CN109657931A - Air control model modeling, business risk appraisal procedure, device and storage medium - Google Patents
Air control model modeling, business risk appraisal procedure, device and storage medium Download PDFInfo
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Abstract
This application involves a kind of air control model modelling approach, device, computer equipment and storage mediums, obtain positive sample data set and negative sample data set, validation data set, validation data set, positive sample data set and negative sample data set are inputted machine learning model to be trained to be trained, until after meeting training condition, obtain enterprise's Rating Model, using validation data set, positive sample data set and negative sample data set as the sample characteristics of abnormal test Rating Model, abnormal test Rating Model is obtained；By enterprise's Rating Model in conjunction with abnormal test Rating Model, air control model is obtained, enterprise's Rating Model combination abnormal test Rating Model, which carries out business risk scoring, reduces resultant error, further improves air control model accuracy rate and stability.
Description
Technical field
This application involves Internet technical field, more particularly to a kind of air control model modeling, business risk appraisal procedure,
Device, computer equipment and storage medium.
Background technique
With the continuous development of Internet technology, according to business needs, need to the enterprise having intention as cooperative relationship
Business risk judgement is carried out, in order to avoid causing unnecessary loss, is converted to utilization interconnection by artificially collecting the relevant information of enterprise
Network technology collects data, judges that business risk is changed to air control model and carries out business risk assessment according to user experience.
The modeling of air control at present generally uses traditional machine learning method, such as is established and scored using Logic Regression Models
Card.This supervised learning method is what the negative sample based on historical data learnt, and needs more balanced known mark
The positive negative sample of label.The relevant acquiring way of needs of positive negative sample obtains, such as: obtaining according in the process of exchange between client
Take, but before being traded, air control system can based on the risk judgment to client, decide whether with the client trading, as the visitor
When family is high-risk client, can refuse with the client trading because the client being rejected often without label, causes to train sample
Larger difference is distributed in this distribution and real data, therefore causes the air control model accuracy rate established and stability not high.
Summary of the invention
Based on this, it is necessary in view of the above technical problems, provide a kind of wind for improving air control model accuracy rate and stability
Control model modeling, business risk appraisal procedure, device, computer equipment and storage medium.
A kind of air control model modelling approach, comprising:
Obtain positive sample data set, negative sample data set and validation data set；
The validation data set, the positive sample data set and the negative sample data set are inputted into engineering to be trained
It practises model to be trained, until obtaining enterprise's Rating Model after meeting training condition；
Using the validation data set, the positive sample data set and the negative sample data set as abnormal test scoring mould
The sample characteristics of type obtain abnormal test Rating Model；
By enterprise's Rating Model in conjunction with abnormal test Rating Model, air control model is obtained.
In one of the embodiments, by the validation data set, the positive sample data set and the negative sample data
Collection inputs machine learning model to be trained and is trained, until after meeting training condition, the step of obtaining enterprise's Rating Model,
Include:
The positive sample data set and the negative sample data set are inputted into machine learning model to be trained；
The machine learning model to be trained is based on the positive sample data set and the negative sample data set, according to phase
The data characteristics vector answered, the machine learning model after being trained；
The machine learning model that the validation data set inputs after the training is scored, business risk is obtained and comments
Point；
When business risk scoring within a preset range, meet training condition, obtain enterprise's Rating Model.
In one of the embodiments, by the validation data set, the positive sample data set and the negative sample data
The step of collecting the sample characteristics as abnormal test Rating Model, obtaining abnormal test Rating Model, comprising:
Using the validation data set, the positive sample data set and the negative sample data set as abnormal test scoring mould
The sample characteristics of type carry out feature extraction, obtain the feature vector of each sample characteristics；
Each described eigenvector is clustered, each group variety is obtained；
It is distributed according to the feature space of each group variety, determines to contribute maximum sample characteristics in each group variety；
Using the maximum sample characteristics of contribution degree as the assessment feature of air control model, abnormal test Rating Model is obtained.
In one of the embodiments, by enterprise's Rating Model in conjunction with abnormal test Rating Model, air control is obtained
The step of model, comprising:
Based on the first business risk scoring of enterprise's Rating Model output and the of the output of abnormal test Rating Model
The mode that the scoring of two business risks is averaged is combined, and obtains air control model.
The step of obtaining positive sample data set and negative sample data set, validation data set in one of the embodiments, is wrapped
It includes:
Obtain the enterprise's financial data sample, invoice information sample, enterprise operation data sample of each enterprise；
The enterprise's financial data sample, the invoice information sample, the enterprise operation data sample are based on data
Label is matched with positive negative sample inventory, obtains positive sample data set and negative sample data set；
By the financial data sample, the invoice information sample, the enterprise operation data sample of non-successful match
As validation data set.
A kind of business risk appraisal procedure, which comprises
Obtain enterprise's financial data, the invoice information, enterprise operation data of enterprise to be scored；
By the enterprise's financial data, the invoice information, the enterprise operation data input air control model into
The assessment of row business risk, obtains business risk assessment result.
