CN115082225A - Enterprise financing risk assessment method and device - Google Patents
Enterprise financing risk assessment method and device Download PDFInfo
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Abstract
The invention provides a method and a device for evaluating enterprise financing risks, which relate to the technical field of financial wind control and comprise the following steps: acquiring enterprise data of financed enterprises, and preprocessing the enterprise data of the financed enterprises to obtain target enterprise data of the financed enterprises; establishing an admission requirement rule set and a repayment risk early warning rule set based on the target enterprise data and the definition label corresponding to the target enterprise data; constructing an operation risk evaluation model and a credit risk evaluation model based on a preset machine learning algorithm and target enterprise data of financed enterprises; after the enterprise data of the enterprise to be evaluated is obtained, the pre-loan assessment result of the enterprise to be evaluated or the in-loan risk early warning result of the enterprise to be evaluated is obtained based on the enterprise data of the enterprise to be evaluated, the admission requirement rule set or the repayment risk early warning rule set, the operation risk assessment model and the credit risk assessment model, and the technical problem that the efficiency and the accuracy of the existing enterprise financing risk assessment method are low is solved.
Description
Technical Field
The invention relates to the technical field of financial wind control, in particular to an enterprise financing risk assessment method and device.
Background
With the rapid development of the economic society, the number of small and medium-sized micro-enterprises is also rapidly increased; meanwhile, when small and medium-sized micro enterprises operate, the demand exists for applying loan to financial institutions by using the production, operation and property conditions of the enterprises. For the financial institution, in order to ensure the economic safety of the financial institution, the overall operation condition, the liability condition and the like of the small and medium-sized micro-enterprises need to be comprehensively judged to determine whether to credit the small and medium-sized micro-enterprises.
In the prior art, the evaluation of the operation financing risk of small and medium-sized micro enterprises is generally judged by deep investigation and analysis under a manual line; after receiving the operation financing requirements of small and medium-sized micro enterprises, financial institutions need to arrange different personnel to visit the enterprises in a field investigation mode according to the internal flow and different flow schedules of the enterprises, and during the period, the financing enterprises still need to continuously provide or manufacture various financing materials according to the requirements of the financial institutions, and provide a financing enterprise risk assessment report to explain the related financing risks of the enterprises.
No effective solution has been proposed to the above problems.
Disclosure of Invention
In view of the above, the present invention provides an enterprise financing risk assessment method and apparatus, so as to alleviate the technical problems of low efficiency and accuracy of the existing enterprise financing risk assessment method.
In a first aspect, an embodiment of the present invention provides an enterprise financing risk assessment method, including: acquiring enterprise data of financed enterprises, and preprocessing the enterprise data of the financed enterprises to obtain target enterprise data of the financed enterprises; establishing an admission requirement rule set and a repayment risk early warning rule set based on the target enterprise data and a definition label corresponding to the target enterprise data, wherein the definition label is used for the operation capacity of the financed enterprise; constructing an operation risk evaluation model and a credit risk evaluation model based on a preset machine learning algorithm and the target enterprise data of the financed enterprise; after enterprise data of an enterprise to be evaluated is acquired, obtaining a pre-loan evaluation result of the enterprise to be evaluated or a pre-loan risk warning result of the enterprise to be evaluated based on the enterprise data of the enterprise to be evaluated, the admission requirement rule set or the repayment risk warning rule set, the operation risk evaluation model and the credit risk evaluation model, wherein the pre-loan evaluation result comprises: the credit result and the financing credit line, wherein the risk early warning result in the loan is as follows: and the risk level and the processing measure corresponding to the risk level.
Further, the enterprise data includes: tax-related operation data, invoice data, industrial and commercial registration data, judicial complaint data and human behavior credit data; the pretreatment comprises the following steps: integrity check processing, structuring processing, availability check processing and characteristic variable derivation processing.
