CN115018619A - Credit assessment method and device - Google Patents

Credit assessment method and device Download PDF

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Publication number
CN115018619A
CN115018619A CN202210545846.8A CN202210545846A CN115018619A CN 115018619 A CN115018619 A CN 115018619A CN 202210545846 A CN202210545846 A CN 202210545846A CN 115018619 A CN115018619 A CN 115018619A
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user
user information
credit
dimensions
training data
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黄秋洁
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Bank of China Ltd
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Bank of China Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • G06N3/08—Learning methods

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Abstract

The application discloses a credit assessment method which can be applied to the financial field or other fields. The method comprises the following steps: user information of a first user in M dimensions is obtained, wherein M is an integer larger than 1. And respectively inputting the user information of the M dimensions into a machine learning model to obtain credit values respectively corresponding to the user information of the M dimensions. And processing the credit values respectively corresponding to the user information of the M dimensions by using a logistic regression equation to obtain a processing result. Determining a credit value for the first user based on the processing result. The credit value determined by the scheme is accurate. And when determining the credit value of the first user, processing the credit values corresponding to the user information of the M dimensions by using a logistic regression equation, so that the user can clearly make the contribution of the user information of the M dimensions when determining the credit value of the first user, and the determined credit value of the first user is easier to be trusted by the user.

