CN111915155A - Small and micro enterprise risk level identification method and device and computer equipment - Google Patents
Small and micro enterprise risk level identification method and device and computer equipment Download PDFInfo
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Abstract
The invention provides a method and a device for identifying the risk level of a small and micro enterprise and computer equipment, and relates to the technical field of network information. Firstly, determining whether an enterprise to be identified is a small and micro enterprise or not through scale identification of the enterprise to be identified; then, when the enterprise to be identified is a small micro enterprise, acquiring a plurality of dimensional information of the enterprise to be identified based on the enterprise name of the enterprise to be identified, and obtaining a dimensional sub-score of each dimensional information by adopting a dimensional model based on an analytic hierarchy process; and finally, inputting each dimensionality sub-score into a comprehensive risk assessment model to obtain a comprehensive risk score of the enterprise to be identified, and determining the risk level of the enterprise to be identified based on the comprehensive risk score. In the scheme, the robustness of the dimensional model can be improved to the maximum extent through the dimensional model based on the business information of the small micro-enterprise and the related enterprises, so that the dimensional model is good in performance in different application scenes, and the comprehensive credit risk assessment of the small micro-enterprise is accurate.
Description
Technical Field
The invention relates to the technical field of network information, in particular to a method and a device for identifying risk levels of small and micro enterprises and computer equipment.
Background
As for the small and micro enterprises which are the key points for supporting the general finance, the difficulty of blocking the general finance is caused by low accuracy of enterprise risk assessment, and how to accurately determine the risk of the small and micro enterprises is an urgent need of technical personnel in the field.
Disclosure of Invention
In order to solve the problems, the invention provides a method and a device for identifying the risk level of a small micro-enterprise and computer equipment.
In a first aspect of the present invention, a method for identifying a risk level of a small micro-enterprise is provided, which is applied to a computer device, and the method includes:
acquiring an enterprise name of an enterprise to be identified;
identifying the scale of the enterprise to be identified in an enterprise business information database according to the enterprise name;
when the enterprise to be identified is a small micro enterprise, acquiring a plurality of dimensional information of the enterprise to be identified according to the name of the enterprise, wherein the dimensional information comprises: enterprise business and business information, enterprise performance dimension information, enterprise operation dimension information and dimension information of associated enterprises which are associated with the enterprise to be identified, wherein the associated enterprises comprise enterprises with corporate investments or duties of the enterprise to be identified, enterprises with director and high outinvestments or duties of the enterprise to be identified and enterprises with outinvestments of the enterprise to be identified;
calculating to obtain a dimensionality sub-score of the enterprise to be identified on each corresponding dimensionality information by adopting a dimensionality model based on an analytic hierarchy process based on the multiple dimensionality information of the enterprise to be identified;
inputting each dimensionality sub-score of the enterprise to be identified into a trained comprehensive risk assessment model to obtain a comprehensive risk score of the enterprise to be identified;
and determining the risk level of the enterprise to be identified according to the comprehensive risk score of the enterprise to be identified.
Further, the multiple pieces of dimension information include enterprise business information, and the step of calculating the dimension sub-score of the to-be-identified enterprise on each corresponding piece of dimension information by using a dimension model based on an analytic hierarchy process includes:
acquiring enterprise business and business information of the enterprise to be identified, wherein the enterprise business and business information comprises the industry to which the enterprise belongs, the enterprise type, the enterprise establishment time, the area to which the enterprise belongs and the registered capital change times of the enterprise in three years;
and calculating the dimensionality sub-score of the enterprise to be identified in the enterprise business information dimensionality by combining an analytic hierarchy process according to the enterprise business information of the enterprise to be identified.
Further, the multiple pieces of dimension information include enterprise performance dimension information, and the step of calculating a dimension sub-score of the enterprise to be identified on each corresponding dimension information by using a dimension model based on an analytic hierarchy process includes:
acquiring enterprise performance dimension information of the enterprise to be identified, wherein the enterprise performance dimension information comprises the tax penalty number of the enterprise in the last three years, the number of times of losing credit of the enterprise in the last 5 years, the legal action number of the enterprise in the last 3 years, the total executed amount of the enterprise in the last two years and the administrative penalty number of the enterprise in the last two years;
and calculating the dimension sub-score of the enterprise to be identified in the enterprise performance dimension according to the enterprise performance dimension information of the enterprise to be identified by combining an analytic hierarchy process.
Further, the multiple pieces of dimensional information include enterprise business dimensional information, and the step of calculating the dimensional sub-score of the to-be-identified enterprise on each corresponding piece of dimensional information by using a dimensional model based on an analytic hierarchy process includes:
acquiring enterprise operation dimension information of the enterprise to be identified, wherein the enterprise operation dimension information comprises the number of administrative licenses of the enterprise in the last three years, the number of patents of the enterprise in the last three years, the number of relevant judicial practices of enterprise operation, the number of administrative awards acquired by the enterprise in the last three years, and the number of labor disputes of the enterprise in the last three years;
and calculating the dimensionality sub-score of the enterprise to be identified in the enterprise operation dimensionality by combining an analytic hierarchy process according to the enterprise operation dimensionality information of the enterprise to be identified.
