CN113723944A - Method and device for setting transfer amount, electronic equipment and computer storage medium - Google Patents

Method and device for setting transfer amount, electronic equipment and computer storage medium Download PDF

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CN113723944A
CN113723944A CN202111073841.1A CN202111073841A CN113723944A CN 113723944 A CN113723944 A CN 113723944A CN 202111073841 A CN202111073841 A CN 202111073841A CN 113723944 A CN113723944 A CN 113723944A
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transfer
client
information
transfer amount
training
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CN113723944B (en
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黄康
侯金波
杨晓明
徐梓丞
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Bank of China Ltd
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Abstract

The application provides a method and a device for setting transfer amount, electronic equipment and a computer storage medium, wherein the method comprises the following steps: acquiring basic information and bank card information of a client; basic information of a client and bank card information are input into a transfer account prediction model to obtain a recommended transfer account of the client; the transfer account prediction model is obtained by training a neural network model through a training sample set; the training sample set includes: training basic information of a sample client, historical information of a bank card and a real transfer amount of the training sample client; basic information for training a sample client includes: training the age, address and income of the sample client; the bank card information comprises consumption information, transfer opposite side information and transfer time; and setting the transfer amount of the client as a recommended transfer amount. The client can be reminded in time in the transaction process by recommending the transfer amount, so that the old client is helped to avoid risks at the first time, and the fund safety is protected.

Description

Method and device for setting transfer amount, electronic equipment and computer storage medium
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for setting a transfer amount, an electronic device, and a computer storage medium.
Background
At present, the bank card can transfer accounts conveniently in modes of mobile banking, internet banking, counter and the like, and a user can transfer accounts with a certain amount subjectively.
Because more and more money is transferred by mobile phone banks or third-party payment products, the risk is increased. The phishing information is increased, and the judgment capability of the old customers is weakened or the operation of mobile phone banks and other unfamiliarity causes a lot of loss to the customers.
Disclosure of Invention
In view of the above, the application provides a method and device for setting a transfer amount, an electronic device and a computer storage medium, which are used for reasonably and intelligently setting the transfer amount for a client, so that the client can be reminded in time, old clients can be helped to avoid risks at the first time, and fund safety is protected.
The application provides a method for setting a transfer amount in a first aspect, which comprises the following steps:
acquiring basic information and bank card information of a client; wherein the basic information of the client comprises: age, address, and income of the customer; the bank card information comprises consumption information, transfer opposite side information and transfer time;
inputting the basic information of the client and the bank card information into a transfer account prediction model to obtain the recommended transfer account of the client; the transfer amount prediction model is obtained by training a neural network model through a training sample set; the training sample set includes: basic information of a training sample client, historical information of a bank card and a real transfer amount of the training sample client; the basic information of the training sample client comprises: training the age, address and income of the sample client; the bank card information comprises consumption information, transfer opposite side information and transfer time;
and setting the transfer amount of the client as the recommended transfer amount.
Optionally, the method for constructing the transfer credit prediction model includes:
constructing a training sample set; wherein, training the sample set includes: basic information of a training sample client, historical information of a bank card and a real transfer amount of the training sample client; the basic information of the training sample client comprises: training the age, address and income of the sample client; the bank card information comprises consumption information, transfer opposite side information and transfer time;
inputting the basic information of the training sample client and the historical information of the bank card into a neural network model to obtain a predicted transfer amount;
and continuously adjusting parameters in the neural network model by using the error between the predicted transfer account and the real transfer account until the error between the predicted transfer account and the real transfer account output by the adjusted neural network model meets a preset convergence condition, and determining the adjusted neural network model as a transfer account prediction model.
Optionally, after the setting of the transfer amount of the client as the recommended transfer amount, the method further includes:
judging whether the transfer amount is larger than the recommended transfer amount or not when the client transfers money each time;
and if the transfer amount is larger than the recommended transfer amount, generating an alarm prompt and sending a reminding message to the relatives and contacts left by the client.
