CN110956385A - Commercial bank public warning method, device, system and storage medium - Google Patents
Commercial bank public warning method, device, system and storage medium Download PDFInfo
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Abstract
The application provides a commercial bank public warning method, device, system and storage medium, the commercial bank public warning system includes: the method is applied to a big data application layer and comprises the following steps: acquiring risk information of a risk event acquired by a real-time data acquisition platform, wherein the risk event represents an event affecting the repayment capacity of a customer of a commercial bank; according to the risk information, determining a target client from the clients of the commercial bank, and determining unfinished business transacted by the target client in the commercial bank; and determining result information based on the risk information and the unfinished business so that the business application layer acquires the result information and sends the result information to the corresponding front end, wherein the corresponding front end is associated with a customer manager managing the unfinished business in a commercial bank. Therefore, a customer manager can work out a solution for an uncompleted service in time, labor and time costs are saved, and the efficiency of service risk monitoring is improved.
Description
Technical Field
The application relates to the field of data processing, in particular to a commercial bank public warning method, device and system and a storage medium.
Background
In recent years, under the background of the policies of gradual increase of macroscopic economy, narrow convergence of rest, financial demeanour and strong supervision, the development trend of the banking industry is changed from rapid expansion to stable and sustainable development. With the advent of the wave of financial science and technology, technologies such as internet, artificial intelligence, big data and the like have great influence on the financial industry, and the application of the technology in bank data assets, risk control, marketing service and the like has huge application space and value. And it is also a trend to utilize technologies such as big data to reduce bank risks and help the financial industry stabilize high-quality development.
In the existing bank business risk early warning mode, risk information is mainly collected manually and is analyzed manually so as to monitor the business risk of a bank. However, this method requires a lot of labor and time, and is inefficient because real-time performance is low.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method, an apparatus, a system and a storage medium for public warning of a commercial bank, so as to automatically collect information, and reduce dependence on manual analysis as much as possible, so as to improve efficiency of monitoring business risks of the commercial bank on the basis of saving labor and time.
In order to achieve the above object, embodiments of the present application are implemented as follows:
in a first aspect, an embodiment of the present application provides a commercial bank public warning method, where the commercial bank public warning system includes: the method is applied to the big data application layer and comprises the following steps: acquiring risk information of a risk event acquired by the real-time data acquisition platform, wherein the risk event represents an event affecting the repayment capacity of a customer of a commercial bank; according to the risk information, determining a target client from the clients of the commercial bank, and determining unfinished business transacted by the target client in the commercial bank; and determining result information based on the risk information and the unfinished business of the target customer so that the business application layer acquires the result information and sends the result information to a corresponding front end, wherein the corresponding front end is associated with a customer manager managing the unfinished business in the commercial bank.
According to the acquired risk information, the corresponding target customer and the unfinished business of the target customer are determined, and the result information containing the risk information and the unfinished business of the target customer is sent to the terminal of the corresponding customer manager, so that the customer manager can work out a related scheme aiming at the unfinished business as soon as possible, and the efficiency of monitoring the business risk of the commercial bank is improved on the basis of saving labor and time.
With reference to the first aspect, in a first possible implementation manner of the first aspect, after obtaining risk information of a risk event, the method further includes: determining the risk level of the risk event according to the risk information; correspondingly, result information is sent to a terminal of a corresponding customer manager in the commercial bank, wherein the result information further comprises the risk level.
And the risk grades determined according to the risk information are sent to the terminal of the client manager together, so that the client manager can know the influence of the risk events on the unfinished business better.
With reference to the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, the determining a risk level of the risk event according to the risk information includes: determining a risk keyword from the risk information; determining the weight of each risk keyword; and determining the risk level of the risk event according to the risk keywords and the corresponding weight.
The risk keywords and the corresponding weights are determined from the risk information to further determine the risk level of the risk event, so that the risk event can be accurately classified according to the risk level.
With reference to the first aspect, in a third possible implementation manner of the first aspect, determining a target customer from customers of the commercial bank according to the risk information includes: acquiring client keywords from the risk information according to a preset word bank, wherein the preset word bank contains the names of the clients of the commercial bank; and determining a target customer from the customers of the commercial bank according to the customer keyword.