Further, the multiple pieces of dimensional information include dimensional information of an associated enterprise related to the to-be-identified enterprise, and the step of obtaining the dimensional sub-score of the to-be-identified enterprise on each corresponding piece of dimensional information by adopting the dimensional model calculation based on the analytic hierarchy process includes:
acquiring dimension information of an associated enterprise associated with the enterprise to be identified, wherein the dimension information of the associated enterprise comprises the average age of the associated enterprise, the average executed times of the associated enterprise, the number of industries of the associated enterprise investing the enterprise, the number of the associated enterprises and the proportion of abnormal enterprises in the associated enterprise;
and calculating by combining an analytic hierarchy process to obtain the dimension sub-score of the enterprise to be identified in the associated enterprise according to the dimension information of the associated enterprise.
Further, the method further comprises the step of pre-training the integrated risk assessment model, which comprises:
obtaining training samples, wherein each training sample comprises each dimensionality sub-score of a sample enterprise, the sample enterprises comprise normal sample enterprises and abnormal sample enterprises, the abnormal sample enterprises comprise enterprises which are used as billed enterprises in financial loan disputes, enterprises which hit a loss-of-credit executed list, and enterprises which hit the executed list and have the executed amount accounting for the registered capital in a proportion exceeding a set proportion;
and inputting the training sample into the comprehensive risk assessment model for training, finishing the training of the comprehensive risk assessment model when the loss function value of the comprehensive risk assessment model is lower than a preset threshold value to obtain a trained comprehensive risk assessment model, and taking the score output by the trained comprehensive risk assessment model as the comprehensive risk score of the enterprise.
Further, the integrated risk assessment model includes: a logistic regression model, a random forest model, a decision tree model and a support vector machine model.
In a second aspect of the present invention, there is provided an apparatus for identifying risk levels of small micro-enterprises, which is applied to computer equipment, the apparatus comprising:
the enterprise name acquisition module is used for acquiring the enterprise name of the enterprise to be identified;
the enterprise scale identification module is used for identifying the scale of the enterprise to be identified in an enterprise business information database according to the enterprise name;
the dimension information acquisition module is used for acquiring a plurality of dimension information of the enterprise to be identified according to the name of the enterprise when the enterprise to be identified is a small micro enterprise, and the dimension information comprises: enterprise business and business information, enterprise performance dimension information, enterprise operation dimension information and dimension information of associated enterprises which are associated with the enterprise to be identified, wherein the associated enterprises comprise enterprises with corporate investments or duties of the enterprise to be identified, enterprises with director and high outinvestments or duties of the enterprise to be identified and enterprises with outinvestments of the enterprise to be identified;
the dimensionality sub-score calculating module is used for calculating dimensionality sub-scores of the to-be-identified enterprise on the corresponding dimensionality information by adopting a dimensionality model based on an analytic hierarchy process based on the multiple dimensionality information of the to-be-identified enterprise;
the comprehensive risk score obtaining module is used for inputting each dimensionality sub-score of the enterprise to be identified into a trained comprehensive risk assessment model to obtain a comprehensive risk score of the enterprise to be identified;
and the risk grade determining module is used for determining the risk grade of the enterprise to be identified according to the comprehensive risk score of the enterprise to be identified.
Further, the apparatus further comprises a model training module configured to:
acquiring training samples, wherein each training sample comprises each dimensionality sub-score of a sample enterprise, the sample enterprises comprise normal sample enterprises and abnormal sample enterprises, and the abnormal sample enterprises comprise enterprises which are used as told enterprises in financial loan disputes, enterprises which mark a loss-of-credit executed list and enterprises which hit the executed list and account for the registration set proportion of the executed amount;
inputting the training sample into the comprehensive risk assessment model to perform capital proportion exceeding training, ending the training of the comprehensive risk assessment model when the loss function value of the comprehensive risk assessment model is lower than a preset threshold value to obtain a trained comprehensive risk assessment model, and taking the score output by the trained comprehensive risk assessment model as the comprehensive risk score of the enterprise.
In a third aspect of the present invention, there is provided a computer device, the computer device comprising a processor and a non-volatile memory storing computer instructions, wherein when the computer instructions are executed by the computer device, the computer device executes the method for identifying a risk level of a small micro-enterprise according to the first aspect of the present invention.
