CN112508674A - Financial risk intelligent analysis method and system based on big data - Google Patents
Financial risk intelligent analysis method and system based on big data Download PDFInfo
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Abstract
The invention discloses a financial risk intelligent analysis method and system based on big data. In the invention, the input ends of the client log acquisition module and the browser page acquisition module are connected, the power output end of the power supply module is connected with the power input ends of the processor module and the big data acquisition module, the output end of the processor module is connected with the input end of the risk analysis module, the output end of the risk analysis module is connected with the input end of the data output module, the output end of the data output module is connected with the input end of the receiving terminal, and the output end of the receiving terminal is connected with the input end of the data display terminal module; the log collection module and the client log collection module can be matched with the browser page collection module to collect various useful information on the network, so that the diversity of data is guaranteed, and the accuracy of the data is improved when the data is used.
Description
Technical Field
The invention belongs to the technical field of financial analysis, and particularly relates to a financial risk intelligent analysis method and system based on big data.
Background
Corporate financial risk refers to the possibility of a corporation incurring losses due to uncertainty in the financial position over the course of each financial activity, due to various unforeseen or uncontrolled factors. According to the main links of financial activities, the method can be divided into liquidity risks, credit risks, financing risks and investment risks. Classified according to controllable degree, the risk can be divided into controllable risk and uncontrollable risk.
However, when a common analysis method is used for analysis, the accuracy of the financial risk analysis is not accurate enough due to the fact that the adopted analysis mode is monotonous, and meanwhile, when the common analysis method is used for collecting data, the adopted collection mode is monotonous.
Disclosure of Invention
The invention aims to: in order to solve the problems proposed above, a financial risk intelligent analysis method and system based on big data are provided.
The technical scheme adopted by the invention is as follows: the financial risk intelligent analysis method and system based on big data comprises a processor module, a big data acquisition module, a risk analysis module, a data output module, a receiving terminal, a data display terminal module, a client log acquisition module, a power supply module, a log acquisition module, a browser page acquisition module, a safety detection module, an AI analysis module, a manual analysis module, a financial statement analysis module, an asset management efficiency analysis module, a cash flow analysis module, a sales and profit analysis module, a repayment capability analysis module, an important client and remark analysis module, a sales and profit analysis module and a liquidity analysis module, wherein the output end of the processor module is connected with the input end of the big data acquisition module, the log acquisition module, the client log acquisition module and the browser page acquisition module are fixedly arranged outside the big data acquisition module, the output of big data acquisition module is connected with log acquisition module client side log acquisition module with browser page acquisition module's input, power module's power output end is connected with processor module with big data acquisition module's power input end, processor module inside fixed mounting have safety inspection module's input, processor module's output is connected with risk analysis module's input, risk analysis module's output is connected with data output module's input, data output module's output is connected with receiving terminal's input, receiving terminal's output is connected with data display terminal module's input.
In a preferred embodiment, the AI analysis module and the manual analysis module are fixedly installed inside the risk analysis module, and an input end of the risk analysis module is connected with output ends of the AI analysis module and the manual analysis module.
In a preferred embodiment, the financial statement analysis module is fixedly installed inside the AI analysis module, an output end of the financial statement analysis module is connected to an input end of the asset management efficiency analysis module, an output end of the asset management efficiency analysis module is connected to an input end of the cash flow analysis module, and an output end of the cash flow analysis module is connected to an input end of the sales and profit analysis module.
In a preferred embodiment, the repayment ability analysis module is fixedly installed inside the manual analysis module, an output end of the repayment ability analysis module is connected with an input end of the important customer and remark analysis module, an output end of the important customer and remark analysis module is connected with an input end of the sales and profit analysis module, and an output end of the sales and profit analysis module is connected with an input end of the liquidity analysis module.