A kind of air control model modeling device includes: by described device
Data set acquisition module, for obtaining positive sample data set and negative sample data set, validation data set；
Enterprise's Rating Model training module is used for the validation data set, the positive sample data set and the negative sample
Notebook data collection inputs machine learning model to be trained and is trained, until obtaining enterprise's Rating Model after meeting training condition；
Abnormal test Rating Model establishes module, for by the validation data set, the positive sample data set and described
Sample characteristics of the negative sample data set as abnormal test Rating Model obtain abnormal test Rating Model；
Models coupling module, for enterprise's Rating Model in conjunction with abnormal test Rating Model, to be obtained air control mould
Type.
A kind of business risk assessment device, described device include:
Data acquisition module, for obtaining enterprise's financial data, the invoice information, enterprise operation data of enterprise to be scored；
Business risk grading module is used for the enterprise's financial data, the invoice information, the enterprise operation data
The input air control model carries out business risk scoring, obtains business risk value.
A kind of computer equipment, including memory and processor, the memory are stored with computer program, the processing
The step of device realizes the method when executing the computer program.
A kind of computer readable storage medium, is stored thereon with computer program, and the computer program is held by processor
The step of method is realized when row.
Above-mentioned air control model modelling approach, device, computer equipment and storage medium obtain positive sample data set and negative sample
Validation data set, positive sample data set and negative sample data set are inputted engineering to be trained by notebook data collection, validation data set
It practises model to be trained, until enterprise's Rating Model is obtained, by validation data set, positive sample data set after meeting training condition
Sample characteristics with negative sample data set as abnormal test Rating Model obtain abnormal test Rating Model；Enterprise is scored
Model obtains air control model in conjunction with abnormal test Rating Model, and enterprise's Rating Model combination abnormal test Rating Model carries out
Business risk scoring reduces resultant error, further improves air control model accuracy rate and stability.
Above-mentioned business risk appraisal procedure, device, computer equipment and storage medium, by the enterprise for obtaining enterprise to be scored
Industry financial data, invoice information, enterprise operation data；The wind that enterprise's financial data, invoice information, enterprise operation data are inputted
Control model carry out business risk assessment, obtain business risk assessment result, air control model by enterprise's financial data, invoice information,
Enterprise operation data carry out business risk scoring by enterprise's Rating Model in conjunction with abnormal test Rating Model reduces result mistake
Difference improves the accuracy rate of business risk assessment.
Detailed description of the invention
Fig. 1 is the application scenario diagram of air control model modelling approach in one embodiment；
Fig. 2 is the flow diagram of air control model modelling approach in one embodiment；
Fig. 3 is the flow diagram of business risk appraisal procedure in another embodiment；
Fig. 4 is the structural block diagram of air control model modeling device in one embodiment；
Fig. 5 is the structural block diagram that business risk assesses device in one embodiment；
Fig. 6 is the internal structure chart of computer equipment in one embodiment.
Specific embodiment
It is with reference to the accompanying drawings and embodiments, right in order to which the objects, technical solutions and advantages of the application are more clearly understood
The application is further elaborated.It should be appreciated that specific embodiment described herein is only used to explain the application, not
For limiting the application.
Air control model modelling approach provided by the present application, can be applied in application environment as shown in Figure 1.Wherein, eventually
End 102 is communicated with server 104 by network by network.User passes through 102 pairs of execution air control model modeling sides of terminal
The server 104 of method is configured, so that server 104 obtains positive sample data set, negative sample data set and validation data set；
Validation data set, positive sample data set and negative sample data set are inputted machine learning model to be trained to be trained, until
After meeting training condition, enterprise's Rating Model is obtained；Using validation data set, positive sample data set and negative sample data set as different
The sample characteristics for often examining Rating Model, obtain abnormal test Rating Model；By enterprise's Rating Model and abnormal test scoring mould
Type combines, and obtains air control model.Wherein, terminal 102 can be, but not limited to be various personal computers, laptop, intelligence
Mobile phone, tablet computer and portable wearable device, server 104 can use independent server either multiple server groups
At server cluster realize.
In one embodiment, as shown in Fig. 2, providing a kind of air control model modelling approach, it is applied to Fig. 1 in this way
In server for be illustrated, including step S220 to step S280:
Step S220 obtains positive sample data set, negative sample data set and validation data set.
Wherein, positive sample data set refers to by not having risky enterprise or risk lower than the enterprise in the enterprise of preset value
The data set of the generations such as financial data, invoice information, enterprise operation data, the data in positive sample data have positive sample label；
Negative sample data set refers to the enterprise's financial data being higher than in the enterprise of preset value by risk, invoice information, enterprise operation number
According to the data set of equal generations, the data in negative sample data set have negative sample label, and validation data set refers to not having risky
Enterprise, risk are higher than the enterprise of preset value or risk lower than the enterprise's financial data in the enterprise of preset value, invoice information, enterprise
The data set of the generations such as management data, the data that verify data is concentrated do not have positive and negative sample label, the data that verify data is concentrated
It may include the positive sample data set of no positive and negative sample label, the data in negative sample data set.