According to the method, the device and the computer equipment for identifying the risk level of the small and micro enterprise, firstly, whether the enterprise to be identified is the small and micro enterprise is determined through the scale identification of the enterprise to be identified; then, when the enterprise to be identified is a small micro enterprise, acquiring a plurality of dimensional information of the enterprise to be identified based on the enterprise name of the enterprise to be identified, and obtaining a dimensional sub-score of each dimensional information by adopting a dimensional model based on an analytic hierarchy process; and finally, inputting each dimensionality sub-score into a comprehensive risk assessment model to obtain a comprehensive risk score of the enterprise to be identified, and determining the risk level of the enterprise to be identified based on the comprehensive risk score. In the scheme, the robustness of the dimensional model can be improved to the maximum extent through the dimensional model based on the business information of the small micro-enterprise and the related enterprises, so that the dimensional model is good in performance in different application scenes, and the comprehensive credit risk assessment of the small micro-enterprise is accurate.
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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 schematic structural diagram of a computer device according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for identifying a risk level of a small micro-enterprise according to an embodiment of the present invention.
Fig. 3 is a block diagram of a risk level identification apparatus for a small micro-enterprise according to an embodiment of the present invention.
Detailed Description
In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some of the embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.
In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, it is to be understood that the following detailed description of the embodiments of the present application, provided in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
The financial institutions will grade the small micro-enterprises so as to output different risk levels for different small micro-enterprises, and provide differentiated quota pricing for the small micro-enterprises when applying the favorable financial policy. However, the inventor finds that the existing risk assessment of the small and micro enterprise only considers the risks of the enterprise itself and does not consider that the risks among the enterprises are conducted with each other, so that the risk assessment of the existing small and micro enterprise is not accurate.
In order to solve the technical problems, the inventor innovatively proposes the following technical scheme.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a computer device 100 according to an embodiment of the present invention. The computer apparatus 100 includes a small micro-enterprise risk level identification device 210, a memory 111, and a processor 112.
The memory 111 and the processor 112 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines.
The memory 111 is used for storing programs, and the processor 112 executes the programs after receiving the execution instructions.
The small micro-enterprise risk level identification means 210 comprises at least one software function module which can be stored in the memory 111 in the form of software or firmware or solidified in the operating system of the server 100. The processor 112 is used for executing executable modules stored in the memory 111, such as software functional modules and computer programs included in the small micro enterprise risk level identification device 210.
Referring to fig. 1 again, in the embodiment of the present invention, the computer device 100 may further include a communication unit 113, and the communication unit 113 may be electrically connected to the memory 111 and the processor 112 directly or indirectly. The communication unit 113 is used for establishing a communication connection between the computer device 100 and another device (such as a cloud server) via a network, and for receiving and transmitting data via the network. For example, in the embodiment of the present invention, when the enterprise business information database is stored in the cloud server, the computer device 100 may communicate with the cloud server through the communication unit 113, and the specific communication process is introduced in the subsequent corresponding steps.
It should be understood that the configuration shown in FIG. 1 is merely illustrative, and that computer device 100 may include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
Referring to fig. 2, fig. 2 is a schematic flow chart of a small-micro enterprise risk level identification method according to an embodiment of the present invention, where the small-micro enterprise risk level identification method specifically includes the following steps.
Step S210, obtaining the enterprise name of the enterprise to be identified.
In the step, the enterprise name of the enterprise to be identified can be input on a visual interface of the computer equipment in a manual input mode; the business name of the business to be identified can be obtained by clicking the business name presented by the visual interface.
And step S220, identifying the scale of the enterprise to be identified in the enterprise business information database based on the enterprise name.
The enterprise business information database may include scale information of each enterprise, where the scale of the enterprise includes super-large scale, medium scale, small scale and micro scale.
In an implementation manner of the embodiment of the present invention, the enterprise and business information database may be stored locally in the computer device 100, and the computer device 100 implements scale identification with the enterprise to be identified at the local end. In another implementation manner of the embodiment of the present invention, the enterprise business information database may also be stored on the cloud server device. When the enterprise business information database is stored in the cloud server device, the computer device 100 sends the enterprise name of the knowledge enterprise to be identified to the cloud server device through the communication unit 113, and after receiving the enterprise name, the cloud server device searches the enterprise scale corresponding to the enterprise to be identified from the enterprise business information database, and feeds back the found enterprise scale to the computer device 100.
And step S230, when the enterprise to be identified is a small micro enterprise, acquiring a plurality of dimensional information of the enterprise to be identified according to the name of the enterprise.
In an embodiment of the present invention, the plurality of dimension information includes: the method comprises the steps of enterprise business information, enterprise performance dimension information, enterprise operation dimension information and dimension information of related enterprises which are related to the to-be-identified enterprise. The related enterprises comprise the enterprises with the corporate invested or held externally of the enterprise to be identified, the enterprises with the director of the enterprise to be identified and held externally or held externally and the enterprises with the enterprise to be identified and held externally.
And S240, calculating to obtain the dimension sub-score of the to-be-identified enterprise on each corresponding dimension information by adopting a dimension model based on an analytic hierarchy process based on the plurality of dimension information of the to-be-identified enterprise.
