CN110930248A - Credit risk prediction model construction method and system, storage medium and electronic equipment - Google Patents
Credit risk prediction model construction method and system, storage medium and electronic equipment Download PDFInfo
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Abstract
The invention relates to a credit risk prediction model construction method and system, a storage medium and electronic equipment, wherein the method comprises the following steps: constructing a feature library, wherein the feature library comprises a plurality of feature variables for predicting the credit risk of the enterprise; constructing a training sample set according to a plurality of predefined risk types, wherein the training samples comprise black samples and white samples; training respectively aiming at each risk type based on the feature library and the training sample set to obtain a corresponding prediction model; and fusing the obtained multiple prediction models to obtain the enterprise credit risk prediction model. The method is particularly suitable for predicting the credit wind direction of small and medium-sized enterprises which lack transaction data, and the accuracy of the prediction result can be improved by adopting the prediction model constructed by the method to predict the credit risk of the small and medium-sized enterprises without being limited by the transaction data.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a credit risk prediction model construction method and system, a storage medium and electronic equipment.
Background
Based on the big data era, the system can effectively help enterprises or others to create more value by collecting and analyzing various data generated in enterprise operation. For example, by analyzing the hot-market type and buying crowd of a product, a business may be helped to make more accurate product marketing strategies. For another example, analysis of the transaction data of the enterprise can help the enterprise form a credit profile, which helps the enterprise to perform financing or loan. Taking the enterprise credit assessment as an example, although the traditional credit assessment model can evaluate the enterprise credit risk to a certain extent, the data of the traditional credit assessment model is too dependent on transaction data, and for small and medium enterprises lacking loan experience and transaction behaviors, the small and medium enterprises with missing or incomplete credit records can be automatically considered as having a higher credit risk, and then financing or loan of the small and medium enterprises is affected. In other words, the existing evaluation model is not targeted and is not suitable for small and medium-sized enterprises lacking transaction data, and accordingly the credit risk prediction result of the small and medium-sized enterprises is inaccurate.
Disclosure of Invention
The invention aims to provide a method and a system for constructing a credit risk prediction model particularly suitable for small and medium-sized enterprises, aiming at small and medium-sized enterprises.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
in a first aspect, an embodiment of the present invention provides a method for constructing a credit risk prediction model, including the following steps:
constructing a feature library, wherein the feature library comprises a plurality of feature variables for predicting the credit risk of the enterprise;
constructing a training sample set according to a plurality of predefined risk types, wherein the training samples comprise black samples and white samples;
training respectively aiming at each risk type based on the feature library and the training sample set to obtain a corresponding prediction model;
and fusing the obtained multiple prediction models to obtain the enterprise credit risk prediction model.
In the method, the characteristic variables relate to multiple aspects of enterprises when the characteristic library is built, not only transaction data, but also multiple risk types are defined when the prediction model is built, and then multiple prediction models are built first, and finally the multiple models are fused to obtain the final prediction model, so that the final prediction model has higher prediction accuracy.
In a first possible implementation manner of the first aspect, in the step of constructing the feature library, each feature variable is initially determined, then the initially determined feature variables are screened again based on the WOE binning processing and IV value measurement, and the screened feature variables are selected into the feature library.
In a second possible implementation of the first aspect, the predefined plurality of risk types includes a cancellation risk, an advertised risk, a loan default risk, a trade default risk, an executed risk, and a loss of credit risk.
In a third possible implementation of the first aspect, for each risk type, a corresponding prediction model is trained based on a logistic regression algorithm, respectively.
In a fourth possible implementation manner of the first aspect, the obtained multiple prediction models are fused based on a logistic regression algorithm to obtain the enterprise credit risk prediction model.