Further, if the enterprise to be assessed is an unfulfilled enterprise, the admission requirement rule set or the repayment risk early warning rule set, the operation risk assessment model and the credit risk assessment model based on the enterprise data of the enterprise to be assessed include: preprocessing enterprise data of the enterprise to be evaluated to obtain target enterprise data of the enterprise to be evaluated; determining an initial credit granting result of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated and the admission requirement rule set; if the initial credit granting result is passed, determining an operation risk level and a credit risk level of the enterprise to be evaluated based on target enterprise data of the enterprise to be evaluated, the operation risk evaluation model and the credit risk evaluation model; constructing a cross matrix based on the operation risk level and the credit risk level, and determining a credit granting result of the enterprise to be evaluated based on the cross matrix; and if the credit granting result of the enterprise to be evaluated passes, determining the credit granting amount of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated.
Further, if the enterprise to be assessed is a financed enterprise, based on enterprise data of the enterprise to be assessed, the admission requirement rule set or the repayment risk early warning rule set, the operation risk assessment model and the credit risk assessment model, the method includes: preprocessing enterprise data of the enterprise to be evaluated to obtain target enterprise data of the enterprise to be evaluated; determining an operation risk level and a credit risk level of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated, the operation risk evaluation model and the credit risk evaluation model; determining the risk level of the enterprise to be evaluated based on the operation risk level and the credit risk level; and determining the processing measures corresponding to the risk level of the enterprise to be evaluated based on the risk level of the enterprise to be evaluated and the repayment risk early warning rule set.
In a second aspect, an embodiment of the present invention further provides an apparatus for evaluating an enterprise financing risk, including: the enterprise data processing system comprises an acquisition unit, a first construction unit, a second construction unit and an evaluation unit, wherein the acquisition unit is used for acquiring enterprise data of financed enterprises and preprocessing the enterprise data of the financed enterprises to obtain target enterprise data of the financed enterprises; the first construction unit is used for constructing an admission requirement rule set and a repayment risk early warning rule set based on the target enterprise data and a definition tag corresponding to the target enterprise data, wherein the definition tag is used for the operational capacity of the financed enterprise; the second construction unit is used for constructing an operation risk evaluation model and a credit risk evaluation model based on a preset machine learning algorithm and the target enterprise data of the financed enterprise; the evaluation unit is configured to obtain a pre-loan evaluation result of the enterprise to be evaluated or a pre-loan risk warning result of the enterprise to be evaluated based on the enterprise data of the enterprise to be evaluated, the admission requirement rule set or the repayment risk warning rule set, the operation risk evaluation model and the credit risk evaluation model after acquiring the enterprise data of the enterprise to be evaluated, where the pre-loan evaluation result includes: the credit result and the financing credit line, wherein the risk early warning result in the loan is as follows: and the risk level and the processing measure corresponding to the risk level.
Further, the enterprise data includes: tax-related operation data, invoice data, industrial and commercial registration data, judicial complaint data and human behavior credit data; the pretreatment comprises the following steps: integrity check processing, structuring processing, availability check processing and characteristic variable derivation processing.
Further, if the enterprise to be evaluated is an unfulfilled enterprise, the evaluation unit is configured to: preprocessing enterprise data of the enterprise to be evaluated to obtain target enterprise data of the enterprise to be evaluated; determining an initial credit granting result of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated and the admission requirement rule set; if the initial credit granting result is passed, determining an operation risk level and a credit risk level of the enterprise to be evaluated based on target enterprise data of the enterprise to be evaluated, the operation risk evaluation model and the credit risk evaluation model; constructing a cross matrix based on the operation risk level and the credit risk level, and determining a credit granting result of the enterprise to be evaluated based on the cross matrix; and if the credit granting result of the enterprise to be evaluated passes, determining the credit granting amount of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated.
Further, if the enterprise to be evaluated is a financed enterprise, the evaluation unit is configured to: preprocessing enterprise data of the enterprise to be evaluated to obtain target enterprise data of the enterprise to be evaluated; determining an operation risk level and a credit risk level of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated, the operation risk evaluation model and the credit risk evaluation model; determining the risk level of the enterprise to be evaluated based on the operation risk level and the credit risk level; and determining the processing measures corresponding to the risk level of the enterprise to be evaluated based on the risk level of the enterprise to be evaluated and the repayment risk early warning rule set.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory is used to store a program that supports the processor to execute the method in the first aspect, and the processor is configured to execute the program stored in the memory.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored.
