Financial risk control method and system based on big data

Abstract

The invention relates to the technical field of enterprise financial risk prediction, in particular to a financial risk control method and system based on big data. The method comprises the following steps: acquiring financial data information of an enterprise; calculating total bank assets and total bank liabilities of the enterprise based on the enterprise bank pledge information, and calculating a first risk index value of the enterprise based on the total bank assets and the total bank liabilities of the enterprise; obtaining enterprise financial risk index information, and substituting the enterprise financial risk index information and the enterprise basic qualification information into the financial risk comparative analysis model to obtain a second risk index value of the enterprise; and calculating a financial risk prediction value of the enterprise based on the first risk index value and the second risk index value, and controlling the financial risk of the enterprise based on the financial risk prediction value of the enterprise. According to the invention, the accuracy rate and the evaluation efficiency of enterprise financial risk prediction are improved.

Description

Technical Field

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

[0002] With the rapid development of information technology and the continuous innovation of financial services, the financial market has become increasingly complex and diversified, and the risks faced by financial institutions have become increasingly complex and changeable. Financial risk management is one of the core businesses of financial institutions and is directly related to the survival and development of financial institutions. Financial risks mainly include market risk, credit risk, interest rate risk, operational risk, liquidity risk, etc., which are inevitable challenges faced by financial institutions in the course of operation. Effective risk management can reduce the losses of financial institutions, maintain the stability of the financial market, and enhance the competitiveness and sustainable development capabilities of financial institutions.

[0003] Traditional financial risk control methods mainly rely on the experience of professionals, rule engines and statistical models, but these methods are easily limited when the data volume is huge and changes rapidly, and cannot meet the rapidly changing needs of modern financial markets. Therefore, there is an urgent need for a financial risk control method and system based on big data to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a financial risk control method and system based on big data: to solve the existing solutions. Traditional financial risk control methods mainly rely on the experience of professionals, rule engines and statistical models, but these methods are easily limited when the data volume is huge and changes rapidly, and cannot meet the rapidly changing needs of the modern financial market, which in turn leads to technical problems such as inaccurate and low efficiency in predicting the financial risks of enterprises.

[0005] The purpose of the present invention can be achieved through the following technical solutions: On the one hand, a financial risk control method based on big data, the method includes: Obtaining the financial data information of the enterprise, where the financial data information of the enterprise includes the bank pledge information and basic qualification information of the enterprise; Based on the bank pledge information of the enterprise, the total bank assets and the total bank liabilities of the enterprise are calculated, and based on the total bank assets and the total bank liabilities of the enterprise, the first risk indicator value of the enterprise is calculated; Obtaining the financial risk indicator information of the enterprise, substituting the financial risk indicator information of the enterprise and the basic qualification information of the enterprise into the financial risk comparison analysis model, and obtaining the second risk indicator value of the enterprise; A financial risk prediction value of the enterprise is calculated based on the first risk indicator value and the second risk indicator value, and the financial risk of the enterprise is controlled based on the financial risk prediction value of the enterprise.

[0006] Furthermore, the calculation of the total bank assets of the enterprise based on the bank pledge information of the enterprise specifically includes the following process: Based on the corporate bank pledge information, the total assets of the enterprise in the bank at the initial moment of statistics are obtained , Enterprise asset turnover rate , standard deviation of the fluctuation of total assets of enterprises and the parameter function of the total assets of corporate banks over time ,in, It obeys normal distribution, that is , each time t corresponds to a parameter function value; The total assets of the enterprise in the bank at the initial moment , Enterprise asset turnover rate , standard deviation of the fluctuation of total assets of enterprises and the parameter function of the total assets of corporate banks over time Substitute the correlation formula to calculate the total bank assets of the enterprise at time t , the association formula is as follows: ; Among them, the time The unit of measurement is days.

[0007] Furthermore, the calculation of the total bank liabilities of an enterprise based on the bank pledge information of the enterprise specifically includes the following process: Based on the corporate bank pledge information, the total debt of the enterprise in the bank at the initial moment of statistics is obtained ; Based on the corporate bank pledge information, the total average interest rate r of the enterprise is obtained; The total debt of the enterprise in the bank at the initial moment will be counted Substitute the total liability average interest rate r of the enterprise into the correlation formula to calculate the total bank debt of the enterprise at time t , the association formula is as follows: ; in, Represents the inflation rate.