In a second aspect, an embodiment of the present invention also provides a credit risk prediction model building system, including:
the system comprises a characteristic library construction module, a characteristic library prediction module and a characteristic analysis module, wherein the characteristic library construction module is used for constructing a characteristic library, and the characteristic library comprises a plurality of characteristic variables for predicting the credit risk of an enterprise;
the system comprises a sample set construction module, a risk classification module and a risk classification module, wherein the sample set construction module is used for constructing a training sample set according to a plurality of predefined risk types, and the training samples comprise black samples and white samples;
the model training module is used for training each risk type respectively based on the feature library and the training sample set to obtain a corresponding prediction model;
and the model fusion module is used for fusing the obtained multiple prediction models to obtain the enterprise credit risk prediction model.
In a first possible implementation manner of the second aspect, when the feature library is constructed by the feature library construction module, each feature variable is preliminarily determined, then the preliminarily determined feature variables are screened again based on the WOE binning processing and the IV value measurement, and the screened feature variables are selected into the feature library.
In a second possible embodiment of the second aspect, the predefined plurality of risk types includes a cancellation risk, an advertised risk, a loan default risk, a trade default risk, an executed risk, and a loss of credit risk.
In still another aspect, the present invention also provides a computer-readable storage medium including computer-readable instructions, which, when executed, cause a processor to perform the operations of the method described in the present invention.
In another aspect, an embodiment of the present invention also provides an electronic device, including: a memory storing program instructions; and the processor is connected with the memory and executes the program instructions in the memory to realize the steps of the method in the embodiment of the invention.
Compared with the prior art, the method and the system have the advantages that the characteristic variables relate to multiple aspects of enterprises when the characteristic library is built, the characteristic variables do not relate to transaction data, multiple risk types are defined when the prediction model is built, then multiple prediction models are built, and finally the multiple models are fused to obtain the final prediction model, so that the finally obtained prediction model has higher prediction accuracy.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a method for constructing an enterprise credit risk prediction model provided in an embodiment.
FIG. 2 is a block diagram of a system for constructing an enterprise credit risk prediction model provided in an embodiment.
Fig. 3 is a block diagram of the electronic device provided in the embodiment.
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. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the method for constructing an enterprise credit risk prediction model provided in this embodiment is particularly suitable for small and medium-sized enterprises. It should be noted that the small and medium-sized enterprises herein mainly refer to enterprises lacking loan experience and/or trading behavior, and not necessarily small and medium-sized enterprises classified on the size of people or registered funds or in other ways. And the judgment on whether the loan experience and the transaction behavior are lack can be defined by self. Theoretically, the method of the embodiment can also be applied to credit risk prediction of large enterprises with large amount of transaction data.
Specifically, the method for constructing the credit risk prediction model provided in this embodiment includes the following steps:
s10, constructing a feature library, wherein a plurality of feature variables for predicting the credit risk of the enterprise are stored in the feature library.
And S20, constructing a training sample set according to the preset multiple risk types, wherein the training samples comprise black samples and white samples. For each sample, the data values of the feature variables stored in the feature library are included, but it is possible that the data value of one or more feature variables is zero or null.
And S30, training for each risk type respectively based on the feature library and the training sample set to obtain a corresponding prediction model.
And S40, fusing the obtained multiple prediction models to obtain a final enterprise credit risk prediction model.
More specifically, in step S10, the feature library is constructed by two steps:
and step A, setting characteristic variables based on the data obtained in the database and expert experience, and selecting some commonly used characteristic variables at present, wherein the characteristic variables mainly relate to several dimensions of comprehensive strength risk, enterprise operation risk, enterprise development risk, enterprise integrity risk, static related party risk and dynamic related party risk.