And collecting client keywords from the risk information according to a preset word bank, so that the target client influenced by the risk information can be quickly and accurately determined.
In a second aspect, an embodiment of the present application provides a commercial bank public warning method, where the commercial bank public warning system includes: the method is applied to the real-time data acquisition platform and comprises the following steps: acquiring risk information of a risk event from news information issued by a news platform, or acquiring the risk information entered by business personnel, so that the big data application layer executes the commercial bank-to-public warning method according to the first aspect or any one of possible implementation manners of the first aspect based on the risk information, wherein the risk event represents an event affecting the repayment capacity of a customer of the commercial bank.
The risk information is acquired from the news information issued by the news platform or acquired in a business personnel input mode, so that the real-time and as comprehensive as possible acquisition of the risk information is facilitated, and the efficiency of monitoring the business risk is further improved.
In a third aspect, an embodiment of the present application provides a commercial bank public warning method, where the commercial bank public warning system includes: the method is applied to the business application layer and comprises the following steps: acquiring a solution of the result information input determined by the commercial bank public warning method based on any one of the possible implementation manners of the first aspect by the customer manager; initiating an approval process corresponding to the risk level based on the solution.
By acquiring the solution of the client manager for unfinished business entry and initiating the approval process corresponding to the risk level, the solution method can be reasonably audited while high efficiency is guaranteed as much as possible, and the quality of the solution is guaranteed.
With reference to the third aspect, in a first possible implementation manner of the third aspect, initiating an approval process corresponding to the risk level based on the solution includes: determining a target approval process matched with the risk level from a plurality of preset approval processes; and sending the solution to the terminal of the approver in the target approval process.
Aiming at the risk information of different risk grades, different approval processes can be determined, so that differentiated process monitoring is realized, and the compliance and the efficiency of the process are balanced.
In a fourth aspect, an embodiment of the present application provides a commercial bank public warning apparatus, and a commercial bank public warning system includes: real-time data acquisition platform, big data application layer, business application layer and front end, the device is applied to big data application layer includes: the information acquisition module is used for acquiring risk information of risk events acquired by the real-time data acquisition platform, wherein the risk events represent events affecting the repayment capacity of customers of commercial banks; the business determining module is used for determining a target customer from the customers of the commercial bank according to the risk information and determining the unfinished business transacted by the target customer in the commercial bank; and the result determining module is used for determining result information based on the risk information and the unfinished business of the target customer so that the business application layer acquires the result information and sends the result information to a corresponding front end, wherein the corresponding front end is associated with a customer manager managing the unfinished business in the commercial bank.
In a fifth aspect, an embodiment of the present application provides a commercial bank public warning system, where the system includes: the system comprises a real-time data acquisition platform, a message middleware, a big data application layer, a business application layer and a front end, wherein the real-time data acquisition platform is connected with an external news platform in a butt joint mode and is used for acquiring risk information of a risk event and sending the risk information to the message middleware, wherein the risk event represents an event which possibly influences the repayment capacity of a customer of a commercial bank; the big data application layer monitors the message middleware and is used for acquiring risk information sent by the real-time data acquisition platform, determining the risk level of the risk event according to the risk information, determining a target client from clients of the commercial bank, determining unfinished business handled by the target client in the commercial bank, and sending result information determined based on the risk level, the risk information and the unfinished business of the target client to the message middleware; and the business application layer monitors the message middleware and is used for acquiring the result information and sending the result information to the corresponding front end, wherein the corresponding front end is associated with a customer manager in the commercial bank for managing the unfinished business.
In a sixth aspect, an embodiment of the present application provides a storage medium, where the storage medium stores one or more programs, and the one or more programs are executable by one or more processors to implement the steps of the commercial bank public warning method according to any one of the first aspect, possible implementations of the first aspect, the second aspect, the third aspect, or possible implementations of the third aspect.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic diagram of a commercial bank public warning system according to an embodiment of the present application.
Fig. 2 is a flowchart of a commercial bank public warning method according to an embodiment of the present disclosure.
