CN111008896A - Financial risk early warning method and device, electronic equipment and storage medium - Google Patents
Financial risk early warning method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN111008896A CN111008896A CN201911232784.XA CN201911232784A CN111008896A CN 111008896 A CN111008896 A CN 111008896A CN 201911232784 A CN201911232784 A CN 201911232784A CN 111008896 A CN111008896 A CN 111008896A
- Authority
- CN
- China
- Prior art keywords
- user
- risk
- early warning
- information
- financial
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q40/03—Credit; Loans; Processing thereof
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Technology Law (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention discloses a financial risk early warning method, a financial risk early warning device, electronic equipment and a storage medium, wherein the method comprises the following steps: public opinion data related to a user is obtained; inputting public opinion data into a pre-trained prediction model to output user risk information; responding to the fact that the user risk information exceeds a preset threshold value, and obtaining related user information related to the user according to public opinion data; and performing risk early warning operation on the user and the associated user according to the user risk information to control the financial risk, wherein the risk early warning operation comprises at least one of the following operations: and adjusting the credit granting parameters of the user and the associated user, generating risk early warning information and sending the risk early warning information to an early warning department. By the invention, financial risks can be controlled, and economic loss of financial institutions is reduced.
Description
Technical Field
The invention relates to the field of data processing, in particular to a financial risk early warning method and device, electronic equipment and a storage medium.
Background
Currently, when a client applies for a loan from a financial institution such as a bank, the financial institution generally performs an evaluation according to the operation status of the client, mortgages provided or available by the client, and the like, and issues a certain credit line to the client according to the evaluation result.
When the business condition of the client is deteriorated or the mortgage is lost, the client can be powerless to repay the loan, namely, the credit risk of the client is generated, and further, financial institutions such as banks can bear economic loss.
Disclosure of Invention
In view of the above, the present invention provides a financial risk early warning method, apparatus, electronic device and storage medium to solve at least one of the above-mentioned problems.
According to a first aspect of the present invention, there is provided a financial risk early warning method, the method comprising: public opinion data related to a user is obtained; inputting the public opinion data into a pre-trained prediction model to output user risk information; responding to the fact that the user risk information exceeds a preset threshold value, and obtaining relevant user information related to a user according to the public opinion data; performing risk early warning operation on the user and the associated user according to the user risk information to control financial risk, wherein the risk early warning operation comprises at least one of the following operations: and adjusting the credit granting parameters of the user and the associated user, generating risk early warning information and sending the risk early warning information to an early warning department.
According to a second aspect of the present invention, there is provided a financial risk early warning apparatus, the apparatus comprising: a data acquisition unit for acquiring public opinion data related to a user; a risk information output unit, configured to input the public opinion data into a pre-trained prediction model to output user risk information; the associated user obtaining unit is used for responding to the situation that the user risk information exceeds a preset threshold value, and obtaining associated user information related to the user according to the public opinion data; the early warning unit is used for performing risk early warning operation on the user and the associated user according to the user risk information so as to control financial risk, and the risk early warning operation comprises at least one of the following operations: and adjusting the credit granting parameters of the user and the associated user, generating risk early warning information and sending the risk early warning information to an early warning department.
According to a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the financial risk warning method when executing the program.
According to a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above-described financial risk warning method.
According to the technical scheme, the acquired user public opinion data is input into the prediction model to generate user risk information, when the user risk information exceeds a preset threshold value, the associated user information is acquired according to the user public opinion data, and then risk early warning operation is performed on the user and the associated user according to the user risk information to control financial risks and reduce economic loss of financial institutions.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a financial risk early warning method according to an embodiment of the invention;
FIG. 2 is an exemplary flow diagram of financial risk early warning according to an embodiment of the invention;
FIG. 3 is a diagram illustrating warning information according to an embodiment of the present invention;
FIG. 4 is a block diagram of a financial risk early warning device according to an embodiment of the present invention;
FIG. 5 is a detailed block diagram of a financial risk early warning device according to an embodiment of the present invention;
FIG. 6 is a block diagram of the structure of model training unit 36 according to an embodiment of the present invention;
fig. 7 is a block diagram of the early warning unit 34 according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. 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 invention.