The comprehensive strength risk mainly reflects the risks of stockholder background, capital background and qualification of the enterprise, and the characteristic variables can be, for example, the industry to which the enterprise belongs, whether the enterprise is on the market, whether the enterprise is a national enterprise, the number of the enterprises in the unnatural stockholder country and the like; the enterprise operation risk mainly reflects risks in aspects of industrial and commercial change conditions, talent structure rationalization, public sentiment and the like of an enterprise, and the characteristic variable can be, for example, the change times of legal people in the last year, the proportion of the local students and the like; the enterprise development risk mainly reflects risks of the enterprise in aspects of external financing expansion, branch management conditions and the like, and the characteristic variable can be the number of branch companies and the like; the enterprise integrity risk mainly reflects the risk of the enterprise in terms of lawsuits, administrative penalties, abnormal operation and tax owed, and the characteristic variable can be, for example, the total number of times the enterprise is executed, the total amount of money the enterprise is executed and the like; the static related party risk mainly reflects the credit of the enterprise related party, illegal financing, legal proceedings, abnormal operation and administrative penalties, and the characteristic variable can be the number of financial companies of the external job class of natural stakeholders, the number of times of the related party being executed and the like; the dynamic associator risk mainly reflects the dynamic change risk of enterprise associator reputation, illegal financing, abnormal operation and administrative penalty, and the characteristic variable can be the growth rate of the associator executed in the last year and the like.
And B, re-screening the characteristic variables selected preliminarily in the step A, determining the finally used characteristic variables, and putting the characteristic variables into a characteristic library. When the screening is carried out in the step, the screening is carried out based on WOE box separation processing and IV value measurement and calculation.
The WOE binning is to divide variables with continuous values into a plurality of discrete classes, so that the interference of extreme data, abnormal data and missing data on the model is avoided, and the training efficiency and the prediction accuracy of the model are improved conveniently. And automatic box separation is adopted in the box separation process. For the class type characteristic variable, one class is a sub-box; for numerical characteristic variables, the number of bins < =4, and if a certain data value is more than a certain proportion among all data values of a certain characteristic variable, the certain data value is taken as a single bin.
The IV value of the single characteristic variable can be measured based on WOE classification to evaluate the distinguishing capability of the single characteristic variable. The IV value is the distinguishing capability of the characteristic variable on the black and white sample, and represents the proportion difference of the characteristic variable on the black and white sample in different value groups, and the larger the IV value is, the larger the distinguishing capability of the characteristic variable on the black and white sample is. The IV value is typically measured as: IV is at [0.02, 0.1), the characteristic variable has weak discriminative power, IV is at [0.1, 0.3), the characteristic variable has medium discriminative power, IV is greater than or equal to 0.3, the characteristic variable has strong discriminative power. Therefore, by WOE binning and IV value measurement, the characteristic variable with the IV value greater than or equal to 0.3 is screened out.
The characteristic variables screened by the method have higher distinguishing capability, and the operation amount can be reduced after the screening process, so that the efficiency is improved.
More specifically, in step S20, each risk type needs black samples and white samples for training, in the embodiment, the enterprises of the six blacklist types shown in table 1 are all defined as black samples for the black samples, and the enterprises outside the black samples are all white samples. The enterprise credit risk is jointly predicted by the 6 risk types.
TABLE 1
| Risk type indication | Blacklist definition | Risk type definitions |
| A | Enterprises being amortized in the presentation period | Risk of lifting pin |
| B | Enterprises with official documents during their presentation period | Is reported the risk |
| C | Enterprises with loan default performance in the performance period | Loan breach risk |
| D | Enterprises with trade default performance in the performance period | Risk of default for business |
| E | Enterprises having records executed during the presentation period and having executed amounts greater than their registered capital | Risk of being executed |
| F | Enterprises released as trust-losing executives during presentation | Risk of losing confidence |
The presentation period is self-set, and the presentation period is a time length (range) of data selection, for example, if the presentation period is set to one year, then for type a, the blacklist is defined as the enterprises that were revoked in the last year, i.e., the enterprises that were revoked in the last year constitute the blacklist, and the enterprises that were not revoked in the last year constitute the whitelist.
The various risk types shown in table 1 are direct reflections of enterprise credit, so that the risk types are defined as shown in table 1, and a plurality of prediction models are constructed based on the risk types, which is helpful for improving the accuracy of the prediction results of the finally obtained prediction models.