Validation data set, positive sample data set and negative sample data set are inputted machine learning to be trained by step S240
Model is trained, until obtaining enterprise's Rating Model after meeting training condition.
Wherein, validation data set, positive sample data set and negative sample data set are inputted into machine learning model to be trained
It is trained, so that machine learning model to be trained is trained, machine learning model to be trained obtains each positive sample number
According to the feature vector of collection and the feature vector of negative sample data set, the feature vector and negative sample number of positive sample data set are arrived in study
Positive sample is exported according to the feature vector of collection, and according to the feature vector of positive sample data set and the feature vector of negative sample data set
The corresponding business risk scoring of each data in data set, negative sample data set, the machine learning model after being trained；Pass through
Validation data set verifies the machine learning model after training, the corresponding enterprise's wind of each data that output verify data is concentrated
Danger scoring after business risk scoring meets training condition, obtains enterprise's Rating Model, and each data that verify data is concentrated are corresponding
When business risk is scored within a preset range, meet training condition, preset range is concentrated for user according to each verify data each
The practical risk situation for the enterprise that data indicate determines.
Step S260, using validation data set, positive sample data set and negative sample data set as abnormal test Rating Model
Sample characteristics, obtain abnormal test Rating Model.
Wherein, the data in validation data set, positive sample data set and negative sample data set are corresponding as every profession and trade
Sample characteristics cluster the sample characteristics of every profession and trade, obtain each group variety, are distributed according to the feature space of each group variety, determine
Maximum sample characteristics are contributed in each group variety out, the sample characteristics in each group variety are subjected to dimensionality reduction, obtain feature space distribution；It is logical
It crosses and is distributed according to the feature space of each group variety, determine to contribute maximum sample characteristics in each group variety；By the maximum sample of contribution degree
Assessment feature of the eigen as air control model obtains abnormal test Rating Model.According to company information to the enterprise of different industries
Industry is divided, and for each different industry, the sample characteristics in the sector is taken to cluster, and cluster is that (sample refers to for research
Mark) classification problem a kind of statistical analysis technique, while be also data mining an important algorithm；The mode of cluster can be
It is several below: k-means algorithm, K-MEDOIDS algorithm etc.；There are many sample characteristics of every profession and trade, provide information abundant,
But the workload of data acquisition is also increased to a certain extent, it is often more important that in many situations, between many variables
There may be correlations, to increase the complexity of case study, if analyzed respectively each index, analysis is often
Be it is isolated, the information in data cannot be fully utilized, therefore blindly reduce index to lose many useful information, to produce
The conclusion of mistake is given birth to, dimensionality reduction mode can have: singular value decomposition (SVD), principal component analysis (PCA), factorial analysis (FA), independence
Constituent analysis (ICA) etc., using abnormal test Rating Model as a part of air control model, without being marked to sample data
Note, the client being rejected can also be used to solve positive and negative sample imbalance as sample data often without label
Problem.
Step S280 obtains air control model by enterprise's Rating Model in conjunction with abnormal test Rating Model.
Wherein, air control model can will need the enterprise characteristic for carrying out business risk assessment to input enterprise's Rating Model respectively
It is exported with abnormal test Rating Model, the business risk scoring and abnormal test Rating Model for obtaining the output of enterprise's Rating Model
Business risk scoring, the business risk of business risk scoring and the output of abnormal test Rating Model that enterprise's Rating Model is exported
Scoring is averaged, and average value is the final business risk scoring of air control model output.
In above-mentioned air control model modelling approach, positive sample data set and negative sample data set, validation data set are obtained, will be tested
Card data set, positive sample data set and negative sample data set input machine learning model to be trained and are trained, until meeting
After training condition, enterprise's Rating Model is obtained, is examined using validation data set, positive sample data set and negative sample data set as abnormal
The sample characteristics of Rating Model are tested, abnormal test Rating Model is obtained；By enterprise's Rating Model and abnormal test Rating Model knot
It closes, obtains air control model, carrying out business risk scoring in conjunction with abnormal test Rating Model reduces resultant error, further improves
Air control model accuracy rate and stability.
In one embodiment, validation data set, positive sample data set and negative sample data set are inputted into machine to be trained
Device learning model is trained, until after meeting training condition, the step of obtaining enterprise's Rating Model, comprising: by positive sample number
Machine learning model to be trained is inputted with negative sample data set according to collection；Machine learning model to be trained is based on positive sample data
Collection and negative sample data set, the machine learning model according to corresponding data characteristics vector, after being trained；By validation data set
Machine learning model after input training scores, and obtains business risk scoring；When business risk scoring within a preset range,
Meet training condition, obtains enterprise's Rating Model.