Optionally, if it is determined that the amount of money transferred is greater than the recommended transfer amount, generating an alert prompt, and sending a prompt message to a parent contact left by the client, further including:
receiving the customer transfer amount modification request; wherein, the money modifying request comprises: the modified transfer amount;
and if the modified transfer amount is smaller than the recommended transfer amount, normally finishing the subsequent transfer.
Optionally, if it is determined that the amount of money transferred is greater than the recommended transfer amount, generating an alert prompt, and sending a prompt message to a parent contact left by the client, further including:
receiving feedback information sent by a relative contact person left by the client; wherein the feedback information is transfer agreement or transfer disagreement;
responding to the feedback information.
This application second aspect provides a setting device of transfer amount, includes:
the acquisition unit is used for acquiring the basic information of the customer and the information of the bank card; wherein the basic information of the client comprises: age, address, and income of the customer; the bank card information comprises consumption information, transfer opposite side information and transfer time;
the first input unit is used for inputting the basic information of the client and the bank card information into a transfer account prediction model to obtain the recommended transfer account of the client; the transfer amount prediction model is obtained by training a neural network model through a training sample set; the training sample set includes: basic information of a training sample client, historical information of a bank card and a real transfer amount of the training sample client; the basic information of the training sample client comprises: training the age, address and income of the sample client; the bank card information comprises consumption information, transfer opposite side information and transfer time;
and the setting unit is used for setting the transfer amount of the client as the recommended transfer amount.
Optionally, the construction unit of the transfer amount prediction model includes:
the construction unit is used for constructing a training sample set; wherein, training the sample set includes: basic information of a training sample client, historical information of a bank card and a real transfer amount of the training sample client; the basic information of the training sample client comprises: training the age, address and income of the sample client; the bank card information comprises consumption information, transfer opposite side information and transfer time;
the second input unit is used for inputting the basic information of the training sample client and the historical information of the bank card into a neural network model to obtain a predicted transfer amount;
and the model determining unit is used for continuously adjusting parameters in the neural network model by using the error between the predicted transfer account and the real transfer account until the error between the predicted transfer account and the real transfer account output by the adjusted neural network model meets a preset convergence condition, and determining the adjusted neural network model as a transfer account prediction model.
Optionally, the device for setting the transfer amount further includes:
the judging unit is used for judging whether the transfer amount is larger than the recommended transfer amount or not when the client transfers money each time;
and the warning unit is used for generating a warning prompt and sending a reminding message to the relatives and contacts left by the client if the judgment unit judges that the transfer amount is larger than the recommended transfer amount.
Optionally, the device for setting the transfer amount further includes:
a receiving unit for receiving the customer transfer amount modification request; wherein, the money modifying request comprises: the modified transfer amount;
and the transfer unit is used for normally finishing the subsequent transfer if the modified transfer amount is less than the recommended transfer amount.
Optionally, the device for setting the transfer amount further includes:
the receiving unit is used for receiving feedback information sent by the relatives and contacts left by the client; wherein the feedback information is transfer agreement or transfer disagreement;
and the response unit is used for responding the feedback information.
A third aspect of the present application provides an electronic device comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of setting a transfer credit as recited in any of the first aspects.
A fourth aspect of the present application provides a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method of setting a transfer credit as recited in any one of the first aspects.
