CN113723944B - Method and device for setting transfer limit, electronic equipment and computer storage medium - Google Patents
Method and device for setting transfer limit, electronic equipment and computer storage medium Download PDFInfo
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Abstract
The application provides a method and a device for setting transfer credit, electronic equipment and a computer storage medium, wherein the method comprises the following steps: acquiring basic information of a customer and bank card information; inputting the basic information and the bank card information of the customer into a transfer line prediction model to obtain the recommended transfer line 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: basic information of a training sample client, historical information of a bank card and real transfer line of the training sample client; basic information of training sample clients includes: training the age, address and income of sample clients; the bank card information comprises consumption information, transfer counterpart information and transfer time; the transfer line of the customer is set as the recommended transfer line. The transfer limit is recommended to prompt the customers in the transaction process in time, so that the elderly customers can avoid risks at the first time, and the fund safety is protected.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for setting transfer accounts, an electronic device, and a computer storage medium.
Background
At present, a bank card can conveniently transfer accounts in a mobile phone bank, an internet bank, a counter and the like, and a user can subjectively transfer accounts for a certain amount.
As more and more transfers are now made through mobile banking or third party payment products, the risk of this is also increasing. The phishing information is increased, and the judgment capability of the old customers is weakened or the old customers are unfamiliar with the operation of mobile banking and the like, so that a lot of losses are brought to the customers.
Disclosure of Invention
In view of this, the application provides a setting method, a device, an electronic device and a computer storage medium for transferring the amount, which are used for reasonably and intelligently setting the transfer amount for a client, and can timely remind the client to help the elderly client avoid risks at the first time and protect fund safety.
The first aspect of the present application provides a method for setting transfer credit, including:
acquiring basic information of a customer and bank card information; wherein, the basic information of the customer comprises: age, address, and income of the customer; the bank card information comprises consumption information, transfer counterpart information and transfer time;
inputting the basic information and the bank card information of the client into a transfer line prediction model to obtain the recommended transfer line of the client; the transfer limit 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 real transfer line of the training sample client; the basic information of the training sample client comprises: training the age, address and income of sample clients; the bank card information comprises consumption information, transfer counterpart information and transfer time;
setting the transfer limit of the client as the recommended transfer limit.
Optionally, the method for constructing the transfer amount prediction model includes:
constructing a training sample set; wherein the training sample set comprises: basic information of a training sample client, historical information of a bank card and real transfer line of the training sample client; the basic information of the training sample client comprises: training the age, address and income of sample clients; the bank card information comprises consumption information, transfer counterpart information and transfer time;
inputting the basic information of the training sample client and the history information of the bank card into a neural network model to obtain a predicted transfer credit;
and continuously adjusting parameters in the neural network model by utilizing the error between the predicted transfer line and the real transfer line until the error between the predicted transfer line and the real transfer line output by the adjusted neural network model meets a preset convergence condition, and determining the adjusted neural network model as a transfer line prediction model.
Optionally, after setting the transfer limit of the customer to the recommended transfer limit, the method further includes:
judging whether the transfer amount is larger than the recommended transfer amount or not when the client transfers each time;
and if the transfer amount is judged to be larger than the recommended transfer amount, generating a warning prompt, and sending a reminding message to the relative contact person left by the client.
Optionally, after generating a warning prompt and sending a warning message to the relatives contact person left by the client if the money amount of the transfer is determined to be greater than the recommended transfer amount, the method further includes:
receiving the customer transfer amount modification request; wherein the amount modification request includes: a modified transfer amount;
and if the modified transfer amount is smaller than the recommended transfer amount, normally completing the subsequent transfer.
Optionally, after generating a warning prompt and sending a warning message to the relatives contact person left by the client if the money amount of the transfer is determined to be greater than the recommended transfer amount, the method further includes:
receiving feedback information sent by the relative contact person left by the client; wherein, the feedback information is agreement transfer or disagreement transfer;
and responding to the feedback information.