[0008] Furthermore, the first risk indicator value of the enterprise is calculated based on the total bank assets of the enterprise and the total liabilities of the enterprise bank, which specifically includes the following process: The net assets of the enterprise are calculated based on the total bank assets of the enterprise and the total liabilities of the enterprise bank. : ; ; in, represents the asset liquidity income at time t, It indicates the ratio of the enterprise's cash and pledged assets in the bank. It indicates the ratio of the value of financial products and assets owned by the enterprise, where the value of financial products includes the value of securities, stocks and government bonds. The ratio of mortgage loans, Indicates the proportion of other loans, express The total bank assets of the enterprise at the time, express The return on investment at that moment; Net assets of the enterprise Real-time calculation is performed, with the execution time of the preset management period as the X-axis and the net assets of the enterprise as the Establish a rectangular coordinate system for the Y axis, and calculate the net assets of the enterprise during the preset management period Draw the net asset curve of the preset management period in the rectangular coordinate system, draw perpendicular lines from the two end points of the net asset curve to the X-axis to obtain two truncation line segments, and form a graph with the net asset curve, the two truncation line segments and the X-axis. Mark the area value of the graph as the first risk indicator value of the enterprise.

[0009] Further, obtaining the enterprise financial risk indicator information, substituting the enterprise financial risk indicator information and the enterprise basic qualification information into the financial risk comparison analysis model, and obtaining the second risk indicator value of the enterprise includes the following process: Obtain the industrial and commercial characteristic data indicators in the enterprise financial risk indicator information, and calculate the ratio Q of the number of existing industrial and commercial characteristic data items in the enterprise basic qualification information to the number of industrial and commercial characteristic data indicator items; Obtain the judicial and public security characteristic data indicators in the enterprise financial risk indicator information, and count the number of existing judicial and public security characteristic data items W in the enterprise basic qualification information; Obtain the financial characteristic data indicators in the enterprise financial risk indicator information, and count the number of financial characteristic data items U that meet the requirements of the financial characteristic data indicators in the enterprise basic qualification information; Obtain the equity structure characteristic data indicators in the enterprise financial risk indicator information, and count the number of existing equity structure characteristic data items P in the enterprise basic qualification information; Input the proportion Q, the number of items W, the number of items U, and the number of items P into the financial risk comparison analysis model. The financial risk comparison analysis model obtains the second risk indicator value of the enterprise according to the financial risk comparison analysis formula. , the financial risk comparison analysis formula is as follows: ; in, , is the weight coefficient, and , the value of e is 2.72.

[0010] Furthermore, the industrial and commercial characteristic data indicators include the enterprise's tax registration address, enterprise type, registered capital and list of abnormal industrial and commercial operations; the judicial and public security characteristic data indicators include the number of times the enterprise is the defendant in legal disputes, the number of times the enterprise is the plaintiff in legal disputes, the number of labor contract disputes, the number of information on the persons subject to execution, the number of judicial documents and the number of court announcements; the financial characteristic data indicators include return on total assets, return on net assets, current ratio, cash flow to liabilities ratio, debt-to-asset ratio, capital preservation and appreciation rate, operating profit growth rate and net capital to net assets ratio; the equity structure characteristic data indicators include the number of equity changes and the number of subsidiaries.

[0011] Furthermore, calculating the predicted financial risk value of the enterprise based on the first risk indicator value and the second risk indicator value specifically includes the following process: Substitute the first risk indicator value and the second risk indicator value into the enterprise's financial risk forecast value calculation formula to calculate the enterprise's financial risk forecast value LMS. The enterprise's financial risk forecast value calculation formula is as follows: ; in, is the first risk indicator value, is the second risk indicator value, , are weight factors, and their values ​​are 0.6 and 0.4 respectively.

[0012] Furthermore, the control of the enterprise's financial risk based on the enterprise's financial risk forecast value specifically includes the following processes: The enterprise's financial risk prediction value threshold is loaded to determine whether the enterprise's financial risk prediction value exceeds the enterprise's financial risk prediction value threshold. If so, the enterprise's financial risk is determined to be high; if not, the enterprise's financial risk is determined to be low.

