CN116842251A - Recommendation method, device, equipment and storage medium of large-amount verification mode - Google Patents

Recommendation method, device, equipment and storage medium of large-amount verification mode Download PDF

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CN116842251A
CN116842251A CN202310611535.1A CN202310611535A CN116842251A CN 116842251 A CN116842251 A CN 116842251A CN 202310611535 A CN202310611535 A CN 202310611535A CN 116842251 A CN116842251 A CN 116842251A
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recommendation
verification
user
candidate
mode
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陈孟妮
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Bank of China Ltd
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Bank of China Ltd
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    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
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    • 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
    • G06Q40/02—Banking, e.g. interest calculation or account maintenance

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Abstract

The application discloses a recommendation method, a recommendation device, recommendation equipment and a recommendation storage medium for a large-amount verification mode, which can be applied to the field of large data or the field of finance. The method comprises the following steps: acquiring personal information of a user in response to a large-amount verification trigger operation for the user; determining a recommended verification mode from a plurality of candidate verification modes according to the personal information; updating the initial recommendation weight of the recommendation verification mode to obtain the recommendation weight corresponding to the recommendation verification mode; based on the recommendation weights respectively corresponding to the candidate verification modes, recommendation results of the candidate verification modes are output. Because the recommendation verification mode is determined based on the personal information of the user, and the final recommendation result refers to the recommendation weights of the candidate proper modes, the service personnel can definitely adapt to the verification mode of the user according to the output recommendation result, thereby being beneficial to shortening the verification time and accelerating the service handling progress.

Description

Recommendation method, device, equipment and storage medium of large-amount verification mode
Technical Field
The present application relates to the field of big data technologies, and in particular, to a recommendation method, apparatus, device, and storage medium for a big amount verification method.
Background
Large-amount verification refers to the process by which a bank verifies and validates the authenticity of large-amount business occurring on an account. As a component of the operation flow of banking business, large-amount verification is a necessary auxiliary means for preventing payment and settlement risk and guaranteeing the fund safety of banks and clients, and aims to verify and confirm large-amount business exceeding a certain amount standard, which occurs to a settlement account, with clients in time, so as to ensure that the business of large-amount money is based on the real intention of the clients.
In the related art, a business person of a bank can adopt different large-amount verification modes aiming at different transaction scenes. For example, when a customer personally handles a large amount of business, the business personnel can verify by face recognition and/or short message authentication; when the customer hosts a large amount of service, the service personnel needs to verify through short message authentication and/or telephone authentication. It can be seen that in a large verification process, a business person may need to try multiple verification methods to obtain a verification result, so that it takes a long time to verify, which affects the business transaction progress.
Disclosure of Invention
The embodiment of the application provides a recommendation method, a recommendation device, recommendation equipment and a storage medium of a large-amount verification mode, which are beneficial to shortening verification time and accelerating business handling progress.
In a first aspect, an embodiment of the present application provides a recommendation method for a large-amount verification method, including:
acquiring personal information of a user in response to a large-amount verification trigger operation for the user;
determining a recommended verification mode from a plurality of candidate verification modes according to the personal information;
updating the initial recommendation weight of the recommendation verification mode to obtain the recommendation weight corresponding to the recommendation verification mode;
and outputting recommendation results of the candidate verification modes based on recommendation weights respectively corresponding to the candidate verification modes.
Optionally, the personal information includes at least one of age information, cultural degree information, usage liveness for banking applications, and asset information;
the determining a recommended verification mode from a plurality of candidate verification modes according to the personal information comprises the following steps:
establishing a user portrait of the user according to at least one of the age information, the cultural degree information, the use liveness for banking application and the asset information;
the recommended verification style is determined from a plurality of candidate verification styles based on the user representation.
Optionally, updating the initial recommendation weight of the recommendation verification mode to obtain a recommendation weight corresponding to the recommendation verification mode, including:
determining a floating range of initial recommendation weights of the recommendation verification mode according to the personal information;
and adding initial recommendation weight of the recommendation verification mode in the floating range to obtain recommendation weight corresponding to the recommendation verification mode.