Wherein, machine learning model to be trained is by the positive sample data set of input and negative sample data set, by each positive sample
Data in notebook data collection and negative sample data set carry out characteristic vector pickup and are learnt according to corresponding data characteristics vector
To corresponding data characteristics vector, machine learning model after being trained, by validation data set to the engineering after training
It practises model to be verified, the feature vector for the data that the machine learning model after training is concentrated based on verify data determines verifying
The feature vector of data in data set is similar, or the and negative sample that carries out feature vector with the data in positive sample data set
Data progress feature vector in data set is similar, the corresponding business risk scoring of each data that output verify data is concentrated, enterprise
After industry risk score meets training condition, enterprise's Rating Model, the corresponding business risk of each data that verify data is concentrated are obtained
When scoring within a preset range, meet training condition, preset range is that user indicates according to each data that each verify data is concentrated
Enterprise practical risk situation determine, the machine learning model after training is verified by validation data set, further
Improve air control model accuracy rate.
In one embodiment, it is commented using validation data set, positive sample data set and negative sample data set as abnormal test
The sample characteristics of sub-model, obtain abnormal test Rating Model the step of, comprising: by validation data set, positive sample data set and
Negative sample data set carries out feature extraction as the sample characteristics of abnormal test Rating Model, obtain the features of each sample characteristics to
Amount；Each feature vector is clustered, each group variety is obtained；It is distributed according to the feature space of each group variety, determines tribute in each group variety
Offer maximum sample characteristics；Using the maximum sample characteristics of contribution degree as the assessment feature of air control model, obtains abnormal test and comment
Sub-model.
Wherein, after establishing abnormal test Rating Model further include: using in industry all characteristic M and enterprise it is extremely special
Business risk scoring is modified greater than the characteristic P of average level in sign, obtains abnormal test Rating Model；Specifically:
All enterprise characteristic numbers of input abnormal test Rating Model are referred to by all characteristic M in industry, all characteristic M
Amount；Enterprise's off-note is analyzed, determines the characteristic P for being greater than average level in each enterprise's off-note, enterprise is abnormal
Feature refers to the enterprise's off-note exported by abnormal test Rating Model；According to all characteristic M in industry and greatly
In the characteristic P of average level, determine that the characteristic accounting of enterprise, the characteristic accounting of enterprise refer to enterprise off-note P
Accounting in all characteristic M；According to the difference of the characteristic accounting of enterprise and intermediate accounting, the deviation journey of enterprise is determined
Degree, intermediate accounting refer to that integral level accounting placed in the middle in the industry, intermediate accounting are 0.5；By the departure degree of enterprise with
Business risk scoring is added, and obtains final business risk scoring；Such as: final business risk probability are as follows:
M: all characteristics of specific enterprise in industry.P: it is greater than the characteristic of average level in enterprise's off-note.
The characteristic accounting of enterprise, intermediate accounting are 0.5.The departure degree of enterprise, score: λ: business risk scoring is adjusted
The hyper parameter of the departure degree of score and enterprise is saved, user can adjust the departure degree of λ enterprise according to the actual situation.Pass through base
The preliminary business risk probability is modified in enterprise's off-note, improves the accuracy of business risk assessment result.
Abnormal test model can will be greater than the feature letter of the characteristic P or the characteristic P less than average level of average level
Breath be collectively labeled as exception, for improve enterprise departure degree precision, when enterprise's off-note is both less than the feature of average level
When, the characteristic accounting of enterprise is subtracted by intermediate accounting, determines the preliminary departure degree of enterprise；When the value of preliminary departure degree
When greater than 0, preliminary departure degree is determined as to the departure degree of enterprise, when the value of preliminary departure degree is less than 0, enterprise it is inclined
It is 0 from degree, such as:
Abnormal test model can will be greater than the feature letter of the characteristic P or the characteristic P less than average level of average level
Breath be collectively labeled as exception, for improve enterprise departure degree precision, when enterprise's off-note is both less than the feature of average level
When, the characteristic accounting of enterprise is subtracted by intermediate accounting, determines the preliminary departure degree of enterprise；When the value of preliminary departure degree
When greater than 0, preliminary departure degree is determined as to the departure degree of enterprise, when the value of preliminary departure degree is less than 0, enterprise it is inclined
It is 0 from degree, such as:
In one embodiment, by enterprise's Rating Model in conjunction with abnormal test Rating Model, the step of air control model is obtained
Suddenly, comprising: the second enterprise based on the first business risk scoring of enterprise's Rating Model output with the output of abnormal test Rating Model
The mode that industry risk score is averaged is combined, and obtains air control model.