[0013] On the other hand, a financial risk control system based on big data includes a financial data acquisition module, a risk index value calculation module, and a financial risk prediction module; The financial data acquisition module is used to acquire the financial data information of the enterprise, wherein the financial data information of the enterprise includes the bank pledge information of the enterprise and the basic qualification information of the enterprise; A risk index value calculation module, used to calculate the total bank assets and total bank liabilities of the enterprise based on the bank pledge information of the enterprise, and to calculate the first risk index value of the enterprise based on the total bank assets and total bank liabilities of the enterprise; Obtaining the financial risk indicator information of the enterprise, substituting the financial risk indicator information of the enterprise and the basic qualification information of the enterprise into the financial risk comparison analysis model, and obtaining the second risk indicator value of the enterprise; The financial risk prediction module is used to calculate the financial risk prediction value of the enterprise based on the first risk indicator value and the second risk indicator value, and control the financial risk of the enterprise based on the financial risk prediction value of the enterprise.

[0014] Compared with the existing solutions, the present invention achieves the following beneficial effects: The present invention calculates a first risk index value and a second risk index value of an enterprise based on the bank pledge information and the basic qualification information of the enterprise, calculates a financial risk prediction value of the enterprise based on the first risk index value and the second risk index value, and controls the financial risk of the enterprise based on the financial risk prediction value of the enterprise, so as to accurately evaluate the financial risk of the enterprise.

[0015] The present invention can effectively improve the risk management level of financial institutions, reduce risk management costs, gradually realize the intelligence and automation of risk management, improve the efficiency of financial risk assessment of enterprises, provide financial institutions with more comprehensive, timely and accurate risk management services, and promote the development of the financial industry in a safer, more stable and sustainable direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 This is a workflow diagram of a financial risk control method based on big data according to an embodiment of the present invention; Figure 2 This is a workflow diagram of another financial risk control method based on big data according to an embodiment of the present invention; Figure 3 It is a system block diagram of a financial risk control system based on big data in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] In addition, the described features, structures or characteristics may be combined in one or more example embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0020] This embodiment provides a financial risk control method based on big data. Figure 1 is a workflow diagram of a financial risk control method based on big data according to an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps: Step S101: Acquire the financial data information of the enterprise, wherein the financial data information of the enterprise includes the bank pledge information of the enterprise and the basic qualification information of the enterprise; It is worth noting that the financial data information of an enterprise can be obtained through legal crawler technology channels, or it can be obtained through financial data information uploaded by the enterprise itself.

[0021] Step S102: Calculate the total bank assets and total bank liabilities of the enterprise based on the bank pledge information of the enterprise, and calculate the first risk indicator value of the enterprise based on the total bank assets and total bank liabilities of the enterprise; Step S103: obtaining the enterprise's financial risk indicator information, substituting the enterprise's financial risk indicator information and the enterprise's basic qualification information into the financial risk comparison analysis model to obtain the enterprise's second risk indicator value; Step S104: Calculate the predicted financial risk value of the enterprise based on the first risk indicator value and the second risk indicator value, and control the financial risk of the enterprise based on the predicted financial risk value of the enterprise.

[0022] In summary, the present invention calculates the first risk index value and the second risk index value of the enterprise based on the enterprise's bank pledge information and the enterprise's basic qualification information, calculates the enterprise's financial risk prediction value based on the first risk index value and the second risk index value, and controls the enterprise's financial risk based on the enterprise's financial risk prediction value, so as to accurately assess the enterprise's financial risk.

[0023] Furthermore, the present invention can effectively improve the risk management level of financial institutions, reduce risk management costs, gradually realize the intelligence and automation of risk management, improve the efficiency of financial risk assessment of enterprises, provide financial institutions with more comprehensive, timely and accurate risk management services, and promote the financial industry to develop in a safer, more stable and sustainable direction.

[0024] In some embodiments, calculating the total bank assets of an enterprise based on the bank pledge information of the enterprise specifically includes the following process: Based on the corporate bank pledge information, the total assets of the enterprise in the bank at the initial moment of statistics are obtained , Enterprise asset turnover rate , standard deviation of the fluctuation of total assets of enterprises and the parameter function of the total assets of corporate banks over time ,in, It obeys normal distribution, that is , each time t corresponds to a parameter function value; The total assets of the enterprise in the bank at the initial moment , Enterprise asset turnover rate , standard deviation of the fluctuation of total assets of enterprises and the parameter function of the total assets of corporate banks over time Substitute the correlation formula to calculate the total bank assets of the enterprise at time t , the association formula is as follows: ; Among them, the time The unit of measurement is days.