In the prior art, when the operation condition of a client is deteriorated or mortgages are lost, the client may be unable to repay the loan, and further financial institutions such as banks and the like bear economic losses. Based on this, the embodiment of the invention provides a financial risk early warning scheme to reduce the economic loss of financial institutions. Embodiments of the present invention are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a financial risk early warning method according to an embodiment of the present invention, as shown in fig. 1, the method includes:
in step 101, public opinion data related to a user is obtained, where the public opinion data may be from each information platform.
And 102, inputting the public opinion data into a pre-trained prediction model to output user risk information.
And 103, in response to the user risk information exceeding a preset threshold value, acquiring associated user information related to the user according to the public opinion data. The predetermined threshold may be set by the financial institution, and when the user risk information exceeds the predetermined threshold, it indicates that the user risk is greater.
104, performing risk early warning operation on the user and the associated user according to the user risk information to control financial risk, wherein the risk early warning operation comprises at least one of the following operations: and adjusting the credit granting parameters of the user and the associated user, generating risk early warning information and sending the risk early warning information to an early warning department.
The acquired user public opinion data is input into the prediction model to generate user risk information, when the user risk information exceeds a preset threshold value, the associated user information is acquired according to the user public opinion data, and then risk early warning operation is performed on the user and the associated user according to the user risk information to control financial risks and reduce economic loss of financial institutions.
In actual operation, trade information related to the user can be obtained; and responding to the fact that the trade information exceeds the trade information threshold range of the user, and carrying out risk early warning operation on the user. The threshold range of trade information herein may be the number of yearly trades or the amount of yearly trades for the user.
When the trade information of the user exceeds the trade information threshold of the user, the trade of the user is in an abnormal state, for example, the transaction amount is increased sharply, such as more than 3 times, or the trade amount is reduced by several times within a period of time, at this time, a risk early warning operation is performed on the user to prompt an early warning department to pay attention to the user so as to avoid financial risk.
When the financial risk occurs to the user, the associated user related to the user can be obtained through the trade information of the user and the public opinion data, the risk early warning operation is also carried out on the associated user at the moment, so that the associated user is prevented from being affected by the risk spread, the early warning prompt can be carried out on the associated user through the risk early warning operation, the related early warning department can also be prompted, the credit granting parameters and the like of the associated user can be properly adjusted, and the economic loss is avoided.
The step 104 may specifically include: determining the risk information of the associated user according to the user risk information and the correlation degree between the user and the associated user; and carrying out risk early warning operation on the user according to the user risk information and carrying out risk early warning operation on the associated user according to the risk information of the associated user.
Specifically, the prediction model in step 102 is trained as follows: obtaining historical user data, the historical user data comprising: historical public opinion data and historical user risk information related to the historical user; training the predictive model based on a predetermined machine learning algorithm according to the historical user data.
Specifically, the topic Recognition is realized by performing a Character Recognition process on the historical public opinion data and the historical user risk information, for example, performing Character Recognition based on an OCR (Optical Character Recognition) technology, and performing a word segmentation process on a title, an abstract, a text, and the like. The prediction model is then trained based on a predetermined machine learning algorithm (e.g., ML algorithm) using the identified information as training data.
After the training of the prediction model is completed, when public opinion data is input, corresponding user risk information can be output (or called as predicted).
In practical operation, the public opinion data may further include: investment project information. When the user risk information is predicted by inputting the public opinion data into the prediction model, the risk information of the investment project can be analyzed according to the user risk information so as to determine whether to execute the investment project. Thus, financial risks can be reduced or avoided, and economic losses are reduced.