According to the scheme, the application provides a setting method and device of transfer amount, electronic equipment and a computer storage medium, wherein the setting method of the transfer amount comprises the following steps: firstly, acquiring basic information and bank card information of a client; wherein the basic information of the client comprises: age, address, and income of the customer; the bank card information comprises consumption information, transfer opposite side information and transfer time; then, inputting the basic information of the customer and the bank card information into a transfer account prediction model to obtain the recommended transfer account of the customer; the transfer amount prediction model is obtained by training a neural network model through a training sample set; the training sample set includes: basic information of a training sample client, historical information of a bank card and a real transfer amount of the training sample client; the basic information of the training sample client comprises: training the age, address and income of the sample client; the bank card information comprises consumption information, transfer opposite side information and transfer time; and finally, setting the transfer amount of the client as the recommended transfer amount. The client can be reminded in time in the transaction process by recommending the transfer amount, so that the old client is helped to avoid risks at the first time, and the fund safety is protected.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a detailed flowchart of a method for setting a transfer amount according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating a method for constructing a model for predicting transfer credits according to another embodiment of the present disclosure;
FIG. 3 is a flowchart illustrating a method for setting a credit transfer according to another embodiment of the present application;
FIG. 4 is a schematic diagram of a device for setting a transfer amount according to another embodiment of the present application;
fig. 5 is a schematic view of an electronic device implementing a method for setting a transfer amount according to another embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first", "second", and the like, referred to in this application, are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence of functions performed by these devices, modules or units, but the terms "include", or any other variation thereof are intended to cover a non-exclusive inclusion, so that a process, method, article, or apparatus that includes a series of elements includes not only those elements but also other elements that are not explicitly listed, or includes elements inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The embodiment of the application provides a method for setting a transfer amount, which specifically comprises the following steps as shown in figure 1:
s101, obtaining basic information of a customer and bank card information.
Wherein, the basic information of the client at least comprises: age, address, and income of the customer; the bank card information at least includes consumption information, transfer partner information, and transfer time.
S102, inputting the basic information of the client and the bank card information into a transfer amount prediction model to obtain the recommended transfer amount of the client.
The transfer account prediction model is obtained by training a neural network model through a training sample set; the training sample set includes: training basic information of a sample client, historical information of a bank card and a real transfer amount of the training sample client; basic information for training a sample client includes: training the age, address and income of the sample client; the bank card information includes consumption information, transfer partner information, and transfer time.
Optionally, in another embodiment of the present application, an implementation of the method for constructing the transfer credit prediction model, as shown in fig. 2, includes:
s201, constructing a training sample set.
Wherein, training the sample set includes: training basic information of a sample client, historical information of a bank card and a real transfer amount of the training sample client; basic information for training a sample client includes: training the age, address and income of the sample client; the bank card information includes consumption information, transfer partner information, and transfer time.
It can be understood that the training sample set comprises at least one training sample client for training the neural network model for multiple times to obtain a final transfer credit prediction model.
S202, inputting the basic information of the training sample client and the historical information of the bank card into a neural network model to obtain a predicted transfer amount.
S203, judging whether the error between the predicted transfer account amount and the real transfer account amount meets a preset convergence condition or not.
The preset convergence condition is set and changed by a technician, a related person with authority, and the like, and is not limited herein.
Specifically, if the error between the predicted transfer amount and the real transfer amount is judged to meet the preset convergence condition, the step S204 is executed; if the error between the predicted transfer amount and the real transfer amount is determined not to satisfy the predetermined convergence condition, step S205 is performed.
And S204, determining the neural network model as a transfer amount prediction model.
And S205, adjusting parameters in the neural network model.
It should be noted that, in the specific implementation process of the present application, the model is not limited to be modified by using a preset convergence condition, and a certain maximum number of iterations may also be set to train the model, which is not limited herein.
S103, setting the transfer amount of the client as a recommended transfer amount.
Optionally, after the transfer amount of the client is set as the recommended transfer amount, in another embodiment of the application, the method further includes:
s301, when the customer transfers money each time, judging whether the transfer money amount is larger than the recommended transfer money amount or not.
Specifically, if the transfer amount is judged to be greater than the recommended transfer amount, step S302 is executed. And if the transfer amount is judged to be not more than the recommended transfer amount, normally finishing the transfer.
S302, generating a warning prompt and sending a reminding message to the relatives and contacts left by the client.
It should be noted that the reminding message may be, but is not limited to, a short message, a mail, a telephone, etc., and is not limited herein.
