CN113971609A - Method and device for determining conversion qualification of securities - Google Patents

Method and device for determining conversion qualification of securities Download PDF

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CN113971609A
CN113971609A CN202111266235.1A CN202111266235A CN113971609A CN 113971609 A CN113971609 A CN 113971609A CN 202111266235 A CN202111266235 A CN 202111266235A CN 113971609 A CN113971609 A CN 113971609A
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李蚌蚌
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Bank of China Ltd
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Abstract

The application discloses a method and a device for determining the conversion qualification of securities, which are used for judging the conversion qualification of the securities of a client through a pre-trained neural network according to the basic information of the client and judging the conversion qualification of the securities of the client through a pre-trained tree model according to the behavior data of the client. Therefore, the method provided by the embodiment of the application can assist the self-service bank security identification and exchange system to verify the securities according to the basic information and behavior data of the customer, and avoids the self-service bank security identification and exchange system from exchanging false securities to a certain extent, so that the security of the exchange of the securities is improved.

Description

Method and device for determining conversion qualification of securities
Technical Field
The application relates to the field of computers, in particular to a method and a device for determining the conversion qualification of securities.
Background
The securities mainly comprise financial documents such as checks, home tickets, money orders and the like. A bank customer may exchange securities at the bank for the currency of their corresponding amount. For example, the customer may go to a bank to exchange a check for the currency of the amount corresponding to the check.
With the advancement of technology, the services provided by banks are more and more convenient. The self-service bank security identification exchange system in the bank can help a customer to exchange the securities conveniently, so that the customer does not need to go to a bank outlet to queue for transaction.
Although the self-service bank security identification exchange system is convenient for customers, the false securities can be judged by the self-service bank security identification exchange system, so that the security of exchanging the securities is poor, and certain loss is brought to banks. At present, a method for improving the security of the exchange of the securities of the self-service bank security identification exchange system is urgently needed in the field.
Disclosure of Invention
In order to solve the technical problem, the application provides a method and a device for determining the conversion qualification of the securities, and the conversion security of the securities of the self-service bank security identification and conversion system is improved.
In order to achieve the above purpose, the technical solutions provided in the embodiments of the present application are as follows:
the embodiment of the application provides a method for determining the conversion qualification of securities, which is applied to a security identification and conversion system of a self-service bank, and comprises the following steps:
inputting the basic information of the client into a pre-trained neural network model to obtain a first evaluation result of the exchange qualification of the client; the customer's basic information includes at least one of the customer's profession, the customer's assets, the customer's academic calendar, and the customer's portfolio exchange experience;
judging whether the first evaluation result exceeds a preset first evaluation standard value or not according to the first evaluation result, and if so, determining that the user has the conversion qualification of the securities;
if not, inputting the behavior data of the customer into a pre-trained tree model to obtain a second evaluation result; the behavior data of the client includes at least one of browsing information of the client and location information of the client;
and judging whether the second evaluation result exceeds a preset second evaluation standard value or not according to the second evaluation result, and if so, determining that the user has the conversion qualification of the securities.
As a possible implementation, the weight and the threshold of the neural network model are obtained by a genetic algorithm.
As a possible implementation manner, before inputting the basic information of the client into a pre-trained neural network model to obtain the first determination result of the client, the method further includes:
checking the securities to be exchanged of the client;
and when the securities to be exchanged are not checked, determining that the client does not have the exchange qualification of the securities.
As a possible implementation, the method further comprises:
dividing historical basic data of a client into a training set and a testing set;
and training the neural network model according to the training set, and testing the neural network model according to the test set.
As a possible implementation, the method further comprises:
and training the tree model according to the historical behavior data of the client and the second security exchange qualification corresponding to the historical behavior data.
As a possible implementation, the method further comprises:
and when the second evaluation result exceeds a preset second evaluation standard value, generating prompt information of the manual exchange bank website, and displaying the prompt information through the client.