In one embodiment, the prediction model can be trained according to the trend relationship between the historical information and the historical investment projects, the trend of the investment projects under the current information situation is predicted through the prediction model, and the risk of the investment projects is analyzed and predicted.
Fig. 2 is a flow chart of financial risk early warning according to an embodiment of the present invention, and the embodiment of the present invention is described below with reference to the flow chart shown in fig. 2. As shown in fig. 2, the early warning process includes:
in step 201, public opinion data related to users are obtained from each information platform (shown as information platforms 1, 2, … …, N).
The information platform may be, for example, a Wind platform, a dow jones platform, a wale street news flash platform, etc., which provide unstructured data such as a financial calendar, news flash, etc. The public opinion data may also include: information posted by financial institution personnel on a social platform, such as a current comment on a certain type of transaction product posted through a WeChat public number, etc.
And step 203, performing message type recognition through an OCR recognition technology to recognize whether the acquired public sentiment data belongs to a positive message or a negative message.
In step 205, when the acquired public opinion data is identified to belong to a negative message, indicating that the risk of the user (and the associated user) is high, the credit line should be reduced and the related credit department should be prompted to avoid generating economic loss.
And step 206, when the acquired public opinion data is identified to belong to the negative message, indicating that the user has a high risk, generating risk prompt information, prompting a related credit granting department, and prompting a related user related to the user so as to further avoid generating economic loss.
In practice, the subject identification of step 202 may be implemented by the existing enterprise information inquiry mechanism or by the user trade information in the financial mechanism. In particular, the subject identification may include information identifying the user and associated users, such as user information on corporate law, major finance, board of directors, and associated user information on the client level of corporate group clients and subordinate members.
The adjustment of the authorized credit line can be completed by the financial institution through an internal credit line system, and the credit line system is used for distributing the credit lines of all enterprise units and individuals. According to the financial risk early warning process, the early warning of the credit line of an individual to an individual and an enterprise to an enterprise can be realized, the warning information prompted by the system can be sent to a credit approval department, a customer relationship department and the like, or can be directly sent to related workers of a financial institution, the workers initiate a business process and confirm whether to adjust the credit rating according to the system suggestion. The credit rating may be divided into multiple levels, e.g., AAA represents the highest level of credit and CCC represents the lowest level of credit.
Fig. 3 is a schematic diagram of the warning information, as shown in fig. 3, according to the obtained public opinion data of the enterprise a, it is predicted by the prediction model that the enterprise X is in a risk (the risk level is shown as high in the figure), and there is a risk of being unable to repay the loan, at this time, it is suggested in the warning information to adjust the credit granting level of the enterprise X to CCC, so as to avoid further dealing with the risk; in addition, when the related enterprises related to the enterprise X are acquired as the enterprise Y and the enterprise Z according to the public opinion data, the trade data and other information, the risk level reached by the enterprise Y is predicted to be middle based on the prediction model, and the risk comparison reached by the enterprise Z is also high, the credit granting levels of the enterprises Y and Z are adjusted to be B and C accordingly.
And sending the early warning information displayed in the figure 3 to an early warning department so as to prompt risk conditions of the enterprise X, Y and the enterprise Z and avoid sending economic risks.
In actual operation, the financial institution may also construct a black and white list for recording the penalty and other items of the user, where the credit level of the user appearing in the black list is lower, and the credit level of the user appearing in the white list is higher.
For a better understanding of embodiments of the present invention, an example is given below.
By identifying public opinion data, existing enterprise information inquiry mechanism, user trade information in financial institution, etc., the equity relationship and legal person, and actual controller information are obtained, such as 10% equity of enterprise A, enterprise B, and 50% equity of enterprise C, which include equity relationship and debt relationship, so when negative public opinion data of company A is obtained, company B and company C are affected (for example, equity or bond will bear credit default, and be paid out in order of payment), at this time, according to credit limits of these three companies in the limit system, and according to predicted risk degree (for example, according to importance degree of public opinion), group personnel such as bank customer relationship, credit approval are prompted to adjust internal rating, and risk occurrence is controlled, thereby avoiding economic loss.
