Financial risk early warning method

Abstract

The invention discloses a financial risk early warning method, and relates to the technical field of financial management, and a risk early warning system adopted by the method comprises a financial data acquisition module, an analysis sensitivity adjustment module and an auxiliary risk identification module. The financial data acquisition module is used for performing data acquisition on financial data change of a fund circulation path and distributed fund flow time points, and the analysis sensitivity adjustment module is used for adjusting the financial analysis frequency of the risk analysis unit according to the acquired data. The auxiliary risk identification module is used for detecting the head end of a financial risk signal by adopting a manual processing method when the risk analysis unit fails to accurately identify the front point of the risk signal; and the financial data acquisition module comprises a fund access recording module, a financial data change acquisition module, a distributed detection unit, a financial structure mapping module and a financial risk analysis unit. The system has the characteristic of automatic risk response.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of financial management, in particular to a financial risk early warning method. BACKGROUND

[0002] In the modern enterprise financial management system, with the complication of business structure and the diversification of fund flow, the financial data presents the characteristics of high-frequency fluctuation, scattered circulation, nonlinear evolution, etc. Especially in the case of multiple branch offices, off-site accounts or temporary fund pools, abnormal fund behavior often has concealment and suddenness.

[0003] In the existing financial management system, in the face of sudden or hidden financial risks such as abnormal fund fluctuation and non-periodic fund flow, due to the dependence on fixed frequency sampling or single algorithm judgment, the problems of risk signal missed detection, misjudgment or delayed response often occur. Especially in the distributed fund circulation environment such as multiple accounts and multiple subsidiary company coordination system, there is a lack of effective dynamic sensitivity adjustment and auxiliary identification mechanism, which leads to the system unable to lock the risk source in real time or adjust the monitoring frequency in time. Therefore, it is necessary to design an automatic risk response financial risk early warning method. SUMMARY

[0004] The present application aims to provide a financial risk early warning method to solve the problems raised in the background.

[0005] In order to solve the above technical problems, the present application provides the following technical scheme: a financial risk early warning method, the risk early warning system used by the method includes a financial data acquisition module, an analysis sensitivity adjustment module and an auxiliary risk identification module. The financial data acquisition module is used for collecting the data of the financial data variation and the scattered fund flow time point of the fund circulation path. The analysis sensitivity adjustment module is used for adjusting the financial analysis frequency of the risk analysis unit according to the collected data. The auxiliary risk identification module is used for detecting the financial risk signal front end by manual processing method before the risk analysis unit fails to accurately identify the risk signal front point.

[0006] According to the technical scheme, the financial data acquisition module comprises a fund in-out recording module, a financial data change acquisition module, a distributed detection unit, a financial structure mapping module, a financial risk analysis unit and a financial data change balancing unit, the fund in-out recording module is electrically connected with the distributed detection unit, the financial structure mapping module is electrically connected with the financial risk analysis unit, the fund in-out recording module is used for recording fund collection and fund withdrawal operations of the distributed detection unit corresponding to each financial index, the financial data change acquisition module is used for acquiring financial data changes connected with the financial index, the distributed detection unit is used for providing fund source detection for the fund circulation path by means of distributed detection, the financial structure mapping module is used for generating a fund flow direction map of each paragraph of the fund circulation path, the financial risk analysis unit is used for detecting abnormal financial signals to locate the risk occurrence position, and the financial data change balancing unit is used for frequency adjustment of the distributed detection unit of the fund circulation path to cope with financial data changes and reduce interference of feedback interference signals.

[0007] The analysis sensitivity adjustment module comprises a financial analysis frequency adjustment module, a financial data change analysis module, a risk judgment feedback module, a fund output strategy adjustment module and a set threshold judgment module, the financial analysis frequency adjustment module is electrically connected with the set threshold judgment module, the fund output strategy adjustment module is electrically connected with the financial data change balancing unit, the financial data change analysis module is used for analyzing the amplitude of the financial data change, the risk judgment feedback module is used for inputting the judgment result of the abnormal financial signal position after on-site detection, the fund output strategy adjustment module is used for adjusting the fund allocation value of the financial data change balancing unit, and the set threshold judgment module is used for judging whether the distributed fund flow point and the financial data change amplitude exceed the set value.

[0008] The auxiliary risk identification module comprises an audit forced review module, a small amount abnormality identification module and a risk cause tracking module, the audit forced review module and the small amount abnormality identification module are electrically connected with the risk judgment feedback module and the risk cause tracking module, the risk cause tracking module is used for positioning the abnormal financial signal by means of auxiliary means, the audit forced review module is used for controlling the forced audit trigger operation of the device configured with the forced audit trigger function, and the small amount abnormality identification module is used for judging the financial starting point by means of the small amount account auxiliary device which is not configured with the forced audit trigger function.