Optionally, the determining, according to the personal information, a floating range of an initial recommendation weight of the recommendation verification method includes:
determining the credibility of the personal information according to the source condition of the personal information;
determining a floating influence proportion of an initial recommendation weight of the recommendation verification mode based on the credibility;
and carrying out floating adjustment on the initial recommended weight of the recommended verification mode according to the floating influence proportion to obtain the floating range.
Optionally, the outputting the recommendation results of the plurality of candidate verification manners based on the recommendation weights respectively corresponding to the plurality of candidate verification manners includes:
sequencing the candidate verification modes according to the sequence from big to small of the recommendation weights respectively corresponding to the candidate verification modes to obtain a sequencing result;
and outputting the sorting result as the recommendation result.
Optionally, before updating the initial recommendation weight of the recommendation verification mode to obtain the recommendation weight corresponding to the recommendation verification mode, the method further includes:
acquiring a verification mode of the user association; the verification means associated with the user is a historical verification means for the user to verify success and is one of the plurality of candidate verification means;
adding initial recommendation weights corresponding to the verification modes of the user association to obtain new initial recommendation weights;
if the recommendation verification mode is the verification mode associated with the user, updating the initial recommendation weight of the recommendation verification mode to obtain the recommendation weight corresponding to the recommendation verification mode, wherein the method comprises the following steps:
and updating the new initial recommendation weight to obtain the recommendation weight corresponding to the recommendation verification mode.
Optionally, the method further comprises:
acquiring historical verification success rates respectively corresponding to the plurality of candidate verification modes;
for a candidate verification means, adjusting initial recommendation weights for the candidate verification means based on historical verification success rates of the candidate verification means.
In a second aspect, an embodiment of the present application provides a recommendation apparatus for a large-amount verification method, including:
the first acquisition module is used for responding to the large-amount verification triggering operation aiming at the user and acquiring personal information of the user;
the first determining module is used for determining a recommended verification mode from a plurality of candidate verification modes according to the personal information;
the updating module is used for updating the initial recommendation weight of the recommendation verification mode to obtain the recommendation weight corresponding to the recommendation verification mode;
and the output module is used for outputting recommendation results of the candidate verification modes based on recommendation weights respectively corresponding to the candidate verification modes.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor, memory, system bus;
the processor and the memory are connected through the system bus;
the memory is for storing one or more programs, the one or more programs comprising instructions, which when executed by the processor, cause the processor to perform any implementation of the method of recommendation of a high-volume verification method described above.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, where instructions are stored, when the instructions are executed on a terminal device, to cause the terminal device to execute any implementation of the recommendation method of the above-mentioned large-volume verification method.
From the above technical solutions, the embodiment of the present application has the following advantages:
in the embodiment of the application, in response to the large-amount verification triggering operation aiming at the user, the personal information of the user can be acquired first, and then the recommendation verification mode is determined from a plurality of candidate verification modes according to the personal information. Then, the initial recommendation weight of the recommendation verification mode is updated to obtain the recommendation weight corresponding to the recommendation verification mode, and recommendation results of the plurality of candidate verification modes are output based on the recommendation weights respectively corresponding to the plurality of candidate verification modes. Because the recommendation verification mode is determined based on the personal information of the user, and the final recommendation result refers to the recommendation weights of the candidate proper modes, the service personnel can definitely adapt to the verification mode of the user according to the output recommendation result, thereby being beneficial to shortening the verification time and accelerating the service handling progress.
Drawings
FIG. 1 is a flowchart of a recommendation method of a large amount verification method according to an embodiment of the present application;
FIG. 2 is a flowchart of another recommendation method for a large-amount verification method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a recommendation device with a large amount verification method according to an embodiment of the present application.
Detailed Description
As described above, in the related art, a business person of a bank may adopt different large-amount verification manners for different transaction scenarios. For example, when a customer personally handles a large amount of business, the business personnel can verify by face recognition and/or short message authentication; when the customer hosts a large amount of service, the service personnel needs to verify through short message authentication and/or telephone authentication. It can be seen that in a large verification process, a business person may need to try multiple verification methods to obtain a verification result, so that it takes a long time to verify, which affects the business transaction progress.