In step S30, the logistic regression algorithm is used to train the 6 risk types shown in table 1, and corresponding prediction models are obtained. The method adopts logistic regression of AddaptiveLasso to carry out feature variable screening and regression coefficient estimation, on one hand, high-correlation and insignificant feature variables in partial modules are removed, and on the other hand, good statistical properties of model coefficient estimation are guaranteed.
The number and the type of the feature variables included in the prediction models corresponding to different risk types may be different, and the same feature variables may also be used. For example, for type A, the prediction model is yA=kA1x1+kA2x2+kA3x3+kA4x4+kA5x5+kA6x6+kA7x7While for type B, the prediction model is yB=kB1x1+kB2x4+kB3x8+kB4x11+kB5x13X represents a characteristic variable, kAOr kBWeight coefficient, k, representing the correspondence of characteristic variablesA1+kA2+…+kA7=1，kB1+kB2+…+kB5And = 1. The two prediction models include the same characteristic variable x1And x4Different characteristic variables are also included, respectively.
Since the logistic regression algorithm is a mature algorithm, the specific logistic regression process will not be described in detail here in this embodiment. Based on the types identified in table 1, 6 prediction models were obtained after this step.
In step S40, the obtained 6 prediction models are fused, and weight coefficients are distributed by using logistic regression, so as to finally obtain the enterprise credit risk prediction model Y = kAyA+kByB+kCyC+kDyD+kEyE+kFyF. By using the prediction model to predict the credit risk of small and medium-sized enterprises, a predicted value with higher accuracy can be obtained.
Referring to fig. 2, in the present embodiment, a system for constructing an enterprise credit risk prediction model is provided, which includes:
and the characteristic library construction module is used for constructing a characteristic library, and the characteristic library comprises a plurality of characteristic variables for predicting the credit risk of the enterprise.
And the sample set construction module is used for constructing a training sample set according to a plurality of predefined risk types, wherein the training samples comprise black samples and white samples.
The model training module is used for training each risk type respectively based on the feature library and the training sample set to obtain a corresponding prediction model; for each risk type, a corresponding prediction model can be obtained based on logistic regression algorithm training.
And the model fusion module is used for fusing the obtained multiple prediction models to obtain the enterprise credit risk prediction model. In specific implementation, the obtained multiple prediction models can be fused based on a logistic regression algorithm to obtain the enterprise credit risk prediction model.
More specifically, when the feature library is constructed by the feature library construction module, each feature variable is preliminarily determined, then the preliminarily determined feature variables are screened again based on WOE (world wide article) binning processing and IV value measurement, and the screened feature variables are selected into the feature library.
More specifically, the predefined plurality of risk types include a suspension risk, an defendant risk, a loan default risk, a trade default risk, an executed risk, and a loss of credit risk.
The model construction system is based on the same inventive concept of the construction method, and for the specific processing process of each module in the system, the related description of the method can be referred, and is not repeated herein.
As shown in fig. 3, the present embodiment also provides an electronic device, which may include a processor 51 and a memory 52, wherein the memory 52 is coupled to the processor 51. It is noted that this diagram is exemplary and that other types of structures may be used in addition to or in place of this structure to implement data extraction, report generation, communication, or other functionality.
As shown in fig. 3, the electronic device may further include: an input unit 53, a display unit 54, and a power supply 55. It is to be noted that the electronic device does not necessarily have to comprise all the components shown in fig. 3. Furthermore, the electronic device may also comprise components not shown in fig. 3, reference being made to the prior art.
The processor 51, also sometimes referred to as a controller or operational control, may comprise a microprocessor or other processor device and/or logic device, the processor 51 receiving input and controlling operation of the various components of the electronic device.