In actual operation, according to the trade information of the financial institution, the common public-to-public transfer and other information, the unregistered associated users can be identified, and may be affected by the risk of company a, at this time, the client manager or the operator needs to be prompted to adjust the credit level, the credit line and the like of the associated users, so as to control the credit risk and avoid the indirect economic influence.
As can be seen from the above description, the embodiments of the present invention analyze the transaction client risk (for example, a has a loan guarantee or trade relationship with B) by obtaining the equity structure and the loan client relationship, and at this time, may provide real-time reminding for the associated user and notify the financial institution staff to adjust the client subject rating as appropriate, so as to avoid economic loss.
In the specific implementation process, for the investment project information in the public opinion data, the analysis of the financial market client transaction information can be carried out, so as to reduce the risk of transactions such as counter bonds, precious metal contracts, business contracts, foreign exchange currencies and the like. By acquiring historical data of the transactions, transaction risks are predicted based on a prediction model (such as an ML prediction model) so as to achieve credit line early warning for public clients or risk prompting for enterprise transaction associated users.
It should be noted that, for the prediction and analysis of the risk information, the prediction and analysis may be implemented based on a prediction model, and in the embodiment of the present invention, the prediction model may be any prediction model capable of predicting the risk based on the input information, which is not limited in this respect.
Based on similar inventive concepts, the embodiment of the invention further provides a financial risk early warning device, and preferably, the device is used for realizing the method flow.
Fig. 4 is a block diagram of a financial risk early warning device according to an embodiment of the present invention, and as shown in fig. 4, the device includes:
a data acquisition unit 31 for acquiring public opinion data related to a user;
a risk information output unit 32, configured to input the public opinion data into a pre-trained prediction model to output user risk information;
an associated user obtaining unit 33, configured to, in response to the user risk information exceeding a predetermined threshold, obtain associated user information related to a user according to the public opinion data;
the early warning unit 34 is configured to perform a risk early warning operation on the user and the associated user according to the user risk information to control a financial risk, where the risk early warning operation includes at least one of: and adjusting the credit granting parameters of the user and the associated user, generating risk early warning information and sending the risk early warning information to an early warning department.
The risk information output unit 32 generates user risk information by inputting the user public opinion data acquired by the data acquisition unit 31 into the prediction model, when the user risk information exceeds a predetermined threshold, the associated user acquisition unit 33 acquires associated user information according to the user public opinion data, and then the early warning unit 34 performs a risk early warning operation on the user and the associated user according to the user risk information to control financial risks and reduce economic loss of financial institutions.
In practical operation, as shown in fig. 5, the apparatus further comprises: a trade information acquisition unit 35 for acquiring trade information relating to the user; at this time, the early warning unit 34 is further configured to perform a risk early warning operation on the user in response to that the trade information exceeds the trade information threshold range of the user. For example, the relevant early warning department is prompted, and the credit granting parameters and the like of the relevant users are properly adjusted to avoid economic loss.
Specifically, the associated user acquiring unit 33 is further configured to: and acquiring the associated user related to the user according to the trade information.
The above-mentioned device still includes: a model training unit 36 for training the prediction model. As shown in fig. 6, the model training unit 36 includes: historical data acquisition module 361 and model training module 362, wherein:
a historical data obtaining module 361, configured to obtain historical user data, where the historical user data includes: historical public opinion data and historical user risk information related to the historical user;
a model training module 362 for training the predictive model based on a predetermined machine learning algorithm based on the historical user data.
Specifically, the historical data acquisition module 361 acquires historical public opinion data, and the model training module 362 performs a character recognition process on the historical public opinion data and the historical user risk information, for example, performs character recognition based on an OCR (Optical character recognition) technique, performs a word segmentation process on a title, an abstract, a text, and the like, realizes a subject recognition, and then trains a prediction model based on a predetermined machine learning algorithm (for example, ML algorithm) using the recognized information as training data.