In order to solve the above-mentioned problems, an embodiment of the present application provides a recommendation method for a large-amount verification method, which includes: in response to a large amount verification trigger operation for a user, personal information of the user may be acquired first, and then a recommended verification manner may be determined from among a plurality of candidate verification manners according to the personal information. Then, the initial recommendation weight of the recommendation verification mode is updated to obtain the recommendation weight corresponding to the recommendation verification mode, and recommendation results of the plurality of candidate verification modes are output based on the recommendation weights respectively corresponding to the plurality of candidate verification modes.
Because the recommendation verification mode is determined based on the personal information of the user, and the final recommendation result refers to the recommendation weights of the candidate proper modes, the service personnel can definitely adapt to the verification mode of the user according to the output recommendation result, thereby being beneficial to shortening the verification time and accelerating the service handling progress.
It should be noted that the recommendation method, device, equipment and storage medium of the large-amount verification method provided by the application can be used in the field of large data or the field of finance. The foregoing is merely exemplary, and the application fields of the recommendation method, the device, the apparatus and the storage medium of the large-amount verification method provided by the present application are not limited. In addition, the embodiment of the present application may also not limit the execution subject of the recommendation method of the large-amount verification method, for example, the recommendation method of the large-amount verification method of the embodiment of the present application may be applied to a data processing device such as a terminal device or a server. The terminal device may be an electronic device such as a smart phone, a computer, a personal digital assistant (Personal Digital Assistant, PDA), a tablet computer, etc. The servers may be stand alone servers, clustered servers, or cloud servers.
In order to make the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Fig. 1 is a flowchart of a recommendation method of a large-amount verification method according to an embodiment of the present application. Referring to fig. 1, the recommendation method of the large-amount verification method provided by the embodiment of the application may include:
s101: personal information of the user is acquired in response to a large-amount verification trigger operation for the user.
In practical application, the large verification trigger operation for the user can be represented as: when the user transacts the financial business, the amount of money related to the financial business exceeds the preset large verification trigger amount. The large amount verification trigger amount may be preset based on a specific financial service, which is not particularly limited herein. For example, the preset large amount verification trigger may be 20 ten thousand when the user handles the personal transfer service, and thus, the large amount verification trigger may be used as the large amount verification trigger for the user when the user's personal transfer service is more than 20 ten thousand. When the user handles the business transfer service, the preset large amount verification trigger amount can be 50 ten thousand, so that when the amount of money related to the business transfer service of the user exceeds 50 ten thousand, the large amount verification trigger operation for the user can be used.
In the embodiment of the present application, the personal information of the user may include at least one of age information, cultural degree information, usage liveness for banking applications, and asset information of the user. The personal information may be used to create a user representation after data analysis that may help to categorize the user with the proper verification means. The frequency of the user using the banking applications such as mobile phone banking, internet banking and the like can be represented according to the using liveness of the banking applications. For example, if the personal information of the user is "23 years old, and the bank application has high activity in use", the user portrait may be represented by "young people like online transactions" as a tag.
In addition, the personal information of the user may not be particularly limited in the embodiment of the present application. For example, the personal information of the user may be stored in an execution body of the embodiment of the present application, such as a server, which may acquire the personal information of the user by means of local reading when necessary. Alternatively, the personal information of the user may be stored in another data storage device, and the server may access the data storage device to obtain the personal information of the user when necessary. It should be noted that, since the personal information of the user may relate to the data associated with the privacy of the user, when the embodiment of the present application is applied to a specific product or technology, the license or consent of the user needs to be obtained, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region. For example, prior to acquiring personal information of a user, the user needs to be queried for permission or consent, thereby acquiring authorization for his personal information.
S102: based on the personal information, a recommended verification means is determined from among a plurality of candidate verification means.
