CN113129139A - Loan interest rate parameter information determination method and device based on artificial intelligence - Google Patents

Loan interest rate parameter information determination method and device based on artificial intelligence Download PDF

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CN113129139A
CN113129139A CN202110543794.6A CN202110543794A CN113129139A CN 113129139 A CN113129139 A CN 113129139A CN 202110543794 A CN202110543794 A CN 202110543794A CN 113129139 A CN113129139 A CN 113129139A
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滕建德
景东亚
王增峰
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Bank of China Ltd
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Abstract

The invention discloses a loan interest rate parameter information determining method and device based on artificial intelligence, relating to the technical field of artificial intelligence, wherein the method comprises the following steps: collecting attribute information of a plurality of preset dimensions of a target loan client; determining a weight value of each preset dimension of the target loan client according to the attribute information of each preset dimension of the target loan client based on a preset client attribute mapping relation table; inputting the attribute information of each preset dimension of the target loan client and the corresponding weight value into a client classification model trained in advance, and outputting a client classification result of the target loan client; and determining loan interest rate parameter information of the target loan client according to the client classification result of the target loan client. The invention trains a client classification model through machine learning, classifies loan clients according to the attribute information of the loan clients, and can provide different loan interest rate parameter information for different clients according to the classification result, thereby realizing the recommendation of personalized loan interest rate.

Description

Loan interest rate parameter information determination method and device based on artificial intelligence
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a loan interest rate parameter information determining method and device based on artificial intelligence.
Background
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.
As is known, in the existing loan service platform, loan interest rates of loan clients are all realized through parameter configuration, and loan interest rates of all clients are fixed and the same, so that the function of providing different interest rates for different clients cannot be realized, and the client experience is poor.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a loan interest rate parameter information determination method based on artificial intelligence, which is used for solving the technical problem that the existing loan service platform cannot provide individualized loan interest rates for different customers, and comprises the following steps: collecting attribute information of a plurality of preset dimensions of a target loan client; determining a weight value of each preset dimension of the target loan client according to the attribute information of each preset dimension of the target loan client based on a preset client attribute mapping relation table; inputting the attribute information of each preset dimension of the target loan client and the corresponding weight value into a client classification model trained in advance, and outputting a client classification result of the target loan client; and determining loan interest rate parameter information of the target loan client according to the client classification result of the target loan client.
The embodiment of the invention also provides a loan interest rate parameter information determining device based on artificial intelligence, which is used for solving the technical problem that the existing loan service platform cannot provide individualized loan interest rates for different customers, and comprises: the client information acquisition module is used for acquiring attribute information of a plurality of preset dimensions of a target loan client; the weight value determining module is used for determining the weight value of each preset dimension of the target loan client according to the attribute information of each preset dimension of the target loan client based on a preset client attribute mapping relation table; the client classification prediction module is used for inputting the attribute information of each preset dimension of the target loan client and the corresponding weight value into a client classification model trained in advance and outputting a client classification result of the target loan client; and the loan interest rate parameter information determining module is used for determining the loan interest rate parameter information of the target loan client according to the client classification result of the target loan client.
The embodiment of the invention also provides computer equipment for solving the technical problem that the existing loan service platform cannot provide personalized loan interest rates for different customers, the computer equipment comprises a memory, a processor and a computer program which is stored on the memory and can be operated on the processor, and the loan interest rate parameter information determination method based on artificial intelligence is realized when the processor executes the computer program.
The embodiment of the invention also provides a computer readable storage medium for solving the technical problem that the existing loan service platform cannot provide personalized loan interest rates for different customers, and the computer readable storage medium stores a computer program for executing the loan interest rate parameter information determination method based on artificial intelligence.
In the embodiment of the invention, after collecting the attribute information of a plurality of preset dimensions of a target loan client, firstly, based on a preset client attribute mapping relation table, according to the attribute information of each preset dimension of the target loan client, determining the weight value of each preset dimension of the target loan client, further inputting the attribute information of each preset dimension of the target loan client and the corresponding weight value into a client classification model trained in advance, outputting the client classification result of the target loan client, and finally, according to the client classification result of the target loan client, determining the loan interest rate parameter information of the target loan client.