In the embodiment of the invention, the target enterprise data of the financed enterprise is obtained by acquiring the enterprise data of the financed enterprise and preprocessing the enterprise data of the financed enterprise; establishing an admission requirement rule set and a repayment risk early warning rule set based on the target enterprise data and a definition label corresponding to the target enterprise data, wherein the definition label is used for the management capacity of the financed enterprise; constructing an operation risk evaluation model and a credit risk evaluation model based on a preset machine learning algorithm and the target enterprise data of the financed enterprise; after enterprise data of an enterprise to be evaluated is acquired, obtaining a pre-loan evaluation result of the enterprise to be evaluated or a pre-loan risk warning result of the enterprise to be evaluated based on the enterprise data of the enterprise to be evaluated, the admission requirement rule set or the repayment risk warning rule set, the operation risk evaluation model and the credit risk evaluation model, wherein the pre-loan evaluation result comprises: the credit result and the financing credit line, wherein the risk early warning result in the loan is as follows: the risk level and the processing measures corresponding to the risk level achieve the purpose of acquiring enterprise data and evaluating enterprise risks without manual offline, and further solve the technical problem that the existing assessment method for enterprise financing risks is low in efficiency and accuracy, so that the technical effect of improving the efficiency and accuracy of enterprise financing assessment is achieved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of an enterprise financing risk assessment method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an apparatus for assessing risk of enterprise financing according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
in accordance with an embodiment of the present invention, there is provided an embodiment of a method for assessing risk of financing an enterprise, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a flowchart of a method for assessing financing risk of an enterprise according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, enterprise data of financed enterprises are obtained, and the enterprise data of the financed enterprises are preprocessed to obtain target enterprise data of the financed enterprises;
it should be noted that the enterprise data includes: tax-related operation data, invoice data, industrial and commercial registration data, judicial complaint data and human credit data.
The pretreatment comprises the following steps: integrity check processing, structuring processing, availability check processing and characteristic variable derivation processing.
Specifically, after the authorization of the financing enterprise, the computer pulls tax-related operation data, invoice data, business registration data, judicial complaint data, human behavior credit data and the like of the financing enterprise on the network.
After the original data obtained by authorization are collected, integrity verification is carried out on the original data;
after verification, cleaning and processing the original data to obtain structured data which can be applied to business financing examination and approval of small and medium-sized micro enterprises, and verifying the availability of the structured data from a business layer; and then, carrying out characteristic variable derivation on the structured data to obtain target enterprise data for subsequent risk judgment of financing examination and approval.
Step S104, establishing an admission requirement rule set and a repayment risk early warning rule set based on the target enterprise data and a definition label corresponding to the target enterprise data, wherein the definition label is used for the operation capacity of the financed enterprise;
it should be noted that statistical analysis is performed on large-scale medium and small micro enterprise operation data with definition labels, and an admission requirement rule set and a repayment risk early warning rule set for representing conditions such as enterprise tax-related operation conditions, industrial and commercial complaint conditions, credit investigation conditions and the like are established according to analysis results.
The definition label is mainly defined according to the condition of differentiating the enterprise operation capacity; if the loan of the enterprise is overdue, whether the business data field of the enterprise is complete or not, whether the enterprise has major and minor changes or not and the like are related.
Step S106, constructing an operation risk assessment model and a credit risk assessment model based on a preset machine learning algorithm and the target enterprise data of the financed enterprise;
it should be noted that the operation risk assessment model mainly comprehensively judges the operation risk of the enterprise from the basic information, the profit situation, the operation situation, the fixed asset situation and the like of the enterprise; the credit risk assessment model is used for measuring and calculating the credit risk distribution rule of the enterprise by utilizing the credit condition of the sample, so that the credit risk of the enterprise is differentiated.
Step S108, after acquiring enterprise data of an enterprise to be evaluated, obtaining a pre-loan evaluation result of the enterprise to be evaluated or a pre-loan risk warning result of the enterprise to be evaluated based on the enterprise data of the enterprise to be evaluated, the admission requirement rule set or the repayment risk warning rule set, the operation risk evaluation model and the credit risk evaluation model, wherein the pre-loan evaluation result comprises: the credit result and the financing credit line, wherein the risk early warning result in the loan is as follows: and the risk level and the processing measure corresponding to the risk level.