[0009] According to the technical scheme, the following steps are included:

[0010] S1. Construct a capital flow diagram by combining the various segments of the capital flow path and the connected distributed detection units, and represent the financial analysis node units including the financial data change balance unit and the financial risk analysis unit in the capital flow diagram;

[0011] S2. When the capital flow path is running, the distributed capital flow time points and the frequency of financial data changes at each detection point are analyzed, and the type of each segment is defined based on the analysis results;

[0012] S3. Adjust the data analysis frequency of the corresponding risk analysis unit according to the type of each segment of the capital flow path, and handle the situation where a single parameter in a segment exceeds the set value separately;

[0013] S4. Judge the detection results of risk financial signals. If the risk signal frontier point cannot be captured or identified after the risk occurs, increase the system financial analysis frequency and use auxiliary judgment methods to analyze the financial risk signal front-end signal.

[0014] According to the above technical solution, in S2, the specific method of defining the type of each paragraph is:

[0015] S2-1. Analyze the dispersed capital flow time points and the frequency of financial data changes in the capital flow paths of each detection point, record the time of each capital collection and withdrawal operation, and calculate the dispersed capital flow time point Δj of the capital flow path. When Δj is greater than the critical value j min When , the capital flow path of this section is defined as a potential high-risk capital flow path, and the variance of the change range f of the collected financial data is calculated. The series {f1, f2…f n}, and calculate the average value of the series Δf, if the variance of the financial data change range f If it is greater than the set value X, then this section is judged to be a high-risk capital path, where f i is any item in the sequence;

[0016] S2-2, when the dispersed capital flow path of a certain detection point Δj is less than the critical value j min If If it is less than the set value X, then this section is judged to be a safe capital path.

[0017] According to the above technical solution, in S3, the specific method of adjusting the data analysis frequency is:

[0018] S3-1, find the high-risk fund path position in the line fund flow map, increase the risk analysis unit financial analysis frequency F of this paragraph, the increasing amplitude is positively correlated with Δj and A, specifically F=[1+kAΔj]F0, wherein F0 is the initial analysis frequency, and k is a sensitivity adjustment factor;

[0019] S3-2, find the safe fund path position in the line fund flow map, reduce the risk analysis unit financial analysis frequency F of this fund path, the reducing amplitude is negatively correlated with Δj and A, specifically

[0020] According to the above technical scheme, in S2-1 and S2-2, if it is detected that Δj is less than the critical value j min and A>X, at this time F does not change, and the analysis weight Q of the fund flow path of this paragraph needs to be increased, if it is detected that Δj is greater than the critical value j min and A<X, it is judged that the abnormal increase of the decentralized detection unit fund collection and fund withdrawal operation, then disconnect the decentralized detection unit connected to the fund flow path of this paragraph, and all use the detection unit of the financial main system itself for analysis, until A>X is detected again to access the decentralized detection unit.

[0021] According to the above technical scheme, in S4, if the risk signal front point cannot be accurately identified after being improved, the following processing method is adopted: for the fund path configured with the forced audit trigger function, the standard financial abnormal signal template after the fund path delay forced audit trigger is used to conform to the abnormal financial signal, the conforming abnormal financial signal and standard financial abnormal signal template signal are captured, and the starting position of the abnormal financial signal is judged.

[0022] According to the above technical scheme, in S4, on the financial analysis node which is not configured with the forced audit trigger function, the account connection at the end of the target fund flow path is temporarily cut off by manual remote control, so as to cause local financial data change, and the change will form a standard financial abnormal signal with structural characteristics. Based on the matching identification of the abnormal signal and the abnormal fluctuation in the current financial data, the potential financial risk occurrence position is accurately located.

[0023] Compared with the prior art, the application has the beneficial effects that: the financial risk early warning method provided by the application realizes accurate identification and response control of abnormal financial signals by constructing a three-layer system architecture including financial data collection, sensitivity adjustment and auxiliary risk identification. According to the risk level of each fund flow section, the frequency and analysis granularity of the risk analysis unit can be dynamically adjusted to avoid resource waste and misjudgment. At the same time, the standard abnormal signal template generation and manual breakpoint induction mechanism are introduced to enhance the fault recovery and judgment ability of the system at the key nodes. Compared with the prior art, the method effectively improves the coverage, identification accuracy and response efficiency of risk early warning, and has good practicability and expansibility especially under the condition of multi-account cooperation and complex fund structure. BRIEF DESCRIPTION OF DRAWINGS