The second aspect of the present application provides a setting device for transfer credit, including:
the acquisition unit is used for acquiring the basic information of the client and the bank card information; wherein, the basic information of the customer comprises: age, address, and income of the customer; the bank card information comprises consumption information, transfer counterpart information and transfer time;
the first input unit is used for inputting the basic information and the bank card information of the client into the transfer credit prediction model to obtain the recommended transfer credit of the client; the transfer limit 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 real transfer line of the training sample client; the basic information of the training sample client comprises: training the age, address and income of sample clients; the bank card information comprises consumption information, transfer counterpart information and transfer time;
and the setting unit is used for setting the transfer limit of the client as the recommended transfer limit.
Optionally, the construction unit of the transfer amount prediction model includes:
the building unit is used for building a training sample set; wherein the training sample set comprises: basic information of a training sample client, historical information of a bank card and real transfer line of the training sample client; the basic information of the training sample client comprises: training the age, address and income of sample clients; the bank card information comprises consumption information, transfer counterpart information and transfer time;
the second input unit is used for inputting the basic information of the training sample client and the history information of the bank card into the neural network model to obtain the predicted transfer line;
and the model determining unit is used for continuously adjusting parameters in the neural network model by utilizing the error between the predicted transfer line and the real transfer line, and determining the adjusted neural network model as a transfer line prediction model when the error between the predicted transfer line and the real transfer line output by the adjusted neural network model meets a preset convergence condition.
Optionally, the setting device of the transfer credit 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 each time;
and the alarm unit is used for generating an alarm prompt and sending a reminding message to the relative contact person left by the client if the judgment unit judges that the transfer amount is larger than the recommended transfer amount.
Optionally, the setting device of the transfer credit further includes:
a receiving unit configured to receive the customer transfer amount modification request; wherein the amount modification request includes: a modified transfer amount;
and the transfer unit is used for normally completing the subsequent transfer if the modified transfer amount is smaller than the recommended transfer amount.
Optionally, the setting device of the transfer credit further includes:
the receiving unit is used for receiving feedback information sent by the relative contact person left by the client; wherein, the feedback information is agreement transfer or disagreement transfer;
and the response unit is used for responding to 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 described in any one 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 for setting a transfer credit according to any one of the first aspects.
As can be seen from the above solutions, the present application provides a method, an apparatus, an electronic device, and a computer storage medium for setting a transfer credit, where the method for setting the transfer credit includes: firstly, acquiring basic information of a customer and bank card information; wherein, the basic information of the customer comprises: age, address, and income of the customer; the bank card information comprises consumption information, transfer counterpart information and transfer time; then, inputting the basic information and the bank card information of the client into a transfer line prediction model to obtain the recommended transfer line of the client; the transfer limit 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 real transfer line of the training sample client; the basic information of the training sample client comprises: training the age, address and income of sample clients; the bank card information comprises consumption information, transfer counterpart information and transfer time; finally, setting the transfer limit of the client as the recommended transfer limit. The transfer limit is recommended to prompt the customers in the transaction process in time, so that the elderly customers can 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 that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 is a specific flowchart of a method for setting transfer accounts according to an embodiment of the present application;
FIG. 2 is a flowchart of a method for constructing a transfer amount prediction model according to another embodiment of the present application;
fig. 3 is a specific flowchart of a method for setting transfer accounts according to another embodiment of the present application;
fig. 4 is a schematic diagram of a device for setting transfer accounts according to another embodiment of the present application;
fig. 5 is a schematic diagram of an electronic device for implementing a method for setting a transfer credit according to another embodiment of the present application.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that the terms "first," "second," and the like in this application are used merely to distinguish between different devices, modules, or units and are not intended to limit the order or interdependence of functions performed by such devices, modules, or units, but 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 also 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 embodiment of the application provides a method for setting transfer credit, as shown in fig. 1, specifically including the following steps:
s101, acquiring basic information of a customer and bank card information.
Wherein, the basic information of the customer at least comprises: age, address, and income of the customer; the bank card information includes at least consumption information, transfer counterpart information, and transfer time.
S102, inputting the basic information of the customer and the bank card information into a transfer line prediction model to obtain the recommended transfer line 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: basic information of a training sample client, historical information of a bank card and real transfer line of the training sample client; basic information of training sample clients includes: training the age, address and income of sample clients; the bank card information includes consumption information, transfer counterpart information, and transfer time.
Optionally, in another embodiment of the present application, an implementation of a method for constructing a transfer amount prediction model, as shown in fig. 2, includes:
s201, constructing a training sample set.