As mentioned above, the personal information, after data analysis, can be used to create a user representation that can help categorize the user with the proper verification means. Based on this, the embodiment of the present application may not be limited specifically for determining the recommended verification manner according to the personal information, that is, the specific implementation manner of S102, and for convenience of understanding, the following description will be made with reference to one possible implementation manner.
In one possible implementation, S102 may specifically include: establishing a user portrait of the user according to at least one of age information, cultural degree information, use liveness for bank application and asset information; based on the user representation, a recommended verification style is determined from a plurality of candidate verification styles. It can be seen that the personal information, after data analysis, can be used to create a user representation that can help categorize the user with the proper verification means. The recommendation verification mode is determined based on personal information of the user, and the final recommendation result refers to recommendation weights of the candidate proper modes, so that service personnel can definitely adapt to the verification mode of the user according to the output recommendation result, thereby being beneficial to shortening verification time and accelerating service handling progress.
The candidate verification modes can include a telephone verification mode, a short message verification mode and a biological characteristic identification verification mode. The telephone verification mode refers to a mode that a banking staff transmits verification information to a user in a call mode and the user outputs the verification information for verification; the short message verification mode refers to a mode that a bank business person issues verification and check information to a user in a short message mode, and the user outputs the verification and check information for verification; the biometric feature recognition verification mode may include fingerprint recognition, face recognition, etc., specifically, a banking person issues a request for identifying the biometric feature to a user through a banking application, and the user inputs a corresponding biometric feature for verification.
In practical applications, referring to the example where the aforementioned user representation is represented by "young people who like an online transaction" as a tag, the biometric verification method among the candidate verification methods can be determined as the recommendation verification method based on the user representation. If the user portrait is indicated by "old people without learning" as a tag, the telephone verification mode among the candidate verification modes is determined as the recommended verification mode based on the user portrait.
S103: and updating the initial recommendation weight of the recommendation verification mode to obtain the recommendation weight corresponding to the recommendation verification mode.
In the embodiment of the application, a plurality of candidate verification modes respectively have respective initial recommendation weights. The initial recommendation weight may be preset based on specific financial services and/or regulatory policies, and is not specifically limited herein. For example, if a financial transaction is set to force that only a phone verification mode can be used, the initial recommendation weight of the phone verification mode is 1; a certain financial business is set to adopt a face recognition verification mode and a short message verification mode, and the initial recommendation weight of the two verification modes is 0.5.
Accordingly, since the recommendation verification mode can be determined from a plurality of candidate verification modes through the personal information of the user, the suitability of the recommendation verification mode and the personal information of the user can be determined to be higher, and the initial recommendation weight of the recommendation verification mode can be updated to obtain the corresponding recommendation weight. Therefore, in the follow-up recommendation results based on the output candidate verification mode, the recommendation verification mode can occupy a larger weight in the decision of banking staff, so that the banking staff can definitely adapt to the verification mode of the user according to the output recommendation result, thereby being beneficial to shortening the verification time and accelerating the business handling progress. In addition, it should be noted that, for other candidate verification methods that do not update the initial recommendation weight, the initial recommendation weight may be used as the final recommendation weight.
In addition, in the embodiment of the present application, the implementation manner of updating the initial recommendation weight of the recommendation verification manner, that is, S103, may not be specifically limited, and for convenience of understanding, the following description is made with reference to one possible implementation manner.
In one possible implementation, S103 may specifically include: determining a floating range of initial recommendation weight of a recommendation verification mode according to personal information; and adding initial recommendation weight of the recommendation verification mode in the floating range to obtain recommendation weight corresponding to the recommendation verification mode. Here, by determining the floating range of the initial recommendation weight of the recommendation verification method through the personal information, the condition that the added excessive initial recommendation weight of the recommendation verification method exceeds the reasonable weight and influences the verification decision of service personnel can be avoided.