Fig. 3 is a block diagram of a structure of a commercial bank public warning device according to an embodiment of the present application.
The figure is as follows: 100-commercial bank public warning system; 110-a real-time data acquisition platform; 120-message middleware; 130-big data application layer; 140-service application layer; 150-front end; 10-commercial bank public warning device; 11-an information acquisition module; 20-commercial bank public warning device; 21-an information acquisition module; 22-a traffic determination module; 23-a result determination module; 30-commercial bank public warning device; 31-a scheme acquisition module; 32-approval determination Module.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
Referring to fig. 1, fig. 1 is a schematic diagram of a commercial bank public warning system 100 according to an embodiment of the present disclosure. In this embodiment, the commercial bank public warning system 100 may include: a real-time data collection platform 110, message middleware 120, a big data application layer 130, a business application layer 140, and a front end 150. The real-time data acquisition platform 110, the big data application layer 130 and the service application layer 140 are respectively connected with the message middleware 120, and the front end 150 is connected with the service application layer 140.
It should be noted that the commercial bank public warning system 100 is only one of various manners, in other implementation manners, the commercial bank public warning system 100 may not include the message middleware 120, and the real-time data collection platform 110 and the service application layer 140 are respectively connected to the big data application layer 130, and the front end 150 is connected to the service application layer 140. In the embodiment, the public warning system 100 is exemplified by a commercial bank including a real-time data collection platform 110, a message middleware 120, a big data application layer 130, a business application layer 140 and a front end 150, but should not be considered as a limitation of the present application.
In this embodiment, the real-time data collection platform 110 may interface with an external news platform to obtain news information published by the news platform in real time. Illustratively, the news platform may include a financial information website (e.g., a chinese financial news web, an eastern financial web, a new and wave financial web, etc.), a news information website (e.g., central news, local news, etc.), and an information distribution platform (e.g., a microblog, a public number, etc.). In order to improve the efficiency of the real-time data acquisition platform, the system can be docked with a news platform which has high reliability of published information and high relevance to finance, economy, industry and the like. Of course, the real-time data collection platform 110 may also provide a channel for the service personnel to manually enter the news information (or risk information), so as to facilitate the real-time and comprehensive acquisition of the news information (or risk information).
The real-time data collection platform 110 is mainly used for collecting risk information of risk events from news information released by the news platform in real time.
In this embodiment, the message middleware 120 generally serves as a place for storing and exchanging information, for example, the real-time data collection platform 110 sends collected risk information to the message middleware 120, the big data application layer 130 acquires the risk information from the message middleware 120, sends result information to the message middleware 120, and the like. Of course, when the commercial bank public warning system 100 does not include the message middleware 120, the message middleware 120 is an object of the information exchange site, and information exchange can be achieved by communicating with an object connected with the message middleware 120 (for example, the real-time data acquisition platform 110 sends acquired risk information to the big data application layer 130).
In the embodiment, the big data application layer 130 is mainly used for processing the risk information, determining the result information and sending the result information to the message middleware 120 (which may be sent to the business application layer 140 when the business bank does not include the message middleware 120 for the public warning system 100), and the operation of the big data application layer 130 will be described in detail later.
In this embodiment, the service application layer 140 may be configured to send the obtained result information to the corresponding front end 150, the terminal, and the like. And, the service application layer 140 may also obtain the solution and initiate a corresponding approval process, which is not limited herein.
It should be noted that the real-time data acquisition platform 110 may use a server as a carrier; and message middleware 120 may be hosted by a device with storage and communication capabilities (e.g., a server, a terminal device, etc.); the big data application layer 130 and the business application layer 140 may also take a server as a carrier; the front end 150 uses the terminal device as a carrier. In addition, the real-time data collection platform 110, the message middleware 120, the big data application layer 130, and the service application layer 140 may use the same server as a carrier, or may use different servers as carriers, which is not limited herein. While the carrier of the front end 150 is a terminal device, the commercial bank public warning system 100 generally includes a plurality of front ends 150 to correspond to different users (e.g., a customer manager, a manager, etc. in this embodiment).