As shown in fig. 7, the warning unit 34 includes: a correlated user risk information determination module 341 and an early warning module 342, wherein:
a relevant user risk information determining module 341, configured to determine risk information of the relevant user according to the user risk information and the correlation between the user and the relevant user;
the early warning module 342 is configured to perform a risk early warning operation on the user according to the user risk information, and perform a risk early warning operation on the associated user according to the risk information of the associated user.
In one embodiment, the public opinion data may further include: investment project information.
With continued reference to fig. 5, the apparatus further comprises: an analysis unit 37 and an investment determination unit 38, wherein:
an analyzing unit 37, configured to analyze risk information of the investment project according to the user risk information;
an investment determination unit 38 for determining whether to execute the investment project based on the risk information of the investment project to control the risk cost.
For specific execution processes of the units and the modules, reference may be made to the description in the foregoing method embodiments, and details are not described here again.
In practical operation, the units and the modules may be combined or may be singly arranged, and the present invention is not limited thereto.
FIG. 8 is a schematic diagram of an electronic device according to an embodiment of the invention. The electronic device shown in fig. 8 is a general-purpose data processing apparatus comprising a general-purpose computer hardware structure including at least a processor 701 and a memory 702. The processor 701 and the memory 702 are connected by a bus 703. The memory 702 is adapted to store one or more instructions or programs that are executable by the processor 701. The one or more instructions or programs are executed by processor 701 to implement the steps in the financial risk warning method described above.
The processor 701 may be an independent microprocessor or a set of one or more microprocessors. Thus, the processor 701 implements the processing of data and the control of other devices by executing commands stored in the memory 702 to thereby execute the method flows of the embodiments of the present invention as described above. The bus 703 connects the above components together, as well as connecting the above components to the display controller 704 and the display device and input/output (I/O) device 705. Input/output (I/O) devices 705 may be a mouse, keyboard, modem, network interface, touch input device, motion sensing input device, printer, and other devices known in the art. Typically, input/output (I/O) devices 705 are connected to the system through an input/output (I/O) controller 706.
The memory 702 may store, among other things, software components such as an operating system, communication modules, interaction modules, and application programs. Each of the modules and applications described above corresponds to a set of executable program instructions that perform one or more functions and methods described in embodiments of the invention.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of the financial risk early warning method.
In summary, the embodiments of the present invention provide an early warning scheme for credit line, which can identify and predict main bodies (clients and related clients) of an event according to received public opinion data such as news information, and adjust the credit line of the clients and the related clients and send an early warning prompt to a financial institution staff by combining a equity structure and a loan client relationship, so as to avoid economic loss.
The preferred embodiments of the present invention have been described above with reference to the accompanying drawings. The many features and advantages of the embodiments are apparent from the detailed specification, and thus, it is intended by the appended claims to cover all such features and advantages of the embodiments which fall within the true spirit and scope thereof. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the embodiments of the invention to the exact construction and operation illustrated and described, and accordingly, all suitable modifications and equivalents may be resorted to, falling within the scope thereof.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (14)
1. A financial risk early warning method, the method comprising:
public opinion data related to a user is obtained;
inputting the public opinion data into a pre-trained prediction model to output user risk information;
responding to the fact that the user risk information exceeds a preset threshold value, and obtaining relevant user information related to a user according to the public opinion data;
performing risk early warning operation on the user and the associated user according to the user risk information to control financial risk, wherein the risk early warning operation comprises at least one of the following operations: and adjusting the credit granting parameters of the user and the associated user, generating risk early warning information and sending the risk early warning information to an early warning department.
2. The financial risk early warning method of claim 1, further comprising:
obtaining trade information associated with the user;
and responding to the fact that the trade information exceeds the trade information threshold range of the user, and carrying out risk early warning operation on the user.