[0024] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the specification, illustrate the application, and are used to explain the application together with the embodiments of the application, and do not constitute a limitation on the application. In the drawings:

[0025] Figure 1 is a schematic diagram of the overall module structure of the application. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0027] Please refer to Figure 1 The application provides a technical solution: a financial risk early warning method, the risk early warning system used in the method includes a financial data collection module, an analysis sensitivity adjustment module and an auxiliary risk identification module. The financial data collection module is used for collecting data of the financial data variation and the dispersed fund flow time point of the fund flow path. The analysis sensitivity adjustment module is used for adjusting the financial analysis frequency of the risk analysis unit according to the collected data. The auxiliary risk identification module is used for detecting the financial risk signal front end by using a manual processing method before the risk analysis unit fails to accurately identify the risk signal front point.

[0028] The financial data collection module comprises a fund in-out record module, a financial data change collection module, a distributed detection unit, a financial structure mapping module, a financial risk analysis unit, and a financial data change balancing unit. The fund in-out record module is electrically connected with the distributed detection unit. The financial structure mapping module is electrically connected with the financial risk analysis unit. The fund in-out record module is used to record the fund collection and fund withdrawal operation of the distributed detection unit corresponding to each financial index. The financial data change collection module is used to collect the financial data change connected with the financial index. The distributed detection unit is used to provide fund source detection for the fund circulation path by using a distributed detection mode. The financial structure mapping module is used to generate a fund flow direction map of each paragraph of the fund circulation path. The financial risk analysis unit is used to detect abnormal financial signals to locate the risk occurrence position. The financial data change balancing unit is used to adjust the frequency of the distributed detection unit of the fund circulation path to respond to the financial data change and reduce the interference of the feedback interference signal.

[0029] The analysis sensitivity adjustment module comprises a financial analysis frequency adjustment module, a financial data change analysis module, a risk judgment feedback module, a fund output strategy adjustment module, and a set threshold judgment module. The financial analysis frequency adjustment module is electrically connected with the set threshold judgment module. The fund output strategy adjustment module is electrically connected with the financial data change balancing unit. The financial data change analysis module is used to analyze the amplitude of the financial data change. The risk judgment feedback module is used to input the judgment result of the abnormal financial signal position after on-site detection. The fund output strategy adjustment module is used to adjust the fund allocation value of the financial data change balancing unit. The set threshold judgment module is used to judge whether the distributed fund flow point and the financial data change amplitude exceed the set value.

[0030] The auxiliary risk identification module comprises an audit forced review module, a small amount abnormality identification module, and a risk cause tracking module. The audit forced review module and the small amount abnormality identification module are electrically connected with the risk judgment feedback module and the risk cause tracking module. The risk cause tracking module is used to locate the abnormal financial signal by using an auxiliary means. The audit forced review module is used to control the forced audit trigger operation of the device configured with a forced audit trigger function. The small amount abnormality identification module is used to judge the financial start point by using a small amount account auxiliary device which is not configured with the forced audit trigger function.

[0031] The method comprises the following steps:

[0032] S1, constructing a fund flow direction map by connecting each paragraph of the fund circulation path and the distributed detection unit, and representing the financial analysis node unit comprising the financial data change balancing unit and the financial risk analysis unit in the fund flow direction map;

[0033] S2. When the capital flow path is running, the distributed capital flow time points and the frequency of financial data changes at each detection point are analyzed, and the type of each segment is defined based on the analysis results;

[0034] S3. Adjust the data analysis frequency of the corresponding risk analysis unit according to the type of each segment of the capital flow path, and handle the situation where a single parameter in a segment exceeds the set value separately;

[0035] S4. Judge the detection results of risk financial signals. If the leading edge of risk signals cannot be captured or identified after the risk occurs, increase the frequency of system financial analysis and use auxiliary judgment methods to analyze the leading edge of financial risk signals.

[0036] In S2, the specific way to define the type of each paragraph is as follows:

[0037] S2-1. Analyze the dispersed capital flow time points and the frequency of financial data changes in the capital flow paths of each detection point, record the time of each capital collection and withdrawal operation, and calculate the dispersed capital flow time point Δj of the capital flow path. When Δj is greater than the critical value j min When , the capital flow path of this section is defined as a potential high-risk capital flow path, and the variance of the change range f of the collected financial data is calculated. The series {f1, f2…f n}, and calculate the average value of the series Δf, if the variance of the financial data change range f If it is greater than the set value X, then this section is judged to be a high-risk capital path, where f i is any item in the sequence;