According to the method for determining the conversion qualification of the securities replaced by the embodiment, the embodiment of the application also provides a device for determining the conversion qualification of the securities, which is characterized in that the device is applied to a self-service bank security identification conversion system, and the device comprises:
the first obtaining module is used for inputting the basic information of the client into a pre-trained neural network model and obtaining a first evaluation result of the exchange qualification of the client; the customer's basic information includes at least one of the customer's profession, the customer's assets, the customer's academic calendar, and the customer's portfolio exchange experience;
the first judgment module is used for judging whether the first evaluation result exceeds a preset first evaluation standard value or not according to the first evaluation result, and if so, determining that the user has the conversion qualification of the securities;
a second obtaining module, configured to, if not, input the behavior data of the customer into a pre-trained tree model to obtain a second evaluation result; the behavior data of the client includes at least one of browsing information of the client and location information of the client;
and the second judgment module is used for judging whether the second evaluation result exceeds a preset second evaluation standard value or not according to the second evaluation result, and if so, determining that the user has the conversion qualification of the securities.
As a possible implementation, the weight and the threshold of the neural network model are obtained by a genetic algorithm.
As a possible implementation, the apparatus further comprises:
the checking module is used for checking the securities to be exchanged of the client; and when the securities to be exchanged are not checked, determining that the client does not have the exchange qualification of the securities.
As a possible implementation, the apparatus further comprises:
the neural network training module is used for dividing the historical basic data of the client into a training set and a testing set; and training the neural network model according to the training set, and testing the neural network model according to the test set.
According to the technical scheme, the method has the following beneficial effects:
the embodiment of the application provides a method for determining the conversion qualification of securities, which comprises the steps of inputting basic information of a client into a pre-trained neural network model to obtain a first judgment result of the client; the basic information of the client comprises at least one of occupation of the client, assets of the client, academic history of the client and exchange experience of securities of the client; judging whether the client has first certificate exchange qualification or not according to the first judgment result, and allowing the client to exchange the securities when the client has the first certificate exchange qualification; when the client does not have the first security exchange qualification, inputting the behavior data of the client into a pre-trained tree model to obtain a second judgment result; the behavior data of the client comprises at least one of browsing information of the client and position information of the client; and judging whether the client has second security exchange qualification according to the second judgment result, and allowing the client to exchange the securities when the client has the second security exchange qualification.
Therefore, the method for determining the conversion qualification of the securities provided by the embodiment of the application judges the conversion qualification of the securities of the client through the pre-trained neural network according to the basic information of the client, and simultaneously judges the conversion qualification of the securities of the client through the pre-trained tree model according to the behavior data of the client. Therefore, the method provided by the embodiment of the application can assist the self-service bank security identification and exchange system to verify the securities according to the basic information and behavior data of the customer, and avoids the self-service bank security identification and exchange system from exchanging false securities to a certain extent, so that the security of the exchange of the securities is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for determining the eligibility for exchange of securities, provided by an embodiment of the present application;
fig. 2 is a schematic structural diagram of a device for determining the redemption qualification of securities, which is provided by an embodiment of the present application.
Detailed Description
In order to help better understand the scheme provided by the embodiment of the present application, before describing the method provided by the embodiment of the present application, a scenario of an application of the scheme of the embodiment of the present application is described.
The securities mainly comprise financial documents such as checks, home tickets, money orders and the like. A bank customer may exchange securities at the bank for the currency of their corresponding amount. For example, the customer may go to a bank to exchange a check for the currency of the amount corresponding to the check.
With the advancement of technology, the services provided by banks are more and more convenient. The self-service bank security identification exchange system in the bank can help a customer to exchange the securities conveniently, so that the customer does not need to go to a bank outlet to queue for transaction.
Although the self-service bank security identification exchange system is convenient for customers, the false securities can be judged by the self-service bank security identification exchange system, so that the security of exchanging the securities is poor, and certain loss is brought to banks. At present, a method for improving the security of the exchange of the securities of the self-service bank security identification exchange system is urgently needed in the field.
