CN111062793A - Financial risk monitoring method, device and storage medium - Google Patents
Financial risk monitoring method, device and storage medium Download PDFInfo
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- CN111062793A CN111062793A CN201911322964.7A CN201911322964A CN111062793A CN 111062793 A CN111062793 A CN 111062793A CN 201911322964 A CN201911322964 A CN 201911322964A CN 111062793 A CN111062793 A CN 111062793A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
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- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
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Abstract
The application discloses a financial risk monitoring method, a financial risk monitoring device and a storage medium, wherein the method comprises the following steps: dividing original invoice data into an invoice main table and an invoice detail table; performing statistical analysis on the invoice main table and the invoice detail table; storing the result obtained by the statistical analysis in a data table; the result includes risk information; pushing risk information of corresponding authorities to terminals with different authorities; the statistical analysis of the invoice main table and the invoice detail table comprises the following steps: trend analysis, swing analysis, and logistic regression calculation. The financial risk monitoring method can dynamically monitor the real-time operation condition of the concerned enterprise, potential risk hidden dangers can be found in time by corresponding user roles based on the sub-right display and push of the risk information, the risk disposal efficiency is improved, and the economic benefit is improved while the operation risk is effectively reduced.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a financial risk monitoring method and device and a storage medium.
Background
In financial activities, financial risks are continuously monitored by a melting institution, and the traditional method adopts a method of expert operation and on-site investigation, so that the working efficiency is low. When facing the increase of information and enterprise quantity, it is difficult to effectively monitor a lot of risks, and based on big data technology, in order to effectively monitor the financial risks increasing day by day, we developed a financial risk monitoring system based on invoice data.
The existing mainstream business risk query software mainly comprises information contents provided by a letter opener, an enterprise investigation, a sky eye investigation and the like, wherein the information contents are mainly industrial and commercial information, court judgment information, associated enterprise information, lost letter information, judicial auction information, recruitment information, enterprise evaluation information and the like of an enterprise. The business software is multi-sided to display data information, and the data analysis is less involved. While financial institutions, enterprises and the like are more willing to know dynamic risk information after analysis, the prior art obviously cannot meet the requirement.
Disclosure of Invention
The application aims to provide a financial risk monitoring method and device and a storage medium. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
According to an aspect of an embodiment of the present application, there is provided a financial risk monitoring method, including:
dividing original invoice data into an invoice main table and an invoice detail table;
and carrying out statistical analysis on the invoice main table and the invoice detail table.
Further, the method further comprises:
storing the result obtained by the statistical analysis in a data table; the results include risk information.
Further, the method further comprises:
and pushing risk information of corresponding authorities to terminals with different authorities.
Further, the performing statistical analysis on the invoice main table and the invoice detail table includes: trend analysis, swing analysis, and logistic regression calculation.
Further, the trend analysis includes:
the prices of securities in a certain period are averaged, and the averages in different periods are connected to form a moving average line which is used for displaying the price variation trend.
Further, the swing analysis includes:
and acquiring a change curve of the random swing index changing along with price fluctuation.
Further, the logistic regression calculation includes:
selecting an explanation variable index according to a business rule;
taking the natural logarithm of the occurrence ratio of the default events, and establishing a linear regression equation; the occurrence ratio of the default events is the ratio of the probability that the client has default to the probability that the client does not have default;
and fitting the linear regression equation.
Further, said fitting the linear regression equation comprises: and performing parameter estimation by adopting a maximum likelihood method.
According to another aspect of the embodiments of the present application, there is provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the financial risk monitoring method.
According to another aspect of embodiments of the present application, there is provided a non-transitory computer readable storage medium having stored thereon a computer program, which is executed by a processor, to implement the financial risk monitoring method.
The technical scheme provided by one aspect of the embodiment of the application can have the following beneficial effects:
the financial risk monitoring method provided by the embodiment of the application can dynamically monitor the real-time operation condition of the concerned enterprise, can timely discover potential risk hazards corresponding to the role of the user based on the sub-authority display and push of the risk information, improves the risk disposal efficiency, and improves the economic benefit while effectively reducing the operation risk.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the application, or may be learned by the practice of the embodiments. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 shows a flow diagram of a financial risk monitoring method of an embodiment of the present application;
FIG. 2 shows a graphical representation of a swing analysis;
FIG. 3 shows a graphical representation of logistic regression;
FIG. 4 shows a block diagram of a unified working platform architecture.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It will be understood by those within the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
One embodiment of the present application provides a financial risk monitoring method, including:
dividing original invoice data into an invoice main table and an invoice detail table;
and carrying out statistical analysis on the invoice main table and the invoice detail table.
In certain embodiments, the method further comprises:
storing the result obtained by the statistical analysis in a data table; the results include risk information.
In certain embodiments, the method further comprises:
and pushing risk information of corresponding authorities to terminals with different authorities.
In some embodiments, said performing a statistical analysis on said invoice master form and said invoice detail form comprises: trend analysis, swing analysis, and logistic regression calculation.
