CN112633635A

CN112633635A - Exhibitor risk assessment method, exhibitor risk assessment device, exhibitor risk assessment server and readable storage medium

Info

Publication number: CN112633635A
Application number: CN202011365923.9A
Authority: CN
Inventors: 聂镭; 邹茂泰; 聂颖
Original assignee: Longma Zhixin Zhuhai Hengqin Technology Co ltd
Current assignee: Longma Zhixin Zhuhai Hengqin Technology Co ltd
Priority date: 2020-11-29
Filing date: 2020-11-29
Publication date: 2021-04-09

Abstract

The embodiment of the application is applicable to the field of data processing, and provides a method, a device, a server and a readable storage medium for risk assessment of exhibitors, wherein the method comprises the following steps: acquiring external risk data of a target exhibitor; carrying out first numerical processing on external risk data to obtain a first mapping value; acquiring internal risk data of a target exhibitor; performing second data processing on the internal risk data to obtain a second mapping value; and inputting the first mapping value and the second mapping value into a preset processing model to obtain a risk evaluation result. Therefore, the risk assessment method and the risk assessment system have the advantages that the internal risk data and the external risk data of the target exhibitor are collected, the internal risk data and the external risk data are directly input into the preset processing model for risk assessment, and therefore the effect of comprehensively assessing the risk of the exhibitor is achieved.

Description

Exhibitor risk assessment method, exhibitor risk assessment device, exhibitor risk assessment server and readable storage medium

Technical Field

The application belongs to the field of data processing, and particularly relates to a method, a device, a server and a readable storage medium for risk assessment of exhibitors.

Background

The exhibition is an emerging service industry with wide influence and high relevance. The risk of the exhibitor needs to be evaluated so as to guarantee the benefits of the audience, but the risk evaluation of the exhibitor in the prior art is not comprehensive and is only simple evaluation.

Disclosure of Invention

In view of this, embodiments of the present application provide a method, an apparatus, a server, and a readable storage medium for risk assessment of exhibitors, so as to solve the problem in the prior art that risk assessment of exhibitors is not comprehensive enough.

A first aspect of an embodiment of the present application provides a method, including:

acquiring external risk data of a target exhibitor;

performing first numerical processing on the external risk data to obtain a first mapping value;

acquiring internal risk data of the target exhibitor;

performing second data processing on the internal risk data to obtain a second mapping value;

and inputting the first mapping value and the second mapping value into a preset processing model to obtain a risk evaluation result.

In a possible implementation manner of the first aspect, performing first data processing on the external risk data to obtain a first mapping value includes:

performing a first classification on the external risk data;

and carrying out first assignment according to the result after the first classification to obtain the first mapping value.

In a possible implementation manner of the first aspect, performing second data processing on the internal risk data to obtain a second mapping value includes:

performing a second classification on the internal risk data;

and carrying out second assignment according to the result after the second classification to obtain the second mapping value.

In a possible implementation manner of the first aspect, performing a second assignment according to the result after the second classification to obtain a second mapping value includes:

and if the target internal risk data appear in the second classified result, inputting the target internal risk data into a preset Z value model to obtain a second mapping value.

In a possible implementation manner of the first aspect, the preset processing model is an XGBoost model;

inputting the first mapping value and the second mapping value into a preset processing model to obtain a risk assessment result, wherein the risk assessment result comprises:

obtaining a risk assessment result according to the following formula:

wherein f is_kAnd expressing a regression tree, K expresses the number of the regression trees, x expresses the sum of the first mapping value and the second mapping value, and y expresses the mapping value corresponding to the risk assessment result.

A second aspect of embodiments of the present application provides an apparatus, comprising:

the first acquisition module is used for acquiring external risk data of a target exhibitor;

the first processing module is used for carrying out first numerical processing on the external risk data to obtain a first mapping value;

the second acquisition module is used for acquiring internal risk data of the target exhibitor;

the second processing module is used for carrying out second data processing on the internal risk data to obtain a second mapping value;

and the generating module is used for inputting the first mapping value and the second mapping value into a preset processing model to obtain a risk evaluation result.

In a possible implementation manner of the second aspect, the first processing module includes:

a first classification unit, configured to perform a first classification on the external risk data;

and the first assignment unit is used for carrying out first assignment according to the result after the first classification to obtain the first mapping value.

In a possible implementation manner of the second aspect, the second processing module includes:

a second classification unit, configured to perform a second classification on the internal risk data;

and the second assignment unit is used for carrying out second assignment according to the result after the second classification to obtain the second mapping value.