Wherein, air control model can will need the enterprise characteristic for carrying out business risk assessment to input enterprise's Rating Model respectively
With abnormal test Rating Model, the business risk scoring (scoring of the first business risk) and exception of the output of enterprise's Rating Model are obtained
The business risk of Rating Model output is examined to score (scoring of the second business risk), the business risk that enterprise's Rating Model is exported
The business risk scoring of scoring and the output of abnormal test Rating Model is averaged, and average value is the final of air control model output
Business risk scoring.
In one embodiment, the step of obtaining positive sample data set and negative sample data set, validation data set includes: to obtain
Enterprise's financial data sample, invoice information sample, the enterprise operation data sample of Qu Ge enterprise；By enterprise's financial data sample,
Invoice information sample, enterprise operation data sample are based on data label and are matched with positive negative sample inventory, obtain positive sample number
According to collection and negative sample data set；The financial data sample, invoice information sample, enterprise operation data sample of non-successful match are made
For validation data set.
Wherein, what positive sample inventory referred to belonging to positive sample data is collected and formulates corresponding label, negative sample
What this inventory referred to belonging to negative sample data is collected and formulates corresponding label, obtains the business finance number of each enterprise
It needs each sample data carrying out positive negative sample division according to sample, invoice information sample, enterprise operation data sample, can be based on
Data label can be matched by positive negative sample inventory, positive sample data set and negative sample data set be obtained, such as positive and negative
Not having the data of data label to belong to uncertain in sample inventory is therefore positive sample or negative sample can be used as verifying number
According to the data of concentration, no label data can also be used in the foundation of air control model, client is solved and be rejected and lose
The problem of negative sample.
In one embodiment, as shown in figure 3, providing a kind of business risk appraisal procedure, the method includes the steps
S420 to step S440:
Step S420 obtains enterprise's financial data, the invoice information, enterprise operation data of enterprise to be scored.
Wherein, the enterprise's financial data of enterprise to be scored, invoice information, enterprise operation data can be obtained to database,
It is also possible to user to input by terminal, the enterprise and each side that enterprise's financial data is embodied by Funds Movement in the process of reproduction
The data of the economic relation in face；Invoice refers to all entity and individual in purchasing and selling commodities, offer or receives service and be engaged in
In other business activities, the business voucher issued and collected, invoice information refers to all information on invoice, such as: the time
Information, invoice codes information, affiliated enterprise's information, tax items information etc.；Enterprise operation data refer to that the enterprise summarizes and comments
The analysis indexes of valence financial position of the enterprise and management performance, including debt paying ability index, operation ability index, Profitability Index
With developing ability index.
Enterprise's financial data, invoice information, enterprise operation data input air control model are carried out business risk by step S440
Assessment obtains business risk assessment result.
Wherein, enterprise's financial data, invoice information, enterprise operation data are inputted into air control model, the enterprise of air control model
Rating Model is analyzed according to enterprise's financial data, invoice information, enterprise operation data, obtains the scoring of the first business risk；
Abnormal test Rating Model is analyzed according to enterprise's financial data, invoice information, enterprise operation data, and output business risk is commented
Point and enterprise's off-note, obtain all characteristics in the affiliated industry of the enterprise；Enterprise's off-note is analyzed, is determined
It is greater than the characteristic of average level in each enterprise's off-note；According to all characteristics in industry and greater than the spy of average level
Number is levied, determines the characteristic accounting of enterprise；According to the difference of the characteristic accounting of enterprise and intermediate accounting, the deviation of enterprise is determined
Degree；The departure degree of enterprise is added with preliminary business risk probability, obtains the scoring of the second business risk；It obtains the first enterprise
The average value of industry risk score and the scoring of the second business risk, which is exported as business risk assessment result.
Above-mentioned business risk appraisal procedure, by the enterprise's financial data, invoice information, the enterprise's warp that obtain enterprise to be scored
Seek data；The air control model of enterprise's financial data, invoice information, the input of enterprise operation data is subjected to business risk assessment, is obtained
Business risk assessment result, air control model score enterprise's financial data, invoice information, enterprise operation data mould by enterprise
Type carries out business risk scoring in conjunction with abnormal test Rating Model reduces resultant error, improves the accurate of business risk assessment
Rate.
It should be understood that although each step in the flow chart of Fig. 2-3 is successively shown according to the instruction of arrow,
These steps are not that the inevitable sequence according to arrow instruction successively executes.Unless expressly stating otherwise herein, these steps
Execution there is no stringent sequences to limit, these steps can execute in other order.Moreover, at least one in Fig. 2-3
Part steps may include that perhaps these sub-steps of multiple stages or stage are not necessarily in synchronization to multiple sub-steps
Completion is executed, but can be executed at different times, the execution sequence in these sub-steps or stage is also not necessarily successively
It carries out, but can be at least part of the sub-step or stage of other steps or other steps in turn or alternately
It executes.
