CN115545095A - Client type determination method and device - Google Patents

Client type determination method and device Download PDF

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CN115545095A
CN115545095A CN202211121949.8A CN202211121949A CN115545095A CN 115545095 A CN115545095 A CN 115545095A CN 202211121949 A CN202211121949 A CN 202211121949A CN 115545095 A CN115545095 A CN 115545095A
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training
client
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任凌然
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Bank of China Ltd
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Bank of China Ltd
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Abstract

The invention discloses a method and a device for determining a client type, which relate to the technical field of artificial intelligence and big data, wherein the method comprises the following steps: acquiring the balance information of a target client; extracting income information of the target customer and historical purchasing information of financial products from the income and expenditure information through a preset text feature extraction model, wherein the text feature extraction model is obtained by training according to a HAAR algorithm model; and inputting the income information and the historical purchase information of the financial products into a preset customer classification model to obtain the customer type of the target customer, wherein the customer classification model is obtained by training according to a support vector machine algorithm model. The method and the device have the advantages of accurately and efficiently determining the client type of the target client, and are beneficial to improving the effect and efficiency of product marketing for the client.

Description

Client type determination method and device
Technical Field
The invention relates to the technical field of artificial intelligence and big data, in particular to a client type determining method and device.
Background
Commercial bank customer marketing is a financial service provided by banks according to various financial product requirements of customers, can be mainly divided into company business marketing, personal business marketing and the like, and is an important part for bank business development. The current customer marketing method mainly determines the types of customers manually through marketing personnel, and then recommends different financial products for different types of customers. The prior art lacks a more efficient and accurate solution for determining the type of customer.
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
Disclosure of Invention
The embodiment of the invention provides a client type determining method, which is used for solving the problems of insufficient accuracy and efficiency of the existing method for manually determining the type of a client by a marketing worker, and comprises the following steps:
acquiring the balance information of a target client;
extracting income information of the target customer and historical purchasing information of financial products from the income and expenditure information through a preset text feature extraction model, wherein the text feature extraction model is obtained by training according to a HAAR algorithm model;
and inputting the income information and the historical purchasing information of the financial products into a preset customer classification model to obtain the customer type of the target customer, wherein the customer classification model is obtained by training according to a support vector machine algorithm model.
Optionally, the method for determining a client type further includes:
acquiring a first training sample, wherein the first training sample is customer data marked with customer classification, and the customer data comprises income information and historical purchase information of financial products;
and training a support vector machine algorithm model according to the first training sample to obtain the customer classification model.
Optionally, the method for determining a client type further includes:
acquiring a second training sample, wherein the second training sample is customer balance information marked with income information and historical purchase information of financial products;
and training the HAAR algorithm model according to the second training sample to obtain the text feature extraction model.
Optionally, the method for determining a client type further includes:
acquiring a plurality of preset customer classifications;
a plurality of the first training samples are generated for each of the customer classifications.
Optionally, the method for determining a client type further includes:
determining a financial product corresponding to the client type of the target client according to a corresponding relation between a preset client type and the financial product;
and generating financial product recommendation information according to the determined financial products, and further sending the financial product recommendation information to the target customer.
The embodiment of the invention also provides a client type determining device, which is used for solving the problems of insufficient accuracy and efficiency of a method for manually determining the type of a client by a marketer, and comprises the following steps:
a balance information acquisition unit for acquiring balance information of a target client;
the text feature extraction unit is used for extracting income information and historical financial product purchase information of the target customer from the income and expenditure information through a preset text feature extraction model, wherein the text feature extraction model is obtained by training according to a HAAR algorithm model;
and the customer type identification unit is used for inputting the income information and the historical purchase information of the financial products into a preset customer classification model to obtain the customer type of the target customer, wherein the customer classification model is obtained by training according to a support vector machine algorithm model.
Optionally, the client type determining apparatus further includes:
the system comprises a first training sample acquisition unit, a second training sample acquisition unit and a financial product classification unit, wherein the first training sample is customer data marked with customer classification, and the customer data comprises income information and historical purchase information of financial products;
and the first model training unit is used for training a support vector machine algorithm model according to the first training sample to obtain the customer classification model.
Optionally, the client type determining apparatus further includes:
the second training sample acquisition unit is used for acquiring a second training sample, wherein the second training sample is customer balance information marked with income information and historical purchase information of financial products;
and the second model training unit is used for training the HAAR algorithm model according to the second training sample to obtain the text feature extraction model.