In the embodiment of the invention, the target enterprise data of the financed enterprise is obtained by acquiring the enterprise data of the financed enterprise and preprocessing the enterprise data of the financed enterprise; establishing an admission requirement rule set and a repayment risk early warning rule set based on the target enterprise data and a definition label corresponding to the target enterprise data, wherein the definition label is used for the operation capacity of the financed enterprise; constructing an operation risk evaluation model and a credit risk evaluation model based on a preset machine learning algorithm and the target enterprise data of the financed enterprise; after enterprise data of an enterprise to be evaluated is acquired, obtaining a pre-loan evaluation result of the enterprise to be evaluated or a pre-loan risk warning result of the enterprise to be evaluated based on the enterprise data of the enterprise to be evaluated, the admission requirement rule set or the repayment risk warning rule set, the operation risk evaluation model and the credit risk evaluation model, wherein the pre-loan evaluation result comprises: the credit result and the financing credit line, wherein the risk early warning result in the loan is as follows: the risk level and the processing measures corresponding to the risk level achieve the purpose of acquiring enterprise data and evaluating enterprise risks without manual offline, and further solve the technical problem that the existing assessment method for enterprise financing risks is low in efficiency and accuracy, so that the technical effect of improving the efficiency and accuracy of enterprise financing assessment is achieved.
In this embodiment of the present invention, if the enterprise to be evaluated is an unfulfilled enterprise, step S108 includes the following steps:
step S11, enterprise data of the enterprise to be evaluated is preprocessed, and target enterprise data of the enterprise to be evaluated is obtained;
step S12, determining an initial credit granting result of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated and the admission requirement rule set;
step S13, if the initial credit granting result is passed, determining the operation risk level and the credit risk level of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated, the operation risk evaluation model and the credit risk evaluation model;
step S14, constructing a cross matrix based on the operation risk level and the credit risk level, and determining the credit granting result of the enterprise to be evaluated based on the cross matrix;
and step S15, if the credit granting result of the enterprise to be evaluated is passed, determining the credit granting amount of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated.
In the embodiment of the invention, firstly, enterprise data of an enterprise to be evaluated is preprocessed to obtain target enterprise data of the enterprise to be evaluated.
Then, the decision flow setting is carried out on the admission requirement rule set, and the operation condition, the continuous operation capacity and the financing capacity of the client are preliminarily judged, so that an initial credit granting result is obtained;
the specific decision flow is as follows:
enterprise fraud identification: and identifying potential fraud suspicion customers through the tax and three-party equipment data, and forbidding the fraud suspicion customers to enter, wherein fraud behaviors such as false tax invoicing income, gambling, money laundering and the like are identified.
Enterprise basic situation assessment: the basic face conditions of the enterprise are identified through data such as industrial and commercial, judicial and tax affairs, and the like, and some customers with basic faces not meeting the product requirements, such as illegal operation enterprises, enterprises with overall regulated main and operation services, and the like, are intercepted.
And (3) enterprise operation condition evaluation: the method comprises the steps of comprehensively evaluating the operation capacity of an enterprise through income, tax payment and financial information in tax data, and intercepting some enterprises with poor operation capacity, such as enterprises with too small operation scale and tax payment scale, poor stability of income and tax payment, poor asset liability level and the like.
Evaluating the credit and financing condition of the enterprise: and evaluating the current liability condition and credit record condition of the enterprise through data such as enterprise and personal credit investigation. And intercepting some clients with higher enterprise liability and poorer credit records.
The method is used for the admittance of the operation and financing of the admitted small and medium-sized micro-enterprises, whether the customers meet the financing threshold of the financial institution can be judged within 10 seconds at the fastest speed, and the customers can be screened better.
And then, if the initial credit granting result is passed, comprehensively evaluating the operation risk and the credit risk of the enterprise by using the operation risk evaluation model and the credit risk evaluation model in a cross matrix mode, and examining and approving the enterprise with lower risk.