Description

Credit assessment method and device
Technical Field
The present application relates to the field of data processing, and in particular, to a credit evaluation method and apparatus.
Background
In some scenarios, the user's credit needs to be evaluated in order to subsequently perform other operations based on the user's credit. For example, the bank determines whether to provide loan services to the user based on the user's credit.
How to accurately determine the credit value of the user is a problem which needs to be solved urgently at present.
Disclosure of Invention
The technical problem to be solved by the application is how to accurately determine the credit value of a user, and a credit evaluation method and a device are provided.
In a first aspect, an embodiment of the present application provides a credit evaluation method, where the method includes:
acquiring user information of a first user in M dimensions, wherein M is an integer larger than 1;
inputting the user information of the M dimensions into a machine learning model respectively to obtain credit values corresponding to the user information of the M dimensions respectively;
processing credit values respectively corresponding to the user information of the M dimensions by using a logistic regression equation to obtain processing results;
determining a credit value for the first user based on the processing result.
Optionally, the machine learning model is obtained by training in the following manner:
acquiring training data and a label corresponding to the training data, wherein the training data comprises training user information, and the label corresponding to the training data is used for indicating a credit value corresponding to the training user information;
and training to obtain the machine learning model based on the training data and the labels corresponding to the training data.
Optionally, the machine learning model is an XGboost model.
Optionally, determining the credit value of the first user based on the processing result includes:
and processing the processing result by using a Wal WOE scoring method to obtain a credit score of the first user.
In a second aspect, an embodiment of the present application provides a credit evaluation apparatus, including:
the device comprises an acquisition unit, a processing unit and a display unit, wherein the acquisition unit is used for acquiring user information of a first user in M dimensions, and M is an integer larger than 1;
the first determining unit is used for respectively inputting the user information of the M dimensions into a machine learning model to obtain credit values corresponding to the user information of the M dimensions;
the processing unit is used for processing the credit values corresponding to the user information of the M dimensions by using a logistic regression equation to obtain a processing result;
a second determining unit configured to determine a credit value of the first user based on the processing result.
Optionally, the machine learning model is obtained by training in the following manner:
acquiring training data and a label corresponding to the training data, wherein the training data comprises training user information, and the label corresponding to the training data is used for indicating a credit value corresponding to the training user information;
and training to obtain the machine learning model based on the training data and the labels corresponding to the training data.
Optionally, the machine learning model is an XGboost model.
Optionally, determining the credit value of the first user based on the processing result includes:
and processing the processing result by using a Wal WOE scoring method to obtain a credit score of the first user.
In a third aspect, an embodiment of the present application provides an apparatus, where the apparatus includes: a processor, a memory, a system bus; the processor and the memory are connected through the system bus; the memory is for storing one or more programs, the one or more programs comprising instructions, which when executed by the processor, cause the processor to perform the method of any of the above first aspects.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, which stores instructions that, when executed on a terminal device, cause the terminal device to perform the method of any one of the above first aspects.
Compared with the prior art, the embodiment of the application has the following advantages:
in an example, user information of a first user in M dimensions may be obtained, where M is an integer greater than 1. And then, inputting the user information of the M dimensions into a machine learning model respectively to obtain credit values corresponding to the user information of the M dimensions respectively. Further, the credit values corresponding to the user information of the M dimensions are processed by using a logistic regression equation to obtain processing results, and the credit value of the first user is determined based on the processing results. It can be seen that, in the embodiment of the application, the credit value of the first user is determined based on the user information of the first user in multiple dimensions, and the determined credit value is relatively accurate. And when determining the credit value of the first user, processing the credit values corresponding to the user information of the M dimensions respectively by using a logistic regression equation, so that the user can make clear the contribution of the user information of the M dimensions in determining the credit value of the first user, that is, the user can know the part of the process of determining the credit value of the first user, and the determined credit value of the first user is more easily trusted by the user.
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 described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flowchart of a credit evaluation method according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a credit evaluation apparatus according to an embodiment of the present application 。
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The inventor of the present application has found through research that, at present, the machine learning model can be used to evaluate the credit of the user, for example, basic information of the user is input into the machine learning model, and the machine learning model outputs the credit value of the user. However, since the machine learning model can only output the final result and cannot embody the determination process of obtaining the credit value based on the basic confidence, the result output by the machine learning model cannot be trusted by the user in some scenarios.
In order to solve the above problem, embodiments of the present application provide a credit evaluation method and apparatus.
Various non-limiting embodiments of the present application are described in detail below with reference to the accompanying drawings.
Exemplary method
Referring to fig. 1, a flowchart of a credit evaluation method according to an embodiment of the present application is schematically shown. In this embodiment, the method may be executed by a terminal device or a server, and the embodiment of the present application is not particularly limited.
In one example, the method described in fig. 1, for example, may include the steps of: S101-S104.
S101: user information of a first user in M dimensions is obtained, wherein M is an integer larger than 1.
In the embodiment of the application, the user information of the first user in M dimensions is obtained and used after the user authorization is obtained.
The user information of the M dimensions is not specifically limited in the embodiments of the present application, and in an example, the user information of the M dimensions may include user information of at least two dimensions: an identity property dimension, a bank card dimension, a transaction dimension, a repayment loan dimension, a loan placement dimension, an application loan dimension, and a multi-headed loan dimension.
S102: and inputting the user information of the M dimensions into a machine learning model respectively to obtain credit values corresponding to the user information of the M dimensions respectively.
In an embodiment of the present application, a machine learning model for determining a corresponding credit value based on user information may be trained in advance. In one example, the machine learning model may be trained by the following steps A1-A2.
A1: the method comprises the steps of obtaining training data and labels corresponding to the training data, wherein the training data comprise training user information, and the labels corresponding to the training data are used for indicating credit values corresponding to the training user information.