Compared with the technical scheme of providing fixed loan interest rates for different customers in the prior art, the method and the system for providing the loan interest rates have the advantages that a customer classification model is trained through machine learning, and then the loan customers are classified according to the attribute information of the loan customers, so that different loan interest rate parameter information can be provided for different customers according to classification results, and the recommendation of personalized loan interest rates is realized.
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 loan interest rate parameter information determination method based on artificial intelligence provided in an embodiment of the invention;
FIG. 2 is a flow chart of machine learning provided in an embodiment of the present invention;
fig. 3 is a flowchart of generating sample data according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an apparatus for determining loan interest rate parameter information based on artificial intelligence according to an embodiment of the invention;
fig. 5 is a schematic diagram of a computer device provided in an 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.
The embodiment of the invention provides a loan interest rate parameter information determining method based on artificial intelligence, and fig. 1 is a flow chart of the loan interest rate parameter information determining method based on artificial intelligence provided by the embodiment of the invention, as shown in fig. 1, the method comprises the following steps:
s101, collecting attribute information of a plurality of preset dimensions of a target loan client.
It should be noted that the target loan client in the embodiment of the present invention refers to a loan client whose client classification is unknown; the collected customer attribute information may be different for different loan products, and the loan customer in the embodiment of the invention may be a house loan customer. The attribute information of the plurality of preset dimensions collected in S101 may include, but is not limited to, information such as age, gender, location, work units, and income.
And S102, determining the weight value of each preset dimension of the target loan customer according to the attribute information of each preset dimension of the target loan customer based on a preset customer attribute mapping relation table.
It should be noted that the customer attribute mapping table provided in the embodiment of the present invention includes attribute information of a plurality of dimensions of the loan customer and corresponding weight values. In specific implementation, the loan interest rate parameter information determining method based on artificial intelligence provided in the embodiment of the present invention may further generate a customer attribute mapping table through the following steps: generating a customer attribute mapping relation table according to attribute information of a plurality of dimensions of a plurality of loan customers, wherein the customer attribute mapping table comprises: and the weight values corresponding to the multiple dimensions and the attribute information of each dimension are different.
Table 1 shows a customer attribute mapping table generated by selecting 50 pieces of dimensional attribute information (age, sex, location, work unit, income per year, credit investigation question, deposit, loan, monthly accumulation, whether there is room or not). The customer attribute mapping relation table contains a weight value corresponding to each dimension attribute information, and can be used for mapping the attribute information of the loan customer into a corresponding weight value in the following process.
TABLE 1 customer Attribute mapping Table
Figure BDA0003072758670000041
And S103, inputting the attribute information of each preset dimension of the target loan client and the corresponding weight value into a client classification model trained in advance, and outputting a client classification result of the target loan client.
It should be noted that the customer classification model in the embodiment of the present invention is a model that is obtained through machine learning training in advance and is capable of predicting the customer classification of a loan customer according to attribute information of each preset dimension of the loan customer and a weight value corresponding to the attribute information of each preset dimension.
And S104, determining loan interest rate parameter information of the target loan client according to the client classification result of the target loan client.
It should be noted that the customer classification result in the embodiment of the present invention includes, but is not limited to, the following five categories: VIP customers, premium customers, better customers, general customers, and poorer customers, which are individually identified as A, B, C, D, E for ease of illustration. The loan interest rate parameter information determined in S104 may be a specific loan interest rate value, or may be a preferential percentage of the loan interest rate, as long as the preferential loan interest rate can reflect different degrees corresponding to different customer classifications.
After determining the loan interest rate parameter information of the target loan client, the method for determining the loan interest rate parameter information based on artificial intelligence provided in the embodiment of the present invention may further include: and transacting the loan business of the target loan client according to the loan interest rate parameter information of the target loan client.
After the client classification result of the target loan client is predicted based on the client classification model, the loan interest rate corresponding to the client classification result can be obtained as the loan interest rate of the target loan client. In specific implementation, the loan interest rate parameter information determining method based on artificial intelligence provided in the embodiment of the present invention may pre-configure loan interest rates or loan interest rate benefit points corresponding to each customer category, for example, may configure loan interest rates of 3.9%, 4.0%, 4.1%, 4.2%, and 4.3% for a class a customer, a class B customer, a class C customer, a class D customer, and a class E customer, respectively; the loan rate preferential points of the class A customer, the class B customer, the class C customer, the class D customer and the class E customer can be respectively set to be 7 points, 6 points, 5 points, 4 points and 3 points.