In some embodiments, the trend analysis comprises:
the prices of securities in a certain period are averaged, and the averages in different periods are connected to form a moving average line which is used for displaying the price variation trend.
In some embodiments, the swing analysis comprises:
and acquiring a change curve of the random swing index changing along with price fluctuation.
In certain embodiments, the logistic regression calculation comprises:
selecting an explanation variable index according to a business rule;
taking the natural logarithm of the occurrence ratio of the default events, and establishing a linear regression equation; the occurrence ratio of the default events is the ratio of the probability that the client has default to the probability that the client does not have default;
and fitting the linear regression equation.
In some embodiments, said fitting said linear regression equation comprises: and performing parameter estimation by adopting a maximum likelihood method.
The embodiment also provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the financial risk monitoring method.
The present embodiments also provide a non-transitory computer readable storage medium having stored thereon a computer program, which is executed by a processor, to implement the financial risk monitoring method.
The financial risk monitoring method provided by the embodiment can dynamically monitor the real-time operation condition of the concerned enterprise, based on the sub-right display and push of the risk information, the corresponding user role can timely find potential risk hidden dangers, the risk disposal efficiency is improved, and the economic benefit is improved while the operation risk is effectively reduced.
This embodiment also provides a financial risk monitoring device, includes:
the system comprises a dividing module, a data processing module and a data processing module, wherein the dividing module is used for dividing invoice original data into an invoice main table and an invoice detail table;
and the analysis module is used for carrying out statistical analysis on the invoice main table and the invoice detail table.
In certain embodiments, the apparatus further comprises:
the storage module is used for storing the result obtained by the statistical analysis in a data table; the results include risk information.
In certain embodiments, the apparatus further comprises:
and the pushing module is used for pushing the risk information of the corresponding authority to the terminals with different authorities.
In certain embodiments, the analysis module is particularly useful for trend analysis, swing analysis, and logistic regression calculation.
In some embodiments, the analysis module is specifically configured to:
carrying out average calculation on the prices of securities in a certain period, and connecting the average values in different periods to form a moving average line, wherein the moving average line is used for displaying the price variation trend;
obtaining a change curve of the random swing index changing along with price fluctuation;
selecting an explanation variable index according to a business rule;
taking the natural logarithm of the occurrence ratio of the default events, and establishing a linear regression equation; the occurrence ratio of the default events is the ratio of the probability that the client has default to the probability that the client does not have default;
and fitting the linear regression equation.
As shown in fig. 1, another embodiment of the present application provides a financial risk monitoring method, including:
firstly, dividing original invoice data into an invoice main table and an invoice detail table;
secondly, embedding various credit risk and operation risk analysis methods in a calculation engine, and performing descriptive and inferred statistical analysis on the data based on the invoice main table and the invoice detail table by the calculation engine;
and finally, storing the calculation result in an operation layer data table. And through the authority distribution, the terminal user acquires the risk information of the corresponding authority and dynamically pushes the risk information.
The algorithm used by the calculation engine for descriptive and inferential statistical analysis is shown in the following table:
the statistical analysis of the data of the invoice main table and the invoice detail table by the computing engine comprises the following steps:
1) trend analysis
The Moving Average line (MA) is a technical index for observing the price variation trend by averaging the prices (indexes) of securities in a certain period of time and connecting the Average values at different times by using a statistical analysis method.
2) Swing analysis
As shown in fig. 2, in a horizontal area, the peak and valley of the swing index and the peak and valley on the price chart appear in a linkage manner, a middle value exists in the change of the swing index, the horizontal area can be divided into an upper half part and a lower half part, and the swing index can be divided into a top part, a bottom part and a middle part by a middle line.
When the market enters a non-trend stage, the price generally fluctuates up and down in an interval, in this case, most of analysis systems following the trend cannot work normally, but the random oscillation index can randomly change along with the fluctuation of the price, and the index is generally defined as the oscillation index which can also be called as the random index.
3) Logistic regression calculation
As shown in fig. 3, a Logistic Regression (Logistic Regression) model is a traditional tool for calculating a probability of breach (PD), and its basic principle is to classify default and non-default samples of existing customers by 0, 1 (for example, a customer has a default marked as 1, and a customer does not have a default marked as 0), and according to business rules, select a set of indexes X ═ (X ═ X)1，x2，x3，x4，x5，…，xn) As an explanatory variable. After taking these samples of the prior data (i.e. the explanatory variables), setting PD ═ P (Y ═ 1| X) as the probability that the customer will have a breach, then the ratio of the probability that the customer will have a breach and will not have a breach is PD/1-PD (referred to as the occurrence ratio of breach events, denoted as 0dds), since 0 < PD < 1, 0dds > 0, and taking the natural logarithm of this ratio, a linear regression equation is established:
In(PD/1-PD)＝β0+β1x1+β2x2+β3x3+β4x4+β5x5+…+βnxn；
the horizontal axis is the value of independent variable (explanatory variable), the vertical axis is the customer default rate, ★ represents default customer sample, ◇ represents non-default customer sample, and the inclined straight line is the linear regression equation, Z is β0+β1x1+β2x2+ β3x3+β4x4+β5x5+…+βnxnThe bold curve is logistic regressionβ equation0+β1x1+β2x2+β3x3+ β4x4+β5x5+…+βnxn。
The logistic regression model is actually the popularization of the general multivariate linear regression model, and the error term of the logistic regression model follows binomial distribution and non-normal distribution, so that a maximum likelihood method is adopted for parameter estimation during fitting. Practical experience with banks has shown that logistic regression analysis is effective in estimating the probability of breach.