[0025] In some embodiments, calculating the total bank liabilities of an enterprise based on the bank pledge information of the enterprise specifically includes the following process: Based on the corporate bank pledge information, the total debt of the enterprise in the bank at the initial moment of statistics is obtained ; Based on the corporate bank pledge information, the total average interest rate r of the enterprise is obtained; The total debt of the enterprise in the bank at the initial moment will be counted Substitute the total liability average interest rate r of the enterprise into the correlation formula to calculate the total bank debt of the enterprise at time t , the association formula is as follows: ; in, Represents the inflation rate.

[0026] In some embodiments, calculating the first risk indicator value of the enterprise based on the total bank assets of the enterprise and the total liabilities of the enterprise bank specifically includes the following process: The net assets of the enterprise are calculated based on the total bank assets of the enterprise and the total liabilities of the enterprise bank. : ; ; in, represents the asset liquidity income at time t, It indicates the ratio of the enterprise's cash and pledged assets in the bank. It indicates the ratio of the value of financial products and assets owned by the enterprise, where the value of financial products includes the value of securities, stocks and government bonds. The ratio of mortgage loans, Indicates the proportion of other loans, express The total bank assets of the enterprise at the time, express The return on investment at that moment; Net assets of the enterprise Real-time calculation is performed, with the execution time of the preset management period as the X-axis and the net assets of the enterprise as the Establish a rectangular coordinate system for the Y axis, and calculate the net assets of the enterprise during the preset management period Draw the net asset curve of the preset management period in the rectangular coordinate system, draw perpendicular lines from the two end points of the net asset curve to the X-axis to obtain two truncation line segments, and form a graph with the net asset curve, the two truncation line segments and the X-axis. Mark the area value of the graph as the first risk indicator value of the enterprise.

[0027] In some embodiments, Figure 2 is a workflow diagram of another financial risk control method based on big data according to an embodiment of the present invention, such as Figure 2 As shown, obtaining the enterprise financial risk indicator information, substituting the enterprise financial risk indicator information and the enterprise basic qualification information into the financial risk comparison analysis model, and obtaining the second risk indicator value of the enterprise includes the following steps: Step S201: Obtain the industrial and commercial characteristic data indicators in the enterprise financial risk indicator information, and calculate the ratio Q of the number of existing industrial and commercial characteristic data items in the enterprise basic qualification information to the number of industrial and commercial characteristic data indicator items; Step S202: Obtain the judicial and public security characteristic data indicators in the enterprise financial risk indicator information, and count the number of judicial and public security characteristic data items W in the enterprise basic qualification information; Step S203: obtaining financial characteristic data indicators in the enterprise financial risk indicator information, and counting the number of financial characteristic data items U that meet the financial characteristic data indicator requirements in the enterprise basic qualification information; Step S204: obtaining the equity structure characteristic data index in the enterprise financial risk indicator information, and counting the number of equity structure characteristic data items P in the enterprise basic qualification information; Step S205: Input the ratio Q, the number of items W, the number of items U, and the number of items P into the financial risk comparison analysis model, and the financial risk comparison analysis model obtains the second risk indicator value of the enterprise according to the financial risk comparison analysis formula , the financial risk comparison analysis formula is as follows: ; in, , is the weight coefficient, and , the value of e is 2.72.

[0028] Furthermore, the industrial and commercial characteristic data indicators include the enterprise's tax registration address, enterprise type, registered capital and list of abnormal industrial and commercial operations; the judicial and public security characteristic data indicators include the number of times the enterprise is the defendant in legal disputes, the number of times the enterprise is the plaintiff in legal disputes, the number of labor contract disputes, the number of information on the persons subject to execution, the number of judicial documents and the number of court announcements; the financial characteristic data indicators include return on total assets, return on net assets, current ratio, cash flow to liabilities ratio, debt-to-asset ratio, capital preservation and appreciation rate, operating profit growth rate and net capital to net assets ratio; the equity structure characteristic data indicators include the number of equity changes and the number of subsidiaries.

[0029] The calculation of the enterprise's financial risk forecast value based on the first risk indicator value and the second risk indicator value specifically includes the following process: Substitute the first risk indicator value and the second risk indicator value into the enterprise's financial risk forecast value calculation formula to calculate the enterprise's financial risk forecast value LMS. The enterprise's financial risk forecast value calculation formula is as follows: ; in, is the first risk indicator value, is the second risk indicator value, , are weight factors, and their values ​​are 0.6 and 0.4 respectively.

[0030] Furthermore, the control of the enterprise's financial risk based on the enterprise's financial risk forecast value specifically includes the following processes: The enterprise's financial risk prediction value threshold is loaded to determine whether the enterprise's financial risk prediction value exceeds the enterprise's financial risk prediction value threshold. If so, the enterprise's financial risk is determined to be high; if not, the enterprise's financial risk is determined to be low.