In an embodiment, before inputting the attribute information of each preset dimension of the target loan client and the corresponding weight value into a pre-trained client classification model and outputting the client classification result of the target loan client, as shown in fig. 2, the loan interest rate parameter information determination method based on artificial intelligence provided in the embodiment of the present invention may further obtain a client classification model through the following machine learning process training:
s201, sample data is obtained.
S202, dividing the sample data into training data and test data according to a preset proportion.
S203, training the SVM model according to the training data to obtain a client classification model.
And S204, testing the client classification model obtained by training according to the test data until the client classification model meeting the preset conditions is obtained.
In specific implementation, the sample data may be divided into training data and test data according to a ratio of 4:1, that is, 80% of the sample data is used as training data for model training, and 20% of the sample data is used as test data for model testing.
In the embodiment of the invention, after the customer classification model is obtained by training the SVM model, the customer classification model can be used for classifying the target loan customers classified by unknown customers.
The SVM model is one of the best algorithms in the classification algorithm at present, and has the following advantages: the machine learning problem under the condition of small samples is solved; the generalization performance of the model is improved; solving the high dimensional problem; solving the non-linear problem; the problems of neural network structure selection and local minimum points are avoided.
In order to improve the training efficiency and accuracy of the model, in an embodiment, as shown in fig. 3, the method for determining loan rate parameter information based on artificial intelligence provided in the embodiment of the present invention may generate sample data by:
s301, collecting attribute information of a plurality of loan clients in a plurality of dimensions;
s302, performing dimension reduction processing on the attribute information of multiple dimensions of each loan client based on a principal component analysis method to obtain the attribute information of multiple preset dimensions of each loan client after dimension reduction;
s303, determining the weight value of each preset dimension after dimension reduction of each loan client according to the attribute information of each preset dimension after dimension reduction of each loan client based on a preset client attribute mapping relation table;
and S304, generating sample data according to the weighted value of each preset dimension after the dimensions of the plurality of loan clients are reduced.
It should be noted that the principal component analysis PCA is a statistical method that converts a set of variables that may have correlation into a set of linearly uncorrelated variables through orthogonal transformation, and the converted set of variables is called principal component. And performing dimensionality reduction on the attribute information by using a Principal Component Analysis (PCA) method, and extracting the attribute information which can most influence the client classification.
Based on the customer attribute mapping relationship table shown in table 1, the attribute information of each customer may be mapped to the weight value shown in table 2, and the weight value shown in table 3 may be obtained after the dimension reduction processing is performed on the attribute information of the customer.
TABLE 2 Attribute weight values for various clients before dimension reduction
Customer Age (age) Sex Location of the place Work unit Annual income … Properties 50 Categories
Customer 1 0.3 0.7 0.2 0.3 … … … A
Customer 2 0.5 0.3 0.8 0.2 … … … C
Client 3 0.3 0.7 0.8 0.15 … … … D
Customer 4 … … … … … … … …
… … … … … … … … …
TABLE 3 Attribute weight values for each client after dimension reduction
Customer Age (age) Sex Location of the place Work unit Annual income …… Properties 20 Categories
Customer 1 0.3 0.7 0.2 0.3 …… …… A
Customer 2 0.5 0.3 0.8 0.2 …… …… C
Client 3 0.3 0.7 0.8 0.15 …… …… D
Customer 4 … … … … … … … …
… … … … … … … … …
In the embodiment of the invention, the PCA is utilized to perform dimensionality reduction on the data after attribute mapping, and finally 20 attribute information with higher gold content is reserved, so that the SVM model is trained, and the model prediction result is more accurate. And outputting A, B, C, D, E five types of customer classification results of the customer classification models obtained by training the SVM model by using the 20 pieces of attribute information shown in the table 3. Taking the 20 dimensions after the dimension reduction processing shown in table 3 as preset dimensions, the above-mentioned S101 collects attribute information of 20 preset dimensions, such as the age, sex, location, work unit, and income of the target loan client.
Based on the same inventive concept, the embodiment of the present invention further provides an artificial intelligence based loan interest rate parameter information determination apparatus, as described in the following embodiments. Because the principle of solving the problems of the device is similar to the method for determining the loan interest rate parameter information based on artificial intelligence, the implementation of the device can refer to the implementation of the method for determining the loan interest rate parameter information based on artificial intelligence, and repeated parts are not described again.