Referring to fig. 2, fig. 2 is a flowchart illustrating a commercial bank public warning method according to an embodiment of the present disclosure. In this embodiment, the commercial bank public warning method may be cooperatively executed by each object in the commercial bank public warning system to implement the function of the commercial bank public warning method. The commercial bank public warning method may include step S11.
Step S11: acquiring risk information of a risk event from news information released by a news platform, or acquiring the risk information input by business personnel, wherein the risk event represents an event affecting the repayment capability of a customer of a commercial bank.
In this embodiment, step S11 may be performed by a real-time data collection platform of the public warning system by the commercial bank.
For example, the real-time data collection platform may collect risk information of a risk event from news information published by a docked news platform. When the real-time data acquisition platform acquires the risk information, the risk information of the risk events related to the existing clients of the commercial bank (which may include clients transacting business and having incomplete business, and may also include clients transacting business but having completed business), wherein the business is incomplete, such as company a transacting a loan in the commercial bank, and the loan is not cleared), and the related risk events represent events which may affect the repayment capability of the existing clients of the commercial bank, such as loss events, operational risks, public opinion information, industry policy information adjustment, regional policy information adjustment, and the like of the clients.
In order to ensure the correlation between the acquired risk information of the risk event and the client, the risk event may be acquired according to the information category in a preset information base, where the preset information base may include the name of the client (which may include a full name, a short name, a code number, etc.), the industry where the client is located, risk words (loss, loss of credit, operational risk, structural adjustment), the region where the client is located, and the like.
For example, a financial website publishes news: company a faces huge losses in amounts of several billion rmb …. Then, the real-time data collection platform may collect the current news as risk information based on the name of the client (company a) in the preset information base.
After the risk information is collected, the real-time data collection platform can send the collected risk information to the message middleware. It should be noted that the risk information sent to the message middleware may be the piece of news information, or may be a link containing the piece of news information, which is not limited herein. For example, the real-time data collection platform may send the contents of the collected risk information to kafka (a high-throughput distributed publish-subscribe messaging system).
The risk information is acquired from news information released by a news platform or is acquired by a business person entering mode, so that the risk information can be acquired in real time (24 multiplied by 7 multiplied by 365, namely, without interruption all year round) and as comprehensively as possible.
After the real-time data acquisition platform sends the acquired risk information to the message middleware, the commercial bank public early warning system can continuously execute the commercial bank public early warning method.
Referring to fig. 2, fig. 2 is a flowchart illustrating a commercial bank public warning method according to an embodiment of the present disclosure. In this embodiment, the commercial bank public warning method may further include step S21, step S22, and step S23, and step S21, step S22, and step S23 may be performed by a big data application layer in the commercial bank public warning system.
After the real-time data collection platform sends the collected risk information to the message middleware, the big data application layer may perform step S21.
Step S21: and acquiring the risk information of the risk event acquired by the real-time data acquisition platform.
In this embodiment, the big data application layer may monitor the message middleware to obtain risk information sent to the message middleware by the real-time data acquisition platform. For example, the big data application layer may obtain the content of the piece of risk information sent by the real-time data acquisition platform by monitoring kafka. And when the commercial bank public early warning system does not contain the message middleware, the big data application layer can receive the content of the risk information sent by the real-time data acquisition platform.
After obtaining the risk information, the big data application layer may perform step S22.
Step S22: and according to the risk information, determining a target client from the clients of the commercial bank, and determining the unfinished business transacted by the target client in the commercial bank.
In order to make the customer manager know the influence of the risk event on the incomplete business, in this embodiment, the risk event may be risk-rated (i.e., the risk level of the risk event is determined) according to the risk information.
For example, the big data application layer may determine risk keywords from the risk information, determine a weight of each risk keyword, and then determine a risk level of the risk event according to the risk keywords and the corresponding weights.
Because the occurrence frequency of the risk keywords can reflect the gravity center of the risk event, for example, the big data application layer can determine the weight corresponding to the risk keywords according to the occurrence frequency of the acquired risk keywords. And because the risk keywords related to the amount and quantity of money can usually reflect the influence degree caused by the risk event, the big data application layer can determine the amount of money from the risk keywords related to the amount of money and determine the risk influence degree of the risk event according to the amount of money. And determining the risk level of the risk event according to the weight and the risk influence degree corresponding to the risk keyword.
