used as the number of data iterations. The financial and tax risk data is iteratively converged until the optimized risk decision conditions are met, thereby obtaining the risk decision data of the financial and tax risk projects.

[0072] S40: Classify and iterate the financial and tax risk data according to the risk decision results, calculate the financial and tax risk decision deviation coefficient according to the iterative results, and correct the risk decision results to obtain financial risk analysis data.

[0073] Specifically, Figure 3 As shown, step S40 includes:

[0074] S401: According to the risk decision result, the financial and tax risk data is secondary classified, and according to the classification result, node data where the financial and tax data has changed is randomly selected from the financial and tax items of each classified data to obtain a first data set and a second data set.

[0075] Specifically, according to the risk decision results, the financial and tax risk data are classified twice, and the financial and tax data with financial and tax risks are divided into one category, and the normal financial and tax data without financial and tax risks are divided into another category. From the financial and tax items of each classified data, node data where the financial and tax data has changed are randomly selected, such as financial and tax nodes with data fluctuations, to obtain the first data set and the second data set.

[0076] S402: According to the number of changes in the financial and taxation data, the first data set and the second data set are respectively iteratively processed and the financial and taxation risk decision limits of the corresponding data sets are analyzed to construct risk decision conditions for the big data analysis model.

[0077] Specifically, the number of changes in financial and tax data is taken as the number of data iterations, and data iteration processing is performed on the first data set and the second data set respectively. The data iteration results are used as the financial and tax risk decision limits of the corresponding data sets to construct the risk decision conditions of the big data analysis model.

[0078] S403: Calculate the financial and tax risk decision deviation coefficient based on the difference between the risk decision condition and the original risk decision result, and correct the original risk decision result based on the financial and tax risk decision deviation coefficient to obtain financial risk data.

[0079] Specifically, the fiscal and tax risk decision deviation coefficient is calculated by formula (1), which is as follows:

[0080]

[0081] Among them, ρ represents the deviation coefficient of fiscal and tax risk decision, n and m represent the number of nodes with changed fiscal and tax data and the number of fiscal and tax items with changed fiscal and tax data, respectively, and Δp ij Represents the data change value of the j-th fiscal and taxation node in the i-th fiscal and taxation project where there is a change in fiscal and taxation data.

[0082] The original risk decision result is corrected according to the fiscal and taxation decision deviation coefficient, such as calculating the fiscal and taxation decision deviation value corresponding to the fiscal and taxation decision deviation coefficient, and taking the difference between the original risk decision value and the fiscal and taxation decision deviation value as the correction result, thereby obtaining the fiscal and taxation risk data.

[0083] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0084] In one embodiment, a financial risk analysis system based on a big data analysis model is provided, and the financial risk analysis system based on a big data analysis model corresponds one-to-one to the financial risk analysis method based on a big data analysis model in the above embodiment. Figure 4 As shown, the financial risk analysis system based on the big data analysis model includes a data processing module, a model building module, a risk decision module and a risk analysis module. The detailed description of each functional module is as follows:

[0085] The data processing module is used to obtain the enterprise's financial and tax data and external industry data within a certain period of time and perform data preprocessing to obtain the preprocessed enterprise internal data and enterprise external data.

[0086] The model building module is used to randomly screen the internal data and external data of the enterprise respectively, analyze the correlation between the screened internal data and external data of the enterprise, and build a big data analysis model for enterprise finance and taxation.

[0087] The risk decision module is used to obtain financial and tax risk data in the company's internal data that exceeds the preset financial and tax project indicators, and input the financial and tax risk data into the big data analysis model to perform risk decision processing on the financial and tax risk data.

[0088] The risk analysis module is used to classify and iteratively process the financial and tax risk data according to the risk decision results, calculate the financial and tax risk decision deviation coefficient according to the iterative results, and correct the risk decision results to obtain financial risk analysis data.

[0089] Preferably, the risk analysis module specifically includes:

[0090] The data partitioning submodule is used to perform secondary classification of the financial and tax risk data according to the risk decision results, and randomly select node data where the financial and tax data has changed from the financial and tax items of each classified data according to the classification results to obtain the first data set and the second data set.

[0091] The condition construction submodule is used to perform data iterative processing on the first data set and the second data set respectively according to the number of changes in the financial and tax data, and analyze the financial and tax risk decision limits of the corresponding data sets to construct the risk decision conditions of the big data analysis model.

[0092] The data correction submodule is used to calculate the financial and tax risk decision deviation coefficient based on the difference between the risk decision conditions and the original risk decision results, and to correct the original risk decision results based on the financial and tax risk decision deviation coefficient to obtain financial risk data.