The analytic hierarchy process is a systematic method which takes a complex multi-target decision problem as a system, decomposes a target into a plurality of targets or criteria, further decomposes the targets into a plurality of layers of multi-index (or criteria, constraint), and calculates the single-layer ordering (weight) and the total ordering of the layers by a qualitative index fuzzy quantization method to be taken as the target (multi-index) and multi-scheme optimization decision.
The analytic hierarchy process includes decomposing the decision problem into different hierarchical structures according to the sequence of the total target, sub targets of each layer, evaluation criteria and specific spare power switching scheme, solving and judging matrix characteristic vector to obtain the priority weight of each element of each layer to one element of the previous layer, and finally conducting hierarchical weighted sum to merge the final weight of each spare power switching scheme to the total target, wherein the maximum weight is the optimal scheme.
In the embodiment of the invention, a dimension model is correspondingly created for each dimension information, and the dimension sub-score is calculated based on the dimension model corresponding to each dimension information.
And S250, inputting each dimensionality sub-score of the enterprise to be identified into the trained comprehensive risk assessment model to obtain the comprehensive risk score of the enterprise to be identified.
And step S260, determining the risk level of the enterprise to be identified according to the comprehensive risk score of the enterprise to be identified.
In the embodiment of the invention, the mapping relation between the comprehensive score and the risk level of the enterprise can be preset, and after the comprehensive risk score of the enterprise to be identified is obtained, the risk level of the enterprise to be identified can be determined through the mapping relation. Specifically, the higher the composite risk score for an enterprise to be identified, the lower the composite risk for that identified enterprise.
Referring to table 1, table 1 illustrates a possible mapping relationship between a composite score and a risk level according to an embodiment of the present invention.
| Grade | Fractional range |
| A | [90，100) |
| B | [80，90) |
| C | [70，80) |
| D | [60，70) |
| E | [0，60) |
TABLE 1
In table 1, the aggregate risk of the enterprise with risk level a is the lowest, and the aggregate risk of the enterprise with risk level E is the highest.
According to the scheme, the robustness of the dimensional model can be improved to the maximum extent through the dimensional model based on the business information of the small micro-enterprise and the related enterprise, so that the dimensional model is good in performance in different application scenes, and the comprehensive credit risk assessment of the small micro-enterprise is accurate.
Further, in the embodiment of the present invention, the dimension information includes enterprise and business information, and step S240 may be implemented in the following manner.
Acquiring enterprise business information of an enterprise to be identified; and taking the industry to which the enterprise belongs, the enterprise type, the enterprise establishment time, the area to which the enterprise belongs and the registered capital change times of the enterprise in the last three years in the enterprise business information as the characteristic vector of the enterprise business information dimensional model.
Referring to table 2, table 2 shows a comparison matrix of the dimensional model corresponding to the enterprise business information.
| Industry of enterprise | Type of business | Time of establishment of enterprise | Region of enterprise | Number of registered capital changes of enterprise in last three years | Weight of | |
| Industry of enterprise | 1 | 0.33 | 0.2 | 0.5 | 3 | 0.082 |
| Type of business | 3 | 1 | 0.5 | 1.5 | 2 | 0.175 |
| Time of establishment of enterprise | 5 | 2 | 1 | 3 | 4 | 0.337 |
| Region of enterprise | 2 | 0.67 | 0.33 | 1 | 2 | 0.126 |
| Number of registered capital changes of enterprise in last three years | 3 | 2 | 4 | 2 | 1 | 0.28 |
TABLE 2
In the comparison matrix, the weight of each feature vector is determined based on an analytic hierarchy process, and dimension sub-scores corresponding to the enterprise business information are output based on the dimension model.
Further, in the embodiment of the present invention, the dimension information includes enterprise performance dimension information, and step S240 may be implemented in the following manner.
Acquiring enterprise performance dimension information of an enterprise to be identified, and taking the tax penalty number of the enterprise in three years, the number of times of losing credit of the enterprise in 5 years, the legal action number of the enterprise in 3 years, the total amount of the enterprise executed in two years and the administrative penalty number of the enterprise in two years as feature vectors of a dimension model.
Referring to table 3, table 3 shows a comparison matrix of the dimension model corresponding to the enterprise performance dimension information.
| Tax punishment for nearly three years of enterprise Number of | The enterprise loses credit in the last 5 years and is executed Number of | Lawsuit of enterprise in near 3 years Number of | Total sum of executed money of enterprise in last two years Forehead (forehead) | Enterprise administrative punishment of nearly 2 years Number of | Weight of | |
| Tax penalty number of enterprise in last three years | 1 | 0.125 | 0.2 | 0.25 | 1 | 0.0562 |
| The enterprise loses credit in the last 5 years and is executed Number of | 8 | 1 | 1.6 | 2 | 8 | 0.5336 |
| Number of court actions of an enterprise in the last 3 years | 5 | 0.625 | 1 | 3 | 5 | 0.0263 |
| Total sum of executed money of enterprise in last two years Forehead (forehead) | 4 | 0.5 | 0.33 | 1 | 4 | 0.2104 |
| Number of administrative punishments of enterprise in last 2 years | 1 | 0.25 | 0.2 | 0.25 | 1 | 0.0526 |
TABLE 3
And in the comparison matrix, calculating the dimension sub-score of the enterprise to be identified in the enterprise performance dimension by combining an analytic hierarchy process according to the enterprise performance dimension information of the enterprise to be identified.