In order to solve the above technical problem, the method for determining the conversion qualification of the securities provided by the embodiment of the application judges the conversion qualification of the securities of the client through a pre-trained neural network according to the basic information of the client, and simultaneously judges the conversion qualification of the securities of the client through a pre-trained tree model according to the behavior data of the client. Therefore, the method provided by the embodiment of the application can assist the self-service bank security identification and exchange system to verify the securities according to the basic information and behavior data of the customer, and avoids the self-service bank security identification and exchange system from exchanging false securities to a certain extent, so that the security of the exchange of the securities is improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the drawings are described in detail below.
Referring to fig. 1, a flowchart of a method for determining the redemption eligibility of securities is provided in the embodiments of the present application.
The method for determining the conversion qualification of the securities provided by the embodiment of the application is applied to a security identification and conversion system of a self-service bank, and as shown in fig. 1, the method for determining the conversion qualification of the securities provided by the embodiment of the application comprises the following steps:
s101: inputting basic information of a client into a pre-trained neural network model to obtain a first evaluation result of the exchange qualification of the client; the basic information of the client includes at least one of occupation of the client, assets of the client, academic history of the client, and exchange experience of securities of the client.
S102: and judging whether the first evaluation result exceeds a preset first evaluation standard value or not according to the first evaluation result, and if so, determining that the user has the conversion qualification of the securities.
S103: if not, inputting the behavior data of the client into a pre-trained tree model to obtain a second evaluation result; the behavior data of the client includes at least one of browsing information of the client and location information of the client.
S104: and judging whether the second evaluation result exceeds a preset second evaluation standard value or not according to the second evaluation result, and if so, determining that the user has the conversion qualification of the securities.
It should be understood that the customer's basic information is typically stored in the bank's back-office system, such as the customer's profession, the customer's assets, the customer's academic history, and the customer's portfolio exchange experience. The self-service bank security identification and exchange system can easily obtain the basic information of the customers, and the data volume of the basic information is small, so that the first evaluation result can be conveniently obtained according to the basic information of the customers, and the conversion qualification of the securities of the customers is determined.
In the embodiment of the application, the client with larger possibility of legally obtaining the securities can be distinguished according to the occupation of the client, the assets of the client and other basic information of the client, so that the conversion qualification of the securities of the client with larger possibility of legally obtaining the securities is determined. However, in actual applications, there are some customers who have acquired securities by accidental factors or who have exchanged securities for the first time, and such customers cannot be identified only by basic information of the customers.
According to the method provided by the embodiment of the application, if the first evaluation result does not exceed the preset first evaluation standard, namely the client cannot obtain the conversion qualification of the securities through the basic information, the judgment is carried out according to the behavior data of the client. It should be appreciated that the customer's behavioral data is typically large and complex and may be stored on an external system of the bank, requiring more computing resources to obtain and identify the customer's behavioral data from the master bank security identification system, and also being slower to obtain the second assessment. Therefore, the method provided by the embodiment of the application preferentially determines the conversion qualification of the securities of the client according to the basic information of the client, and judges according to the behavior data of the client when the basic information of the client cannot be determined.
It should be understood that the method provided by the embodiment of the present application is implemented by using behavior data of a client, for example: the method has the advantages that information and position information are browsed, the conversion qualification of the securities of the client is judged, whether the client contacts illegal channels for obtaining the securities can be identified to a certain degree, so that the securities obtained by the clients through the illegal channels can be prevented from being converted to a certain degree, and the security of the conversion of the securities is improved.
As a possible implementation manner, the weight and the threshold of the neural network model provided in the embodiment of the present application are obtained by a genetic algorithm. Dividing the historical basic data of the client into a training set and a testing set; and training the neural network model according to the training set, and testing the neural network model according to the testing set. As a possible implementation manner, the method provided by the embodiment of the application can collect the customer data of the website for exchanging the securities as historical basic data, such as occupation, assets, academic calendar and whether the exchange experience is used as model input before, and whether the exchange is allowed or not is used as output. And (3) combining the advantages of the BP neural network and the genetic algorithm, introducing the genetic algorithm in the aspect of optimizing the weight and the threshold of the BP neural network, and constructing a GA-BP neural network model (genetic algorithm optimized neural network). Determining a GA-BP neural network structure, determining the BP neural network structure according to the number of network input and output, and further determining the number of parameters needing to be optimized in a genetic algorithm. According to the kolmogorov (kolmogorov) principle, a three-layer BP neural network is enough to complete any mapping from n dimension to m dimension, generally only one hidden layer is needed, and the number of hidden layer nodes is determined by a trial and error method, so that the GA-BP neural network structure is determined. And (4) training and learning the BP neural network by taking the optimal individual output by the genetic algorithm as the initial weight and the threshold of the BP neural network. And dividing historical data into a training set and a testing set, training the GA-BP neural network model based on historical data analysis, and verifying the prediction accuracy of the model by using a testing sample.