Optionally, after sending the reminding message to the related contact left by the client, in another embodiment of the application, the method further includes:
a customer transfer amount modification request is received.
Wherein, the money modification request comprises: the modified transfer amount.
And if the modified transfer amount is less than the recommended transfer amount, normally finishing the subsequent transfer. And if the modified transfer amount is larger than the recommended transfer amount, generating an alarm prompt and sending a reminding message to the relatives and contacts left by the client.
It can be understood that in the specific implementation process of the present application, an alarm threshold may be set, for example, three times, and if the transfer amount modified by the client is still greater than the recommended transfer amount after the three alarm prompts are generated, it can be understood that the account may be stolen by an illegal party to transfer, and the account is temporarily frozen.
Optionally, after sending the reminding message to the related contact left by the client, in another embodiment of the application, the method further includes:
and receiving feedback information sent by the relatives and contacts left by the client.
Wherein the feedback information is transfer agreement or transfer disagreement.
In response to the feedback information.
Specifically, if the feedback information is that the transfer is agreed, the recommended transfer amount is broken through, and the transfer is carried out; and if the feedback information is that the transfer is not approved, ending the transfer operation.
According to the scheme, the method for setting the transfer amount comprises the following steps: firstly, acquiring basic information and bank card information of a client; wherein, the basic information of the client comprises: age, address, and income of the customer; the bank card information comprises consumption information, transfer opposite side information and transfer time; then, inputting the basic information of the client and the information of the bank card into a transfer account prediction model to obtain the recommended transfer account of the client; the transfer account prediction model is obtained by training a neural network model through a training sample set; the training sample set includes: training basic information of a sample client, historical information of a bank card and a real transfer amount of the training sample client; basic information for training a sample client includes: training the age, address and income of the sample client; the bank card information comprises consumption information, transfer opposite side information and transfer time; and finally, setting the transfer amount of the client as a recommended transfer amount. The client can be reminded in time in the transaction process by recommending the transfer amount, so that the old client is helped to avoid risks at the first time, and the fund safety is protected.
Another embodiment of the present application provides a device for setting a transfer amount, as shown in fig. 4, including:
an obtaining unit 401, configured to obtain basic information of a customer and bank card information.
Wherein, the basic information of the client comprises: age, address, and income of the customer; the bank card information includes consumption information, transfer partner information, and transfer time.
And a first input unit 402, configured to input basic information of the client and bank card information into the transfer amount prediction model to obtain a recommended transfer amount of the client.
The transfer account prediction model is obtained by training a neural network model through a training sample set; the training sample set includes: training basic information of a sample client, historical information of a bank card and a real transfer amount of the training sample client; basic information for training a sample client includes: training the age, address and income of the sample client; the bank card information includes consumption information, transfer partner information, and transfer time.
And a setting unit 403 for setting the transfer amount of the client as a recommended transfer amount.
For a specific working process of the unit disclosed in the above embodiment of the present application, reference may be made to the content of the corresponding method embodiment, as shown in fig. 1, which is not described herein again.
Optionally, in another embodiment of the present application, an implementation manner of the construction unit of the transfer credit prediction model includes:
and the construction unit is used for constructing a training sample set.
Wherein, training the sample set includes: training basic information of a sample client, historical information of a bank card and a real transfer amount of the training sample client; basic information for training a sample client includes: training the age, address and income of the sample client; the bank card information includes consumption information, transfer partner information, and transfer time.
And the second input unit is used for inputting the basic information of the training sample client and the historical information of the bank card into the neural network model to obtain the predicted transfer amount.
And the model determining unit is used for continuously adjusting parameters in the neural network model by using the error between the predicted transfer amount and the real transfer amount until the error between the predicted transfer amount and the real transfer amount output by the adjusted neural network model meets a preset convergence condition, and determining the adjusted neural network model as the transfer amount prediction model.