Further, in the embodiment of the present invention, the dimension information includes enterprise business dimension information, and step S240 may be implemented in the following manner.
Acquiring enterprise operation dimension information of an enterprise to be identified, and taking the administrative license number of the enterprise in three years, the patent number of the enterprise in three years, the related judicial number of enterprise operation, the acquired administrative bonus number of the enterprise in three years and the labor dispute number of the enterprise in three years as feature vectors of a dimension model.
Referring to table 4, table 4 shows a comparison matrix of the dimensional model corresponding to the enterprise business dimensional information.
| Administrative permission of enterprise in last three years Number of | Nearly three years patent of enterprise Number of | Enterprise business related judicial Number of | Enterprise awards for administrative services in three years Number of | Three years of labor dispute of enterprise Number of | Weight of | |
| Number of administrative licenses of enterprise in last three years | 1 | 0.166667 | 0.25 | 0.25 | 1 | 0.0625 |
| Number of patents in nearly three years for an enterprise | 6 | 1 | 1.6 | 1.5 | 6 | 0.375 |
| Enterprise business related judicial counting | 4 | 0.625 | 1 | 3 | 5 | 0.025 |
| Enterprise awards for administrative services in three years Number of | 4 | 0.666667 | 0.33 | 1 | 4 | 0.25 |
| Number of labor disputes of enterprises in nearly three years | 1 | 0.166667 | 0.2 | 0.25 | 1 | 0.0625 |
TABLE 4
And in the comparison matrix, calculating the dimension sub-score of the enterprise to be identified in the enterprise operation dimension by combining an analytic hierarchy process according to the enterprise operation dimension information of the enterprise to be identified.
Further, in the embodiment of the present invention, the dimension information includes dimension information of an associated enterprise that is associated with the existence of the enterprise to be identified, and step S240 may be implemented in the following manner.
Obtaining dimension information of associated enterprises which are associated with the enterprise to be identified, and taking the average age of the associated enterprises, the average executed times of the associated enterprises, the external investment enterprise industry number of the associated enterprises, the number of the associated enterprises and the proportion of abnormal enterprises in the associated enterprises in the dimension information of the associated enterprises as feature vectors of a dimension model.
Referring to table 5, table 5 shows a comparison matrix of the dimension models corresponding to the dimension information of the associated enterprise.
| Average year of related enterprises Age (age) | Enterprise associated Enterprise average executed number Number of | Enterprise external investment enterprise industry Number of | Associating enterprise abnormal business occupancies Ratio of | Number of related businesses | Weight of | |
| Average age of related enterprises | 1 | 0.25 | 0.25 | 0.25 | 1 | 0.0714 |
| Enterprise associated Enterprise average executed number Number of | 4 | 1 | 1 | 1 | 4 | 0.2856 |
| Number of business of enterprise investing in external | 4 | 1 | 1 | 1 | 4 | 0.2856 |
| Related enterprise abnormal enterprise percentage | 4 | 1 | 1 | 1 | 4 | 0.2856 |
| Number of related businesses | 1 | 0.25 | 0.25 | 0.25 | 1 | 0.0714 |
TABLE 5
And in the comparison matrix, calculating by combining an analytic hierarchy process according to the dimension information of the associated enterprises to obtain the dimension sub-scores of the enterprises to be identified in the associated enterprises.
In the embodiment of the invention, the method for identifying the risk level of the small and micro enterprise further comprises the step of training the comprehensive risk assessment model.
First, training samples are obtained, wherein each training sample comprises all dimension sub-scores of a sample enterprise.
Specifically, the sample enterprises include normal sample enterprises and abnormal sample enterprises, in the prior art, when model training is performed, the selected abnormal sample enterprises are generally enterprises in which loss of credit is executed, which is different from the definition of the abnormal sample enterprises in the actual business of the financial institution to a certain extent, the financial institution prefers to find enterprises in which the loan is bad or the loan is not longer than 60 days, and the actual requirements of the financial institution cannot be objectively reflected on the basis of the abnormal enterprise samples of the loss of credit executives at present. The abnormal enterprise sample is adopted for model training, so that the finally obtained comprehensive risk assessment model cannot accurately assess the actual risk of the small and micro enterprise.
Therefore, according to the embodiment of the present invention, based on the actual business risk of the financial institution, the following enterprises are adopted as the abnormal sample enterprises, and specifically, the abnormal sample enterprises include an enterprise which is billed in the financial loan dispute, an enterprise which hits the loss-of-credit executed list, and an enterprise which hits the executed list and has an executed amount accounting for the registered capital in a proportion exceeding a set proportion.