The memory 52 may be one or more of a buffer, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable devices, and may store the configuration information of the processor 51, the instructions executed by the processor 51, the recorded table data, and other information. The processor 51 may execute a program stored in the memory 52 to realize information storage or processing, or the like. In one embodiment, a buffer memory, i.e., a buffer, is also included in the memory 52 to store the intermediate information.
The input unit 53 is for example used to provide the processor 51 with data of the respective samples. The display unit 54 is used for displaying various results in the processing procedure, such as characteristic variables in the characteristic library, the obtained various prediction models, and the like, and may be, for example, an LCD display, but the present invention is not limited thereto. The power supply 55 is used to provide power to the electronic device.
Embodiments of the present invention further provide a computer readable instruction, where when the instruction is executed in an electronic device, the program causes the electronic device to execute the operation steps included in the method of the present invention.
Embodiments of the present invention further provide a storage medium storing computer-readable instructions, where the computer-readable instructions cause an electronic device to execute the operation steps included in the method of the present invention.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that the various illustrative modules described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided in the present application, it should be understood that the disclosed system may be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not implemented.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A credit risk prediction model construction method is characterized by comprising the following steps:
constructing a feature library, wherein the feature library comprises a plurality of feature variables for predicting the credit risk of the enterprise;
constructing a training sample set according to a plurality of predefined risk types, wherein the training samples comprise black samples and white samples;
training respectively aiming at each risk type based on the feature library and the training sample set to obtain a corresponding prediction model;
and fusing the obtained multiple prediction models to obtain the enterprise credit risk prediction model.
2. The method as claimed in claim 1, wherein the step of constructing the feature library comprises the steps of firstly preliminarily determining each feature variable, then screening the preliminarily determined feature variables again based on WOE binning processing and IV value measurement, and selecting the screened feature variables into the feature library.
3. The method of claim 1, wherein the predefined plurality of risk types includes a cancellation risk, an announced risk, a loan default risk, a trade default risk, an executed risk, and a loss of credit risk.
4. The method of claim 1, wherein for each risk type, a corresponding prediction model is trained based on a logistic regression algorithm.
5. The method according to claim 1, wherein the obtained multiple prediction models are fused based on a logistic regression algorithm to obtain the enterprise credit risk prediction model.
6. A system for building a credit risk prediction model, comprising:
the system comprises a characteristic library construction module, a characteristic library prediction module and a characteristic analysis module, wherein the characteristic library construction module is used for constructing a characteristic library, and the characteristic library comprises a plurality of characteristic variables for predicting the credit risk of an enterprise;
the system comprises a sample set construction module, a risk classification module and a risk classification module, wherein the sample set construction module is used for constructing a training sample set according to a plurality of predefined risk types, and the training samples comprise black samples and white samples;
the model training module is used for training each risk type respectively based on the feature library and the training sample set to obtain a corresponding prediction model;
and the model fusion module is used for fusing the obtained multiple prediction models to obtain the enterprise credit risk prediction model.
7. The system as claimed in claim 6, wherein the feature library construction module is configured to, when constructing the feature library, initially determine each feature variable, and then re-screen the initially determined feature variables based on the WOE binning processing and IV value measurement, and the screened feature variables are selected into the feature library.
8. The system of claim 6, wherein the predefined plurality of risk types includes a cancellation risk, an announced risk, a loan default risk, a trade default risk, an executed risk, and a loss of credit risk.
9. A computer readable storage medium comprising computer readable instructions that, when executed, cause a processor to perform the operations of the method of any of claims 1-5.
10. An electronic device, comprising:
a memory storing program instructions;
a processor coupled to the memory and executing the program instructions in the memory to implement the steps of the method of any of claims 1-5.
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| CN112348353A (en) * | 2020-11-05 | 2021-02-09 | 浪潮软件股份有限公司 | Enterprise confidence loss risk prediction method based on transfer learning |
| CN112580992A (en) * | 2020-12-23 | 2021-03-30 | 成都数联铭品科技有限公司 | Illegal collective risk monitoring system of similar financial enterprises |
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