In a preferred embodiment, the financial statement analysis module issues an audit report without the insurance opinion, an audit report without the reservation opinion with the description section, an audit report with the reservation opinion, or an audit report rejecting the opinion, and the reasons should be analyzed in detail for the latter three cases; if the audit is not carried out, a client manager firstly notes the source of the financial statement and the evaluation of the financial statement, and the client manager is accurate, credible and approximately credible but has a little part, suspicious or inaccurate, unreliable and the like so as to be convenient for analysis and grasp of market searching batch personnel in the subsequent market; whether the accounting policy adopted by the client is reasonable or not is analyzed, whether the accounting policy is consistent with the industry condition and the previous accounting year or not is analyzed, and if any condition that the assets, net assets and profits are highly estimated or the liabilities are underestimated is found, the analysis is explained; in principle, the summary report is not accepted, and the borrower report and the group combined report are analyzed simultaneously for the local part of the group company or the core enterprise of the group.
In a preferred embodiment, the internal primary analysis basis of the asset management efficiency analysis module is a turn-around ratio; each index in the turnover rate expresses the average held days or updated times of related subjects of the balance sheet in one year; the number of receivables turnover days may be affected by the proportion of sales revenue of the portion not counted in receivables, such as the proportion of cash sales; if the specific gravity about cash sales is known, adjusting the data to output meters and recalculating the maximum receivables turnover; the accounts receivable and accounts payable are approximately consistent with the operation condition of the credit object; the number of days for turnover of the stock is mainly dependent on the nature of the business of the credit object; for all turnover ratios, attention should be paid to trends and changes, and reasons should be understood to measure the influence of cash flow when the indexes are changed significantly.
In a preferred embodiment, the main indexes used by the internal analysis of the repayment ability analysis module are tangible net assets and guarantee ratios in the absolute value index; the coverage rate refers to the ability of the credited object to generate sufficient profits to cover the total debt; although profit does not necessarily provide the necessary security for paying debts, profit can be used as a reference index for evaluating the long-term repayment ability of a credit object.
In a preferred embodiment, the important client and remarks analysis module is used for analyzing the account and financial statement remarks with larger amount in the recent year report and the recent report so as to have clear understanding of the asset structure and quality, the source structure and stability of the funds, the condition of or liability and the like of the company.
In a preferred embodiment, the main indexes used by the internal analysis of the sales and profit analysis module are net sales and changes thereof, net earnings and changes thereof, business profits and changes thereof in the absolute index, net sales growth rate, business cost growth rate, business profit growth rate and profitability rate in the growth rate index.
In a preferred embodiment, the main index for the internal analysis of the flowability analysis module is the flowability ratio; these rates evaluate the resources available to the company for repayment; the most important indicator is the flow ratio, but the flow ratio has great limitations; a flow ratio equal to 2 is generally considered sufficient, whereas for companies with high quality of flowing assets, reasonably reliable accounting data and stable operation, the ratio of accounts receivable to flowing liability is considered sufficient.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. in the invention, the log acquisition module and the client log acquisition module can be matched with the browser page acquisition module to acquire various useful information on the network, thereby ensuring the diversity of data and improving the accuracy of the data when the data is used.
2. In the invention, each analysis module arranged in the AI analysis module is matched with each analysis module in the artificial analysis module, so that the artificial analysis module is matched with the intelligent AI analysis module, thereby improving the capability of carrying out operational analysis on financial risks and lightening the labor burden of workers.
Drawings
FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a system block diagram of an AI analysis module according to the present invention;
FIG. 3 is a block diagram of a manual analysis module system according to the present invention;
FIG. 4 is a block diagram of a risk analysis module system according to the present invention.