And then, inputting the training sample into the comprehensive risk assessment model for training, finishing the training of the comprehensive risk assessment model when the loss function value of the comprehensive risk assessment model is lower than a preset threshold value, obtaining the trained comprehensive risk assessment model, and taking the score output by the trained comprehensive risk assessment model as the comprehensive risk score of the enterprise.
In the process, the abnormal enterprise sample is determined based on the actual business requirements of the financial institution, and the comprehensive risk assessment model is trained, so that the final output comprehensive risk score can be matched with the risk level in the actual business of the financial institution, the risk level of the small and micro enterprise can be reflected more truly and accurately, the financial institution can adopt a corresponding quota pricing strategy based on the risk level, and the fair, fair and safe implementation and popularization of the general financial policy can be promoted.
In the embodiment of the invention, the comprehensive risk assessment model can be realized by adopting a logistic regression model, and also can be realized by adopting a random forest model, a decision tree model and a support vector machine model.
According to the technical scheme, firstly, whether an enterprise to be identified is a small micro enterprise or not is determined through scale identification of the enterprise to be identified; then, when the enterprise to be identified is a small micro enterprise, acquiring a plurality of dimensional information of the enterprise to be identified based on the enterprise name of the enterprise to be identified, and obtaining a dimensional sub-score of each dimensional information by adopting a dimensional model based on an analytic hierarchy process; and finally, inputting each dimensionality sub-score into a comprehensive risk assessment model to obtain a comprehensive risk score of the enterprise to be identified, and determining the risk level of the enterprise to be identified based on the comprehensive risk score. In the scheme, the robustness of the dimensional model can be improved to the maximum extent through the dimensional model based on the business information of the small micro-enterprise and the related enterprises, so that the dimensional model is good in performance in different application scenes, and the comprehensive credit risk assessment of the small micro-enterprise is accurate.
On the basis, please refer to fig. 3 in combination, which is a block diagram of the small-micro enterprise risk level identification apparatus 210 according to the embodiment of the present invention, where the small-micro enterprise risk level identification apparatus 210 may include an enterprise name acquisition module 2101, an enterprise scale identification module 2102, a dimension information acquisition module 2103, a dimension sub-score calculation module 2104, a comprehensive risk score acquisition module 2105, and a risk level determination module 2106.
The enterprise name obtaining module 2101 is configured to obtain an enterprise name of an enterprise to be identified.
The enterprise name acquisition module 2101 may input the enterprise name of the enterprise to be identified on a visual interface of the computer device by a manual input mode; the business name of the business to be identified can be obtained by clicking the business name presented by the visual interface.
And an enterprise scale identifying module 2102, configured to identify the scale of the enterprise to be identified in the enterprise business information database according to the enterprise name.
In an implementation manner of the embodiment of the present invention, the enterprise business information database may be stored locally in the computer device 100, and the enterprise scale identifying module 2102 implements scale identification of the enterprise to be identified on the local side. In another implementation manner of the embodiment of the present invention, the enterprise business information database may also be stored on the cloud server device. When the enterprise business information database is stored in the cloud server device, the enterprise scale recognition module 2102 sends the enterprise name of the knowledge enterprise to be recognized to the cloud server device through the communication unit 113, and after the cloud server device receives the enterprise name, the cloud server device searches for the enterprise scale corresponding to the enterprise to be recognized from the enterprise business information database and feeds the found enterprise scale back to the enterprise scale recognition module 2102.
The dimension information acquiring module 2103 is configured to acquire, according to the name of the enterprise, a plurality of dimension information of the enterprise to be identified when the enterprise to be identified is a small micro enterprise.
In an embodiment of the present invention, the plurality of dimension information includes: the method comprises the steps of enterprise business information, enterprise performance dimension information, enterprise operation dimension information and dimension information of related enterprises which are related to the to-be-identified enterprise. The related enterprises comprise the enterprises with the corporate invested or held externally of the enterprise to be identified, the enterprises with the director of the enterprise to be identified and held externally or held externally and the enterprises with the enterprise to be identified and held externally.
And a dimension sub-score calculating module 2104 for calculating, based on the multiple dimension information of the enterprise to be identified, a dimension model based on an analytic hierarchy process to obtain a dimension sub-score of the enterprise to be identified on the corresponding dimension information.
In the embodiment of the invention, a dimension model is correspondingly created for each dimension information, and the dimension sub-score is calculated based on the dimension model corresponding to each dimension information.
And the comprehensive risk score obtaining module 2105 is configured to input each dimensionality sub-score of the enterprise to be identified into the trained comprehensive risk assessment model to obtain a comprehensive risk score of the enterprise to be identified.
A risk level determining module 2106, configured to determine a risk level of the enterprise to be identified according to the composite risk score of the enterprise to be identified.