For a specific working process of the unit disclosed in the above embodiment of the present application, reference may be made to the content of the corresponding method embodiment, as shown in fig. 2, which is not described herein again.
Optionally, in another embodiment of the present application, an implementation manner of the device for setting the transfer amount further includes:
and the judging unit is used for judging whether the transfer amount is greater than the recommended transfer amount or not when the client transfers money each time.
And the warning unit is used for generating a warning prompt and sending a reminding message to the relatives and contacts left by the client if the judgment unit judges that the transfer amount is larger than the recommended transfer amount.
For a specific working process of the unit disclosed in the above embodiment of the present application, reference may be made to the content of the corresponding method embodiment, as shown in fig. 3, which is not described herein again.
Optionally, in another embodiment of the present application, an implementation manner of the device for setting the transfer amount further includes:
and the receiving unit is used for receiving the client transfer amount modification request.
Wherein, the money modification request comprises: the modified transfer amount.
And the transfer unit is used for normally finishing subsequent transfer if the modified transfer amount is less than the recommended transfer amount.
For specific working processes of the units disclosed in the above embodiments of the present application, reference may be made to the contents of the corresponding method embodiments, which are not described herein again.
Optionally, in another embodiment of the present application, an implementation manner of the device for setting the transfer amount further includes:
and the receiving unit is used for receiving the feedback information sent by the relatives and contacts left by the client. Wherein the feedback information is transfer agreement or transfer disagreement.
And the response unit is used for responding the feedback information.
For specific working processes of the units disclosed in the above embodiments of the present application, reference may be made to the contents of the corresponding method embodiments, which are not described herein again.
According to the scheme, the application provides a device for setting the transfer amount: firstly, an obtaining unit 401 obtains basic information of a customer and bank card information; wherein, the basic information of the client comprises: age, address, and income of the customer; the bank card information comprises consumption information, transfer opposite side information and transfer time; then, the first input unit 402 inputs the basic information of the customer and the bank card information into a transfer amount prediction model to obtain the recommended transfer amount of the customer; the transfer account prediction model is obtained by training a neural network model through a training sample set; the training sample set includes: training basic information of a sample client, historical information of a bank card and a real transfer amount of the training sample client; basic information for training a sample client includes: training the age, address and income of the sample client; the bank card information comprises consumption information, transfer opposite side information and transfer time; finally, the setting unit 403 sets the transfer amount of the client as a recommended transfer amount. The client can be reminded in time in the transaction process by recommending the transfer amount, so that the old client is helped to avoid risks at the first time, and the fund safety is protected.
Another embodiment of the present application provides an electronic device, as shown in fig. 5, including:
one or more processors 501.
A storage device 502 on which one or more programs are stored.
The one or more programs, when executed by the one or more processors 501, cause the one or more processors 501 to implement a method of setting a transfer credit as described in any of the above embodiments.
Another embodiment of the present application provides a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method of setting a transfer amount as described in any one of the above embodiments.
In the above embodiments disclosed in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present disclosure 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. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a live broadcast device, or a network device) to execute all or part of the steps of the method according to the embodiments of the present disclosure. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Those skilled in the art can 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 method for setting a transfer amount is characterized by comprising the following steps:
acquiring basic information and bank card information of a client; wherein the basic information of the client comprises: age, address, and income of the customer; the bank card information comprises consumption information, transfer opposite side information and transfer time;
inputting the basic information of the client and the bank card information into a transfer account prediction model to obtain the recommended transfer account of the client; the transfer amount prediction model is obtained by training a neural network model through a training sample set; the training sample set includes: basic information of a training sample client, historical information of a bank card and a real transfer amount of the training sample client; the basic information of the training sample client comprises: training the age, address and income of the sample client; the bank card information comprises consumption information, transfer opposite side information and transfer time;
and setting the transfer amount of the client as the recommended transfer amount.