The specific matrix judgment method is as follows:
ranking the operation risk model and the credit risk model from high to low, and dividing the model grades;
2 models are put into a two-dimensional cross matrix according to high and low levels;
in the cross matrix, through modeling sample conditions, a risk preference value meeting the admission requirement of the financial institution is found through a method of score intersection or union of 2 models, admission score thresholds of the 2 models are set, and if the admission score thresholds are greater than a preset threshold, the admission result is a pass.
And finally, according to the mobile capital requirement of the enterprise to be evaluated, comprehensively evaluating factors such as tax income, tax total, tax rate condition, growth condition, operation capacity and the like of the enterprise to be evaluated, and giving a mobile capital limit to the enterprise to be evaluated.
Specifically, for example, in the aspect of asset assessment value, the traditional financial depreciation method, the trading price of the second-hand market and the current industry capacity condition are combined for comprehensive assessment, and the asset assessment limit is determined.
On the specific financable limit, the financial institution can allocate according to the requirements of the enterprise, and if the mobile fund limit meets the requirements of the enterprise, the assets can not be provided for the evaluation of the asset limit. If the mobile fund limit cannot meet the requirements of the enterprise to be evaluated, the asset evaluation limit of the enterprise to be evaluated can be superposed on the basis of the mobile fund limit.
It should be noted that the pre-operation financing loan approval of the small and medium-sized micro enterprises refers to a process in which the financial risk of the enterprises is evaluated by the financial institution aiming at the small and medium-sized micro enterprises with financing requirements according to the operation condition, financial condition, credit risk and other factors of the enterprises before credit granting, and whether the financing bodies meet the credit granting requirements of the financial institution is determined.
In the embodiment of the invention, the pre-loan approval evaluation specifically comprises the steps of collecting various operation data and external data of the financing enterprise, evaluating the access risk of the financing enterprise, cross-evaluating the operation risk and credit risk of the financing enterprise, and calculating the financing credit line of the financing enterprise.
In this embodiment of the present invention, if the enterprise to be evaluated is a financed enterprise, step S108 includes the following steps:
step S21, enterprise data of the enterprise to be evaluated is preprocessed, and target enterprise data of the enterprise to be evaluated is obtained;
step S22, based on the target enterprise data of the enterprise to be evaluated, the operation risk evaluation model and the credit risk evaluation model, determining the operation risk level and the credit risk level of the enterprise to be evaluated;
step S23, determining the risk level of the enterprise to be evaluated based on the operation risk level and the credit risk level;
and step S24, determining the processing measures corresponding to the risk level of the enterprise to be evaluated based on the risk level of the enterprise to be evaluated and the repayment risk early warning rule set.
The risk levels correspond to the enterprises as shown in the following table:
| serial number | Risk rating | Risk performance |
| 1 | Is normal | Enterprise with normal repayment and normal operation condition |
| 2 | First degree risk | Enterprises with overdue repayment or illegal behaviors |
| 3 | Second degree risk | Enterprises with downslide or change of business behavior |
The corresponding measures for the risk levels are shown in the following table:
| serial number | Risk rating | Corresponding measures |
| 1 | Is normal | Increase the amount of money, reduce interest rate, normal management, etc |
| 2 | First degree risk | Hastening, receiving credit in advance, freezing the amount, etc |
| 3 | Second degree risk | Lower the limit, increase the interest rate, etc |
It should be noted that the risk early warning monitoring during loan refers to that the financial institution monitors the operation condition, the credit condition and the like of the financing enterprise with the loan in the loan term of the financing enterprise, and knows whether the enterprise has related risks affecting loan recovery.
According to the embodiment of the invention, risk early warning and monitoring in loan is realized by actively contacting the relevant business managers of the financial institutions, specifically, various operation data and external data of financing enterprises are collected at a certain time point in loan, whether the enterprises have related risks influencing loan recovery is predicted, and different processing measures are matched according to the risk conditions.
Compared with the evaluation method of enterprise financing risk recorded in the embodiment of the application in the prior art, the method has the following advantages:
(1) the applied enterprise data source is basically obtained on line in a passive mode, so that the approval efficiency of the current operation financing can be improved, and various operation risks in the conventional manual approval can be avoided; meanwhile, the actual operation condition of the enterprise can be more accurately and efficiently evaluated.