Fig. 4 is a schematic diagram of an apparatus for determining loan interest rate parameter information based on artificial intelligence according to an embodiment of the present invention, as shown in fig. 4, the apparatus includes: a customer information collecting module 41, a weight value determining module 42, a customer classification predicting module 43 and a loan interest rate parameter information determining module 44.
The client information acquisition module 41 is used for acquiring attribute information of a plurality of preset dimensions of a target loan client; the weight value determining module 42 is configured to determine a weight value of each preset dimension of the target loan client according to the attribute information of each preset dimension of the target loan client based on a preconfigured client attribute mapping relation table; the client classification prediction module 43 is used for inputting the attribute information of each preset dimension of the target loan client and the corresponding weight value into a client classification model trained in advance and outputting the client classification result of the target loan client; and the loan interest rate parameter information determining module 44 is used for determining the loan interest rate parameter information of the target loan client according to the client classification result of the target loan client.
In one embodiment, as shown in fig. 4, the apparatus for determining artificial intelligence based loan rate parameter information provided in the embodiment of the present invention may further include: a machine learning module 45 to: acquiring sample data; dividing the sample data into training data and test data according to a preset proportion; training a Support Vector Machine (SVM) model according to the training data to obtain a client classification model; and testing the client classification model obtained by training according to the test data until the client classification model meeting the preset conditions is obtained.
In one embodiment, as shown in fig. 4, the apparatus for determining artificial intelligence based loan rate parameter information provided in the embodiment of the present invention may further include: a dimension reduction processing module 46; the client information collecting module 41 is further configured to collect attribute information of multiple dimensions of multiple loan clients; the dimension reduction processing module 46 is configured to perform dimension reduction processing on the attribute information of multiple dimensions of each loan client based on a principal component analysis method to obtain the attribute information of multiple preset dimensions after the dimension reduction of each loan client. In this embodiment, the weight value determining module 42 is further configured to determine, based on a pre-configured customer attribute mapping table, a weight value of each preset dimension after dimension reduction of each loan customer according to attribute information of each preset dimension after dimension reduction of each loan customer; the machine learning module 45 is further configured to generate sample data according to the weighted values of the preset dimensions after the dimensions of the plurality of loan clients are reduced.
In one embodiment, as shown in fig. 4, the apparatus for determining artificial intelligence based loan rate parameter information provided in the embodiment of the present invention may further include: a customer attribute mapping table generating module 47, configured to generate a customer attribute mapping table according to attribute information of multiple dimensions of multiple loan customers, where the customer attribute mapping table includes: and the weight values corresponding to the multiple dimensions and the attribute information of each dimension are different.
Based on the same inventive concept, an embodiment of the present invention further provides a computer device, so as to solve the technical problem that the existing loan service platform cannot provide personalized loan interest rates to different customers, fig. 5 is a schematic diagram of the computer device provided in the embodiment of the present invention, as shown in fig. 5, the computer device 50 includes a memory 501, a processor 502, and a computer program stored on the memory 501 and capable of being run on the processor 502, and the processor 502 implements the above method for determining artificial intelligence-based loan interest rate parameter information when executing the computer program.
Based on the same inventive concept, the embodiment of the present invention further provides a computer-readable storage medium, for solving the technical problem that the existing loan service platform cannot provide personalized loan interest rates to different customers, wherein the computer-readable storage medium stores a computer program for executing the above loan interest rate parameter information determination method based on artificial intelligence.
In summary, according to the loan interest rate parameter information determining method, apparatus, computer device, and computer readable storage medium provided in the embodiments of the present invention, after acquiring attribute information of a plurality of preset dimensions of a target loan client, a weight value of each preset dimension of the target loan client is determined according to the attribute information of each preset dimension of the target loan client based on a pre-configured client attribute mapping table, and then the attribute information of each preset dimension of the target loan client and the corresponding weight value are input into a pre-trained client classification model, a client classification result of the target loan client is output, and finally, the loan interest rate parameter information of the target loan client is determined according to the client classification result of the target loan client.