Wherein the training sample set comprises: basic information of a training sample client, historical information of a bank card and real transfer line of the training sample client; basic information of training sample clients includes: training the age, address and income of sample clients; the bank card information includes consumption information, transfer counterpart information, and transfer time.
It can be understood that the training sample set should include at least one training sample client for training the neural network model for multiple times to obtain a final transfer value unit prediction model.
S202, inputting basic information of the training sample client and historical information of the bank card into a neural network model to obtain predicted transfer line.
S203, judging whether the error between the predicted transfer allowance and the real transfer allowance meets a preset convergence condition.
The preset convergence condition is set and changed by technicians, authorized related personnel and the like, and is not limited herein.
Specifically, if it is determined that the error between the predicted transfer allowance and the real transfer allowance meets the preset convergence condition, step S204 is executed; if it is determined that the error between the predicted transfer allowance and the real transfer allowance does not satisfy the preset convergence condition, step S205 is performed.
S204, determining the neural network model as a transfer amount prediction model.
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 modifying the model by using a preset convergence condition, but may also set a certain maximum iteration number, and the model is trained, which is not limited herein.
S103, setting the transfer limit of the client as the recommended transfer limit.
Optionally, after setting the transfer limit of the customer to the recommended transfer limit, in another embodiment of the present application, the method further includes:
s301, judging whether the transfer amount is larger than the recommended transfer amount or not when the client transfers each time.
Specifically, if it is determined that the transfer amount is greater than the recommended transfer amount, step S302 is performed. If the transfer amount is judged not to be larger than the recommended transfer amount, the transfer is normally completed.
S302, generating a warning prompt and sending a reminding message to the related contact person left by the client.
Note that the alert message may be, but is not limited to, a short message, a mail, a phone, etc., which is not limited herein.
Optionally, after sending the alert message to the related contact left by the client, in another embodiment of the present application, the method further includes:
a customer transfer amount modification request is received.
Wherein the amount modification request includes: and the modified transfer amount.
If the modified transfer amount is smaller than the recommended transfer amount, the subsequent transfer is completed normally. If the modified transfer amount is greater than the recommended transfer amount, generating a warning prompt and sending a reminding message to the relative contact person left by the client.
It may 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 customer is still greater than the recommended transfer amount after three alarm prompts are generated, it may be understood that the account may be stolen by an illegal person to transfer, and the account is temporarily frozen.
Optionally, after sending the alert message to the related contact left by the client, in another embodiment of the present application, the method further includes:
and receiving feedback information sent by the related contact person left by the client.
Wherein the feedback information is agreement or disagreement of transfer.
And responding to the feedback information.
Specifically, if the feedback information is that the transfer is agreed, the recommended transfer limit is broken through, and the transfer is performed; and if the feedback information is that the transfer is not agreed, ending the transfer operation.
As can be seen from the above solutions, the present application provides a method for setting transfer allowance: firstly, acquiring basic information of a customer and bank card information; wherein, the basic information of the customer comprises: age, address, and income of the customer; the bank card information comprises consumption information, transfer counterpart information and transfer time; then, inputting the basic information and the bank card information of the customer into a transfer line prediction model to obtain the recommended transfer line 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: basic information of a training sample client, historical information of a bank card and real transfer line of the training sample client; basic information of training sample clients includes: training the age, address and income of sample clients; the bank card information comprises consumption information, transfer counterpart information and transfer time; finally, the transfer line of the customer is set as the recommended transfer line. The transfer limit is recommended to prompt the customers in the transaction process in time, so that the elderly customers can avoid risks at the first time, and the fund safety is protected.
Another embodiment of the present application provides a device for setting transfer credit, as shown in fig. 4, including:
an acquiring unit 401, configured to acquire basic information of a customer and bank card information.
Wherein, the basic information of the customer comprises: age, address, and income of the customer; the bank card information includes consumption information, transfer counterpart information, and transfer time.
The first input unit 402 is configured to input basic information and bank card information of a customer into the transfer amount prediction model, and obtain a 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: basic information of a training sample client, historical information of a bank card and real transfer line of the training sample client; basic information of training sample clients includes: training the age, address and income of sample clients; the bank card information includes consumption information, transfer counterpart information, and transfer time.
