CN113379532A - Credit consciousness level prediction method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses a credit consciousness level prediction method, a device, equipment and a storage medium, wherein the method comprises the following steps: determining a user needing to realize credit consciousness level prediction at present as a current user, and acquiring basic data of the current user; inputting basic data of the current user into a prediction model, and obtaining data output by the prediction model as the repayment willingness of the current user; providing credit service for the current user based on the repayment willingness of the current user; the basic data comprise user information, asset information and credit information, and the prediction model is obtained by training in advance by using the basic data and repayment willingness of a plurality of historical users. The method and the device can realize quantification of repayment willingness of the user and provide convenience for credit wind control.
Description
Technical Field
The invention relates to the technical field of internet data processing, in particular to a credit consciousness level prediction method, a device, equipment and a storage medium.
Background
In the process of rural financial development, farmers generally have the condition of weak credit consciousness, even have the condition that the farmers think that the money is not needed or not needed for national subsidy in credit business; in credit wind control, the failure to quantify the repayment willingness of farmers becomes one of the more troublesome obstacles in wind control means.
Disclosure of Invention
The invention aims to provide a credit consciousness level prediction method, a credit consciousness level prediction device, credit consciousness level prediction equipment and a credit consciousness level storage medium, which can realize quantification of payment willingness of a user and provide convenience for credit wind control.
In order to achieve the above purpose, the invention provides the following technical scheme:
a method of credit awareness level prediction, comprising:
determining a user needing to realize credit consciousness level prediction at present as a current user, and acquiring basic data of the current user; wherein the basic data comprises user information, asset information, and credit information;
inputting basic data of a current user into a prediction model, and obtaining the data output by the prediction model as the repayment willingness of the current user; the prediction model is obtained by utilizing historical basic data of a plurality of users and corresponding repayment willingness training in advance;
and providing credit service for the current user based on the repayment willingness of the current user.
Preferably, training the predictive model comprises:
acquiring historical basic data and corresponding repayment willingness of a plurality of users, and storing the acquired historical basic data and the corresponding repayment willingness of the plurality of users into a training sample set;
and training a preset algorithm by using the training sample set to obtain a corresponding prediction model.
Preferably, after obtaining the corresponding prediction model, the method further includes:
and acquiring new user basic data and corresponding repayment willingness generated in the preset time period before the current time closest to the current time every time when the preset time period passes, storing the acquired new user basic data and corresponding repayment willingness into the training sample set, and executing the step of training a preset algorithm by using the training sample set.
Preferably, the training of the preset algorithm by using the training sample set to obtain a corresponding prediction model includes:
and training a logistic regression algorithm, an xgboost algorithm or a neural network algorithm by using the training sample set to obtain a corresponding prediction model.
A credit awareness level prediction apparatus, comprising:
an acquisition module to: determining a user needing to realize credit consciousness level prediction at present as a current user, and acquiring basic data of the current user; wherein the basic data comprises user information, asset information, and credit information;
a prediction module to: inputting basic data of a current user into a prediction model, and obtaining the data output by the prediction model as the repayment willingness of the current user; the prediction model is obtained by utilizing historical basic data of a plurality of users and corresponding repayment willingness training in advance;
providing a module for: and providing credit service for the current user based on the repayment willingness of the current user.
Preferably, the method further comprises the following steps:
a training module to: acquiring historical basic data and corresponding repayment willingness of a plurality of users, and storing the acquired historical basic data and the corresponding repayment willingness of the plurality of users into a training sample set; and training a preset algorithm by using the training sample set to obtain a corresponding prediction model.
Preferably, the method further comprises the following steps:
an update module to: and after the corresponding prediction model is obtained, acquiring new user basic data and corresponding repayment willingness generated in a preset time period before the current time closest to the current time every time the preset time period passes, storing the acquired new user basic data and corresponding repayment willingness into the training sample set, and instructing the training module to execute a step of training a preset algorithm by using the training sample set.
Preferably, the training module comprises:
a training unit to: and training a logistic regression algorithm, an xgboost algorithm or a neural network algorithm by using the training sample set to obtain a corresponding prediction model.
A credit awareness level predicting device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the credit awareness level prediction method as described in any one of the above when executing the computer program.
A computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the credit awareness level prediction method according to any one of the preceding claims.
The invention provides a credit consciousness level prediction method, a device, equipment and a storage medium, wherein the method comprises the following steps: determining a user needing to realize credit consciousness level prediction at present as a current user, and acquiring basic data of the current user; inputting basic data of a current user into a prediction model, and obtaining the data output by the prediction model as the repayment willingness of the current user; providing credit service for the current user based on the repayment willingness of the current user; the basic data comprise user information, asset information and credit information, and the prediction model is obtained by training in advance by using the basic data and corresponding repayment willingness of a plurality of historical users. According to the method, the basic data and repayment willingness of a plurality of historical users are utilized in advance to train the prediction model, and then when the credit consciousness level prediction needs to be realized, the basic data of the user needing to realize the credit consciousness level prediction is input into the prediction model, so that the repayment willingness of the user output by the prediction model can be obtained, wherein the basic data comprises user information, asset information and credit information related to the user reduction willingness, quantification of the repayment willingness of the user is realized, and convenience is provided for credit wind control.
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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 embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flowchart of a method for predicting a level of confidence in credit according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a credit awareness level prediction apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
Referring to fig. 1, a flowchart of a method for predicting a level of credit awareness according to an embodiment of the present invention is shown, which may include:
s11: determining a user needing to realize credit consciousness level prediction at present as a current user, and acquiring basic data of the current user; wherein the basic data includes user information, asset information, and credit information.
The method for predicting the credit consciousness level can be a corresponding device and equipment for predicting the credit consciousness level. It should be noted that the user may be a farmer, or may be another user determined according to actual needs. Any user needing to realize the credit consciousness level prediction at present can be called as a current user, and after the current user is determined, basic data of the current user can be obtained to be used as characteristics for predicting the credit consciousness level of the current user. The basic data may include user information, asset information, and credit information, where the user information is information of the user itself, and may include a name, an age, an organization, and the like of the user, and the information is used as a personal qualification tag, the asset information is information in the user's asset aspect, and may include transaction flow, a real estate, a deposit, and the like of the user, and the credit information is information in the user's credit aspect, and may include a credit record, and the like.
S12: inputting basic data of the current user into a prediction model, and obtaining data output by the prediction model as the repayment willingness of the current user; the prediction model is obtained by training in advance by using historical basic data and corresponding repayment willingness of a plurality of users.
And inputting the basic data of the current user into a pre-trained prediction model, wherein the data output by the prediction model is the repayment willingness of the current user, namely the willingness of the current user to repay the loan, so that the credit consciousness level is predicted. The basic data and the repayment willingness of a plurality of users in a period of time (the duration of the period of time can be determined according to actual needs) before and closest to the current time can be obtained in advance, the repayment willingness of the users is determined according to the actual conditions of the users, and the repayment willingness can be divided into low repayment willingness and high repayment willingness, or can be divided into more conditions according to actual needs, and the repayment willingness are all within the protection scope of the invention; and training by using the basic data and repayment willingness of the users to obtain a corresponding prediction model, and then predicting the credit consciousness level by using the prediction model.
S13: and providing credit service for the current user based on the repayment willingness of the current user.
After the repayment willingness of the current user is determined, if the repayment willingness is high, loan service can be provided for the user, otherwise, the loan service can be refused to be provided for the user, and therefore credit service is provided for the current user based on the repayment willingness of the current user. In addition, for users with high probability of low repayment willingness, risk decision information can be supplemented based on the repayment willingness, and credit consciousness popularization education projects can be developed repeatedly to improve the credit consciousness.
According to the method, the basic data and repayment willingness of a plurality of historical users are utilized in advance to train the prediction model, and then when the credit consciousness level prediction needs to be realized, the basic data of the user needing to realize the credit consciousness level prediction is input into the prediction model, so that the repayment willingness of the user output by the prediction model can be obtained, wherein the basic data comprises user information, asset information and credit information related to the user reduction willingness, quantification of the repayment willingness of the user is realized, and convenience is provided for credit wind control.
The method for predicting the credit consciousness level provided by the embodiment of the invention trains a prediction model, and comprises the following steps:
acquiring historical basic data and corresponding repayment willingness of a plurality of users, and storing the acquired historical basic data and the corresponding repayment willingness of the plurality of users into a training sample set;
and training a preset algorithm by using a training sample set to obtain a corresponding prediction model.
When the prediction model training is realized, acquired basic data and corresponding restoration intentions of a plurality of users in history can be stored in a training sample set, so that the training of the prediction model is directly performed by using the training sample set, and the basic data and the corresponding repayment intentions used for the prediction model training can be conveniently stored and used.
The method for predicting the credit consciousness level provided by the embodiment of the invention can further comprise the following steps after obtaining the corresponding prediction model:
and acquiring new user basic data and corresponding repayment willingness generated in a preset time period before the current time closest to the current time every time when the preset time period passes, storing the acquired new user basic data and corresponding repayment willingness into a training sample set, and executing the step of training the preset algorithm by using the training sample set.
The preset time period can be set according to actual needs; the method and the device can regularly acquire the basic data and the repayment willingness of the user which are newly generated historically based on the preset time period, then store the basic data and the repayment willingness of the user which are newly generated into the training sample set, and then use the training sample set to realize the training of the prediction model, thereby realizing the real-time updating of the training sample set and iterating the repayment willingness model algorithm, and further ensuring the real-time property and the effectiveness of the prediction model.
Specifically, when the prediction model is trained, marking and warehousing can be performed on a farmer with a weak repayment intention as a target feature, the repayment intention model is trained through supervised learning (including algorithms such as logistic regression, xgboost, neural network models and the like) by combining the existing features of the farmer transaction flow, assets, credit records, farmer personal qualification labels and the like in a certain row, and the credit consciousness level of the farmer is predicted for a user without the credit consciousness label through the prediction model, so that the repayment intention of the farmer is quantified.
The method for predicting the credit consciousness level provided by the embodiment of the invention utilizes the training sample set to train the preset algorithm to obtain the corresponding prediction model, and can comprise the following steps:
and training the logistic regression algorithm, the xgboost algorithm or the neural network algorithm by using the training sample set to obtain a corresponding prediction model.
The preset algorithm can be set according to actual needs, for example, a logistic regression algorithm, an xgboost algorithm or a neural network algorithm can be selected, so that the effective realization of the prediction model is realized.
In a specific implementation manner, a method for predicting a credit consciousness level provided in an embodiment of the present invention may specifically include:
1 a credit awareness questionnaire was conducted.
2 accumulating questionnaire survey sample libraries:
2-1 marking the farmer without the repayment will as a target characteristic.
3 training a prediction model:
3-1, combining the characteristics of existing farmer transaction flow, assets, credit records, personal qualification tags and the like of a certain row;
3-2, taking the no-repayment willingness as a target, and training a prediction model through algorithms such as logistic regression, xgboost, neural network and the like.
4 Credit consciousness level prediction:
4-1 making credit consciousness level prediction for users uncovered by questionnaires and quantifying repayment willingness.
5, rejecting farmers with high probability and low repayment willingness:
5-1 developing the credit consciousness popularization education with emphasis.
And 6, carrying out credit consciousness popularization education and updating a label library and an iterative model result for farmers with high probability and low repayment willingness.
Therefore, in the credit granting process of the peasant households, the method for quantifying the credit level of the peasant households is provided, and rural financial risks such as road crossing fraud and the like in the high-probability and low-payment-willingness peasant households are prevented from the source; and the peasant households with high probability and low repayment willingness develop credit consciousness popularization education for a long time, and improve the rural financial credit consciousness ecology.
An embodiment of the present invention further provides a device for predicting a credit consciousness level, as shown in fig. 2, which may specifically include:
an obtaining module 11, configured to: determining a user needing to realize credit consciousness level prediction at present as a current user, and acquiring basic data of the current user; wherein the basic data comprises user information, asset information and credit information;
a prediction module 12 for: inputting basic data of the current user into a prediction model, and obtaining data output by the prediction model as the repayment willingness of the current user; the prediction model is obtained by utilizing historical basic data of a plurality of users and corresponding repayment willingness training in advance;
providing a module 13 for: and providing credit service for the current user based on the repayment willingness of the current user.
The device for predicting the credit consciousness level provided by the embodiment of the invention can also comprise:
a training module to: acquiring historical basic data and corresponding repayment willingness of a plurality of users, and storing the acquired historical basic data and the corresponding repayment willingness of the plurality of users into a training sample set; and training a preset algorithm by using a training sample set to obtain a corresponding prediction model.
The device for predicting the credit consciousness level provided by the embodiment of the invention can also comprise:
an update module to: and after the corresponding prediction model is obtained, acquiring new basic data and corresponding repayment willingness of the user generated in a preset time period before the current time closest to the current time every time when a preset time period passes, storing the acquired new basic data and corresponding repayment willingness of the user into a training sample set, and instructing a training module to execute a step of training a preset algorithm by using the training sample set.
In the device for predicting the level of credit consciousness provided by the embodiment of the present invention, the training module may include:
a training unit to: and training the logistic regression algorithm, the xgboost algorithm or the neural network algorithm by using the training sample set to obtain a corresponding prediction model.
An embodiment of the present invention further provides a device for predicting a credit consciousness level, which may include:
a memory for storing a computer program;
a processor for implementing the steps of any of the above methods of predicting a level of credit awareness when executing a computer program.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, can implement any one of the above steps of the method for predicting a level of credit consciousness.
It should be noted that for the description of the relevant parts of the credit consciousness level prediction apparatus, the device and the storage medium provided in the embodiment of the present invention, reference is made to the detailed description of the corresponding parts of the credit consciousness level prediction method provided in the embodiment of the present invention, and details are not repeated herein. In addition, parts of the above technical solutions provided in the embodiments of the present invention that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail, so as to avoid redundant description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for predicting a level of credit awareness, comprising:
determining a user needing to realize credit consciousness level prediction at present as a current user, and acquiring basic data of the current user; wherein the basic data comprises user information, asset information, and credit information;
inputting basic data of a current user into a prediction model, and obtaining the data output by the prediction model as the repayment willingness of the current user; the prediction model is obtained by utilizing historical basic data of a plurality of users and corresponding repayment willingness training in advance;
and providing credit service for the current user based on the repayment willingness of the current user.
2. The method of claim 1, wherein training the predictive model comprises:
acquiring historical basic data and corresponding repayment willingness of a plurality of users, and storing the acquired historical basic data and the corresponding repayment willingness of the plurality of users into a training sample set;
and training a preset algorithm by using the training sample set to obtain a corresponding prediction model.
3. The method of claim 2, further comprising, after obtaining the corresponding predictive model:
and acquiring new user basic data and corresponding repayment willingness generated in the preset time period before the current time closest to the current time every time when the preset time period passes, storing the acquired new user basic data and corresponding repayment willingness into the training sample set, and executing the step of training a preset algorithm by using the training sample set.
4. The method of claim 2, wherein training a predetermined algorithm using the training sample set to obtain a corresponding predictive model comprises:
and training a logistic regression algorithm, an xgboost algorithm or a neural network algorithm by using the training sample set to obtain a corresponding prediction model.
5. A credit awareness level prediction apparatus, comprising:
an acquisition module to: determining a user needing to realize credit consciousness level prediction at present as a current user, and acquiring basic data of the current user; wherein the basic data comprises user information, asset information, and credit information;
a prediction module to: inputting basic data of a current user into a prediction model, and obtaining the data output by the prediction model as the repayment willingness of the current user; the prediction model is obtained by utilizing historical basic data of a plurality of users and corresponding repayment willingness training in advance;
providing a module for: and providing credit service for the current user based on the repayment willingness of the current user.
6. The apparatus of claim 5, further comprising:
a training module to: acquiring historical basic data and corresponding repayment willingness of a plurality of users, and storing the acquired historical basic data and the corresponding repayment willingness of the plurality of users into a training sample set; and training a preset algorithm by using the training sample set to obtain a corresponding prediction model.
7. The apparatus of claim 6, further comprising:
an update module to: and after the corresponding prediction model is obtained, acquiring new user basic data and corresponding repayment willingness generated in a preset time period before the current time closest to the current time every time the preset time period passes, storing the acquired new user basic data and corresponding repayment willingness into the training sample set, and instructing the training module to execute a step of training a preset algorithm by using the training sample set.
8. The apparatus of claim 7, wherein the training module comprises:
a training unit to: and training a logistic regression algorithm, an xgboost algorithm or a neural network algorithm by using the training sample set to obtain a corresponding prediction model.
9. A credit awareness level predicting device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the credit awareness level prediction method of any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of predicting a level of credit as set forth in any one of claims 1 to 4.
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