The labels in the figure are: 1-a processor module, 2-a big data acquisition module, 3-a risk analysis module, 4-a data output module, 5-a receiving terminal, 6-a data display terminal module, 7-a client log acquisition module, 8-a power supply module, 9-a log acquisition module, 10-a browser page acquisition module, 11-a safety detection module, the system comprises a 12-AI analysis module, a 13-manual analysis module, a 14-financial statement analysis module, a 15-asset management efficiency analysis module, a 16-cash flow analysis module, a 17-sales and profit analysis module, an 18-repayment capability analysis module, a 19-important customer and remarks analysis module, a 20-sales and profit analysis module and a 21-liquidity analysis module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1-4, the financial risk intelligent analysis method and system based on big data comprises a processor module 1, a big data acquisition module 2, a risk analysis module 3, a data output module 4, a receiving terminal 5, a data display terminal module 6, a client log acquisition module 7, a power supply module 8, a log acquisition module 9, a browser page acquisition module 10, a safety detection module 11, an AI analysis module 12, an artificial analysis module 13, a financial statement analysis module 14, an asset management efficiency analysis module 15, a cash flow analysis module 16, a sales and profit analysis module 17, a repayment capability analysis module 18, an important client and remark analysis module 19, a sales and profit analysis module 20 and a liquidity analysis module 21, wherein the output end of the processor module 1 is connected with the input end of the big data acquisition module 2, and the log acquisition module 9, the risk analysis module 10, the browser page acquisition module 10, the safety detection, The system comprises a client log acquisition module 7 and a browser page acquisition module 10, wherein the output end of a big data acquisition module 2 is connected with log acquisition modules 9, the input ends of the client log acquisition module 7 and the browser page acquisition module 10, the power output end of a power supply module 8 is connected with the power input ends of a processor module 1 and the big data acquisition module 2, the input end of a safety detection module 11 is fixedly installed in the processor module 1, the output end of the processor module 1 is connected with the input end of a risk analysis module 3, the interior of the risk analysis module 3 is fixedly provided with an AI analysis module 12 and a manual analysis module 13, and the input end of the risk analysis module 3 is connected with the output ends of the AI analysis module 12 and the manual analysis module 13; a financial statement analysis module 14 is fixedly installed inside the AI analysis module 12, an audit report without a guarantee opinion, an audit report without a reservation opinion with a description section, an audit report with a reservation opinion or an audit report rejecting an opinion presentation is issued inside the financial statement analysis module 14, and reasons should be analyzed in detail for the latter three cases; if the audit is not carried out, a client manager firstly notes the source of the financial statement and the evaluation of the financial statement, and the client manager is accurate, credible and approximately credible but has a little part, suspicious or inaccurate, unreliable and the like so as to be convenient for analysis and grasp of market searching batch personnel in the subsequent market; whether the accounting policy adopted by the client is reasonable or not is analyzed, whether the accounting policy is consistent with the industry condition and the previous accounting year or not is analyzed, and if any condition that the assets, net assets and profits are highly estimated or the liabilities are underestimated is found, the analysis is explained; in principle, the local part of the group company or the core enterprise of the group should analyze the borrower report and the group combined report at the same time; the output end of the financial statement analysis module 14 is connected with the input end of an asset management efficiency analysis module 15, the output end of the asset management efficiency analysis module 15 is connected with the input end of a cash flow analysis module 16, and the internal main analysis basis of the asset management efficiency analysis module 15 is the turnover ratio; each index in the turnover rate expresses the average held days or updated times of related subjects of the balance sheet in one year; the number of receivables turnover days may be affected by the proportion of sales revenue of the portion not counted in receivables, such as the proportion of cash sales; if the specific gravity about cash sales is known, adjusting the data to output meters and recalculating the maximum receivables turnover; the accounts receivable and accounts payable are approximately consistent with the operation condition of the credit object; the number of days for turnover of the stock is mainly dependent on the nature of the business of the credit object; for all the turnover ratios, attention should be paid to trends and changes, reasons should be known, and the influence conditions of cash flow generated when the indexes are changed greatly are measured; the output end of the cash flow analysis module 16 is connected with the input end of a sale and profit analysis module 17; a repayment ability analysis module 18 is fixedly installed inside the manual analysis module 13, and main indexes used for internal analysis