And a setting unit 403 for setting the transfer line of the customer as the recommended transfer line.
The specific working process of the unit disclosed in the foregoing embodiments of the present application may refer to the content of the corresponding method embodiment, as shown in fig. 1, which is not repeated herein.
Optionally, in another embodiment of the present application, an implementation manner of the construction unit of the transfer amount prediction model includes:
and the construction unit is used for constructing the training sample set.
Wherein the training sample set comprises: basic information of a training sample client, historical information of a bank card and real transfer line of the training sample client; basic information of training sample clients includes: training the age, address and income of sample clients; the bank card information includes consumption information, transfer counterpart information, and transfer time.
And the second input unit is used for inputting the basic information of the training sample client and the history information of the bank card into the neural network model to obtain the predicted transfer limit.
The model determining unit is used for continuously adjusting parameters in the neural network model by utilizing the error between the predicted transfer line and the real transfer line, and determining the adjusted neural network model as the transfer line prediction model until the error between the predicted transfer line and the real transfer line output by the adjusted neural network model meets the preset convergence condition.
The specific working process of the unit disclosed in the foregoing embodiments of the present application may refer 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 setting device of the transfer allowance further includes:
and the judging unit is used for judging whether the transfer amount is larger than the recommended transfer amount or not when the client transfers each time.
And the alarm unit is used for generating an alarm prompt and sending a reminding message to the relative contact person left by the client if the judgment unit judges that the transfer amount is larger than the recommended transfer amount.
The specific working process of the unit disclosed in the foregoing embodiments of the present application may refer 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 setting device of the transfer allowance further includes:
and a receiving unit for receiving the customer transfer amount modification request.
Wherein the amount modification request includes: and the modified transfer amount.
And the transfer unit is used for normally completing the subsequent transfer if the modified transfer amount is smaller than the recommended transfer amount.
The specific working process of the unit disclosed in the foregoing embodiments of the present application may refer to the content of the corresponding method embodiment, which is not described herein again.
Optionally, in another embodiment of the present application, an implementation manner of the setting device of the transfer allowance further includes:
and the receiving unit is used for receiving feedback information sent by the relative contact person left by the client. Wherein the feedback information is agreement or disagreement of transfer.
And the response unit is used for responding to the feedback information.
The specific working process of the unit disclosed in the foregoing embodiments of the present application may refer to the content of the corresponding method embodiment, which is not described herein again.
According to the scheme, the application provides a transfer limit setting device: first, the acquisition unit 401 acquires basic information of a customer and bank card information; wherein, the basic information of the customer comprises: age, address, and income of the customer; the bank card information comprises consumption information, transfer counterpart information and transfer time; then, the first input unit 402 inputs the basic information and the bank card information of the customer into the transfer line prediction model to obtain the recommended transfer line 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: basic information of a training sample client, historical information of a bank card and real transfer line of the training sample client; basic information of training sample clients includes: training the age, address and income of sample clients; the bank card information comprises consumption information, transfer counterpart information and transfer time; finally, the setting unit 403 sets the transfer amount of the customer as the recommended transfer amount. The transfer limit is recommended to prompt the customers in the transaction process in time, so that the elderly customers can 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 the method of setting transfer credit as described in any one 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 for setting transfer credit according to any one of the above embodiments.
In the above embodiments of the disclosure, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus and method embodiments described above are merely illustrative, for example, flow diagrams 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 various embodiments of the present disclosure may be integrated together to form a single portion, or each module may exist alone, or two or more modules may be integrated to form a single portion. 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 essence or a part contributing to the prior art or a part of the technical solution, or in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a live device, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present disclosure. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, randomAccess Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
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 (7)
1. A method for setting transfer credit, comprising:
acquiring basic information of a customer and bank card information; wherein, the basic information of the customer comprises: age, address, and income of the customer; the bank card information comprises consumption information, transfer counterpart information and transfer time;
inputting the basic information and the bank card information of the client into a transfer line prediction model to obtain the recommended transfer line of the client; the transfer limit 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 real transfer line of the training sample client; the basic information of the training sample client comprises: training the age, address and income of sample clients; the bank card information comprises consumption information, transfer counterpart information and transfer time;
setting the transfer limit of the client as the recommended transfer limit;
judging whether the transfer amount is larger than the recommended transfer amount or not when the client transfers each time;
if the transfer amount is judged to be larger than the recommended transfer amount, generating a warning prompt, and sending a reminding message to the relative contact person left by the client;
receiving the customer transfer amount modification request; wherein the amount modification request includes: a modified transfer amount;
if the modified transfer amount is smaller than the recommended transfer amount, normally completing subsequent transfer;
if the modified transfer amount is still greater than the recommended transfer amount, continuing to generate a warning prompt and sending a warning message to the related contact person left by the client;
and if the transfer amount modified by the customer is still larger than the recommended transfer amount after the three times of alarm information are generated, temporarily freezing the account of the customer so as to avoid the account being replaced by an illegal molecule.