3. The financial risk early warning method of claim 2, further comprising:
and acquiring the associated user related to the user according to the trade information.
4. The financial risk early warning method according to claim 1, wherein the prediction model is trained by:
obtaining historical user data, the historical user data comprising: historical public opinion data and historical user risk information related to the historical user;
training the predictive model based on a predetermined machine learning algorithm according to the historical user data.
5. The financial risk early warning method according to claim 1, wherein performing risk early warning operation on the user and the associated user according to the user risk information comprises:
determining the risk information of the associated user according to the user risk information and the correlation degree between the user and the associated user;
and carrying out risk early warning operation on the user according to the user risk information and carrying out risk early warning operation on the associated user according to the risk information of the associated user.
6. The financial risk early warning method according to claim 1, wherein the public opinion data comprises: investment project information, the method further comprising:
analyzing the risk information of the investment project according to the user risk information;
and determining whether to execute the investment project according to the risk information of the investment project.
7. A financial risk early warning device, the device comprising:
a data acquisition unit for acquiring public opinion data related to a user;
a risk information output unit, configured to input the public opinion data into a pre-trained prediction model to output user risk information;
the associated user obtaining unit is used for responding to the situation that the user risk information exceeds a preset threshold value, and obtaining associated user information related to the user according to the public opinion data;
the early warning unit is used for performing risk early warning operation on the user and the associated user according to the user risk information so as to control financial risk, and the risk early warning operation comprises at least one of the following operations: and adjusting the credit granting parameters of the user and the associated user, generating risk early warning information and sending the risk early warning information to an early warning department.
8. The financial risk early warning device of claim 7, further comprising:
a trade information acquisition unit for acquiring trade information related to the user;
the early warning unit is further used for responding to the fact that the trade information exceeds the trade information threshold range of the user, and carrying out risk early warning operation on the user.
9. The financial risk early warning device of claim 8, wherein the associated user acquisition unit is further configured to:
and acquiring the associated user related to the user according to the trade information.
10. The financial risk early warning device of claim 7, further comprising: a model training unit for training the prediction model,
the model training unit includes:
a historical data obtaining module, configured to obtain historical user data, where the historical user data includes: historical public opinion data and historical user risk information related to the historical user;
and the model training module is used for training the prediction model based on a preset machine learning algorithm according to the historical user data.
11. The financial risk early warning device according to claim 7, wherein the early warning unit comprises:
the associated user risk information determining module is used for determining the risk information of the associated user according to the user risk information and the correlation degree between the user and the associated user;
and the early warning module is used for carrying out risk early warning operation on the user according to the user risk information and carrying out risk early warning operation on the associated user according to the risk information of the associated user.
12. The financial risk early warning device as claimed in claim 7, wherein the public opinion data comprises: investment project information, the apparatus further comprising:
the analysis unit is used for analyzing the risk information of the investment project according to the user risk information;
and the investment determining unit is used for determining whether to execute the investment project according to the risk information of the investment project.
13. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the financial risk warning method of any one of claims 1 to 6 when executing the program.
14. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the financial risk warning method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911232784.XA CN111008896A (en) | 2019-12-05 | 2019-12-05 | Financial risk early warning method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911232784.XA CN111008896A (en) | 2019-12-05 | 2019-12-05 | Financial risk early warning method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111008896A true CN111008896A (en) | 2020-04-14 |
Family
ID=70115662
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911232784.XA Pending CN111008896A (en) | 2019-12-05 | 2019-12-05 | Financial risk early warning method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111008896A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111784492A (en) * | 2020-07-10 | 2020-10-16 | 讯飞智元信息科技有限公司 | Public opinion analysis and financial early warning method, device, electronic equipment and storage medium |
| CN111858903A (en) * | 2020-06-11 | 2020-10-30 | 创新工场(北京)企业管理股份有限公司 | Method and device for negative news early warning |
| CN112053174A (en) * | 2020-09-29 | 2020-12-08 | 中国银行股份有限公司 | Method, device and equipment for determining relation between bank customer and teller |
| CN112348520A (en) * | 2020-10-21 | 2021-02-09 | 上海淇玥信息技术有限公司 | XGboost-based risk assessment method and device and electronic equipment |
| CN112734566A (en) * | 2021-01-19 | 2021-04-30 | 中国农业银行股份有限公司 | Credit limit acquisition method and device and computer equipment |
| CN113205409A (en) * | 2021-05-28 | 2021-08-03 | 中国工商银行股份有限公司 | Loan transaction processing method and device |
| CN113239290A (en) * | 2021-06-10 | 2021-08-10 | 杭州安恒信息技术股份有限公司 | Data analysis method and device for public opinion monitoring and electronic device |
| CN113392185A (en) * | 2021-06-10 | 2021-09-14 | 中国联合网络通信集团有限公司 | Public opinion early warning method, device, equipment and storage medium |
| CN115460059A (en) * | 2022-07-28 | 2022-12-09 | 浪潮通信信息系统有限公司 | Risk early warning method and device |
| CN116630050A (en) * | 2023-06-16 | 2023-08-22 | 深圳市弘裕金联科技有限公司 | Online gold transaction method, system, device and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104408157A (en) * | 2014-12-05 | 2015-03-11 | 四川诚品电子商务有限公司 | Funnel type data gathering, analyzing and pushing system and method for online public opinion |
| WO2017125936A1 (en) * | 2016-01-19 | 2017-07-27 | Minacs Private Limited | System and method for holistically predicting risk associated with trade transactions in real-time |
| CN108280760A (en) * | 2018-01-25 | 2018-07-13 | 树根互联技术有限公司 | A kind of financial risks on-line monitoring method and apparatus |
| CN108846547A (en) * | 2018-05-06 | 2018-11-20 | 成都信息工程大学 | A kind of Enterprise Credit Risk Evaluation method of dynamic adjustment |
| CN109657894A (en) * | 2018-09-27 | 2019-04-19 | 深圳壹账通智能科技有限公司 | Credit Risk Assessment of Enterprise method for early warning, device, equipment and storage medium |
-
2019
- 2019-12-05 CN CN201911232784.XA patent/CN111008896A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104408157A (en) * | 2014-12-05 | 2015-03-11 | 四川诚品电子商务有限公司 | Funnel type data gathering, analyzing and pushing system and method for online public opinion |
| WO2017125936A1 (en) * | 2016-01-19 | 2017-07-27 | Minacs Private Limited | System and method for holistically predicting risk associated with trade transactions in real-time |
| CN108280760A (en) * | 2018-01-25 | 2018-07-13 | 树根互联技术有限公司 | A kind of financial risks on-line monitoring method and apparatus |
| CN108846547A (en) * | 2018-05-06 | 2018-11-20 | 成都信息工程大学 | A kind of Enterprise Credit Risk Evaluation method of dynamic adjustment |
| CN109657894A (en) * | 2018-09-27 | 2019-04-19 | 深圳壹账通智能科技有限公司 | Credit Risk Assessment of Enterprise method for early warning, device, equipment and storage medium |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111858903A (en) * | 2020-06-11 | 2020-10-30 | 创新工场(北京)企业管理股份有限公司 | Method and device for negative news early warning |
| CN111784492A (en) * | 2020-07-10 | 2020-10-16 | 讯飞智元信息科技有限公司 | Public opinion analysis and financial early warning method, device, electronic equipment and storage medium |