Referring again to fig. 3, the small micro-enterprise risk level identification apparatus 210 may further include a model training module 2107, where the model training module 2107 is configured to:
acquiring training samples, wherein each training sample comprises each dimensionality sub-score of a sample enterprise, the sample enterprises comprise normal sample enterprises and abnormal sample enterprises, and the abnormal sample enterprises comprise enterprises which are used as told enterprises in financial loan disputes, enterprises which mark a loss-of-credit executed list and enterprises which hit the executed list and account for the registration set proportion of the executed amount;
inputting the training sample into the comprehensive risk assessment model to perform capital proportion exceeding training, ending the training of the comprehensive risk assessment model when the loss function value of the comprehensive risk assessment model is lower than a preset threshold value to obtain a trained comprehensive risk assessment model, and taking the score output by the trained comprehensive risk assessment model as the comprehensive risk score of the enterprise.
In summary, according to the method, the apparatus, and the computer device for identifying the risk level of the small and micro enterprise provided by the embodiment of the present invention, first, whether the enterprise to be identified is the small and micro enterprise is determined by identifying the scale of the enterprise to be identified; then, when the enterprise to be identified is a small micro enterprise, acquiring a plurality of dimensional information of the enterprise to be identified based on the enterprise name of the enterprise to be identified, and obtaining a dimensional sub-score of each dimensional information by adopting a dimensional model based on an analytic hierarchy process; and finally, inputting each dimensionality sub-score into a comprehensive risk assessment model to obtain a comprehensive risk score of the enterprise to be identified, and determining the risk level of the enterprise to be identified based on the comprehensive risk score. In the scheme, the robustness of the dimensional model can be improved to the maximum extent through the dimensional model based on the business information of the small micro-enterprise and the related enterprises, so that the dimensional model is good in performance in different application scenes, and the comprehensive credit risk assessment of the small micro-enterprise is accurate.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for identifying risk level of small micro-enterprise is applied to computer equipment, and the method comprises the following steps:
acquiring an enterprise name of an enterprise to be identified;
identifying the scale of the enterprise to be identified in an enterprise business information database according to the enterprise name;
when the enterprise to be identified is a small micro enterprise, acquiring a plurality of dimensional information of the enterprise to be identified according to the name of the enterprise, wherein the dimensional information comprises: enterprise business and business information, enterprise performance dimension information, enterprise operation dimension information and dimension information of associated enterprises which are associated with the enterprise to be identified, wherein the associated enterprises comprise enterprises with corporate investments or duties of the enterprise to be identified, enterprises with director and high outinvestments or duties of the enterprise to be identified and enterprises with outinvestments of the enterprise to be identified;
calculating to obtain a dimensionality sub-score of the enterprise to be identified on each corresponding dimensionality information by adopting a dimensionality model based on an analytic hierarchy process based on the multiple dimensionality information of the enterprise to be identified;
inputting each dimensionality sub-score of the enterprise to be identified into a trained comprehensive risk assessment model to obtain a comprehensive risk score of the enterprise to be identified;
and determining the risk level of the enterprise to be identified according to the comprehensive risk score of the enterprise to be identified.
2. The method for identifying the risk level of the small micro-enterprise as claimed in claim 1, wherein the dimension information comprises enterprise business information, and the step of calculating the dimension sub-score of the enterprise to be identified on each corresponding dimension information by using a dimension model based on an analytic hierarchy process comprises the following steps:
acquiring enterprise business and business information of the enterprise to be identified, wherein the enterprise business and business information comprises the industry to which the enterprise belongs, the enterprise type, the enterprise establishment time, the area to which the enterprise belongs and the registered capital change times of the enterprise in three years;
and calculating the dimensionality sub-score of the enterprise to be identified in the enterprise business information dimensionality by combining an analytic hierarchy process according to the enterprise business information of the enterprise to be identified.
3. The method for identifying the risk level of the small micro-enterprise as claimed in claim 1, wherein the dimension information includes enterprise performance dimension information, and the step of calculating the dimension sub-score of the enterprise to be identified on each corresponding dimension information by using a dimension model based on an analytic hierarchy process includes:
acquiring enterprise performance dimension information of the enterprise to be identified, wherein the enterprise performance dimension information comprises the tax penalty number of the enterprise in the last three years, the number of times of losing credit of the enterprise in the last 5 years, the legal action number of the enterprise in the last 3 years, the total executed amount of the enterprise in the last two years and the administrative penalty number of the enterprise in the last two years;
and calculating the dimension sub-score of the enterprise to be identified in the enterprise performance dimension according to the enterprise performance dimension information of the enterprise to be identified by combining an analytic hierarchy process.