[0038] S2-2, when the dispersed capital flow path of a certain detection point Δj is less than the critical value j min If If it is less than the set value X, then this section is judged to be a safe capital path;

[0039] In S3, the specific method of adjusting the data analysis frequency is as follows:

[0040] S3-1. Find the high-risk capital path in the capital flow map and increase the financial analysis frequency F of the risk analysis unit in this section. The increase is positively correlated with Δj and A. Specifically:

[0041] F = [1 + kAΔj]F0, where F0 is the initial analysis frequency and k is the sensitivity adjustment factor;

[0042] S3-2. Find the safe capital path in the capital flow map and reduce the risk analysis unit financial analysis frequency F of this capital path. The reduction is negatively correlated with Δj and A. Specifically,

[0043] In S2-1 and S2-2, if Δj is detected to be less than the critical value j min And in the case of A>X, F does not change at this time, and the analysis weight Q of the capital flow path of this section needs to be increased. If it is detected that Δj is greater than the critical value j min And A <X的情况,则判断分散式检测单元资金归集和资金撤离操作的异常增加,则断开此段落的资金流通路径连接的分散式检测单元,全部采用财务主系统本身的检测单元进行分析,直到检测出A> At X, the distributed detection unit is connected;

[0044] In S4, if the risk signal frontier point is still not accurately identified after the improvement, the following processing method is adopted: for the fund path configured with the mandatory audit trigger function, the standard financial abnormal signal template after the fund path delay mandatory audit trigger is used to match the abnormal financial signal, capture the abnormal financial signal that matches the standard financial abnormal signal template signal, and determine the starting position of the abnormal financial signal;

[0045] In S4, on the financial analysis node that is not configured with the mandatory audit trigger function, the account connection at the end of the target capital flow path is temporarily cut off through manual remote control to cause local changes in financial data. This change will form a standard financial anomaly signal with structural characteristics. Based on this anomaly signal, it is matched and identified with abnormal fluctuations in the current financial data, thereby accurately locating the location of potential financial risks.

[0046] The financial risk warning method provided by the present invention achieves accurate identification and response control of abnormal financial signals by constructing a three-layer system architecture including financial data collection, sensitivity adjustment, and auxiliary risk identification. This solution can dynamically adjust the frequency and analysis granularity of risk analysis units according to the risk level of each capital flow segment, avoiding resource waste and misjudgment. At the same time, it introduces standard abnormal signal template generation and artificial breakpoint induction mechanisms to enhance the system's fault recovery and judgment capabilities at key nodes. Compared with existing technologies, this method effectively improves the coverage, identification accuracy, and response efficiency of risk warnings, and has good practicality and scalability, especially in the context of multi-account collaboration and complex capital structures.

[0047] It is to be noted that, in the present text, relational terms such as first and second and the like can 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. Moreover, the terms "comprises", "comprising", or any other variations 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 can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0048] Finally, it should be noted that the above-mentioned only constitutes preferred embodiments of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications, equivalent replacements, improvements and the like of the technical solutions described in the foregoing embodiments can still be made. Any modifications, equivalent replacements, improvements and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A financial risk early warning method, characterized by: The risk warning system adopted by the method includes a financial data acquisition module, an analysis sensitivity adjustment module, and an auxiliary risk identification module. The financial data acquisition module is used to collect data on financial data changes in the capital circulation path and the time points of decentralized capital flow. The analysis sensitivity adjustment module is used to adjust the financial analysis frequency of the risk analysis unit according to the collected data. The auxiliary risk identification module is used to detect the front end of the financial risk signal using a manual processing method when the risk analysis unit fails to accurately identify the leading point of the risk signal.

2. A financial risk early warning method according to claim 1, characterized in that: The financial data acquisition module includes a fund inflow and outflow recording module, a financial data change acquisition module, a distributed detection unit, a financial structure mapping module, a financial risk analysis unit, and a financial data change balance unit. The fund inflow and outflow recording module is used to record the fund collection and withdrawal operations of the distributed detection units corresponding to each financial indicator. The financial data change acquisition module is used to collect changes in financial data connected to the financial indicators. The distributed detection unit is used to provide fund source detection for the fund flow path using a distributed detection method. The financial structure mapping module is used to generate a fund flow map for each section of the fund flow path. The financial risk analysis unit is used to detect abnormal financial signals to locate the location of risk. The financial data change balance unit is used to adjust the frequency of the distributed detection units of the fund flow path to respond to financial data changes and reduce interference from feedback interference signals. The analysis sensitivity adjustment module includes a financial analysis frequency adjustment module, a financial data change analysis module, a risk determination feedback module, a capital output strategy adjustment module, and a threshold setting judgment module. The financial data change analysis module is used to analyze the amplitude of financial data changes. The risk determination feedback module is used to input the judgment result of the abnormal financial signal location after on-site detection. The capital output strategy adjustment module is used to adjust the capital allocation value of the financial data change balance unit. The threshold setting judgment module is used to determine whether the dispersed capital flow timing and the financial data change amplitude exceed the set value. The auxiliary risk identification module includes an audit mandatory review module, a small amount anomaly identification module, and a risk cause tracking module. The risk cause tracking module is used to locate abnormal financial signals using auxiliary means. The audit mandatory review module is used to control the mandatory audit trigger operation of devices configured with a mandatory audit trigger function. The small amount anomaly identification module is used to use small accounts to assist in determining the financial starting point for devices that are not configured with a mandatory audit trigger function.