2. The setting method according to claim 1, wherein the method for constructing the transfer amount prediction model includes:
constructing a training sample set; wherein the training sample set comprises: basic information of a training sample client, historical information of a bank card and real transfer line of the training sample client; the basic information of the training sample client comprises: training the age, address and income of sample clients; the bank card information comprises consumption information, transfer counterpart information and transfer time;
inputting the basic information of the training sample client and the history information of the bank card into a neural network model to obtain a predicted transfer credit;
and continuously adjusting parameters in the neural network model by utilizing the error between the predicted transfer line and the real transfer line until the error between the predicted transfer line and the real transfer line output by the adjusted neural network model meets a preset convergence condition, and determining the adjusted neural network model as a transfer line prediction model.
3. The setting method according to claim 1, wherein after generating a warning prompt and sending a warning message to the related contact left by the customer if it is determined that the amount of transfer is greater than the recommended transfer amount, further comprising:
receiving feedback information sent by the relative contact person left by the client; wherein, the feedback information is agreement transfer or disagreement transfer;
and responding to the feedback information.
4. A setting device of transfer allowance, characterized by comprising:
the acquisition unit is used for acquiring the basic information of the client and the bank card information; wherein, the basic information of the customer comprises: age, address, and income of the customer; the bank card information comprises consumption information, transfer counterpart information and transfer time;
the first input unit is used for inputting the basic information and the bank card information of the client into the transfer credit prediction model to obtain the recommended transfer credit of the client; the transfer limit 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 real transfer line of the training sample client; the basic information of the training sample client comprises: training the age, address and income of sample clients; the bank card information comprises consumption information, transfer counterpart information and transfer time;
a setting unit configured to set a transfer value unit of the customer as the recommended transfer value unit;
the judging unit is used for judging whether the transfer amount is larger than the recommended transfer amount or not when the client transfers each time;
the alarm unit is used for generating an alarm 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;
a receiving unit for receiving a customer transfer amount modification request; wherein the amount modification request includes: a modified transfer amount;
the transfer unit is used for normally completing subsequent transfer if the modified transfer amount is smaller than the recommended transfer amount;
the alarm unit is further configured to continuously generate an alarm prompt and send a reminder message to the related contacts left by the client if the modified transfer amount is still greater than the recommended transfer amount;
and if the transfer amount modified by the customer is still larger than the recommended transfer amount after the three times of alarm information are generated, temporarily freezing the account of the customer so as to avoid the account being replaced by an illegal molecule.
5. The setting apparatus according to claim 4, wherein the construction unit of the transfer amount prediction model includes:
the building unit is used for building a training sample set; wherein the training sample set comprises: basic information of a training sample client, historical information of a bank card and real transfer line of the training sample client; the basic information of the training sample client comprises: training the age, address and income of sample clients; the bank card information comprises consumption information, transfer counterpart information and transfer time;
the second input unit is used for inputting the basic information of the training sample client and the history information of the bank card into the neural network model to obtain the predicted transfer line;
and the model determining unit is used for continuously adjusting parameters in the neural network model by utilizing the error between the predicted transfer line and the real transfer line, and determining the adjusted neural network model as a transfer line prediction model when the error between the predicted transfer line and the real transfer line output by the adjusted neural network model meets a preset convergence condition.
6. 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 transfer amount setting method of any one of claims 1 to 3.
7. A computer storage medium, wherein a computer program is stored thereon, wherein the computer program, when executed by a processor, implements the method of setting a transfer credit as claimed in any one of claims 1 to 3.
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