of the repayment ability analysis module 18 are tangible net assets and guarantee ratios in absolute indexes; the coverage rate refers to the ability of the credited object to generate sufficient profits to cover the total debt, including paying interest and amortizing principal amortization; although the profit does not necessarily provide necessary guarantee for paying the debt, the profit can be used as a reference index for evaluating the long-term repayment capability of the credit granting object; the output end of the repayment capacity analysis module 18 is connected with the input end of an important client and remark analysis module 19, the important client and remark analysis module 19 is internally used for analyzing the subjects with larger or decorated money and the financial statement remarks in the recent annual report and the recent report so as to clearly know the asset structure and quality, the source structure and stability of the money, the condition of or liability and the like of a company; the output end of the important customer and remark analysis module 19 is connected with the input end of a sales and profit analysis module 20, and the main indexes used for the internal analysis of the sales and profit analysis module 20 are net sales and change conditions thereof, net earnings and change conditions thereof, business profits and change conditions thereof in the absolute value index, and net sales growth rate, business cost growth rate, business profit growth rate and profit capacity ratio in the culture rate index; the output end of the marketing and profit analysis module 20 is connected with the input end of the fluidity analysis module 21, and the main index used for the internal analysis of the fluidity analysis module 21 is the fluidity ratio; these rates evaluate the resources available to the company for repayment; the most important indicator is the flow ratio, but the flow ratio has great limitations; a flow ratio equal to 2 is generally considered sufficient, whereas for companies with high quality of mobile assets, reasonably reliable accounting data and stable operation, the ratio of accounts receivable to mobile liability; the output end of the risk analysis module 3 is connected with the input end of the data output module 4, the output end of the data output module 4 is connected with the input end of the receiving terminal 5, and the output end of the receiving terminal 5 is connected with the input end of the data display terminal module 6.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. The financial risk intelligent analysis method and system based on big data comprises a processor module (1), a big data acquisition module (2), a risk analysis module (3), a data output module (4), a receiving terminal (5), a data display terminal module (6), a client log acquisition module (7), a power supply module (8), a log acquisition module (9), a browser page acquisition module (10), a safety detection module (11), an AI analysis module (12), a manual analysis module (13), a financial statement analysis module (14), an asset management efficiency analysis module (15), a cash flow analysis module (16), a sales and profit analysis module (17), a repayment capacity analysis module (18), an important client and remarks analysis module (19), a sales and profit analysis module (20) and a liquidity analysis module (21), the method is characterized in that: the output end of the processor module (1) is connected with the input end of the big data acquisition module (2), the log acquisition module (9), the client log acquisition module (7) and the browser page acquisition module (10) are fixedly installed outside the big data acquisition module (2), the output end of the big data acquisition module (2) is connected with the input ends of the log acquisition module (9), the client log acquisition module (7) and the browser page acquisition module (10), the power output end of the power supply module (8) is connected with the power input ends of the processor module (1) and the big data acquisition module (2), the input end of the safety detection module (11) is fixedly installed inside the processor module (1), the output end of the processor module (1) is connected with the input end of the risk analysis module (3), the output end of the risk analysis module (3) is connected with the input end of the data output module (4), the output end of the data output module (4) is connected with the input end of the receiving terminal (5), and the output end of the receiving terminal (5) is connected with the input end of the data display terminal module (6).
2. The big-data based intelligent financial risk analysis method and system according to claim 1, wherein: the internal part of the risk analysis module (3) is fixedly provided with the AI analysis module (12) and the manual analysis module (13), and the input end of the risk analysis module (3) is connected with the output ends of the AI analysis module (12) and the manual analysis module (13).
3. The big-data based intelligent financial risk analysis method and system according to claim 1, wherein: the financial statement analysis module (14) is fixedly installed inside the AI analysis module (12), the output end of the financial statement analysis module (14) is connected with the input end of the asset management efficiency analysis module (15), the output end of the asset management efficiency analysis module (15) is connected with the input end of the cash flow analysis module (16), and the output end of the cash flow analysis module (16) is connected with the input end of the sales and profit analysis module (17).