Further, the determining process of the floating range of the initial recommendation weight of the recommendation verification mode specifically may include: determining the credibility of the personal information according to the source condition of the personal information; determining a floating influence proportion of an initial recommendation weight of a recommendation verification mode based on the credibility; and carrying out floating adjustment on the initial recommended weight of the recommended verification mode according to the floating influence proportion to obtain a floating range. Therefore, when the credibility is higher, the recommendation verification mode determined according to the personal information can be more accurately attached to the actual situation of the user, so that the floating influence proportion can be set larger, and the initial recommendation weight of the recommendation verification mode can be obviously increased. When the credibility angle is adopted, a certain gap exists between the recommendation proper mode determined according to the personal information and the real situation of the user, so that the floating influence proportion can be set smaller, and the influence on the verification decision of service personnel caused by the fact that the initial recommendation weight of the recommendation verification mode is obviously increased and exceeds the reasonable weight is avoided.
In practical applications, the source of personal information can be generally divided into two types, one is personal information captured by networking, and the other is personal information self-filled by users in advance. In response to this, the personal information captured by the network, for example, the asset information, the usage activity for banking applications, and the like, may have a high reliability, and the corresponding floating influence ratio may have a value of 30%, and in this case, if the initial recommendation weight is 0.5, the corresponding floating range may be 0.35 to 0.65. The confidence level of the personal information pre-filled by the user, such as the cultural level information pre-filled by the user, is lower than that of the personal information captured by the network, the value of the corresponding floating influence proportion can be 20%, and the corresponding floating range can be 0.4-0.6 if the initial recommendation weight is still 0.5.
In addition, in order to improve the probability of success of one verification, the verification efficiency is further improved, and in the embodiment of the application, the initial recommendation weight of the corresponding candidate proper mode can be adjusted through the historical verification success rate of the candidate verification mode, so that the subsequent service personnel can determine the corresponding verification mode based on the recommendation results of the multiple candidate verification modes, thereby being beneficial to shortening the verification time and accelerating the service handling progress. Based on this, the recommendation method of the large amount verification method may further include: acquiring historical verification success rates respectively corresponding to a plurality of candidate verification modes; for a candidate verification means, the initial recommendation weight of the candidate verification means is adjusted based on the historical verification success rate of the candidate verification means. Specifically, based on the historical verification success rate of the candidate verification means, the means for adjusting the initial recommendation weight of the candidate verification means may be embodied as: when the history verification success rate is higher, the initial recommendation weight of the candidate verification means may be increased, and when the history verification success rate is lower, the initial recommendation weight of the candidate verification means may be decreased.
S104: based on the recommendation weights respectively corresponding to the candidate verification modes, recommendation results of the candidate verification modes are output.
In the embodiment of the present application, the implementation manner for outputting the recommended results of the plurality of candidate verification manners may be not particularly limited, and will be exemplified below.
As an example, a candidate verification style corresponding to a maximum value may be determined from a plurality of candidate verification styles based on a maximum value in recommended weights respectively corresponding to the plurality of candidate verification styles, and the candidate verification style corresponding to the maximum value may be used as a recommendation result. Therefore, the business personnel can definitely adapt to the verification mode of the user according to the output recommendation result, thereby being beneficial to shortening the verification time and accelerating the business handling progress.
As another example, the candidate verification manners may be ranked in order of the recommendation weights corresponding to the candidate verification manners from large to small, to obtain a ranking result; and outputting the sorting result as a recommendation result. Therefore, all the candidate verification modes can be output to the business personnel, and the ranking result can represent the recommendation degree of the candidate verification modes, so that under the condition that the candidate verification mode with the largest recommendation weight fails to be verified, the business personnel can further carry out verification decision according to the ranking result, thereby being beneficial to shortening the verification time and accelerating the business handling progress.
Based on the above description of S101-S104, in the embodiment of the present application, in response to a large-amount verification trigger operation for a user, personal information of the user may be acquired first, and then a recommendation verification manner may be determined from a plurality of candidate verification manners according to the personal information. Then, the initial recommendation weight of the recommendation verification mode is updated to obtain the recommendation weight corresponding to the recommendation verification mode, and recommendation results of the plurality of candidate verification modes are output based on the recommendation weights respectively corresponding to the plurality of candidate verification modes. Because the recommendation verification mode is determined based on the personal information of the user, and the final recommendation result refers to the recommendation weights of the candidate proper modes, the service personnel can definitely adapt to the verification mode of the user according to the output recommendation result, thereby being beneficial to shortening the verification time and accelerating the service handling progress.