In an embodiment of the present application, the training data may include user information in multiple dimensions. In this embodiment of the application, the training data may be data obtained by preprocessing raw data, where the preprocessing raw data may be, for example, deleting data missing more in the raw data, or interpolating partial data in the raw data by using a K-nearest neighbor (KNN) algorithm, so as to obtain the training data with richer data. In the embodiment of the present application, the label corresponding to the training data may be determined by, for example, a manual labeling manner.
A2: and training to obtain the machine learning model based on the training data and the labels corresponding to the training data.
After the training data and the labels corresponding to the training data are obtained, the machine learning model may be trained based on the training data and the labels corresponding to the training data. It should be noted that, when training the machine learning model, the training samples may be divided into a training set and a test set, where the training set is used for training the machine learning model, and the test set is used for testing the accuracy of the machine learning model.
The embodiment of the application does not specifically limit the machine learning model, which may be a Convolutional Neural Network (CNN) model, a Deep Neural Network (DNN) model, or another model. For example, in one example, the XGboost model may be an XGboost model, which may be found to work well based on user information to determine a user credit value based on actual testing.
In this embodiment of the application, after the user information of M dimensions is respectively input into the machine learning model, based on the user information of each dimension, the machine model may output a credit value. In one example, the credit value input by the machine learning model may be a value between 0 and 1, and a larger credit value indicates a higher credit.
S103: and processing the credit values respectively corresponding to the user information of the M dimensions by using a logistic regression equation to obtain a processing result.
In one example, credit values respectively corresponding to the user information of the M dimensions are used as independent variables of the logistic regression equation, and the processing result is a dependent variable of the logistic regression equation. In one example, the processing result is a probability value between 0 and 1.
The determination method of the logistic regression equation is not specifically limited in the embodiments of the present application. In one example, the logistic regression equation may be a logistic regression equation.
S104: determining a credit value for the first user based on the processing result.
After the processing result of the logistic regression equation is obtained, the credit value of the first user may be obtained based on the processing result.
When the S104 is implemented specifically, there may be a plurality of implementations.
In one example, the processing result may be directly determined as a credit value of the first user.
In yet another example, the processing result may be processed using a Woll (WOE) scoring method to obtain a credit score for the first user. In this case, the obtained credit score of the first user is a value within a certain range, for example, a value greater than 0 and less than 800, and the value range of the credit score is not particularly limited in this embodiment of the application.
As can be seen from the above description, with the scheme of the embodiment of the present application, the credit value of the first user is determined based on the user information of the first user in multiple dimensions, and the determined credit value is relatively accurate. Moreover, when the credit value of the first user is determined, the credit values corresponding to the M dimensions of user information are processed by using a logistic regression equation, so that the user can make clear the contribution of the M dimensions of user information when determining the credit value of the first user, that is, the user can know the part of the process of determining the credit value of the first user, and the determined credit value of the first user is more easily trusted by the user.
Moreover, with the scheme, more user information is used than when the logistic regression equation is directly used for processing the user information of multiple dimensions to determine the credit value of the first user. When the logistic regression equation is directly used for processing the user information with multiple dimensions, part of valid user data can be directly deleted, and the user data cannot actually participate in the process of determining the credit value of the user. Therefore, by adopting the scheme, compared with the method that the credit value of the first user is determined by directly using the logistic regression equation to process the user information with multiple dimensions, the determined credit value is more accurate.
Exemplary device
Based on the method provided by the above embodiment, the embodiment of the present application further provides an apparatus, which is described below with reference to the accompanying drawings.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a credit evaluation apparatus according to an embodiment of the present application. The apparatus 200 may specifically include, for example: an acquisition unit 201, a first determination unit 202, a processing unit 203, and a second determination unit 204.
An obtaining unit 201, configured to obtain user information of a first user in M dimensions, where M is an integer greater than 1;
a first determining unit 202, configured to input the M-dimensional user information into a machine learning model respectively, so as to obtain credit values corresponding to the M-dimensional user information respectively;
the processing unit 203 is configured to process the credit values corresponding to the user information of the M dimensions by using a logistic regression equation, so as to obtain a processing result;
a second determining unit 204, configured to determine a credit value of the first user based on the processing result.
Optionally, the machine learning model is obtained by training in the following manner:
acquiring training data and a label corresponding to the training data, wherein the training data comprises training user information, and the label corresponding to the training data is used for indicating a credit value corresponding to the training user information;
and training to obtain the machine learning model based on the training data and the labels corresponding to the training data.
Optionally, the machine learning model is an XGboost model.
Optionally, determining the credit value of the first user based on the processing result includes:
and processing the processing result by using a Wal WOE scoring method to obtain a credit score of the first user.
Since the apparatus 200 is an apparatus corresponding to the method provided in the above method embodiment, and the specific implementation of each unit of the apparatus 200 is the same as that of the above method embodiment, for the specific implementation of each unit of the apparatus 200, reference may be made to the description part of the above method embodiment, and details are not repeated here.
An embodiment of the present application further provides an apparatus, including: a processor, a memory, a system bus; the processor and the memory are connected through the system bus; the memory is for storing one or more programs, the one or more programs comprising instructions, which when executed by the processor, cause the processor to perform the method of the above method embodiments.
The embodiment of the present application further provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed on a terminal device, the terminal device is caused to execute the method described in the above method embodiment.
It should be noted that the credit evaluation method and device provided by the invention can be used in the financial field or other fields. For example, the method can be applied to a user credit evaluation scene in the financial field. The other fields are arbitrary fields other than the financial field, for example, the information processing field. The above is merely an example, and the application fields of the information processing method and apparatus provided by the present invention are not limited.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice in the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (10)