For example, company a faces a huge loss, the amount of the loss reaches … billion rmb, the amount of the loss is "billion rmb", the influence degree is very deep, and the weight of the loss risk keyword in the risk keywords determined by the whole news content reaches a higher value (e.g. 0.2), so that the risk level can be determined as high risk (for example, the risk levels can be classified into a low risk level, a medium risk level, and a high risk level.
In this embodiment, a Spark Streaming (a set of frames that extend with Spark as a core, can implement a high throughput, and has a fault-tolerant mechanism) may be used to match the keywords according to a preset information base: the name class- "company a", the risk class- "loss", the amount class- "billion", determines the risk level as high risk (which may be labeled with a color, such as red, to highlight).
The risk keywords and the corresponding weights are determined from the risk information to further determine the risk level of the risk event, so that the risk event can be accurately classified according to the risk level. And the risk information and the determined risk level are sent to the terminal of the client manager together, so that the client manager can know the influence of the risk event on the unfinished business more accurately.
It should be noted that the determined risk level may also be used as a criterion for screening risk information, for example, when the risk level is extremely low (hardly affecting the incomplete business), subsequent steps may not be required to save resources (e.g., computing resources, human resources, time resources, etc. of the system). In addition, the evaluation of the risk level is not necessary, and in other implementations, the evaluation of the risk level may not be performed, and therefore, the evaluation of the risk level herein should not be considered as limiting the present application. The order of the evaluation of the risk level and the execution of step S22 is not limited to this, and step S22 may be executed after the evaluation of the risk level is performed, step S22 may be executed before the evaluation of the risk level is performed, or the evaluation of the risk level and step S22 may be performed simultaneously, which is not limited herein.
To improve the efficiency of risk forewarning in commercial banks to make the customer manager work out the appropriate solution as soon as possible, the big data application layer may perform step S22.
In this embodiment, the big data application layer may collect a client keyword from the risk information according to a preset lexicon, determine a target client from clients of the commercial bank according to the client keyword, and determine an incomplete business that the target client transacts in the commercial bank, where the preset lexicon includes names of the clients of the commercial bank. It should be noted that the relationship between the preset lexicon (including the name of the client, such as a full name, an abbreviation, a code, a company code, a stock code, etc.) and the preset information base (including the name of the client, the industry where the client is located, a risk word, the region where the client is located, etc.) may be a relationship (including a part of the preset information base of the preset lexicon, for example), an association relationship (a change in the preset information base caused by a change in the preset lexicon), or may be independent of each other, which is not limited herein.
Illustratively, the big data application layer can collect client keywords from the risk information according to a preset lexicon, so that a target client can be determined from clients of a commercial bank according to the client keywords. After the target customer is determined, the big data application layer can determine the incomplete business of the target customer in the commercial bank according to the target customer.
For example, the big data application layer may query the number of a client in the bank of the company according to the keyword "a corporation" in ES (Elastic Search, a Search server), and query the corresponding stock quantity service bank HBase (a distributed and column-oriented open source database) according to the number of the client, for example, the company a has an outstanding credit service (i.e., unfinished service) in the line (the commercial bank), and the service number is XXXX.
After determining the target client and the corresponding incomplete service, the big data application layer may perform step S23.
Step S23: and determining result information based on the risk information and the unfinished business of the target customer so that the business application layer acquires the result information and sends the result information to a corresponding front end, wherein the corresponding front end is associated with a customer manager managing the unfinished business in the commercial bank.
In this embodiment, the big data application layer may generate result information according to the risk information, the incomplete business of the target client, and the risk level, where the result information includes the risk information, the incomplete business of the target client, and the risk level. Of course, in the case where the big data application layer does not need to determine the risk level, the result information may not include the risk level. After the result information is determined, the big data application layer can send the result information to the message middleware (when the message middleware is not included in the commercial bank public warning system, the message middleware can be sent to the business application layer). For example, the big data application layer sends result information (containing corresponding news content, company a, service number, etc.) to kafka.