(2) The current advanced machine learning algorithm is introduced to construct the enterprise operation risk scoring card and the credit risk scoring card, so that the operation risk and the credit risk of the enterprise can be more comprehensively and accurately assessed, and the data image of the enterprise operation condition can be more comprehensively and objectively drawn.
(3) The credit line of the enterprise is comprehensively evaluated according to the business condition, the profit condition and the asset condition of the enterprise, so that the actual condition of the enterprise can be better matched, and the credit line within a more reasonable and bearing range can be given to the enterprise.
(4) By utilizing active triggering type risk early warning and risk relieving measures in the loan, credit clients are managed more scientifically and efficiently, and the substantial risk of an enterprise is identified more accurately and quickly under the condition of saving labor cost of management in the loan.
The second embodiment:
the embodiment of the present invention further provides an enterprise financing risk assessment apparatus, which is configured to execute the enterprise financing risk assessment method provided in the foregoing content of the embodiment of the present invention, and the following is a specific introduction of the apparatus provided in the embodiment of the present invention.
As shown in fig. 2, fig. 2 is a schematic view of the above-mentioned enterprise financing risk assessment device, which includes: an acquisition unit 10, a first building unit 20, a second building unit 30 and an evaluation unit 40.
The acquiring unit 10 is configured to acquire enterprise data of a financed enterprise, and preprocess the enterprise data of the financed enterprise to obtain target enterprise data of the financed enterprise;
the first constructing unit 20 is configured to construct an admission requirement rule set and a repayment risk early warning rule set based on the target enterprise data and a definition tag corresponding to the target enterprise data, where the definition tag is used for the operational capacity of the financed enterprise;
the second construction unit 30 is configured to construct an operation risk assessment model and a credit risk assessment model based on a preset machine learning algorithm and target enterprise data of the financed enterprise;
the evaluation unit 40 is configured to obtain a pre-loan evaluation result of the enterprise to be evaluated or a pre-loan risk warning result of the enterprise to be evaluated based on the enterprise data of the enterprise to be evaluated, the admission requirement rule set or the repayment risk warning rule set, the operation risk evaluation model and the credit risk evaluation model after acquiring the enterprise data of the enterprise to be evaluated, where the pre-loan evaluation result includes: the credit result and the financing credit line, wherein the risk early warning result in the loan is as follows: and the risk level and the processing measure corresponding to the risk level.
In the embodiment of the invention, the target enterprise data of the financed enterprise is obtained by acquiring the enterprise data of the financed enterprise and preprocessing the enterprise data of the financed enterprise; establishing an admission requirement rule set and a repayment risk early warning rule set based on the target enterprise data and a definition label corresponding to the target enterprise data, wherein the definition label is used for the operation capacity of the financed enterprise; constructing an operation risk evaluation model and a credit risk evaluation model based on a preset machine learning algorithm and the target enterprise data of the financed enterprise; after enterprise data of an enterprise to be evaluated is acquired, obtaining a pre-loan evaluation result of the enterprise to be evaluated or a pre-loan risk warning result of the enterprise to be evaluated based on the enterprise data of the enterprise to be evaluated, the admission requirement rule set or the repayment risk warning rule set, the operation risk evaluation model and the credit risk evaluation model, wherein the pre-loan evaluation result comprises: the credit result and the financing credit line, wherein the risk early warning result in the loan is as follows: the risk level and the processing measures corresponding to the risk level achieve the purpose of acquiring enterprise data and evaluating enterprise risks without manual offline, and further solve the technical problem that the existing assessment method for enterprise financing risks is low in efficiency and accuracy, so that the technical effect of improving the efficiency and accuracy of enterprise financing assessment is achieved.
Preferably, the enterprise data includes: tax-related operation data, invoice data, industrial and commercial registration data, judicial complaint data and human bank credit data; the pretreatment comprises the following steps: integrity check processing, structuring processing, availability check processing and characteristic variable derivation processing.