Optionally, the client type determining apparatus further includes:
the first training sample generating unit is used for obtaining a plurality of preset customer classifications and generating a plurality of first training samples aiming at each customer classification.
Optionally, the client type determining apparatus further includes:
the financial product determining unit is used for determining a financial product corresponding to the client type of the target client according to the corresponding relation between the preset client type and the financial product;
and the product recommending unit is used for generating financial product recommending information according to the determined financial products and further sending the financial product recommending information to the target client.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the client type determination method when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the client type determining method.
An embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program, and when executed by a processor, the computer program implements the client type determining method described above.
In the embodiment of the invention, the income information and the historical purchasing information of the financial products of the target customer are extracted from the income information and the expenditure information of the target customer through the preset text feature extraction model, and the income information and the historical purchasing information of the financial products are input into the preset customer classification model to obtain the customer type of the target customer, so that the beneficial effect of accurately and efficiently determining the customer type of the target customer is realized, and the product marketing effect and efficiency of the customer are improved.
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 some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a flow chart of a method for determining a client type in an embodiment of the present invention;
FIG. 2 is a flowchart illustrating the training of a client classification model according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating the training of a text feature extraction model according to an embodiment of the present invention;
fig. 4 is a first configuration diagram of the client type determining apparatus according to the embodiment of the present invention;
fig. 5 is a second schematic structural diagram of a client type determining apparatus according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a third structure of the client type determining apparatus according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
It should be noted that, in the technical solution of the present application, the acquisition, storage, use, processing, etc. of data all conform to the relevant regulations of the national laws and regulations.
Fig. 1 is a flowchart of a client type determining method in an embodiment of the present invention, and as shown in fig. 1, in an embodiment of the present invention, the client type determining method of the present invention includes steps S101 to S103.
Step S101, acquiring the balance information of the target client.
In an embodiment of the present invention, the step may obtain the balance information of the target customer by accessing the information data resource inside the bank. The income and expense information includes various types of income and indication information of the customers, and the income information specifically includes: payroll income, financial product income, other income and the like, and the indication information specifically can include: consumer instructions, financial product instructions, etc.
It should be noted that the balance information of the target client is data permitted to be used by the target client, and the acquisition, storage, use, processing and the like of the data in the scheme of the invention are authorized by the client and all meet the relevant regulations of national laws and regulations.
And S102, extracting income information and historical purchasing information of the target customer from the income and expenditure information through a preset text feature extraction model, wherein the text feature extraction model is obtained through training according to a HAAR algorithm model.
According to the invention, text feature extraction is carried out by using a HAAR algorithm (HAAR feature extraction algorithm), so that income information and historical purchasing information of financial products can be accurately extracted from the income and expenditure information of the customer, and the accuracy of determining the type of the customer can be further improved.
Step S103, inputting the income information and the historical purchasing information of the financial products into a preset customer classification model to obtain the customer type of the target customer, wherein the customer classification model is obtained by training according to a support vector machine algorithm model.
In the invention, the client classification model is obtained by training according to a Support Vector Machine (SVM) algorithm model.
In one embodiment of the invention, the marketing personnel firstly divide the customers with different receiving levels and investment directions into a plurality of customer types, and then set the corresponding financial products according to experience for each customer type. Further, after determining the client type of the target client, the financial products to be recommended to the target client can be directly determined.
In an embodiment of the present invention, after the step S103, the method of the present invention further includes:
determining a financial product corresponding to the client type of the target client according to a corresponding relation between a preset client type and the financial product;
and generating financial product recommendation information according to the determined financial products, and further sending the financial product recommendation information to the target customer.
In one embodiment of the invention, the marketing personnel firstly divide the customers with different receiving levels and investment directions into a plurality of customer types, and then set the corresponding financial products according to experience for each customer type. And then after the client type of the target client is determined, the financial products to be recommended to the target client can be directly determined, so that the accuracy and the efficiency of recommending the financial products to the client can be improved.
Fig. 2 is a flowchart illustrating a training process of a customer classification model according to an embodiment of the present invention, and as shown in fig. 2, in an embodiment of the present invention, the customer classification model in step S103 is specifically obtained by training in step S201 and step S202.