The trend and swing analysis can be used for time series analysis, the logistic regression model can be used for risk prediction, and dynamic early warning and risk information pushing can be performed on various risk conditions through the data analysis system.
The method of the embodiment solves the fusion risk discovery and early warning problems in commercial activities, effectively saves labor resources and expenses, and greatly improves the financial risk management capability.
Another embodiment of the present application provides a financial risk monitoring system, which includes a unified working platform, as shown in fig. 4, where the unified working platform includes a sub-authority system login module, various risk monitoring modules, and a risk early warning module.
And the sub-authority system login module is used for formulating corresponding user authority for different user roles through background configuration authority.
The risk monitoring module comprises a credit risk dynamic monitoring module, an operation risk dynamic monitoring module and a plurality of sub-modules, and is used for carrying out data analysis through an algorithm and then visually presenting through the unified working platform.
And the risk early warning module is used for carrying out information push on the early warning information discovered by the risk monitoring module, and the user with the corresponding authority acquires the corresponding risk information.
Financial risk monitoring is realized at a PC terminal and an APP terminal through a unified working platform, including and not limited to page interaction and risk information display.
And establishing user roles and distributing corresponding authorities through the unified working platform.
And dynamic monitoring of credit risk and operation risk is realized through a unified working platform. Various risk management algorithms can be embedded into the system, and risk discovery is realized by analyzing invoice data.
The real-time pushing of the corresponding user roles of the early warning information of the credit risk and the operation risk is realized through the unified working platform. The discovery user based on risks in the system can set whether to push information of the risks.
And integrating the algorithms of credit risk and operation risk based on invoice data through a unified working platform.
The risk monitoring module is used for dynamically monitoring credit risk and operation risk, and the credit risk monitoring comprises the following steps: 1) monomer comprehensive credit risk monitoring 2) group comprehensive credit risk monitoring 3) industry landscape monitoring. Operational risk monitoring includes: 4) on-garage management compliance monitoring 5) off-garage management compliance monitoring 6) vehicle allocation management compliance monitoring. The risk monitoring content can be increased along with the application scene, and meanwhile, early warning pushing can be set for the monitoring content.
Compared with the prior art, the system can effectively save the cost of human resources, greatly improves the working efficiency, is simple and convenient to use, and has lower maintenance cost.
By means of the system, invoice data are described, inferred and statistically analyzed, and organically combined with data display and risk pushing, enterprises or financial institutions can dynamically monitor real-time operation conditions of concerned enterprises through the system, and business activity efficiency is improved.
It should be noted that:
the term "module" is not intended to be limited to a particular physical form. Depending on the particular application, a module may be implemented as hardware, firmware, software, and/or combinations thereof. Furthermore, different modules may share common components or even be implemented by the same component. There may or may not be clear boundaries between the various modules.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. In addition, this application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the present application.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the creation apparatus of a virtual machine according to embodiments of the present application. The present application may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present application may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The above-mentioned embodiments only express the embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. A financial risk monitoring method, comprising:
dividing original invoice data into an invoice main table and an invoice detail table;
and carrying out statistical analysis on the invoice main table and the invoice detail table.
2. The method of claim 1, further comprising:
storing the result obtained by the statistical analysis in a data table; the results include risk information.
3. The method of claim 2, further comprising:
and pushing risk information of corresponding authorities to terminals with different authorities.
4. The method of claim 1, wherein said performing a statistical analysis of said invoice master form and said invoice detail form comprises: trend analysis, swing analysis, and logistic regression calculation.
5. The method of claim 4, wherein the trend analysis comprises:
the prices of securities in a certain period are averaged, and the averages in different periods are connected to form a moving average line which is used for displaying the price variation trend.
6. The method of claim 4, wherein the swing analysis comprises:
and acquiring a change curve of the random swing index changing along with price fluctuation.
7. The method of claim 4, wherein the logistic regression computation comprises:
selecting an explanation variable index according to a business rule;
taking the natural logarithm of the occurrence ratio of the default events, and establishing a linear regression equation; the occurrence ratio of the default events is the ratio of the probability that the client has default to the probability that the client does not have default;
and fitting the linear regression equation.
8. The method of claim 7, wherein said fitting the linear regression equation comprises: and performing parameter estimation by adopting a maximum likelihood method.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the financial risk monitoring method of any one of claims 1-8.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the program is executed by a processor to implement the financial risk monitoring method of any one of claims 1-8.
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| CN113706013A (en) * | 2021-08-27 | 2021-11-26 | 上海见兴信息科技有限公司 | Labor relation contradiction risk analysis method combining financial technical indexes |
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