4. The method for identifying the risk level of the small micro-enterprise as claimed in claim 1, wherein the dimension information comprises enterprise business dimension information, and the step of calculating the dimension sub-score of the enterprise to be identified on each corresponding dimension information by using a dimension model based on an analytic hierarchy process comprises the following steps:
acquiring enterprise operation dimension information of the enterprise to be identified, wherein the enterprise operation dimension information comprises the number of administrative licenses of the enterprise in the last three years, the number of patents of the enterprise in the last three years, the number of relevant judicial practices of enterprise operation, the number of administrative awards acquired by the enterprise in the last three years, and the number of labor disputes of the enterprise in the last three years;
and calculating the dimensionality sub-score of the enterprise to be identified in the enterprise operation dimensionality by combining an analytic hierarchy process according to the enterprise operation dimensionality information of the enterprise to be identified.
5. The method for identifying the risk level of the small micro-enterprise as claimed in claim 1, wherein the dimension information includes dimension information of related enterprises related to the enterprise to be identified, and the step of calculating the dimension sub-score of the enterprise to be identified on each corresponding dimension information by using a dimension model based on an analytic hierarchy process includes:
acquiring dimension information of an associated enterprise associated with the enterprise to be identified, wherein the dimension information of the associated enterprise comprises the average age of the associated enterprise, the average executed times of the associated enterprise, the number of industries of the associated enterprise investing the enterprise, the number of the associated enterprises and the proportion of abnormal enterprises in the associated enterprise;
and calculating by combining an analytic hierarchy process to obtain the dimension sub-score of the enterprise to be identified in the associated enterprise according to the dimension information of the associated enterprise.
6. The small micro-enterprise risk classification identification method of any one of claims 1-5, further comprising the step of pre-training the integrated risk assessment model, comprising:
obtaining training samples, wherein each training sample comprises each dimensionality sub-score of a sample enterprise, the sample enterprises comprise normal sample enterprises and abnormal sample enterprises, the abnormal sample enterprises comprise enterprises which are used as billed enterprises in financial loan disputes, enterprises which hit a loss-of-credit executed list, and enterprises which hit the executed list and have the executed amount accounting for the registered capital in a proportion exceeding a set proportion;
and inputting the training sample into the comprehensive risk assessment model for training, finishing the training of the comprehensive risk assessment model when the loss function value of the comprehensive risk assessment model is lower than a preset threshold value to obtain a trained comprehensive risk assessment model, and taking the score output by the trained comprehensive risk assessment model as the comprehensive risk score of the enterprise.
7. The small micro-enterprise risk level identification method of claim 6, wherein the integrated risk assessment model comprises: a logistic regression model, a random forest model, a decision tree model and a support vector machine model.
8. An apparatus for identifying risk level of small micro-enterprise, applied to a computer device, the apparatus comprising:
the enterprise name acquisition module is used for acquiring the enterprise name of the enterprise to be identified;
the enterprise scale identification module is used for identifying the scale of the enterprise to be identified in an enterprise business information database according to the enterprise name;
the dimension information acquisition module is used for acquiring a plurality of dimension information of the enterprise to be identified according to the name of the enterprise when the enterprise to be identified is a small micro enterprise, and the dimension information comprises: enterprise business and business information, enterprise performance dimension information, enterprise operation dimension information and dimension information of associated enterprises which are associated with the enterprise to be identified, wherein the associated enterprises comprise enterprises with corporate investments or duties of the enterprise to be identified, enterprises with director and high outinvestments or duties of the enterprise to be identified and enterprises with outinvestments of the enterprise to be identified;
the dimensionality sub-score calculating module is used for calculating dimensionality sub-scores of the to-be-identified enterprise on the corresponding dimensionality information by adopting a dimensionality model based on an analytic hierarchy process based on the multiple dimensionality information of the to-be-identified enterprise;
the comprehensive risk score obtaining module is used for inputting each dimensionality sub-score of the enterprise to be identified into a trained comprehensive risk assessment model to obtain a comprehensive risk score of the enterprise to be identified;
and the risk grade determining module is used for determining the risk grade of the enterprise to be identified according to the comprehensive risk score of the enterprise to be identified.
9. The small micro-enterprise risk level identification apparatus of claim 8, further comprising a model training module to:
acquiring training samples, wherein each training sample comprises each dimensionality sub-score of a sample enterprise, the sample enterprises comprise normal sample enterprises and abnormal sample enterprises, and the abnormal sample enterprises comprise enterprises which are used as told enterprises in financial loan disputes, enterprises which mark a loss-of-credit executed list and enterprises which hit the executed list and account for the registration set proportion of the executed amount;
inputting the training sample into the comprehensive risk assessment model to perform capital proportion exceeding training, ending the training of the comprehensive risk assessment model when the loss function value of the comprehensive risk assessment model is lower than a preset threshold value to obtain a trained comprehensive risk assessment model, and taking the score output by the trained comprehensive risk assessment model as the comprehensive risk score of the enterprise.
10. A computer device comprising a processor and a non-volatile memory storing computer instructions, wherein the computer instructions, when executed by the computer device, cause the computer device to perform the method for small micro-enterprise risk level identification of any one of claims 1-7.
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