Furthermore, in order to improve the success rate of the recommendation results of multiple candidate verification modes and further improve the verification efficiency, the embodiment of the application can provide another recommendation method of a large-scale verification mode. The recommended method of the large-amount verification mode is described below with reference to the embodiments and drawings, respectively.
Fig. 2 is a flowchart of another recommendation method of a large-amount verification method according to an embodiment of the present application.
Referring to fig. 2, the recommendation method of the large amount verification method may specifically include:
s201: personal information of the user is acquired in response to a large-amount verification trigger operation for the user.
In the embodiment of the present application, the technical details of S201 may be referred to the description related to S101 in the above embodiment, which is not described herein.
S202: based on the personal information, a recommended verification means is determined from among a plurality of candidate verification means.
In the embodiment of the present application, the technical details of S202 may be referred to the description related to S102 in the above embodiment, which is not described herein.
S203: a verification of the user's association is obtained.
Here, the verification means of the user association may be a history verification means of user verification success, and one of a plurality of candidate verification means. Specifically, in the case of successful user verification, the verification mode of successful verification can be stored as the verification mode of the user association, so that the verification mode of the user association can be conveniently obtained when needed.
S204: and adding initial recommendation weights corresponding to the verification modes of the user association to obtain new initial recommendation weights.
Because the verification mode of the user association is a history verification mode of successful user verification, the recommendation of the verification mode can be further improved by increasing the initial recommendation weight corresponding to the verification mode, and the new initial recommendation weight corresponding to the verification mode can be updated conveniently. It should be noted that, in the embodiment of the present application, the initial recommendation weight of the verification mode associated with the user still needs to be increased within the floating influence proportion of the initial recommendation weight of the verification mode associated with the user, so as to avoid that the initial recommendation weight of the verification mode associated with the user is significantly increased beyond a reasonable weight, and the verification decision of service personnel is influenced.
S205: if the recommendation verification mode is a verification mode associated with the user, updating the new initial recommendation weight to obtain the recommendation weight corresponding to the recommendation verification mode.
In the embodiment of the present application, for the update method of the new initial recommendation weight, reference may be made to the update process of the initial recommendation method in S103 in the above embodiment, which is not described herein.
S206: based on the recommendation weights respectively corresponding to the candidate verification modes, recommendation results of the candidate verification modes are output.
In the embodiment of the present application, the technical details of S206 may be referred to the description related to S104 in the above embodiment, which is not described herein.
Based on the above description of S201-S206, in the embodiment of the present application, in response to the large-amount verification trigger operation for the user, the personal information of the user may be acquired first, and then the recommendation verification mode is determined from multiple candidate verification modes according to the personal information. And then, further acquiring a verification mode associated with the user, and adding initial recommendation weights corresponding to the verification mode to obtain new initial recommendation weights. Because the verification mode related to the user is a history verification mode of successful user verification, when the determined recommendation verification mode is the verification mode, the new initial recommendation weight corresponding to the verification mode can be updated to obtain the corresponding recommendation weight, and recommendation results of multiple candidate verification modes are output based on the recommendation weights respectively corresponding to multiple candidate verification modes. Because the recommendation verification mode is determined based on the personal information of the user, and the final recommendation result refers to the recommendation weights of the candidate proper modes, the service personnel can definitely adapt to the verification mode of the user according to the output recommendation result, thereby being beneficial to shortening the verification time and accelerating the service handling progress.
Based on the recommendation method of the large-amount verification mode provided by the embodiment, the embodiment of the application can also provide a recommendation device of the large-amount verification mode. The recommendation device of the large-amount verification mode will be described below with reference to the embodiments and the drawings, respectively.