In one embodiment, as shown in figure 4, providing a kind of air control model modeling device, comprising: data set obtains mould
Block 310, enterprise's Rating Model training module 320, abnormal test Rating Model establish module 330 and models coupling module 340,
In:
Data set acquisition module 310, for obtaining positive sample data set and negative sample data set, validation data set；
Enterprise's Rating Model training module 320, for validation data set, positive sample data set and negative sample data set is defeated
Enter machine learning model to be trained to be trained, until obtaining enterprise's Rating Model after meeting training condition；
Abnormal test Rating Model establishes module 330, is used for validation data set, positive sample data set and negative sample data
Collect the sample characteristics as abnormal test Rating Model, obtains abnormal test Rating Model；
Models coupling module 340, for enterprise's Rating Model in conjunction with abnormal test Rating Model, to be obtained air control mould
Type.
In one embodiment, enterprise's Rating Model training module 320 includes: data input cell, is used for positive sample
Data set and negative sample data set input machine learning model to be trained；Enterprise's Rating Model training unit, for wait train
Machine learning model be based on positive sample data set and the negative sample data set, according to corresponding data characteristics vector, obtain
Machine learning model after training；Authentication unit, for commenting the machine learning model after validation data set input training
Point, obtain business risk scoring；Judging unit meets training condition, obtains for scoring within a preset range when business risk
Enterprise's Rating Model.
In one embodiment, it includes: characteristic vector pickup unit that abnormal test Rating Model, which establishes module 330, is used for
Feature is carried out using validation data set, positive sample data set and negative sample data set as the sample characteristics of abnormal test Rating Model
It extracts, obtains the feature vector of each sample characteristics；Cluster cell obtains each group variety for clustering each feature vector；Sample
Eigen determination unit is determined to contribute maximum sample characteristics in each group variety for being distributed according to the feature space of each group variety；
Characteristics determining unit is assessed, for obtaining abnormal inspection using the maximum sample characteristics of contribution degree as the assessment feature of air control model
Test Rating Model.
In one embodiment, models coupling module 340 includes being used for: the first enterprise based on the output of enterprise's Rating Model
The mode that the second business risk scoring that risk score is exported with abnormal test Rating Model is averaged is combined, and obtains wind
Control model.
In one embodiment, data set acquisition module 310 includes being used for: obtaining the enterprise's financial data sample of each enterprise
Sheet, invoice information sample, enterprise operation data sample；By enterprise's financial data sample, invoice information sample, enterprise operation data
Sample is based on data label and is matched with positive negative sample inventory, obtains positive sample data set and negative sample data set；It will not
With successful financial data sample, invoice information sample, enterprise operation data sample as validation data set.
In one embodiment, as shown in figure 5, providing a kind of business risk assessment device, comprising:
Data acquisition module 510, for obtaining enterprise's financial data, invoice information, the enterprise operation number of enterprise to be scored
According to；
Business risk grading module 520, the wind for inputting enterprise's financial data, invoice information, enterprise operation data
It controls model and carries out business risk scoring, obtain business risk value.
Specific about air control model modeling device limits the limit that may refer to above for air control model modelling approach
Fixed, specific about business risk assessment device limits the restriction that may refer to above for business risk appraisal procedure,
This is repeated no more.Modules in above-mentioned air control model modeling device, business risk assessment device can be fully or partially through
Software, hardware and combinations thereof are realized.Above-mentioned each module can be embedded in the form of hardware or independently of the place in computer equipment
It manages in device, can also be stored in a software form in the memory in computer equipment, in order to which processor calls execution or more
The corresponding operation of modules.
In one embodiment, a kind of computer equipment is provided, which can be server, internal junction
Composition can be as shown in Figure 6.The computer equipment include by system bus connect processor, memory, network interface and
Database.Wherein, the processor of the computer equipment is for providing calculating and control ability.The memory packet of the computer equipment
Include non-volatile memory medium, built-in storage.The non-volatile memory medium is stored with operating system, computer program and data
Library.The built-in storage provides environment for the operation of operating system and computer program in non-volatile memory medium.The calculating
The database of machine equipment is for storing the data such as sample characteristics.The network interface of the computer equipment is used for logical with external terminal
Cross network connection communication.To realize a kind of air control model modeling, business risk assessment when the computer program is executed by processor
Method.
It will be understood by those skilled in the art that structure shown in Fig. 6, only part relevant to application scheme is tied
The block diagram of structure does not constitute the restriction for the computer equipment being applied thereon to application scheme, specific computer equipment
It may include perhaps combining certain components or with different component layouts than more or fewer components as shown in the figure.
In one embodiment, a kind of computer equipment, including memory and processor are provided, which is stored with
Computer program, the processor perform the steps of when executing computer program
Obtain positive sample data set, negative sample data set and validation data set；By validation data set, positive sample data set and
Negative sample data set inputs machine learning model to be trained and is trained, until obtaining enterprise's scoring after meeting training condition
Model；Using validation data set, positive sample data set and negative sample data set as the sample characteristics of abnormal test Rating Model, obtain
Obtain abnormal test Rating Model；By enterprise's Rating Model in conjunction with abnormal test Rating Model, air control model is obtained.