As a possible implementation manner, the tree model provided in this embodiment of the present application may be obtained by training according to the historical behavior data of the client and the second security exchange qualification corresponding to the historical behavior data. The bank is linked with the external system to inquire the behavior data of the client, such as the information recently browsed by the client, the main place to go and the like, the information is input into the tree model, and whether the client can be helped to exchange the securities or not is finally judged through the tree model.
As a possible implementation manner, before inputting the basic information of the client into the pre-trained neural network model to obtain the first determination result of the client, the method provided by the embodiment of the present application further includes: checking the securities to be exchanged of the client; and when the securities to be exchanged are not checked, determining that the client does not have the exchange qualification of the securities. It should be understood that the method provided by the embodiment of the application can firstly check the authenticity of the securities of the client when checking whether the client has the securities exchange qualification. If the client's securities are judged to be unqualified, the exchange qualification of the client's securities is not required to be checked, and the exchange can be directly terminated.
As a possible implementation manner, the method for determining the redemption qualification of the securities, provided by the embodiments of the present application, further includes: and when the second evaluation result exceeds a preset second evaluation standard value, generating prompt information of the manual exchange bank website, and displaying the prompt information through the client. It should be understood that when the first evaluation result and the second evaluation result can not determine the conversion qualification of the securities of the client, prompt information of the manual conversion bank network point can be sent to prompt the client to go to the bank network point supporting the manual conversion for conversion.
In summary, the method provided by the embodiment of the application judges the conversion qualification of the securities of the client through the pre-trained neural network according to the basic information of the client, and simultaneously judges the conversion qualification of the securities of the client through the pre-trained tree model according to the behavior data of the client. Therefore, the method provided by the embodiment of the application can assist the self-service bank security identification and exchange system to verify the securities according to the basic information and behavior data of the customer, and avoids the self-service bank security identification and exchange system from exchanging false securities to a certain extent, so that the security of the exchange of the securities is improved.
According to the method for determining the conversion qualification of the securities provided by the embodiment, the embodiment of the application also provides a device for determining the conversion qualification of the securities.
Referring to fig. 2, a schematic structural diagram of a device for determining the redemption qualification of securities according to an embodiment of the present application is shown.
The device is applied to self-service bank securities discernment exchange system, and the device includes:
the first obtaining module 100 is used for inputting basic information of a client into a pre-trained neural network model to obtain a first evaluation result of the exchange qualification of the client; the basic information of the client includes at least one of occupation of the client, assets of the client, academic history of the client, and exchange experience of securities of the client.
The first judging module 200 is configured to judge whether the first evaluation result exceeds a preset first evaluation criterion value according to the first evaluation result, and if so, determine that the user has the qualification of exchanging the securities.
A second obtaining module 300, configured to, if not, input behavior data of the customer into a pre-trained tree model to obtain a second evaluation result; the behavior data of the client includes at least one of browsing information of the client and location information of the client.
And the second judging module 400 is configured to judge whether the second evaluation result exceeds a preset second evaluation standard value according to the second evaluation result, and if so, determine that the user has the qualification of exchanging the securities.
As a possible implementation manner, the weight and the threshold of the neural network model in the device for determining the redemption qualification of securities provided in the embodiments of the present application are obtained by a genetic algorithm.
As a possible implementation manner, the apparatus for determining the redemption qualification of the securities provided in the embodiments of the present application further includes: the checking module is used for checking the securities to be exchanged of the client; and when the securities to be exchanged are not checked, determining that the client does not have the exchange qualification of the securities.