3. A financial risk early warning method according to claim 2, characterized in that: The following steps are involved: S1. Construct a capital flow diagram by combining the various segments of the capital flow path and the connected distributed detection units, and represent the financial analysis node units including the financial data change balance unit and the financial risk analysis unit in the capital flow diagram; S2. When the capital flow path is running, the distributed capital flow time points and the frequency of financial data changes at each detection point are analyzed, and the type of each segment is defined based on the analysis results; S3. Adjust the data analysis frequency of the corresponding risk analysis unit according to the type of each segment of the capital flow path, and handle the situation where a single parameter in a segment exceeds the set value separately; S4. Judge the detection results of risk financial signals. If the risk signal frontier point cannot be captured or identified after the risk occurs, increase the system financial analysis frequency and use auxiliary judgment methods to analyze the financial risk signal front-end signal.

4. A financial risk early warning method according to claim 3, characterized in that: In S2, the specific method of defining the type of each paragraph is: S2-1. Analyze the dispersed capital flow time points and the frequency of financial data changes in the capital flow paths of each detection point, record the time of each capital collection and withdrawal operation, and calculate the dispersed capital flow time point Δj of the capital flow path. When Δj is greater than the critical value j min When , the capital flow path of this section is defined as a potential high-risk capital flow path, and the variance of the change range f of the collected financial data is calculated. The series {f1, f2…f n }, and calculate the average value of the series Δf, if the variance of the financial data change range f If it is greater than the set value X, then this section is judged to be a high-risk capital path, where f i is any item in the sequence; S2-2, when the dispersed capital flow path of a certain detection point Δj is less than the critical value j min If If it is less than the set value X, then this section is judged to be a safe capital path.

5. A financial risk early warning method according to claim 4, characterized in that: In S3, the specific method of adjusting the data analysis frequency is as follows: S3-1. Find the high-risk capital path in the capital flow map and increase the financial analysis frequency F of the risk analysis unit in this section. The increase is positively correlated with Δj and A. Specifically, F = [1 + kAΔj]F0, where F0 is the initial analysis frequency and k is the sensitivity adjustment factor. S3-2. Find the safe capital path in the capital flow map and reduce the risk analysis unit financial analysis frequency F of this capital path. The reduction is negatively correlated with Δj and A. Specifically, 6. A financial risk early warning method according to claim 5, characterized in that: In S2-1 and S2-2, if it is detected that Δj is less than the critical value j min and A > X, at this time F remains unchanged, and the analysis weight Q of the capital circulation path of this paragraph needs to be increased. If it is detected that Δj is greater than the critical value j min and A < X, then it is judged that there is an abnormal increase in the capital collection and capital withdrawal operations of the decentralized detection unit, and the decentralized detection unit connected to the capital circulation path of this paragraph is disconnected, and all are analyzed using the detection unit of the financial main system itself until it is detected that A > X and then the decentralized detection unit is connected.

7. A financial risk early warning method according to claim 6, characterized in that: In said S4, if the risk signal frontier point still cannot be accurately identified after the improvement, the following processing method is adopted: for the capital path configured with the mandatory audit trigger function, the standard financial abnormality signal template after the capital path delay mandatory audit trigger is used to match the abnormal financial signal, capture the matched abnormal financial signal and the standard financial abnormality signal template signal, and judge the starting position of the abnormal financial signal.

8. A financial risk early warning method according to claim 7, characterized in that: In said S4, on the financial analysis node that is not configured with the mandatory audit trigger function, the account connection at the end of the target capital flow path is temporarily cut off by manual remote control to cause local changes in financial data. This change will form a standard financial anomaly signal with structural characteristics. Based on this anomaly signal, it is matched and identified with abnormal fluctuations in the current financial data, thereby accurately locating the location of potential financial risks.

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