In one embodiment, processor execute computer program when also perform the steps of by positive sample data set with
Negative sample data set inputs machine learning model to be trained；Machine learning model to be trained is based on positive sample data set and bears
Sample data set, the machine learning model according to corresponding data characteristics vector, after being trained；Validation data set is inputted and is instructed
Machine learning model after white silk scores, and obtains business risk scoring；When business risk scores within a preset range, satisfaction is instructed
The condition of white silk obtains enterprise's Rating Model.
In one embodiment, it also performs the steps of when processor executes computer program by validation data set, positive sample
The sample characteristics progress feature extraction of notebook data collection and negative sample data set as abnormal test Rating Model, it is special to obtain each sample
The feature vector of sign；Each feature vector is clustered, each group variety is obtained；It is distributed, is determined according to the feature space of each group variety
Maximum sample characteristics are contributed in each group variety；Using the maximum sample characteristics of contribution degree as the assessment feature of air control model, obtain
Abnormal test Rating Model.
In one embodiment, it also performs the steps of when processor executes computer program based on enterprise's Rating Model
The mode that the first business risk scoring of output is averaged with the second business risk scoring of abnormal test Rating Model output
It is combined, obtains air control model.
In one embodiment, the enterprise for obtaining each enterprise is also performed the steps of when processor executes computer program
Financial data sample, invoice information sample, enterprise operation data sample；By enterprise's financial data sample, invoice information sample, enterprise
Industry is managed data sample and is matched based on data label with positive negative sample inventory, and positive sample data set and negative sample data are obtained
Collection；Using the financial data sample of non-successful match, invoice information sample, enterprise operation data sample as validation data set.
In one embodiment, a kind of computer readable storage medium is provided, computer program is stored thereon with, is calculated
Machine program performs the steps of when being executed by processor
Obtain positive sample data set, negative sample data set and validation data set；By validation data set, positive sample data set and
Negative sample data set inputs machine learning model to be trained and is trained, until obtaining enterprise's scoring after meeting training condition
Model；Using validation data set, positive sample data set and negative sample data set as the sample characteristics of abnormal test Rating Model, obtain
Obtain abnormal test Rating Model；By enterprise's Rating Model in conjunction with abnormal test Rating Model, air control model is obtained.
In one embodiment, it is also performed the steps of when computer program is executed by processor by positive sample data set
Machine learning model to be trained is inputted with negative sample data set；Machine learning model to be trained be based on positive sample data set with
Negative sample data set, the machine learning model according to corresponding data characteristics vector, after being trained；Validation data set is inputted
Machine learning model after training scores, and obtains business risk scoring；When business risk scoring within a preset range, meet
Training condition obtains enterprise's Rating Model.
In one embodiment, it is also performed the steps of by validation data set, just when computer program is executed by processor
The sample characteristics progress feature extraction of sample data set and negative sample data set as abnormal test Rating Model, obtains each sample
The feature vector of feature；Each feature vector is clustered, each group variety is obtained；It is distributed, is determined according to the feature space of each group variety
Maximum sample characteristics are contributed in each group variety out；Using the maximum sample characteristics of contribution degree as the assessment feature of air control model, obtain
Obtain abnormal test Rating Model.
In one embodiment, it is also performed the steps of when computer program is executed by processor based on enterprise's scoring mould
The side that the first business risk scoring of type output is averaged with the second business risk scoring of abnormal test Rating Model output
Formula is combined, and obtains air control model.
In one embodiment, the enterprise for obtaining each enterprise is also performed the steps of when computer program is executed by processor
Industry financial data sample, invoice information sample, enterprise operation data sample；By enterprise's financial data sample, invoice information sample,
Enterprise operation data sample is based on data label and is matched with positive negative sample inventory, obtains positive sample data set and negative sample number
According to collection；Using the financial data sample of non-successful match, invoice information sample, enterprise operation data sample as validation data set.
In one embodiment, a kind of computer equipment, including memory and processor are provided, which is stored with
Computer program, the processor execute computer program when perform the steps of obtain enterprise to be scored enterprise's financial data,
Invoice information, enterprise operation data；By enterprise's financial data, invoice information, the input of enterprise operation data the air control model
Business risk assessment is carried out, business risk assessment result is obtained.
In one embodiment, a kind of computer readable storage medium is provided, computer program is stored thereon with, is calculated
Machine program performs the steps of the enterprise's financial data for obtaining enterprise to be scored, invoice information, enterprise's warp when being executed by processor
Seek data；Enterprise's financial data, invoice information, the input of enterprise operation data the air control model are carried out business risk to comment
Estimate, obtains business risk assessment result.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the computer program can be stored in a non-volatile computer
In read/write memory medium, the computer program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein,
To any reference of memory, storage, database or other media used in each embodiment provided herein,
Including non-volatile and/or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM
(PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include
Random access memory (RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms,
Such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhancing
Type SDRAM (ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
Each technical characteristic of above embodiments can be combined arbitrarily, for simplicity of description, not to above-described embodiment
In each technical characteristic it is all possible combination be all described, as long as however, the combination of these technical characteristics be not present lance
Shield all should be considered as described in this specification.