1. A credit evaluation method, the method comprising:
acquiring user information of a first user in M dimensions, wherein M is an integer larger than 1;
inputting the user information of the M dimensions into a machine learning model respectively to obtain credit values corresponding to the user information of the M dimensions respectively;
processing credit values respectively corresponding to the user information of the M dimensions by using a logistic regression equation to obtain processing results;
determining a credit value for the first user based on the processing result.
2. The method of claim 1, wherein the machine learning model is trained by:
acquiring training data and a label corresponding to the training data, wherein the training data comprises training user information, and the label corresponding to the training data is used for indicating a credit value corresponding to the training user information;
and training to obtain the machine learning model based on the training data and the labels corresponding to the training data.
3. The method of claim 1, wherein the machine learning model is an XGboost model.
4. The method of claim 1, wherein determining a credit value for the first user based on the processing result comprises:
and processing the processing result by using a Wal WOE scoring method to obtain a credit score of the first user.
5. A credit evaluation apparatus, the apparatus comprising:
the device comprises an acquisition unit, a processing unit and a display unit, wherein the acquisition unit is used for acquiring user information of a first user in M dimensions, and M is an integer larger than 1;
the first determining unit is used for respectively inputting the user information of the M dimensions into a machine learning model to obtain credit values corresponding to the user information of the M dimensions;
the processing unit is used for processing the credit values corresponding to the user information of the M dimensions by using a logistic regression equation to obtain a processing result;
a second determining unit configured to determine a credit value of the first user based on the processing result.
6. The apparatus of claim 5, wherein the machine learning model is trained by:
acquiring training data and a label corresponding to the training data, wherein the training data comprises training user information, and the label corresponding to the training data is used for indicating a credit value corresponding to the training user information;
and training to obtain the machine learning model based on the training data and the labels corresponding to the training data.
7. The apparatus of claim 5, wherein the machine learning model is an XGboost model.
8. The apparatus of claim 5, wherein determining the credit value of the first user based on the processing result comprises:
and processing the processing result by using a Wal WOE scoring method to obtain a credit score of the first user.
9. An apparatus, characterized in that the apparatus comprises: a processor, a memory, a system bus; the processor and the memory are connected through the system bus; the memory is to store one or more programs, the one or more programs comprising instructions, which when executed by the processor, cause the processor to perform the method of any of claims 1 to 4.
10. A computer-readable storage medium having stored therein instructions which, when run on a terminal device, cause the terminal device to perform the method of any one of claims 1 to 4.
CN202210545846.8A 2022-05-19 2022-05-19 Credit assessment method and device Pending CN115018619A (en)

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CN202210545846.8A CN115018619A (en) 2022-05-19 2022-05-19 Credit assessment method and device

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Application Number Priority Date Filing Date Title
CN202210545846.8A CN115018619A (en) 2022-05-19 2022-05-19 Credit assessment method and device

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CN115018619A true CN115018619A (en) 2022-09-06

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