[0031] The present invention also provides a financial risk control system based on big data. Figure 3 is a system block diagram of a financial risk control system based on big data according to an embodiment of the present invention. Figure 3 As shown, the system includes a financial data acquisition module, a risk index value calculation module, and a financial risk prediction module; The financial data acquisition module is used to acquire the financial data information of the enterprise, wherein the financial data information of the enterprise includes the bank pledge information of the enterprise and the basic qualification information of the enterprise; A risk index value calculation module, used to calculate the total bank assets and total bank liabilities of the enterprise based on the bank pledge information of the enterprise, and to calculate the first risk index value of the enterprise based on the total bank assets and total bank liabilities of the enterprise; Obtaining the financial risk indicator information of the enterprise, substituting the financial risk indicator information of the enterprise and the basic qualification information of the enterprise into the financial risk comparison analysis model, and obtaining the second risk indicator value of the enterprise; The financial risk prediction module is used to calculate the financial risk prediction value of the enterprise based on the first risk indicator value and the second risk indicator value, and control the financial risk of the enterprise based on the financial risk prediction value of the enterprise.

[0032] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are changed or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0033] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0034] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0035] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0036] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0037] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A financial risk control method based on big data, characterized in that the method include: Obtaining the financial data information of the enterprise, where the financial data information of the enterprise includes the bank pledge information and basic qualification information of the enterprise; Based on the bank pledge information of the enterprise, the total bank assets and the total bank liabilities of the enterprise are calculated, and based on the total bank assets and the total bank liabilities of the enterprise, the first risk indicator value of the enterprise is calculated; Obtaining the financial risk indicator information of the enterprise, substituting the financial risk indicator information of the enterprise and the basic qualification information of the enterprise into the financial risk comparison analysis model, and obtaining the second risk indicator value of the enterprise; A financial risk prediction value of the enterprise is calculated based on the first risk indicator value and the second risk indicator value, and the financial risk of the enterprise is controlled based on the financial risk prediction value of the enterprise.

2. According to the financial risk control method based on big data according to claim 1, it is characterized in that: Based on the corporate bank pledge information, the total bank assets of the enterprise are calculated. The process includes: Based on the corporate bank pledge information, the total assets of the enterprise in the bank at the initial moment of statistics are obtained , Enterprise asset turnover rate , standard deviation of the fluctuation of total assets of enterprises and the parameter function of the total assets of corporate banks over time ,in, It obeys normal distribution, that is , each time t corresponds to a parameter function value; The total assets of the enterprise in the bank at the initial moment , Enterprise asset turnover rate , standard deviation of the fluctuation of total assets of enterprises and the parameter function of the total assets of corporate banks over time Substitute the correlation formula to calculate the total bank assets of the enterprise at time t , the association formula is as follows: ； Among them, the time The unit of measurement is days.

3. The financial risk control method based on big data according to claim 2 is characterized in that: Based on the corporate bank pledge information, the total bank liabilities of the enterprise are calculated. The process includes: Based on the corporate bank pledge information, the total debt of the enterprise in the bank at the initial moment of statistics is obtained ; Based on the corporate bank pledge information, the total average interest rate r of the enterprise is obtained; The total debt of the enterprise in the bank at the initial moment will be counted Substitute the total liability average interest rate r of the enterprise into the correlation formula to calculate the total bank debt of the enterprise at time t , the association formula is as follows: ； in, Represents the inflation rate.

4. The financial risk control method based on big data according to claim 1, characterized in that: The first risk index value of the enterprise is calculated based on the total bank assets of the enterprise and the total liabilities of the enterprise bank. The process includes: The net assets of the enterprise are calculated based on the total bank assets of the enterprise and the total liabilities of the enterprise bank. : ； ； in, represents the asset liquidity income at time t, It indicates the ratio of the enterprise's cash and pledged assets in the bank. It indicates the ratio of the value of financial products and assets owned by the enterprise, where the value of financial products includes the value of securities, stocks and government bonds. The ratio of mortgage loans, Indicates the proportion of other loans, express Total bank assets of the enterprise at the time, express The return on investment at that moment; Net assets of the enterprise Real-time calculation is performed, with the execution time of the preset management period as the X-axis and the net assets of the enterprise as the Establish a rectangular coordinate system for the Y axis, and calculate the net assets of the enterprise during the preset management period Draw the net asset curve of the preset management period in the rectangular coordinate system, draw perpendicular lines from the two end points of the net asset curve to the X-axis to obtain two truncation line segments, and form a graph with the net asset curve, the two truncation line segments and the X-axis. Mark the area value of the graph as the first risk indicator value of the enterprise.