[0093] Preferably, the data correction submodule specifically includes:

[0094] The deviation coefficient of financial and tax risk decision is calculated by formula (1), which is as follows:

[0095]

[0096] Among them, ρ represents the deviation coefficient of fiscal and tax risk decision, n and m represent the number of nodes with changed fiscal and tax data and the number of fiscal and tax items with changed fiscal and tax data, respectively, and Δp ij Represents the data change value of the j-th fiscal and taxation node in the i-th fiscal and taxation project where there is a change in fiscal and taxation data.

[0097] Preferably, the risk decision module specifically includes:

[0098] The risk analysis submodule is used to construct the risk association relationship between financial and tax risk data and external data of the enterprise, analyze the financial and tax risk distribution of the financial and tax risk data according to the risk association relationship, and obtain financial and tax risk analysis data.

[0099] The conditional optimization submodule is used to calculate the risk partial derivative coefficient of the current financial and tax risks based on the financial and tax risk analysis data, and optimize the risk decision-making conditions of the big data analysis model based on the risk partial derivative coefficient.

[0100] The data iteration submodule is used to iterate and converge the financial and tax risk data according to the optimized risk decision conditions and the number of financial and tax projects with risks, so as to obtain the risk decision data of the risky financial and tax projects.

[0101] Preferably, the condition optimization submodule specifically includes:

[0102] The risk partial derivative coefficient of the current fiscal and tax risk is calculated by formula (2), which is as follows:

[0103]

[0104] Among them, ω represents the risk partial derivative coefficient of the current fiscal and taxation risk, n and m represent the number of nodes with changed fiscal and taxation data and the number of fiscal and taxation items with changed fiscal and taxation data, respectively, and λ ij represents the constraint coefficient of the jth taxation item node with taxation data changes in the i-th taxation item, which is related to the importance of the taxation item to the enterprise value target. ij ,y ij It represents the coordinate value of the j-th fiscal and taxation project node of the ith fiscal and taxation project with changes in fiscal and taxation data in the coordinate axis with fiscal and taxation data as the x-axis and the enterprise value of the corresponding fiscal and taxation project as the y-axis.

[0105] The risk decision-making conditions of the big data analysis model are optimized according to the risk partial derivative coefficient, wherein the optimization expression of the risk decision-making conditions is expressed by formula (3), which is as follows:

[0106]

[0107] Among them, min ω L(ω,x,y) represents the minimum constraint factor of the risk decision condition, λ i , j They represent the constraint coefficients of the i-th fiscal and taxation item and the j-th fiscal and taxation data change node, respectively. i ,y i represents the coordinate value of the i-th fiscal and taxation item, x j ,y j Represents the coordinate value of the jth fiscal and taxation item node where fiscal and taxation data has changed.

[0108] For the specific limitations of the financial risk analysis system based on the big data analysis model, please refer to the limitations of the financial risk analysis method based on the big data analysis model mentioned above, which will not be repeated here. Each module in the above-mentioned financial risk analysis system based on the big data analysis model can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0109] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store financial risk analysis data of a big data analysis model. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a financial risk analysis method based on a big data analysis model is implemented.

[0110] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of a financial risk analysis method based on a big data analysis model are implemented.

[0111] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0112] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0113] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A financial risk analysis method based on a big data analysis model, characterized in that: include: Obtain the enterprise's financial and tax data and external industry data within a certain period of time and perform data preprocessing to obtain the preprocessed internal enterprise data and external enterprise data; Randomly screening the internal data of the enterprise and the external data of the enterprise respectively, analyzing the correlation between the screened internal data of the enterprise and the external data of the enterprise, and building a big data analysis model for enterprise finance and taxation; Acquire the financial and tax risk data exceeding the preset financial and tax item indicators in the internal data of the enterprise, input the financial and tax risk data into the big data analysis model, and perform risk decision processing on the financial and tax risk data; According to the risk decision results, the financial and tax risk data are classified and iteratively processed, and the financial and tax risk decision deviation coefficient is calculated based on the iterative results and the risk decision results are corrected to obtain financial risk analysis data.

2. The financial risk analysis method based on the big data analysis model according to claim 1 is characterized in that: The financial and tax risk data are classified and iterated according to the risk decision results, and the financial and tax risk decision deviation coefficient is calculated according to the iterative results and the risk decision results are corrected to obtain the financial risk analysis data, specifically including: According to the risk decision results, the financial and tax risk data are secondary classified, and the node data where the financial and tax data has changed is randomly selected from the financial and tax items of each classified data according to the classification results to obtain the first data set and the second data set; According to the number of changes in the financial and tax data, respectively, the first data set and the second data set are iterated and the financial and tax risk decision limits of the corresponding data sets are analyzed to construct the risk decision conditions of the big data analysis model; According to the difference between the risk decision conditions and the original risk decision results, the financial and tax risk decision deviation coefficient is calculated, and the original risk decision results are corrected according to the financial and tax risk decision deviation coefficient to obtain financial risk data.