After the big data application layer sends the result information to the message middleware, the business application layer of the commercial bank public warning system can acquire the result information from the message middleware and send the result information to the corresponding front end, wherein the corresponding front end is associated with a customer manager who manages the unfinished business in the commercial bank (namely, the front end on the terminal device used by the customer manager who processes the business). In order to inform the client manager of processing in time, the service application layer can also send prompt information to a mobile terminal (such as a mobile phone) used by the client manager to remind the client manager of processing in time.
According to the acquired risk information, the corresponding target customer and the unfinished business of the target customer are determined, and the result information containing the risk information and the unfinished business of the target customer is sent to the terminal of the corresponding customer manager, so that the customer manager can work out a related solution for the unfinished business as soon as possible, and the efficiency of monitoring the business risk of the commercial bank is improved on the basis of saving labor and time.
After the business application layer sends the result information to the corresponding front end, the commercial bank public warning system can execute the commercial bank public warning method. Referring to fig. 2 again, fig. 2 is a flowchart of a commercial bank public warning method according to an embodiment of the present disclosure. In this embodiment, the commercial bank public warning method may further include steps S31 and S32, and steps S31 and S32 may be performed by the business application layer.
The customer manager may enter the solution formulated based on the result information through the front end, and the business application layer may perform step S31.
Step S31: and acquiring a solution entered by the customer manager based on the result information.
In this embodiment, the service application layer may obtain a solution that the customer manager formulates based on the result information and enters through the front end.
After acquiring the solution, the service application layer may perform step S32.
Step S32: initiating an approval process corresponding to the risk level based on the solution.
In this embodiment, the business application layer may determine a target approval process matched with the risk level from a plurality of preset approval processes, so as to send the solution to the terminal of the approver in the target approval process.
For example, the business application layer may determine the corresponding target approval process according to the risk level, for example, the approval person of the target approval process corresponding to the high risk level is the superior supervisor and the leader, the business application layer may first send the solution to the superior supervisor terminal and/or the front end, and after the superior supervisor passes through the solution, the business application layer may send the solution to the leader terminal and/or the front end.
By acquiring the solution of the client manager for unfinished business entry and initiating the approval process corresponding to the risk level, the solution method can be reasonably audited while high efficiency is guaranteed as much as possible, and the quality of the solution is guaranteed. Different approval processes can be determined according to risk information of different risk levels, so that differentiated process monitoring is realized, and the compliance and the efficiency of the process are balanced.
Referring to fig. 3, fig. 3 is a block diagram illustrating a public warning device for a commercial bank according to an embodiment of the present disclosure.
In the present embodiment, there is provided a commercial bank public warning apparatus 20, and the commercial bank public warning system includes: real-time data acquisition platform, big data application layer, business application layer and front end, commercial bank is applied to public early warning device 20 in big data application layer includes: the information acquisition module 21 is configured to acquire risk information of a risk event acquired by the real-time data acquisition platform, where the risk event represents an event affecting the repayment capability of a customer of a commercial bank; a business determining module 22, configured to determine a target client from the clients of the commercial bank according to the risk information, and determine an unfinished business that the target client transacts in the commercial bank; a result determining module 23, configured to determine result information based on the risk information and the incomplete business of the target customer, so that the business application layer obtains the result information and sends the result information to a corresponding front end, where the corresponding front end is associated with a customer manager in the commercial bank that manages the incomplete business.
In this embodiment, after the business determining module 22 obtains the risk information of the risk event, it is further configured to determine the risk level of the risk event according to the risk information; correspondingly, result information is sent to a terminal of a corresponding customer manager in the commercial bank, wherein the result information further comprises the risk level.
In this embodiment, the service determination module 22 is further configured to determine a risk keyword from the risk information; determining the weight of each risk keyword; and determining the risk level of the risk event according to the risk keywords and the corresponding weight.
In this embodiment, the service determination module 22 is further configured to collect a client keyword from the risk information according to a preset lexicon, where the preset lexicon includes names of clients of the commercial bank; and determining a target customer from the customers of the commercial bank according to the customer keyword.