Fig. 3 is a schematic structural diagram of a recommendation device with a large amount verification method according to an embodiment of the present application. Referring to fig. 3, a recommendation device 300 of a large-amount verification method according to an embodiment of the present application includes:
a first obtaining module 301, configured to obtain personal information of a user in response to a large-amount verification trigger operation for the user;
a first determining module 302, configured to determine a recommended verification mode from a plurality of candidate verification modes according to the personal information;
an updating module 303, configured to update the initial recommendation weight of the recommendation verification method to obtain a recommendation weight corresponding to the recommendation verification method;
and the output module 304 is configured to output recommendation results of the multiple candidate verification manners based on recommendation weights respectively corresponding to the multiple candidate verification manners.
As one embodiment, the personal information includes at least one of age information, cultural degree information, usage liveness for banking applications, and asset information;
the first determining module 302 includes:
the establishing module is used for establishing a user portrait of the user according to at least one of the age information, the cultural degree information, the using liveness aiming at the bank application and the asset information;
and the second determining module is used for determining the recommended verification mode from a plurality of candidate verification modes based on the user portrait.
As an embodiment, the updating module 303 includes:
the third determining module is used for determining the floating range of the initial recommendation weight of the recommendation verification mode according to the personal information;
and the first increasing module is used for increasing the initial recommendation weight of the recommendation verification mode in the floating range to obtain the recommendation weight corresponding to the recommendation verification mode.
As an embodiment, the third determining module includes:
a fourth determining module, configured to determine, according to a source condition of the personal information, a credibility of the personal information;
a fifth determining module, configured to determine a floating impact proportion of an initial recommendation weight of the recommendation verification method based on the reliability;
and the first adjusting module is used for carrying out floating adjustment on the initial recommended weight of the recommended verification mode according to the floating influence proportion to obtain the floating range.
As an embodiment, the output module 304 includes:
the sorting module is used for sorting the candidate verification modes according to the sequence from big to small of the recommended weights respectively corresponding to the candidate verification modes to obtain a sorting result;
and the output sub-module is used for outputting the sorting result as the recommended result.
As an embodiment, the recommendation device 300 of the large-amount verification method further includes:
the second acquisition module is used for acquiring the verification mode of the user association; the verification means associated with the user is a historical verification means for the user to verify success and is one of the plurality of candidate verification means;
the second adding module is used for adding initial recommendation weights corresponding to the verification modes of the user association to obtain new initial recommendation weights;
if the recommended verification mode is the verification mode associated with the user, the updating module 303 includes:
and the updating sub-module is used for updating the new initial recommendation weight to obtain the recommendation weight corresponding to the recommendation verification mode.
As an embodiment, the recommendation device 300 of the large-amount verification method further includes:
the third acquisition module is used for acquiring the history verification success rates respectively corresponding to the plurality of candidate verification modes;
and the second adjustment module is used for adjusting the initial recommendation weight of the candidate verification mode based on the historical verification success rate of the candidate verification mode for one candidate verification mode.
Further, an embodiment of the present application further provides an electronic device, including: a processor, memory, system bus;
the processor and the memory are connected through the system bus;
the memory is for storing one or more programs, the one or more programs comprising instructions, which when executed by the processor, cause the processor to perform any implementation of the method of recommendation of a high-volume verification method described above.
Further, the embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores instructions, and when the instructions run on the terminal equipment, the terminal equipment is caused to execute any implementation mode of the recommendation method of the large-amount verification mode.
From the above description of embodiments, it will be apparent to those skilled in the art that all or part of the steps of the above described example methods may be implemented in software plus necessary general purpose hardware platforms. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network communication device such as a media gateway, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present application. It should be noted that, in the present description, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different manner from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
It is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A recommendation method for a large amount verification method, comprising:
acquiring personal information of a user in response to a large-amount verification trigger operation for the user;
determining a recommended verification mode from a plurality of candidate verification modes according to the personal information;
updating the initial recommendation weight of the recommendation verification mode to obtain the recommendation weight corresponding to the recommendation verification mode;
and outputting recommendation results of the candidate verification modes based on recommendation weights respectively corresponding to the candidate verification modes.