3. The financial risk analysis method based on the big data analysis model according to claim 2 is characterized in that: The calculation of the financial and tax risk decision deviation coefficient based on the difference between the risk decision condition and the original risk decision result specifically includes: The fiscal and tax risk decision deviation coefficient is calculated by formula (1), which is as follows: Among them, ρ represents the deviation coefficient of fiscal and tax risk decision, n and m represent the number of nodes with changed fiscal and tax data and the number of fiscal and tax items with changed fiscal and tax data, respectively, and Δp ij Represents the data change value of the j-th fiscal and taxation node in the i-th fiscal and taxation project where there is a change in fiscal and taxation data.

4. The financial risk analysis method based on the big data analysis model according to claim 1 is characterized in that: The step of obtaining the financial and tax risk data exceeding the preset financial and tax item index in the internal data of the enterprise, inputting the financial and tax risk data into the big data analysis model, and performing risk decision processing on the financial and tax risk data specifically includes: Constructing a risk association relationship between the financial and tax risk data and the enterprise's external data, analyzing the financial and tax risk distribution of the financial and tax risk data according to the risk association relationship, and obtaining financial and tax risk analysis data; Calculate the risk partial derivative coefficient of the current financial and tax risk according to the financial and tax risk analysis data, and optimize the risk decision-making conditions of the big data analysis model according to the risk partial derivative coefficient; According to the optimized risk decision conditions and the number of financial and tax projects with risks, the financial and tax risk data are iteratively converged to obtain risk decision data for risky financial and tax projects.

5. The financial risk analysis method based on the big data analysis model according to claim 4 is characterized in that: The step of calculating the risk partial derivative coefficient of the current financial and tax risk according to the financial and tax risk analysis data, and optimizing the risk decision-making conditions of the big data analysis model according to the risk partial derivative coefficient specifically includes: The risk partial derivative coefficient of the current fiscal and tax risk is calculated by formula (2), which is as follows: Among them, ω represents the risk partial derivative coefficient of the current fiscal and taxation risk, n and m represent the number of nodes with changed fiscal and taxation data and the number of fiscal and taxation items with changed fiscal and taxation data, respectively, and λ ij represents the constraint coefficient of the jth taxation item node with taxation data changes in the i-th taxation item, which is related to the importance of the taxation item to the enterprise value target. ij ,y ij Indicates the coordinate value of the jth fiscal and taxation project node with fiscal and taxation data changes in the i-th fiscal and taxation project, in the coordinate axis with fiscal and taxation data as the x-axis and the enterprise value of the corresponding fiscal and taxation project as the y-axis; The risk decision-making condition of the big data analysis model is optimized according to the risk partial derivative coefficient, wherein the optimization expression of the risk decision-making condition is represented by formula (3), and formula (3) is as follows: Among them, min ω L(ω,x,y) represents the minimum constraint factor of the risk decision condition, λ i , j They represent the constraint coefficients of the i-th fiscal and taxation item and the j-th fiscal and taxation data change node, respectively. i ,y i represents the coordinate value of the i-th fiscal and taxation item, x j ,y j Represents the coordinate value of the jth fiscal and taxation item node where fiscal and taxation data has changed.

6. A financial risk analysis system based on a big data analysis model, characterized in that: The system is applied to the financial risk analysis method based on the big data analysis model according to any one of claims 1 to 5 above, and the system comprises: The data processing module is used to obtain the enterprise's financial and tax data and external industry data within a certain period of time and perform data preprocessing to obtain the preprocessed internal enterprise data and external enterprise data; A model building module, used to randomly screen the internal data of the enterprise and the external data of the enterprise respectively, analyze the correlation between the screened internal data of the enterprise and the external data of the enterprise, and build a big data analysis model for enterprise finance and taxation; A risk decision module, used to obtain the financial and tax risk data exceeding the preset financial and tax item indicators in the internal data of the enterprise, and input the financial and tax risk data into the big data analysis model to perform risk decision processing on the financial and tax risk data; The risk analysis module is used to classify and iteratively process the financial and tax risk data according to the risk decision results, calculate the financial and tax risk decision deviation coefficient according to the iterative results, and correct the risk decision results to obtain financial risk analysis data.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the financial risk analysis method based on the big data analysis model as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the financial risk analysis method based on the big data analysis model as described in any one of claims 1 to 5 are implemented.

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