As a possible implementation manner, the apparatus for determining the redemption qualification of the securities provided in the embodiments of the present application further includes: the neural network training module is used for dividing the historical basic data of the client into a training set and a testing set; and training the neural network model according to the training set, and testing the neural network model according to the testing set.
As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the above embodiment methods can be implemented by software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present application may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling 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 according to the embodiments or some parts of the embodiments of the present application.
It should be noted that, in the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. The method disclosed by the embodiment corresponds to the system disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the system part for description.
It should also be noted that, in this document, 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 an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing description of the disclosed embodiments will enable those skilled in the art to make or use the invention in various modifications to these embodiments, which will be apparent to those skilled in the art, and the general principles defined herein may be implemented in 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 determining the conversion qualification of securities, which is applied to a self-service bank security identification conversion system, and comprises the following steps:
inputting the basic information of the client into a pre-trained neural network model to obtain a first evaluation result of the exchange qualification of the client; the customer's basic information includes at least one of the customer's profession, the customer's assets, the customer's academic calendar, and the customer's portfolio exchange experience;
judging whether the first evaluation result exceeds a preset first evaluation standard value or not according to the first evaluation result, and if so, determining that the user has the conversion qualification of the securities;
if not, inputting the behavior data of the customer into a pre-trained tree model to obtain a second evaluation result; the behavior data of the client includes at least one of browsing information of the client and location information of the client;
and judging whether the second evaluation result exceeds a preset second evaluation standard value or not according to the second evaluation result, and if so, determining that the user has the conversion qualification of the securities.
2. The method of claim 1, wherein the weights and thresholds of the neural network model are obtained by a genetic algorithm.
3. The method of claim 1, before inputting the basic information of the client into a pre-trained neural network model to obtain the first determination result of the client, further comprising:
checking the securities to be exchanged of the client;
and when the securities to be exchanged are not checked, determining that the client does not have the exchange qualification of the securities.
4. The method of claim 1, further comprising:
dividing historical basic data of a client into a training set and a testing set;
and training the neural network model according to the training set, and testing the neural network model according to the test set.
5. The method of claim 1, further comprising:
and training the tree model according to the historical behavior data of the client and the second security exchange qualification corresponding to the historical behavior data.
6. The method of claim 1, further comprising:
and when the second evaluation result exceeds a preset second evaluation standard value, generating prompt information of the manual exchange bank website, and displaying the prompt information through the client.
7. An apparatus for determining the eligibility of exchange of securities, which is applied to a self-service bank security identification exchange system, the apparatus comprising:
the first obtaining module is used for inputting the basic information of the client into a pre-trained neural network model and obtaining a first evaluation result of the exchange qualification of the client; the customer's basic information includes at least one of the customer's profession, the customer's assets, the customer's academic calendar, and the customer's portfolio exchange experience;
the first judgment module is used for judging whether the first evaluation result exceeds a preset first evaluation standard value or not according to the first evaluation result, and if so, determining that the user has the conversion qualification of the securities;
a second obtaining module, configured to, if not, input the behavior data of the customer into a pre-trained tree model to obtain a second evaluation result; the behavior data of the client includes at least one of browsing information of the client and location information of the client;
and the second judgment module is used for judging whether the second evaluation result exceeds a preset second evaluation standard value or not according to the second evaluation result, and if so, determining that the user has the conversion qualification of the securities.
8. The apparatus of claim 7, wherein the weights and thresholds of the neural network model are obtained by a genetic algorithm.
9. The apparatus of claim 7, further comprising:
the checking module is used for checking the securities to be exchanged of the client; and when the securities to be exchanged are not checked, determining that the client does not have the exchange qualification of the securities.
10. The apparatus of claim 7, further comprising:
the neural network training module is used for dividing the historical basic data of the client into a training set and a testing set; and training the neural network model according to the training set, and testing the neural network model according to the test set.
CN202111266235.1A 2021-10-28 2021-10-28 Method and device for determining conversion qualification of securities Pending CN113971609A (en)

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