In this embodiment, there is also provided a commercial bank public warning device 10, and the commercial bank public warning system includes: the device comprises a real-time data acquisition platform and a big data application layer, wherein the device is applied to the big data application layer and comprises an information acquisition module 11, and the information acquisition module is used for acquiring risk information of a risk event from news information issued by a news platform or acquiring risk information input by business personnel, so that the big data application layer operates the commercial bank public early warning device provided by the embodiment of the application based on the risk information, wherein the risk event represents an event affecting the repayment capacity of a customer of the commercial bank.
In this embodiment, there is also provided a commercial bank public warning device 30, and the commercial bank public warning system includes: big data application layer, business application layer and front end, the device is applied to the business application layer, including: a scheme obtaining module 31, configured to obtain a solution entered by the customer manager based on a big data application layer to execute the result information determined by the public warning method for a commercial bank provided in the embodiment of the present application; and an approval determining module 32, configured to initiate an approval process corresponding to the risk level based on the solution.
In this embodiment, the approval determining module 32 is further configured to determine a target approval process matched with the risk level from a plurality of preset approval processes; and sending the solution to an approver in the target approval process.
The embodiment of the present application further provides a storage medium, where one or more programs are stored, and the one or more programs may be executed by one or more processors to implement the steps of the commercial bank public warning method provided in the embodiment of the present application.
In summary, the embodiments of the present application provide a commercial bank public warning method, apparatus, system, and storage medium, to determine incomplete business of a corresponding target customer and a corresponding target customer according to acquired risk information, and send result information including the risk information and the incomplete business of the target customer to a terminal of a corresponding customer manager, so that the customer manager can make a relevant scheme for the incomplete business as soon as possible, which is beneficial to improving efficiency of business risk monitoring of the commercial bank on the basis of saving manpower and time.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and there may be other divisions in actual implementation, and for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement 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 (10)
1. A commercial bank public warning method is characterized in that a commercial bank public warning system comprises: the method is applied to the big data application layer and comprises the following steps:
acquiring risk information of a risk event acquired by the real-time data acquisition platform, wherein the risk event represents an event affecting the repayment capacity of a customer of a commercial bank;
according to the risk information, determining a target client from the clients of the commercial bank, and determining unfinished business transacted by the target client in the commercial bank;
and determining result information based on the risk information and the unfinished business of the target customer so that the business application layer acquires the result information and sends the result information to a corresponding front end, wherein the corresponding front end is associated with a customer manager managing the unfinished business in the commercial bank.
2. The commercial bank public warning method of claim 1, wherein after obtaining risk information for a risk event, the method further comprises:
determining the risk level of the risk event according to the risk information;
correspondingly, result information is sent to a terminal of a corresponding customer manager in the commercial bank, wherein the result information further comprises the risk level.
3. The commercial bank public warning method as claimed in claim 2, wherein determining the risk level of the risk event according to the risk information comprises:
determining a risk keyword from the risk information;
determining the weight of each risk keyword;
and determining the risk level of the risk event according to the risk keywords and the corresponding weight.
4. The commercial bank public warning method as claimed in claim 1, wherein determining a target customer from customers of the commercial bank based on the risk information comprises:
acquiring client keywords from the risk information according to a preset word bank, wherein the preset word bank contains the names of the clients of the commercial bank;
and determining a target customer from the customers of the commercial bank according to the customer keyword.
5. A commercial bank public warning method is characterized in that a commercial bank public warning system comprises: the method is applied to the real-time data acquisition platform and comprises the following steps:
acquiring risk information of a risk event from news information released by a news platform, or acquiring the risk information input by business personnel, so that the big data application layer executes the commercial bank-to-public warning method according to any one of claims 1 to 4 based on the risk information, wherein the risk event represents an event affecting the repayment capacity of a customer of a commercial bank.
6. A commercial bank public warning method is characterized in that a commercial bank public warning system comprises: the method is applied to the business application layer and comprises the following steps:
acquiring a solution of the result information entry determined by the customer manager based on the public warning method of the commercial bank as claimed in any one of claims 2 to 4;
initiating an approval process corresponding to the risk level based on the solution.