Preferably, if the enterprise to be assessed is an unfurled enterprise, the assessing unit is configured to: preprocessing enterprise data of the enterprise to be evaluated to obtain target enterprise data of the enterprise to be evaluated; determining an initial credit granting result of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated and the admission requirement rule set; if the initial credit granting result is passed, determining an operation risk level and a credit risk level of the enterprise to be evaluated based on target enterprise data of the enterprise to be evaluated, the operation risk evaluation model and the credit risk evaluation model; constructing a cross matrix based on the operation risk level and the credit risk level, and determining a credit granting result of the enterprise to be evaluated based on the cross matrix; and if the credit granting result of the enterprise to be evaluated passes, determining the credit granting amount of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated.
Preferably, if the enterprise to be assessed is a financed enterprise, the assessment unit is configured to: preprocessing enterprise data of the enterprise to be evaluated to obtain target enterprise data of the enterprise to be evaluated; determining an operation risk level and a credit risk level of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated, the operation risk evaluation model and the credit risk evaluation model; determining the risk level of the enterprise to be evaluated based on the operation risk level and the credit risk level; and determining the processing measures corresponding to the risk level of the enterprise to be evaluated based on the risk level of the enterprise to be evaluated and the repayment risk early warning rule set.
Example three:
an embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory is used to store a program that supports the processor to execute the method described in the first embodiment, and the processor is configured to execute the program stored in the memory.
Referring to fig. 3, an embodiment of the present invention further provides an electronic device 100, including: the device comprises a processor 50, a memory 51, a bus 52 and a communication interface 53, wherein the processor 50, the communication interface 53 and the memory 51 are connected through the bus 52; the processor 50 is arranged to execute executable modules, such as computer programs, stored in the memory 51.
The Memory 51 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 53 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The bus 52 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus.
The memory 51 is used for storing a program, the processor 50 executes the program after receiving an execution instruction, and the method performed by the apparatus defined by the flow program disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 50, or implemented by the processor 50.
The processor 50 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 50. The Processor 50 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, etc. as is well known in the art. The storage medium is located in the memory 51, and the processor 50 reads the information in the memory 51 and completes the steps of the method in combination with the hardware thereof.
Example four:
the embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the method in the first embodiment.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An assessment method for enterprise financing risk, comprising:
acquiring enterprise data of financed enterprises, and preprocessing the enterprise data of the financed enterprises to obtain target enterprise data of the financed enterprises;
establishing an admission requirement rule set and a repayment risk early warning rule set based on the target enterprise data and a definition label corresponding to the target enterprise data, wherein the definition label is used for the management capacity of the financed enterprise;
constructing an operation risk evaluation model and a credit risk evaluation model based on a preset machine learning algorithm and the target enterprise data of the financed enterprise;
after enterprise data of an enterprise to be evaluated is acquired, obtaining a pre-loan evaluation result of the enterprise to be evaluated or a pre-loan risk warning result of the enterprise to be evaluated based on the enterprise data of the enterprise to be evaluated, the admission requirement rule set or the repayment risk warning rule set, the operation risk evaluation model and the credit risk evaluation model, wherein the pre-loan evaluation result comprises: the credit result and the financing credit line, wherein the risk early warning result in the loan is as follows: and the risk level and the processing measure corresponding to the risk level.
2. The method of claim 1,
the enterprise data includes: tax-related operation data, invoice data, industrial and commercial registration data, judicial complaint data and human behavior credit data;
the pretreatment comprises the following steps: integrity check processing, structuring processing, availability check processing and characteristic variable derivation processing.
3. The method of claim 1, wherein if the enterprise to be evaluated is an unfulfilled enterprise, the admission requirement rule set or the repayment risk pre-warning rule set, and the business risk evaluation model and the credit risk evaluation model based on enterprise data of the enterprise to be evaluated comprise:
preprocessing enterprise data of the enterprise to be evaluated to obtain target enterprise data of the enterprise to be evaluated;
determining an initial credit granting result of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated and the admission requirement rule set;
if the initial credit granting result is passed, determining an operation risk level and a credit risk level of the enterprise to be evaluated based on target enterprise data of the enterprise to be evaluated, the operation risk evaluation model and the credit risk evaluation model;
constructing a cross matrix based on the operation risk level and the credit risk level, and determining a credit granting result of the enterprise to be evaluated based on the cross matrix;
and if the credit granting result of the enterprise to be evaluated passes, determining the credit granting amount of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated.