2. The setting method as claimed in claim 1, wherein the construction method of the transfer credit prediction model comprises the following steps:
constructing a training sample set; wherein, training the sample set includes: basic information of a training sample client, historical information of a bank card and a real transfer amount of the training sample client; the basic information of the training sample client comprises: training the age, address and income of the sample client; the bank card information comprises consumption information, transfer opposite side information and transfer time;
inputting the basic information of the training sample client and the historical information of the bank card into a neural network model to obtain a predicted transfer amount;
and continuously adjusting parameters in the neural network model by using the error between the predicted transfer account and the real transfer account until the error between the predicted transfer account and the real transfer account output by the adjusted neural network model meets a preset convergence condition, and determining the adjusted neural network model as a transfer account prediction model.
3. The setting method as claimed in claim 1, wherein after setting the transfer amount of the client as the recommended transfer amount, further comprising:
judging whether the transfer amount is larger than the recommended transfer amount or not when the client transfers money each time;
and if the transfer amount is larger than the recommended transfer amount, generating an alarm prompt and sending a reminding message to the relatives and contacts left by the client.
4. The setting method of claim 3, wherein after generating a warning prompt and sending a reminding message to a related contact left by the client if the transfer amount is judged to be larger than the recommended transfer amount, the method further comprises:
receiving the customer transfer amount modification request; wherein, the money modifying request comprises: the modified transfer amount;
and if the modified transfer amount is smaller than the recommended transfer amount, normally finishing the subsequent transfer.
5. The setting method of claim 3, wherein after generating a warning prompt and sending a reminding message to a related contact left by the client if the transfer amount is judged to be larger than the recommended transfer amount, the method further comprises:
receiving feedback information sent by a relative contact person left by the client; wherein the feedback information is transfer agreement or transfer disagreement;
responding to the feedback information.
6. A setting device of transfer amount is characterized by comprising:
the acquisition unit is used for acquiring the basic information of the customer and the information of the bank card; wherein the basic information of the client comprises: age, address, and income of the customer; the bank card information comprises consumption information, transfer opposite side information and transfer time;
the first input unit is used for inputting the basic information of the client and the bank card information into a transfer account prediction model to obtain the recommended transfer account of the client; the transfer amount prediction model is obtained by training a neural network model through a training sample set; the training sample set includes: basic information of a training sample client, historical information of a bank card and a real transfer amount of the training sample client; the basic information of the training sample client comprises: training the age, address and income of the sample client; the bank card information comprises consumption information, transfer opposite side information and transfer time;
and the setting unit is used for setting the transfer amount of the client as the recommended transfer amount.
7. The setting device as claimed in claim 6, wherein the construction unit of the transfer credit prediction model comprises:
the construction unit is used for constructing a training sample set; wherein, training the sample set includes: basic information of a training sample client, historical information of a bank card and a real transfer amount of the training sample client; the basic information of the training sample client comprises: training the age, address and income of the sample client; the bank card information comprises consumption information, transfer opposite side information and transfer time;
the second input unit is used for inputting the basic information of the training sample client and the historical information of the bank card into a neural network model to obtain a predicted transfer amount;
and the model determining unit is used for continuously adjusting parameters in the neural network model by using the error between the predicted transfer account and the real transfer account until the error between the predicted transfer account and the real transfer account output by the adjusted neural network model meets a preset convergence condition, and determining the adjusted neural network model as a transfer account prediction model.
8. The setting apparatus according to claim 6, further comprising:
the judging unit is used for judging whether the transfer amount is larger than the recommended transfer amount or not when the client transfers money each time;
and the warning unit is used for generating a warning prompt and sending a reminding message to the relatives and contacts left by the client if the judgment unit judges that the transfer amount is larger than the recommended transfer amount.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method of setting a transfer credit as recited in any of claims 1 to 5.
10. A computer storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method of setting a transfer credit as recited in any one of claims 1 to 5.
CN202111073841.1A 2021-09-14 2021-09-14 Method and device for setting transfer limit, electronic equipment and computer storage medium Active CN113723944B (en)

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