5. The financial risk control method based on big data according to claim 1, characterized in that: Obtaining the enterprise's financial risk indicator information, substituting the enterprise's financial risk indicator information and the enterprise's basic qualification information into the financial risk comparison analysis model, and obtaining the enterprise's second risk indicator value includes the following process: Obtain the industrial and commercial characteristic data indicators in the enterprise financial risk indicator information, and calculate the ratio Q of the number of existing industrial and commercial characteristic data items in the enterprise basic qualification information to the number of industrial and commercial characteristic data indicator items; Obtain the judicial and public security characteristic data indicators in the enterprise financial risk indicator information, and count the number of existing judicial and public security characteristic data items W in the enterprise basic qualification information; Obtain the financial characteristic data indicators in the enterprise financial risk indicator information, and count the number of financial characteristic data items U that meet the requirements of the financial characteristic data indicators in the enterprise basic qualification information; Obtain the equity structure characteristic data indicators in the enterprise financial risk indicator information, and count the number of existing equity structure characteristic data items P in the enterprise basic qualification information; Input the proportion Q, the number of items W, the number of items U, and the number of items P into the financial risk comparison analysis model. The financial risk comparison analysis model obtains the second risk indicator value of the enterprise according to the financial risk comparison analysis formula. , the financial risk comparative analysis formula is as follows: ； in, , is the weight coefficient, and , the value of e is 2.

72.

6. A financial risk control method based on big data according to claim 5, characterized in that: The industrial and commercial characteristic data indicators include the enterprise's tax registration address, enterprise type, registered capital and list of abnormal industrial and commercial operations; the judicial and public security characteristic data indicators include the number of times the enterprise is the defendant in legal disputes, the number of times the enterprise is the plaintiff in legal disputes, the number of labor contract disputes, the number of information on the persons subject to execution, the number of judicial documents and the number of court announcements; the financial characteristic data indicators include return on total assets, return on net assets, current ratio, cash flow to liabilities ratio, debt-to-asset ratio, capital preservation and appreciation rate, operating profit growth rate and net capital to net assets ratio; the equity structure characteristic data indicators include the number of equity changes and the number of subsidiaries.

7. The financial risk control method based on big data according to claim 1, characterized in that: The specific financial risk forecast value of the enterprise is calculated based on the first risk indicator value and the second risk indicator value. The process includes: Substitute the first risk indicator value and the second risk indicator value into the enterprise's financial risk forecast value calculation formula to calculate the enterprise's financial risk forecast value LMS. The enterprise's financial risk forecast value calculation formula is as follows: ； in, is the first risk indicator value, is the second risk indicator value, , are weight factors, and their values ​​are 0.6 and 0.4 respectively.

8. The financial risk control method based on big data according to claim 1, characterized in that: Control the financial risks of enterprises based on their predicted financial risk values. The process includes: The enterprise's financial risk prediction value threshold is loaded to determine whether the enterprise's financial risk prediction value exceeds the enterprise's financial risk prediction value threshold. If so, the enterprise's financial risk is determined to be high; if not, the enterprise's financial risk is determined to be low.

9. A financial risk control system based on big data, characterized in that: A financial risk control method based on big data applicable to any one of claims 1 to 8, the system comprising a financial data acquisition module, a risk index value calculation module, and a financial risk prediction module; The financial data acquisition module is used to acquire the financial data information of the enterprise, wherein the financial data information of the enterprise includes the bank pledge information of the enterprise and the basic qualification information of the enterprise; A risk index value calculation module, used to calculate the total bank assets and total bank liabilities of the enterprise based on the bank pledge information of the enterprise, and to calculate the first risk index value of the enterprise based on the total bank assets and total bank liabilities of the enterprise; Obtaining the financial risk indicator information of the enterprise, substituting the financial risk indicator information of the enterprise and the basic qualification information of the enterprise into the financial risk comparison analysis model, and obtaining the second risk indicator value of the enterprise; The financial risk prediction module is used to calculate the financial risk prediction value of the enterprise based on the first risk indicator value and the second risk indicator value, and control the financial risk of the enterprise based on the financial risk prediction value of the enterprise.

Images