The several embodiments of the application above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the concept of this application, various modifications and improvements can be made, these belong to the protection of the application
Range.Therefore, the scope of protection shall be subject to the appended claims for the application patent.
Claims (10)
1. a kind of air control model modelling approach, comprising:
Obtain positive sample data set, negative sample data set and validation data set；
The validation data set, the positive sample data set and the negative sample data set are inputted into machine learning mould to be trained
Type is trained, until obtaining enterprise's Rating Model after meeting training condition；
Using the validation data set, the positive sample data set and the negative sample data set as abnormal test Rating Model
Sample characteristics obtain abnormal test Rating Model；
By enterprise's Rating Model in conjunction with abnormal test Rating Model, air control model is obtained.
2. air control model modelling approach according to claim 1, which is characterized in that by the validation data set, it is described just
Sample data set inputs machine learning model to be trained with the negative sample data set and is trained, until meeting training condition
Afterwards, the step of obtaining enterprise's Rating Model, comprising:
The positive sample data set and the negative sample data set are inputted into machine learning model to be trained；
The machine learning model to be trained is based on the positive sample data set and the negative sample data set, according to corresponding
Data characteristics vector, the machine learning model after being trained；
The machine learning model that the validation data set inputs after the training is scored, business risk scoring is obtained；
When business risk scoring within a preset range, meet training condition, obtain enterprise's Rating Model.
3. air control model modelling approach according to claim 1, which is characterized in that by the validation data set, it is described just
The sample characteristics of sample data set and the negative sample data set as abnormal test Rating Model obtain abnormal test scoring mould
The step of type, comprising:
Using the validation data set, the positive sample data set and the negative sample data set as abnormal test Rating Model
Sample characteristics carry out feature extraction, obtain the feature vector of each sample characteristics；
Each described eigenvector is clustered, each group variety is obtained；
It is distributed according to the feature space of each group variety, determines to contribute maximum sample characteristics in each group variety；
Using the maximum sample characteristics of contribution degree as the assessment feature of air control model, abnormal test Rating Model is obtained.
4. air control model modelling approach according to claim 1, which is characterized in that by enterprise's Rating Model and exception
The step of examining Rating Model to combine, obtaining air control model, comprising:
The second enterprise based on the first business risk scoring of enterprise's Rating Model output with the output of abnormal test Rating Model
The mode that industry risk score is averaged is combined, and obtains air control model.
5. air control model modelling approach according to claim 1, which is characterized in that obtain positive sample data set and negative sample
The step of data set, validation data set includes:
Obtain the enterprise's financial data sample, invoice information sample, enterprise operation data sample of each enterprise；
The enterprise's financial data sample, the invoice information sample, the enterprise operation data sample are based on data label
It is matched with positive negative sample inventory, obtains positive sample data set and negative sample data set；
Using the financial data sample of non-successful match, the invoice information sample, the enterprise operation data sample as
Validation data set.
6. a kind of business risk appraisal procedure, which comprises
Obtain enterprise's financial data, the invoice information, enterprise operation data of enterprise to be scored；
By the enterprise's financial data, the invoice information, the enterprise operation data input such as any one of claim 1-5
The air control model carries out business risk assessment, obtains business risk assessment result.
7. a kind of air control model modeling device, which is characterized in that include: by described device
Data set acquisition module, for obtaining positive sample data set and negative sample data set, validation data set；
Enterprise's Rating Model training module is used for the validation data set, the positive sample data set and the negative sample number
It inputs machine learning model to be trained according to collection to be trained, until obtaining enterprise's Rating Model after meeting training condition；
Abnormal test Rating Model establishes module, is used for the validation data set, the positive sample data set and the negative sample
Sample characteristics of the notebook data collection as abnormal test Rating Model obtain abnormal test Rating Model；
Models coupling module, for enterprise's Rating Model in conjunction with abnormal test Rating Model, to be obtained air control model.
8. a kind of business risk assesses device, described device includes:
Data acquisition module, for obtaining enterprise's financial data, the invoice information, enterprise operation data of enterprise to be scored；
Business risk grading module, for inputting the enterprise's financial data, the invoice information, the enterprise operation data
Air control model according to any one of claims 1 to 5 carries out business risk scoring, obtains business risk value.
9. a kind of computer equipment, including memory and processor, the memory are stored with computer program, feature exists
In the step of processor realizes any one of claims 1 to 5 the method when executing the computer program.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of method described in any one of claims 1 to 5 is realized when being executed by processor.
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| CN110569904A (en) * | 2019-09-10 | 2019-12-13 | 福建榕基软件股份有限公司 | method for constructing machine learning model and computer-readable storage medium |
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