Step S201, obtaining a first training sample, wherein the first training sample is customer data marked with customer classification, and the customer data comprises income information and historical purchase information of financial products.
And S202, training a support vector machine algorithm model according to the first training sample to obtain the customer classification model.
In an embodiment of the present invention, before the step S201, the method of the present invention further includes:
acquiring a plurality of preset customer classifications; a plurality of the first training samples are generated for each of the customer classifications.
In one embodiment of the invention, the marketer first classifies customers of different levels of income and directions of investment into a plurality of customer types. And generating a plurality of corresponding first training samples aiming at each divided client type.
Fig. 3 is a flowchart illustrating training of a text feature extraction model in an embodiment of the present invention, and as shown in fig. 3, in an embodiment of the present invention, the text feature extraction model in step S102 is specifically obtained by training in step S301 and step S302.
Step S301, obtaining a second training sample, wherein the second training sample is customer balance information marked with income information and historical purchase information of financial products;
and S302, training the HAAR algorithm model according to the second training sample to obtain the text feature extraction model.
The HAAR feature extraction algorithm and the SVM classification algorithm are very popular and powerful corresponding field algorithms, and are excellent choices for feature extraction and classification algorithms.
The embodiment can show that the invention replaces manual service by the machine learning algorithm, greatly improves the accuracy of determining the client type, shortens the working time of determining the client type and enhances the efficiency of determining the client type.
The embodiment of the present invention further provides a client type determining apparatus, as described in the following embodiments. Because the principle of the device for solving the problems is similar to the client type determination method, the implementation of the device can refer to the implementation of the client type determination method, and repeated details are not repeated.
Fig. 4 is a schematic diagram of a first structure of a client type determining apparatus in an embodiment of the present invention, and as shown in fig. 4, in an embodiment of the present invention, the client type determining apparatus specifically includes:
a balance information acquiring unit 1 for acquiring balance information of a target client;
the text feature extraction unit 2 is used for extracting income information and historical financial product purchase information of the target customer from the income and expenditure information through a preset text feature extraction model, wherein the text feature extraction model is obtained by training according to a HAAR algorithm model;
and the customer type identification unit 3 is used for inputting the income information and the historical purchase information of the financial products into a preset customer classification model to obtain the customer type of the target customer, wherein the customer classification model is obtained by training according to a support vector machine algorithm model.
Fig. 5 is a schematic diagram of a second structure of a client type determining apparatus in an embodiment of the present invention, and as shown in fig. 5, in an embodiment of the present invention, the client type determining apparatus of the present invention further includes:
a first training sample acquiring unit 4, configured to acquire a first training sample, where the first training sample is customer data indicating a customer classification, and the customer data includes income information and historical purchase information of financial products;
and the first model training unit 5 is used for training a support vector machine algorithm model according to the first training sample to obtain the customer classification model.
Fig. 6 is a schematic diagram of a third structure of a client type determining apparatus in an embodiment of the present invention, as shown in fig. 6, in an embodiment of the present invention, the client type determining apparatus of the present invention further includes:
a second training sample acquiring unit 6, configured to acquire a second training sample, where the second training sample is customer balance information indicating income information and historical purchase information of financial products;
and the second model training unit 7 is used for training the HAAR algorithm model according to the second training sample to obtain the text feature extraction model.
In an embodiment of the present invention, the client type determining apparatus of the present invention further includes:
the first training sample generating unit is used for acquiring a plurality of preset customer classifications and generating a plurality of first training samples aiming at each customer classification.
In an embodiment of the present invention, the client type determining apparatus of the present invention further includes:
the financial product determining unit is used for determining a financial product corresponding to the client type of the target client according to the corresponding relation between the preset client type and the financial product;
and the product recommending unit is used for generating financial product recommending information according to the determined financial products and further sending the financial product recommending information to the target client.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the client type determination method when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for determining a client type is implemented.
An embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program, and when the computer program is executed by a processor, the method for determining a client type is implemented.
According to the embodiment of the invention, income information and historical purchasing information of financial products of a target customer are extracted from income and expense information of the target customer through a preset text feature extraction model, the income information and the historical purchasing information of the financial products are input into a preset customer classification model to obtain the customer type of the target customer, and then the financial products corresponding to the customer type of the target customer are determined according to the corresponding relation between the preset customer type and the financial products, so that the customer type of the target customer is determined according to the determined financial products, and the beneficial effect of improving the efficiency of product marketing for the customer is realized.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, and it should be understood that the above-mentioned embodiments are only examples of the present invention and should not be used to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (13)