7. The commercial bank public warning method according to claim 6, wherein initiating an approval process corresponding to the risk level based on the solution comprises:
determining a target approval process matched with the risk level from a plurality of preset approval processes;
and sending the solution to the terminal of the approver in the target approval process.
8. A commercial bank public warning device is characterized in that a commercial bank public warning system comprises: real-time data acquisition platform, big data application layer, business application layer and front end, the device is applied to big data application layer includes:
the information acquisition module is used for acquiring risk information of risk events acquired by the real-time data acquisition platform, wherein the risk events represent events affecting the repayment capacity of customers of commercial banks;
the business determining module is used for determining a target customer from the customers of the commercial bank according to the risk information and determining the unfinished business transacted by the target customer in the commercial bank;
and the result determining module is used for determining result information based on the risk information and the unfinished business of the target customer so that the business application layer acquires the result information and sends the result information to a corresponding front end, wherein the corresponding front end is associated with a customer manager managing the unfinished business in the commercial bank.
9. A commercial bank public warning system, the system comprising: a real-time data acquisition platform, a message middleware, a big data application layer, a service application layer and a front end,
the real-time data acquisition platform is in butt joint with an external news platform and is used for acquiring risk information of a risk event and sending the risk information to the message middleware, wherein the risk event represents an event which possibly influences the repayment capacity of a customer of a commercial bank;
the big data application layer monitors the message middleware and is used for acquiring risk information sent by the real-time data acquisition platform, determining the risk level of the risk event according to the risk information, determining a target client from clients of the commercial bank, determining unfinished business handled by the target client in the commercial bank, and sending result information determined based on the risk level, the risk information and the unfinished business of the target client to the message middleware;
and the business application layer monitors the message middleware and is used for acquiring the result information and sending the result information to the corresponding front end, wherein the corresponding front end is associated with a customer manager in the commercial bank for managing the unfinished business.
10. A storage medium storing one or more programs, the one or more programs being executable by one or more processors to perform the steps of the commercial bank to public warning method as claimed in any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112991040A (en) * | 2021-02-20 | 2021-06-18 | 中国建设银行股份有限公司 | Risk management method and device for public account by bank |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101908194A (en) * | 2010-08-09 | 2010-12-08 | 中国建设银行股份有限公司 | Method for monitoring corporate bank loan |
| US20110055852A1 (en) * | 2009-08-31 | 2011-03-03 | Bank Of America Corporation | Event processing for detection of suspicious financial activity |
| US20130332338A1 (en) * | 2010-01-13 | 2013-12-12 | Corelogic Solutions, Llc | System and method of detecting and assessing multiple types of risks related to mortgage lending |
| CN107169860A (en) * | 2016-12-30 | 2017-09-15 | 中国建设银行股份有限公司 | A kind of method for prewarning risk and device |
| CN109409633A (en) * | 2018-08-23 | 2019-03-01 | 山东省城市商业银行合作联盟有限公司 | Business monitoring and Warning System |
-
2019
- 2019-11-26 CN CN201911179178.6A patent/CN110956385A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110055852A1 (en) * | 2009-08-31 | 2011-03-03 | Bank Of America Corporation | Event processing for detection of suspicious financial activity |
| US20130332338A1 (en) * | 2010-01-13 | 2013-12-12 | Corelogic Solutions, Llc | System and method of detecting and assessing multiple types of risks related to mortgage lending |
| CN101908194A (en) * | 2010-08-09 | 2010-12-08 | 中国建设银行股份有限公司 | Method for monitoring corporate bank loan |
| CN107169860A (en) * | 2016-12-30 | 2017-09-15 | 中国建设银行股份有限公司 | A kind of method for prewarning risk and device |
| CN109409633A (en) * | 2018-08-23 | 2019-03-01 | 山东省城市商业银行合作联盟有限公司 | Business monitoring and Warning System |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112991040A (en) * | 2021-02-20 | 2021-06-18 | 中国建设银行股份有限公司 | Risk management method and device for public account by bank |
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