Compared with the technical scheme of providing fixed loan interest rates for different customers in the prior art, the method and the system for providing the loan interest rates have the advantages that a customer classification model is trained through machine learning, and then the loan customers are classified according to the attribute information of the loan customers, so that different loan interest rate parameter information can be provided for different customers according to classification results, and the recommendation of personalized loan interest rates 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, 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 (10)

1. A loan interest rate parameter information determination method based on artificial intelligence is characterized by comprising the following steps:
collecting attribute information of a plurality of preset dimensions of a target loan client;
determining a weight value of each preset dimension of the target loan client according to the attribute information of each preset dimension of the target loan client based on a preset client attribute mapping relation table;
inputting the attribute information of each preset dimension of the target loan client and the corresponding weight value into a client classification model trained in advance, and outputting a client classification result of the target loan client;
and determining loan interest rate parameter information of the target loan client according to the client classification result of the target loan client.
2. The method as claimed in claim 1, wherein before inputting the attribute information and the corresponding weight value of each preset dimension of the target loan client into a pre-trained client classification model and outputting the client classification result of the target loan client, the method further comprises:
acquiring sample data;
dividing the sample data into training data and test data according to a preset proportion;
training a Support Vector Machine (SVM) model according to the training data to obtain a client classification model;
and testing the client classification model obtained by training according to the test data until the client classification model meeting the preset conditions is obtained.
3. The method of claim 2, wherein the method further comprises:
collecting attribute information of a plurality of dimensions of a plurality of loan clients;
performing dimension reduction processing on the attribute information of multiple dimensions of each loan client based on a principal component analysis method to obtain the attribute information of multiple preset dimensions of each loan client after dimension reduction;
determining the weight value of each preset dimension after dimension reduction of each loan client according to the attribute information of each preset dimension after dimension reduction of each loan client based on a pre-configured client attribute mapping relation table;
and generating sample data according to the weighted value of each preset dimension after the dimensions of the plurality of loan clients are reduced.
4. The method of claim 1, wherein the method further comprises:
generating a customer attribute mapping relation table according to attribute information of multiple dimensions of multiple loan customers, wherein the customer attribute mapping table comprises: and the weight values corresponding to the multiple dimensions and the attribute information of each dimension are different.
5. An artificial intelligence-based loan interest rate parameter information determination device, comprising:
the client information acquisition module is used for acquiring attribute information of a plurality of preset dimensions of a target loan client;
the weight value determining module is used for determining the weight value of each preset dimension of the target loan client according to the attribute information of each preset dimension of the target loan client based on a preset client attribute mapping relation table;
the client classification prediction module is used for inputting the attribute information of each preset dimension of the target loan client and the corresponding weight value into a client classification model trained in advance and outputting the client classification result of the target loan client;
and the loan interest rate parameter information determining module is used for determining the loan interest rate parameter information of the target loan client according to the client classification result of the target loan client.
6. The apparatus of claim 5, wherein the apparatus further comprises:
a machine learning module to: acquiring sample data; dividing the sample data into training data and test data according to a preset proportion; training a Support Vector Machine (SVM) model according to the training data to obtain a client classification model; and testing the client classification model obtained by training according to the test data until the client classification model meeting the preset conditions is obtained.
7. The apparatus of claim 6, wherein the apparatus further comprises: a dimension reduction processing module;
the client information acquisition module is also used for acquiring attribute information of a plurality of loan clients in a plurality of dimensions;
the dimension reduction processing module is used for carrying out dimension reduction processing on the attribute information of multiple dimensions of each loan client based on a principal component analysis method to obtain the attribute information of multiple preset dimensions of each loan client after dimension reduction;
the weight value determining module is further used for determining the weight value of each preset dimension after dimension reduction of each loan client according to the attribute information of each preset dimension after dimension reduction of each loan client based on a pre-configured client attribute mapping relation table;
and the machine learning module is also used for generating sample data according to the weighted value of each preset dimension after the dimension reduction of the plurality of loan clients.
8. The apparatus of claim 5, wherein the apparatus further comprises:
a customer attribute mapping table generating module, configured to generate a customer attribute mapping table according to attribute information of multiple dimensions of multiple loan customers, where the customer attribute mapping table includes: and the weight values corresponding to the multiple dimensions and the attribute information of each dimension are different.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the artificial intelligence based loan rate parameter information determination method of any one of claims 1 to 4.
10. A computer-readable storage medium storing a computer program for executing the artificial intelligence based loan rate parameter information determination method according to any one of claims 1 to 4.
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