1. A client type determination method, comprising:
acquiring the balance information of a target client;
extracting income information of the target customer and historical purchasing information of financial products from the income and expenditure information through a preset text feature extraction model, wherein the text feature extraction model is obtained by training according to a HAAR algorithm model;
and inputting the income information and the historical purchasing information of the financial products into a preset customer classification model to obtain the customer type of the target customer, wherein the customer classification model is obtained by training according to a support vector machine algorithm model.
2. The customer type determination method of claim 1, further comprising:
acquiring a first training sample, wherein the first training sample is customer data marked with customer classification, and the customer data comprises income information and historical purchase information of financial products;
and training a support vector machine algorithm model according to the first training sample to obtain the customer classification model.
3. The customer type determination method of claim 1, further comprising:
acquiring a second training sample, wherein the second training sample is customer balance information marked with income information and historical purchase information of financial products;
and training the HAAR algorithm model according to the second training sample to obtain the text feature extraction model.
4. The customer type determination method of claim 2, further comprising:
acquiring a plurality of preset customer classifications;
a plurality of the first training samples are generated for each of the customer classifications.
5. The customer type determination method of claim 1, further comprising:
determining a financial product corresponding to the client type of the target client according to a corresponding relation between a preset client type and the financial product;
and generating financial product recommendation information according to the determined financial products, and further sending the financial product recommendation information to the target customer.
6. A client type determining apparatus, comprising:
a balance information acquisition unit for acquiring balance information of a target client;
the text feature extraction unit is used for extracting income information and historical financial product purchase information of the target customer from the income and expenditure information through a preset text feature extraction model, wherein the text feature extraction model is obtained by training according to a HAAR algorithm model;
and the customer type identification unit is used for inputting the income information and the historical purchase information of the financial products into a preset customer classification model to obtain the customer type of the target customer, wherein the customer classification model is obtained by training according to a support vector machine algorithm model.
7. The client type determining apparatus of claim 6, further comprising:
the system comprises a first training sample acquisition unit, a second training sample acquisition unit and a financial product classification unit, wherein the first training sample is customer data marked with customer classification, and the customer data comprises income information and historical purchase information of financial products;
and the first model training unit is used for training a support vector machine algorithm model according to the first training sample to obtain the customer classification model.
8. The client type determining apparatus of claim 6, further comprising:
the second training sample acquisition unit is used for acquiring a second training sample, wherein the second training sample is customer balance information marked with income information and historical purchase information of financial products;
and the second model training unit is used for training the HAAR algorithm model according to the second training sample to obtain the text feature extraction model.
9. The client type determining apparatus of claim 7, further comprising:
the first training sample generating unit is used for obtaining a plurality of preset customer classifications and generating a plurality of first training samples aiming at each customer classification.
10. The client type determining apparatus of claim 7, further comprising:
the financial product determining unit is used for determining a financial product corresponding to the client type of the target client according to the corresponding relation between the preset client type and the financial product;
and the product recommending unit is used for generating financial product recommending information according to the determined financial products and further sending the financial product recommending information to the target client.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 5 when executing the computer program.
12. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the method of any of claims 1 to 5.
13. A computer program product, characterized in that the computer program product comprises a computer program which, when being executed by a processor, carries out the method of any one of claims 1 to 5.
CN202211121949.8A 2022-09-15 2022-09-15 Client type determination method and device Pending CN115545095A (en)

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Application Number Priority Date Filing Date Title
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