2. The recommendation method according to claim 1, wherein the personal information includes at least one of age information, cultural degree information, usage liveness for banking applications, and asset information;
the determining a recommended verification mode from a plurality of candidate verification modes according to the personal information comprises the following steps:
establishing a user portrait of the user according to at least one of the age information, the cultural degree information, the use liveness for banking application and the asset information;
the recommended verification style is determined from a plurality of candidate verification styles based on the user representation.
3. The recommendation method according to claim 1, wherein updating the initial recommendation weight of the recommendation verification means to obtain the recommendation weight corresponding to the recommendation verification means comprises:
determining a floating range of initial recommendation weights of the recommendation verification mode according to the personal information;
and adding initial recommendation weight of the recommendation verification mode in the floating range to obtain recommendation weight corresponding to the recommendation verification mode.
4. The recommendation method according to claim 3, wherein said determining a floating range of initial recommendation weights of said recommendation verification means based on said personal information comprises:
determining the credibility of the personal information according to the source condition of the personal information;
determining a floating influence proportion of an initial recommendation weight of the recommendation verification mode based on the credibility;
and carrying out floating adjustment on the initial recommended weight of the recommended verification mode according to the floating influence proportion to obtain the floating range.
5. The recommendation method according to claim 1, wherein the outputting of the recommendation results of the plurality of candidate verification means based on the recommendation weights respectively corresponding to the plurality of candidate verification means includes:
sequencing the candidate verification modes according to the sequence from big to small of the recommendation weights respectively corresponding to the candidate verification modes to obtain a sequencing result;
and outputting the sorting result as the recommendation result.
6. The recommendation method according to any one of claims 1 to 5, wherein before updating the initial recommendation weight of the recommendation verification means to obtain the recommendation weight corresponding to the recommendation verification means, the method further comprises:
acquiring a verification mode of the user association; the verification means associated with the user is a historical verification means for the user to verify success and is one of the plurality of candidate verification means;
adding initial recommendation weights corresponding to the verification modes of the user association to obtain new initial recommendation weights;
if the recommendation verification mode is the verification mode associated with the user, updating the initial recommendation weight of the recommendation verification mode to obtain the recommendation weight corresponding to the recommendation verification mode, wherein the method comprises the following steps:
and updating the new initial recommendation weight to obtain the recommendation weight corresponding to the recommendation verification mode.
7. The recommendation method according to any one of claims 1 to 5, further comprising:
acquiring historical verification success rates respectively corresponding to the plurality of candidate verification modes;
for a candidate verification means, adjusting initial recommendation weights for the candidate verification means based on historical verification success rates of the candidate verification means.
8. A recommendation device of a large-volume verification method, comprising:
the first acquisition module is used for responding to the large-amount verification triggering operation aiming at the user and acquiring personal information of the user;
the first determining module is used for determining a recommended verification mode from a plurality of candidate verification modes according to the personal information;
the updating module is used for updating the initial recommendation weight of the recommendation verification mode to obtain the recommendation weight corresponding to the recommendation verification mode;
and the output module is used for outputting recommendation results of the candidate verification modes based on recommendation weights respectively corresponding to the candidate verification modes.
9. An electronic device, the device comprising: a processor, memory, system bus;
the processor and the memory are connected through the system bus;
the memory is for storing one or more programs, the one or more programs comprising instructions, which when executed by the processor, cause the processor to perform the recommendation method of the high volume verification method of any one of claims 1 to 7.
10. A computer readable storage medium having instructions stored therein which, when executed on a terminal device, cause the terminal device to perform the recommendation method of the high volume verification method according to any one of claims 1 to 7.
CN202310611535.1A 2023-05-26 2023-05-26 Recommendation method, device, equipment and storage medium of large-amount verification mode Pending CN116842251A (en)

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CN202310611535.1A CN116842251A (en) 2023-05-26 2023-05-26 Recommendation method, device, equipment and storage medium of large-amount verification mode

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