4. The big-data based intelligent financial risk analysis method and system according to claim 1, wherein: the internal of the manual analysis module (13) is fixedly provided with the repayment capacity analysis module (18), the output end of the repayment capacity analysis module (18) is connected with the input end of the important customer and remark analysis module (19), the output end of the important customer and remark analysis module (19) is connected with the input end of the sales and profit analysis module (20), and the output end of the sales and profit analysis module (20) is connected with the input end of the liquidity analysis module (21).
5. The big-data based intelligent financial risk analysis method and system according to claim 1, wherein: the financial statement analysis module (14) internally issues an audit report without the insurance opinion, an audit report without the reservation opinion with the description section, an audit report with the reservation opinion or an audit report rejecting the opinion.
6. The big-data based intelligent financial risk analysis method and system according to claim 1, wherein: the internal primary analysis basis of the asset management efficiency analysis module (15) is a turnover ratio; each index in the turnover rate is a representation of the average number of days or updates held by the subject associated with the balance sheet over the year.
7. The big-data based intelligent financial risk analysis method and system according to claim 1, wherein: the main indexes used by the internal analysis of the repayment capacity analysis module (18) are tangible net assets and guarantee ratios in absolute value indexes; the coverage rate refers to the ability of a trusted subject to generate sufficient profits to cover a total debt (including paying interest and amortizing principal).
8. The big-data based intelligent financial risk analysis method and system according to claim 1, wherein: the important customer and remark analysis module (19) is internally used for analyzing the subjects with larger or decorated larger money and the financial statement remarks in the recent annual report and the recent report so as to have clear understanding on the asset structure and quality, the source structure and stability of the money, the condition of or liability and the like of the company.
9. The big-data based intelligent financial risk analysis method and system according to claim 1, wherein: the main indexes used by the internal analysis of the sales and profit analysis module (20) are the net sales and the change thereof, the net earnings and the change thereof, the business profits and the change thereof in the absolute value index, and the net sales growth rate, the main business cost growth rate, the business profit growth rate and the profit capacity ratio in the cultivation rate index.
10. The big-data based intelligent financial risk analysis method and system according to claim 1, wherein: the main index for the internal analysis of the fluidity analysis module (21) is the fluidity ratio; the most important indicator is the flow ratio, but the flow ratio has great limitations; a flow ratio equal to 2 is generally considered sufficient, whereas for companies with high quality of flowing assets, reasonably reliable accounting data and stable operation, the ratio of accounts receivable to flowing liability is considered sufficient.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113487399A (en) * | 2021-05-18 | 2021-10-08 | 广州城建职业学院 | Accounting book management method based on block chain technology |
| CN113487274A (en) * | 2021-06-22 | 2021-10-08 | 北京德风新征程科技有限公司 | Intelligent service management method based on Internet big data |
| CN113487398A (en) * | 2021-05-17 | 2021-10-08 | 广州城建职业学院 | Financial risk intelligent analysis method based on big data |
| US20220164886A1 (en) * | 2020-11-24 | 2022-05-26 | VFD SAAS Technology, Ltd. | Artificial intelligence financial analysis and reporting platform |
-
2021
- 2021-01-13 CN CN202110039524.1A patent/CN112508674A/en not_active Withdrawn
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220164886A1 (en) * | 2020-11-24 | 2022-05-26 | VFD SAAS Technology, Ltd. | Artificial intelligence financial analysis and reporting platform |
| CN113487398A (en) * | 2021-05-17 | 2021-10-08 | 广州城建职业学院 | Financial risk intelligent analysis method based on big data |
| CN113487399A (en) * | 2021-05-18 | 2021-10-08 | 广州城建职业学院 | Accounting book management method based on block chain technology |
| CN113487274A (en) * | 2021-06-22 | 2021-10-08 | 北京德风新征程科技有限公司 | Intelligent service management method based on Internet big data |
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Application publication date: 20210316 |