4. The method of claim 1, wherein if the enterprise to be assessed is a financed enterprise, the admission requirement rule set or the repayment risk pre-warning rule set, and the business risk assessment model and the credit risk assessment model based on enterprise data of the enterprise to be assessed comprise:
preprocessing enterprise data of the enterprise to be evaluated to obtain target enterprise data of the enterprise to be evaluated;
determining an operation risk level and a credit risk level of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated, the operation risk evaluation model and the credit risk evaluation model;
determining the risk level of the enterprise to be evaluated based on the operation risk level and the credit risk level;
and determining the processing measures corresponding to the risk level of the enterprise to be evaluated based on the risk level of the enterprise to be evaluated and the repayment risk early warning rule set.
5. An apparatus for assessing financing risk of an enterprise, comprising: an acquisition unit, a first construction unit, a second construction unit and an evaluation unit, wherein,
the acquisition unit is used for acquiring enterprise data of financed enterprises and preprocessing the enterprise data of the financed enterprises to obtain target enterprise data of the financed enterprises;
the first construction unit is used for constructing an admission requirement rule set and a repayment risk early warning rule set based on the target enterprise data and a definition tag corresponding to the target enterprise data, wherein the definition tag is used for the management capacity of the financed enterprise;
the second construction unit is used for constructing an operation risk evaluation model and a credit risk evaluation model based on a preset machine learning algorithm and the target enterprise data of the financed enterprise;
the evaluation unit is configured to obtain a pre-loan evaluation result of the enterprise to be evaluated or a pre-loan risk warning result of the enterprise to be evaluated based on the enterprise data of the enterprise to be evaluated, the admission requirement rule set or the repayment risk warning rule set, the operation risk evaluation model and the credit risk evaluation model after acquiring the enterprise data of the enterprise to be evaluated, where the pre-loan evaluation result includes: the credit result and the financing credit line, wherein the risk early warning result in the loan is as follows: and the risk level and the processing measure corresponding to the risk level.
6. The apparatus of claim 5,
the enterprise data includes: tax-related operation data, invoice data, industrial and commercial registration data, judicial complaint data and human behavior credit data;
the pretreatment comprises the following steps: integrity check processing, structuring processing, availability check processing and characteristic variable derivation processing.
7. The apparatus of claim 5, wherein if the enterprise to be evaluated is an unfulfilled enterprise, the evaluation unit is configured to:
preprocessing the enterprise data of the enterprise to be evaluated to obtain target enterprise data of the enterprise to be evaluated;
determining an initial credit granting result of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated and the admission requirement rule set;
if the initial credit granting result is passed, determining an operation risk level and a credit risk level of the enterprise to be evaluated based on target enterprise data of the enterprise to be evaluated, the operation risk evaluation model and the credit risk evaluation model;
constructing a cross matrix based on the operation risk level and the credit risk level, and determining a credit granting result of the enterprise to be evaluated based on the cross matrix;
and if the credit granting result of the enterprise to be evaluated passes, determining the credit granting amount of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated.
8. The apparatus of claim 5, wherein if the enterprise to be evaluated is a financed enterprise, the evaluation unit is configured to:
preprocessing enterprise data of the enterprise to be evaluated to obtain target enterprise data of the enterprise to be evaluated;
determining an operation risk level and a credit risk level of the enterprise to be evaluated based on the target enterprise data of the enterprise to be evaluated, the operation risk evaluation model and the credit risk evaluation model;
determining the risk level of the enterprise to be evaluated based on the operation risk level and the credit risk level;
and determining the processing measures corresponding to the risk level of the enterprise to be evaluated based on the risk level of the enterprise to be evaluated and the repayment risk early warning rule set.
9. An electronic device comprising a memory for storing a program that enables a processor to perform the method of any of claims 1 to 4 and a processor configured to execute the program stored in the memory.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of the claims 1 to 4.
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