| CN112053174B (en) * | 2020-09-29 | 2024-03-29 | 中国银行股份有限公司 | Method, device and equipment for determining relationship between bank clients and teller |
| CN112053174A (en) * | 2020-09-29 | 2020-12-08 | 中国银行股份有限公司 | Method, device and equipment for determining relation between bank customer and teller |
| CN112348520A (en) * | 2020-10-21 | 2021-02-09 | 上海淇玥信息技术有限公司 | XGboost-based risk assessment method and device and electronic equipment |
| CN112734566A (en) * | 2021-01-19 | 2021-04-30 | 中国农业银行股份有限公司 | Credit limit acquisition method and device and computer equipment |
| CN113205409A (en) * | 2021-05-28 | 2021-08-03 | 中国工商银行股份有限公司 | Loan transaction processing method and device |
| CN113239290A (en) * | 2021-06-10 | 2021-08-10 | 杭州安恒信息技术股份有限公司 | Data analysis method and device for public opinion monitoring and electronic device |
| CN113392185B (en) * | 2021-06-10 | 2023-06-23 | 中国联合网络通信集团有限公司 | Public opinion early warning method, device, equipment and storage medium |
| CN113392185A (en) * | 2021-06-10 | 2021-09-14 | 中国联合网络通信集团有限公司 | Public opinion early warning method, device, equipment and storage medium |
| CN115460059A (en) * | 2022-07-28 | 2022-12-09 | 浪潮通信信息系统有限公司 | Risk early warning method and device |
| CN115460059B (en) * | 2022-07-28 | 2024-03-08 | 浪潮通信信息系统有限公司 | Risk early warning method and device |
| CN116630050A (en) * | 2023-06-16 | 2023-08-22 | 深圳市弘裕金联科技有限公司 | Online gold transaction method, system, device and storage medium |
| CN116630050B (en) * | 2023-06-16 | 2024-02-02 | 深圳市弘裕金联科技有限公司 | Online gold transaction method, system, device and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111008896A (en) | Financial risk early warning method and device, electronic equipment and storage medium | |
| WO2020132026A1 (en) | Computer-projected risk assessment using voluntarily contributed information | |
| CN111340616B (en) | Method, device, equipment and medium for approving online loan | |
| KR102009309B1 (en) | Management automation system for financial products and management automation method using the same | |
| WO2010037030A1 (en) | Evaluating loan access using online business transaction data | |
| KR20190064749A (en) | Method and device for intelligent decision support in stock investment | |
| US20110153536A1 (en) | Computer-Implemented Systems And Methods For Dynamic Model Switching Simulation Of Risk Factors | |
| US20200104911A1 (en) | Dynamic monitoring and profiling of data exchanges within an enterprise environment | |
| CA3081569A1 (en) | Entity segmentation for analysis of sensitivities to potential disruptions | |
| WO2018216592A1 (en) | System, method, and program for assisting banking operations | |
| US20140278774A1 (en) | In the market model systems and methods | |
| CN111179051A (en) | Financial target customer determination method and device and electronic equipment | |
| CN111708883A (en) | Credit credit limit determination method and device based on machine learning and equipment fingerprint | |
| JP2016099915A (en) | Server for credit examination, system for credit examination, and program for credit examination | |
| Jena et al. | Dynamics of FII flows and stock market returns in a major developing country: How does economic uncertainty matter? | |
| JP6771513B2 (en) | Devices and methods for calculating default probability and programs for it | |
| JP7298286B2 (en) | Model providing program, model providing method and model providing apparatus | |
| JP7344609B2 (en) | Data quantification method based on confirmed and estimated values | |
| CN115759283A (en) | Model interpretation method and device, electronic equipment and storage medium | |
| CN115860889A (en) | Financial loan big data management method and system based on artificial intelligence | |
| KR20130100614A (en) | Method and apparatus for managing credit information | |
| EP2838065A1 (en) | Customer income estimator with confidence intervals | |
| Kamruzzaman et al. | Measuring systemic and systematic risk in the financial markets using artificial intelligence | |
| CA3019195A1 (en) | Dynamic monitoring and profiling of data exchanges within an enterprise environment | |
| Hargreaves | Machine learning application to identify good credit customers |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |