CN111932367A - Pre-credit evaluation method and device - Google Patents
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Abstract
The invention discloses a method and a device for evaluating credit before loan, wherein the method comprises the following steps: the method comprises the steps of obtaining customer account information, loan blacklist user information and loan whitelist user information of a plurality of banking institutions; abnormal data cleaning is carried out on the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions; constructing a training sample set according to the customer account information, the loan blacklist user information and the loan whitelist user information of the plurality of banking institutions subjected to abnormal data cleaning; training the pre-credit evaluation model according to the training sample set to obtain a trained pre-credit evaluation model; and carrying out pre-credit evaluation by using the trained pre-credit evaluation model. The invention can realize data combination of a plurality of banking institutions, improve the performance of the credit evaluation model before credit, avoid revealing privacy data of users, facilitate supervision and reduce the risk of data violation.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for evaluating credit before credit.
Background
With the development of the fields of machine learning and artificial intelligence, the application of machine learning in the field of loan credit assessment of commercial banks is an exploration direction at present. However, machine learning needs a large amount of data as a learning basis, data of each commercial bank is limited, and a good-quality machine model is difficult to train only by means of the data of the commercial bank. If the data of other banking institutions are combined for training, the privacy of the user data of each banking institution can be revealed.
Specifically, in the prior art, when a machine learning algorithm is used for loan credit assessment, multiple institution data are generally centralized, and a machine learning algorithm model is used for loan credit assessment, so that privacy data of a user can be leaked, supervision is difficult, and the risk of data violation is increased.
Disclosure of Invention
The embodiment of the invention provides a pre-credit assessment method, which is used for realizing data combination of a plurality of banking institutions, improving the performance of a pre-credit assessment model, avoiding disclosure of privacy data of users, facilitating supervision and reducing the risk of data violation, and comprises the following steps:
the method comprises the steps of obtaining customer account information, loan blacklist user information and loan whitelist user information of a plurality of banking institutions;
abnormal data cleaning is carried out on the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions;
constructing a training sample set according to customer account information, loan blacklist user information and loan whitelist user information of a plurality of banking institutions subjected to abnormal data cleaning, wherein the training sample set comprises positive sample information and negative sample information, the loan whitelist user information is positive sample information, and the loan blacklist user information is negative sample information;
training a pre-credit evaluation model according to the training sample set to obtain a trained pre-credit evaluation model;
and carrying out pre-credit evaluation by using the trained pre-credit evaluation model.
Optionally, after abnormal data cleaning is performed on the customer account information, the loan blacklist user information, and the loan whitelist user information of the plurality of banking institutions, the method further includes:
and carrying out quantitative processing on the customer account information, the loan blacklist user information and the loan whitelist user information of the plurality of banking institutions after abnormal data cleaning so as to obtain the feature vector of the user.
Optionally, the method further includes:
and updating and aggregating the trained model to obtain the parameters of the aggregated model.
Optionally, after performing update aggregation processing on the trained model, the method further includes:
and detecting whether the credit evaluation model before credit is converged, and if not, returning the parameters of the aggregated model to a plurality of banking institutions to continue iterative training.
The embodiment of the invention also provides a pre-credit assessment device, which is used for realizing data combination of a plurality of banking institutions, improving the performance of a pre-credit assessment model, avoiding disclosure of privacy data of users, facilitating supervision and reducing the risk of data violation, and comprises the following components:
the information acquisition module is used for acquiring the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions;
the data cleaning module is used for cleaning abnormal data of the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions;
the system comprises a training sample set construction module, a data processing module and a data processing module, wherein the training sample set construction module is used for constructing a training sample set according to customer account information, loan blacklist user information and loan whitelist user information of a plurality of banking institutions subjected to abnormal data cleaning, and the training sample set comprises positive sample information and negative sample information, wherein the loan whitelist user information is positive sample information, and the loan blacklist user information is negative sample information;
the model training module is used for training the pre-credit evaluation model according to the training sample set to obtain a trained pre-credit evaluation model;
and the credit evaluation module is used for carrying out pre-credit evaluation by utilizing the trained pre-credit evaluation model.
Optionally, the apparatus further comprises:
and the characteristic vector acquisition module is used for carrying out quantitative processing on the customer account information, the loan blacklist user information and the loan whitelist user information of the plurality of banking institutions after abnormal data cleaning so as to acquire the characteristic vector of the user.
Optionally, the apparatus further comprises:
and the updating and aggregating processing module is used for updating and aggregating the trained model to obtain the parameters of the aggregation model.
Optionally, the apparatus further comprises:
the model detection module is used for detecting whether the pre-credit evaluation model is converged or not, and if not, returning the aggregated model parameters to a plurality of banking institutions to continue iterative training
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 method when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program for executing the above method is stored.
In the embodiment of the invention, by acquiring the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions, abnormal data cleaning is carried out on the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions, and constructing a training sample set according to the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions after abnormal data cleaning, training the pre-credit assessment model according to the training sample set to obtain the trained pre-credit assessment model, and then performing pre-credit assessment by using the trained pre-credit assessment model, so that data combination of a plurality of banking institutions is realized, the performance of the pre-credit assessment model is improved, the privacy data of users are prevented from being revealed, the monitoring is convenient, and the risk of data violation is reduced.
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 pre-credit evaluation in accordance with an embodiment of the present invention;
FIG. 2 is a flowchart of obtaining a user feature vector according to an embodiment of the present invention;
FIG. 3 is a flowchart of a pre-credit evaluation method according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a pre-credit evaluation apparatus according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of obtaining a user feature vector according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of another embodiment of a pre-credit evaluation apparatus according to the present invention;
FIG. 7 is a schematic diagram of a computer apparatus according to 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.
With the development of the fields of machine learning and artificial intelligence, the application of machine learning in the field of loan credit assessment of commercial banks is an exploration direction at present. However, machine learning needs a large amount of data as a learning basis, data of each commercial bank is limited, and a good-quality machine model is difficult to train only by means of the data of the commercial bank. If the data of other banking institutions are combined for training, the privacy of the user data of each banking institution can be revealed.
Specifically, in the prior art, when a machine learning algorithm is used for loan credit assessment, multiple institution data are generally centralized, and a machine learning algorithm model is used for loan credit assessment, so that privacy data of a user can be leaked, supervision is difficult, and the risk of data violation is increased.
Fig. 1 is a flowchart of a method for evaluating credit before credit according to an embodiment of the present invention, as shown in fig. 1, the method includes:
In an embodiment, the customer account information includes: basic information of customers, liabilities of assets, income types, cross-border frequency, credit card limits, overdue credit cards and other dimension information.
And 102, carrying out abnormal data cleaning on the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions.
In specific implementation, data cleaning and processing are carried out on the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions to obtain a wide table, then abnormal data cleaning is carried out on the wide table by using a null filling method, and invalid data are removed.
103, constructing a training sample set according to the customer account information, the loan blacklist user information and the loan whitelist user information of the plurality of banking institutions subjected to abnormal data cleaning, wherein the training sample set comprises positive sample information and negative sample information, the loan whitelist user information is positive sample information, and the loan blacklist user information is negative sample information.
And 104, training the pre-credit evaluation model according to the training sample set to obtain the trained pre-credit evaluation model.
In specific implementation, the method participates in federal learning of each commercial bank data end based on the training sample set samples to obtain a credit evaluation model before commercial bank credit.
And 105, evaluating the credit before the credit by using the trained credit before the credit evaluating model.
The method for evaluating the credit before credit provided by the embodiment of the invention comprises the steps of cleaning abnormal data of the client account information, the loan blacklist user information and the loan white list user information of a plurality of banking institutions by acquiring the client account information, the loan blacklist user information and the loan white list user information of the banking institutions, constructing a training sample set according to the client account information, the loan blacklist user information and the loan white list user information of the banking institutions after cleaning the abnormal data, training a credit evaluation model before credit according to the training sample set to obtain a trained credit before credit evaluation model, and evaluating the credit before credit by using the trained credit evaluation model before credit evaluation, so that data combination of the banking institutions is realized, the performance of the credit evaluation model before credit is improved, and the privacy data of a user is prevented from being leaked, the monitoring is convenient, and the data violation risk is reduced.
In order to facilitate to obtain the user information intuitively, after performing abnormal data cleaning on the client account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions, as shown in fig. 2, the method further includes:
In specific implementation, the continuous features can be discretized by using an isopachous method, and the WOE (weight of Evidence weight) is used for quantifying the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions after abnormal data cleaning to obtain the feature vector of the user.
Fig. 3 is a flowchart of a method for evaluating credit before credit according to an embodiment of the present invention, as shown in fig. 3, the method further includes:
In specific implementation, based on the training set samples, each banking institution locally uses the lightGBM algorithm to complete training of a local model, each model parameter is encrypted and uploaded to the aggregation server, and the aggregation server updates and aggregates the model parameters uploaded by each banking institution to obtain the aggregation model parameters. Among them, lightGBM is a gradient boosting framework, using a tree-based learning algorithm. It is a version of XGB boost performance with similar accuracy and 20 times faster training speed than other GBMs.
Further, after performing update aggregation processing on the trained model, the method further includes:
and detecting whether the credit evaluation model before credit is converged, and if not, returning the parameters of the aggregated model to a plurality of banking institutions to continue iterative training.
And during specific implementation, detecting whether the pre-credit evaluation model is converged, if not, returning the parameters of the aggregation model to a plurality of banking institutions to continue iterative training, and taking the parameters of the aggregation model as final parameters of the model to be trained when the model to be trained is in a convergence state to obtain the trained pre-credit evaluation model.
And if the convergence occurs, the aggregation model parameters in the iteration and the converged aggregation model parameters are downloaded to each banking institution.
Based on the same inventive concept, the embodiment of the present invention further provides a pre-credit evaluation device, as described in the following embodiments. Since the principle of the pre-credit evaluation apparatus for solving the problem is similar to that of the pre-credit evaluation method, the implementation of the pre-credit evaluation apparatus can be referred to the implementation of the pre-credit evaluation method, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 4 is a schematic structural diagram of a pre-credit evaluation apparatus according to an embodiment of the present invention, as shown in fig. 4, the apparatus includes:
the information obtaining module 401 is configured to obtain client account information, loan blacklist user information, and loan whitelist user information of multiple banking institutions.
And the data cleaning module 402 is used for performing abnormal data cleaning on the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions.
The training sample set constructing module 403 is configured to construct a training sample set according to the customer account information, the loan blacklist user information, and the loan whitelist user information of the multiple banking institutions subjected to abnormal data cleaning, where the training sample set includes positive sample information and negative sample information, where the loan whitelist user information is positive sample information, and the loan blacklist user information is negative sample information.
And the model training module 404 is configured to train the pre-credit evaluation model according to the training sample set to obtain a trained pre-credit evaluation model.
And a credit evaluation module 405 for performing pre-credit evaluation using the trained pre-credit evaluation model.
In the embodiment of the present invention, as shown in fig. 5, the apparatus further includes:
the feature vector obtaining module 501 is configured to perform quantization processing on the customer account information, the loan blacklist user information, and the loan whitelist user information of the multiple banking institutions after the abnormal data is cleaned, so as to obtain a feature vector of the user.
Fig. 6 is a schematic diagram of another structure of a pre-credit evaluation apparatus according to an embodiment of the present invention, as shown in fig. 6, the apparatus further includes:
and an update aggregation processing module 601, configured to perform update aggregation processing on the trained model to obtain an aggregation model parameter.
In an embodiment of the present invention, the apparatus further includes:
and the model detection module is used for detecting whether the pre-credit evaluation model is converged or not, and if not, returning the aggregated model parameters to a plurality of banking institutions to continue iterative training.
To achieve the above object, according to another aspect of the present application, there is also provided a computer apparatus. As shown in fig. 7, the computer device comprises a memory, a processor, a communication interface and a communication bus, wherein a computer program that can be run on the processor is stored in the memory, and the steps of the method of the above embodiment are realized when the processor executes the computer program.
The processor may be a Central Processing Unit (CPU). The Processor may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or a combination thereof.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and units, such as the corresponding program units in the above-described method embodiments of the present invention. The processor executes various functional applications of the processor and the processing of the work data by executing the non-transitory software programs, instructions and modules stored in the memory, that is, the method in the above method embodiment is realized.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor, and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and such remote memory may be coupled to the processor via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more units are stored in the memory and when executed by the processor perform the method of the above embodiments.
An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program for executing the above method is stored.
In summary, the present invention obtains the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions, abnormal data cleaning is carried out on the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions, and constructing a training sample set according to the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions after abnormal data cleaning, training the pre-credit assessment model according to the training sample set to obtain the trained pre-credit assessment model, and then performing pre-credit assessment by using the trained pre-credit assessment model, so that data combination of a plurality of banking institutions is realized, the performance of the pre-credit assessment model is improved, the privacy data of users are prevented from being revealed, the monitoring is convenient, and the risk of data violation is reduced.
In addition, the prior art generally adopts the following method when performing credit evaluation:
1. and evaluating the credit of the user by using information such as a credit investigation report of the user and adopting a complex auditing process, and approving the credit service. The method adopts a complicated audit process for auditing manually, has high labor cost, long audit period, low efficiency and strong subjective factor period, and is easy to introduce uncertain risks.
2. The credit scoring model is constructed by utilizing historical consumption behaviors and user personal credit investigation and mainly using statistical methods such as discriminant analysis, linear regression, Logistic regression and the like. The method has the advantages that the dimension of analysis data is single, so that the accuracy of a model is poor, most of the analysis data are linear modeling methods, and the relation between user information and credit score cannot be accurately reproduced.
Based on the above, the invention utilizes the machine learning technology to automatically judge the credit condition of the borrower, thereby improving the examination and approval efficiency, reducing the operation cost and eliminating the subjective factors. The invention can comprehensively analyze the multi-dimensional data of the user to predict the credit performance capability of the user and overcome the defect of single dimension of the analysis data of the original scheme. The method is driven by data, and lightegbm is used for predicting the performance capability of a client, so that the highly nonlinear and non-monotonic relation between a dependent variable and an independent variable is solved, and the defect that the traditional analysis method only analyzes the linear relation is overcome.
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 method for assessing pre-credit, comprising:
the method comprises the steps of obtaining customer account information, loan blacklist user information and loan whitelist user information of a plurality of banking institutions;
abnormal data cleaning is carried out on the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions;
constructing a training sample set according to customer account information, loan blacklist user information and loan whitelist user information of a plurality of banking institutions subjected to abnormal data cleaning, wherein the training sample set comprises positive sample information and negative sample information, the loan whitelist user information is positive sample information, and the loan blacklist user information is negative sample information;
training a pre-credit evaluation model according to the training sample set to obtain a trained pre-credit evaluation model;
and carrying out pre-credit evaluation by using the trained pre-credit evaluation model.
2. The method of claim 1, wherein after performing exception data cleansing on customer account information, loan blacklist user information, and loan whitelist user information for a plurality of banking institutions, the method further comprises:
and carrying out quantitative processing on the customer account information, the loan blacklist user information and the loan whitelist user information of the plurality of banking institutions after abnormal data cleaning so as to obtain the feature vector of the user.
3. The method of claim 1, further comprising:
and updating and aggregating the trained model to obtain the parameters of the aggregated model.
4. The method of claim 3, wherein after performing the update aggregation process on the trained models, the method further comprises:
and detecting whether the credit evaluation model before credit is converged, and if not, returning the parameters of the aggregated model to a plurality of banking institutions to continue iterative training.
5. A pre-credit evaluation apparatus, comprising:
the information acquisition module is used for acquiring the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions;
the data cleaning module is used for cleaning abnormal data of the customer account information, the loan blacklist user information and the loan whitelist user information of a plurality of banking institutions;
the system comprises a training sample set construction module, a data processing module and a data processing module, wherein the training sample set construction module is used for constructing a training sample set according to customer account information, loan blacklist user information and loan whitelist user information of a plurality of banking institutions subjected to abnormal data cleaning, and the training sample set comprises positive sample information and negative sample information, wherein the loan whitelist user information is positive sample information, and the loan blacklist user information is negative sample information;
the model training module is used for training the pre-credit evaluation model according to the training sample set to obtain a trained pre-credit evaluation model;
and the credit evaluation module is used for carrying out pre-credit evaluation by utilizing the trained pre-credit evaluation model.
6. The apparatus of claim 5, wherein the apparatus further comprises:
and the characteristic vector acquisition module is used for carrying out quantitative processing on the customer account information, the loan blacklist user information and the loan whitelist user information of the plurality of banking institutions after abnormal data cleaning so as to acquire the characteristic vector of the user.
7. The apparatus of claim 5, further comprising:
and the updating and aggregating processing module is used for updating and aggregating the trained model to obtain the parameters of the aggregation model.
8. The apparatus of claim 7, wherein the apparatus further comprises:
and the model detection module is used for detecting whether the pre-credit evaluation model is converged or not, and if not, returning the aggregated model parameters to a plurality of banking institutions to continue iterative training.
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 implements the method of any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 4.
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| CN112598311A (en) * | 2020-12-29 | 2021-04-02 | 中国农业银行股份有限公司 | Risk operation identification model construction method and risk operation identification method |
| CN113592623A (en) * | 2021-07-20 | 2021-11-02 | 浙江惠瀜网络科技有限公司 | Construction method of risk assessment system before vehicle loan and credit and risk assessment method |
| CN113793210A (en) * | 2021-09-17 | 2021-12-14 | 吉林亿联银行股份有限公司 | Method for evaluating network loan credit, related device and computer storage medium |
| CN113744049A (en) * | 2021-09-18 | 2021-12-03 | 中国银行股份有限公司 | Personal credit investigation score obtaining method, device, server and medium |
| CN113935826A (en) * | 2021-10-21 | 2022-01-14 | 阿尔法时刻科技(深圳)有限公司 | Credit account management method and system based on user privacy |
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