In a possible implementation manner of the second aspect, the second assignment unit includes:

and the assignment subunit is used for inputting the target internal risk data into a preset Z value model to obtain a second mapping value if the target internal risk data are determined to appear in the result after the second classification.

In a possible implementation manner, the preset processing model is an XGBoost model;

the generation module comprises:

obtaining a risk assessment result according to the following formula:

A third aspect of an embodiment of the present application provides a server, including: a memory, a processor, an image pick-up device and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method as described in the first aspect above when executing the computer program.

A fourth aspect of an embodiment of the present application provides a readable storage medium, including: the readable storage medium stores a computer program which, when executed by a processor, performs the steps of the method according to the first aspect as described above.

Compared with the prior art, the embodiment of the application has the advantages that: the risk assessment method and the risk assessment device have the advantages that the internal risk data and the external risk data of the target exhibitors are collected, the internal risk data and the external risk data are directly input into the preset processing model to carry out risk assessment, and therefore the effect of carrying out comprehensive risk assessment on the exhibitors is achieved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.

Fig. 1 is a schematic flow chart of a risk assessment method for exhibitors provided in the embodiment of the present application;

fig. 2 is a schematic specific flowchart of step S102 in fig. 1 according to an embodiment of the present disclosure;

FIG. 3 is a flowchart illustrating an implementation of step S104 of FIG. 1 for risk assessment of an exhibitor, according to an embodiment of the present disclosure;

fig. 4 is a schematic structural diagram of a risk assessment apparatus for exhibitors according to an embodiment of the present application;

fig. 5 is a server provided in an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It should be noted that the application scenario applicable to the embodiment of the present application includes not only performing risk assessment on exhibitors participating in the exhibition, but also applying to any other application scenario requiring risk assessment on enterprises, that is, the embodiment of the present application does not limit the application scenario.

In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.

Example one

Referring to fig. 1, a schematic flow chart of a method for risk assessment of an exhibitor provided in an embodiment of the present application is applied to a server, where the server is a computing device such as a cloud server, and the method includes the following steps:

step 101, obtaining external risk data of a target exhibitor.

Wherein, the target exhibitor refers to an exhibitor needing risk assessment, and the exhibitor can refer to an enterprise and the like; external risk data includes, but is not limited to, national risk data, policy risk data, market risk data, and non-adversarial risk data. It should be noted that the external risk may be obtained by crawling from a regular website by using a data collection means such as a crawler.

The following illustrates the definition of each external risk:

national risks, i.e., risks associated with the economic, social, and political environmental aspects of the country in which the borrower is located. The exhibitors may be from all over the world, and so there is a need to collect relevant information about the country in which the enterprise is located. The news of the country where the enterprise is located is collected and classified into related country categories through text classification. Each country corresponds to a respective national risk.

Policy risk, the policy risk (foreign language name) refers to the risk brought to investors by the fluctuation of the securities market caused by the major change of the government policies related to the securities market or the emergence of important actions and regulations. Under the market economic condition, due to the influence of value laws and competitive mechanisms, enterprises strive for market resources and hope to obtain greater freedom of movement, so that the related policies of the country can be violated, and the national policies have mandatory restriction on the behaviors of the enterprises. In addition, the state can change the policy according to the change of the macroscopic environment at different periods, which necessarily influences the economic benefit of the enterprise. Therefore, the existence and adjustment of policies between countries and enterprises can create contradictions in economic interests, thereby creating policy risks. And the relevant policies of the country where the enterprise is located are collected, so that subsequent policy classification is facilitated.

The ineffectiveness refers to an objective situation that cannot be predicted, avoided, or overcome at the time of contracting. Including natural disasters such as typhoons, earthquakes, floods, hails; government actions, such as levy, levy; the social abnormal events are three aspects of strikes and harassment. The invalidity news related to the company is collected.

And (4) collecting the inelasticity data related to the enterprise, and facilitating the subsequent calculation of the inelasticity risk value.

Step S102, carrying out first numerical processing on the external risk data to obtain a first mapping value.

In a possible implementation manner, referring to fig. 2, a specific flowchart of step S102 in fig. 1 provided in this embodiment of the application is that performing a first numerical processing on external risk data to obtain a first mapping value includes:

step S201, performing a first classification on the external risk data.

Preferably, before the first classification of the external risk data, data cleaning may be performed on the external risk data, for example, cleaning the collected text data, and filtering stop words. The stop word list adopts a Hadamard stop word list, so that subsequent text classification is facilitated.

Specifically, the external risk data after data cleaning is classified by adopting fastText.

Step S202, carrying out first assignment according to the result after the first classification to obtain a first mapping value.

In specific application, the national risk is evaluated in an average mode.

For example, if there are 10 data, 8 of which present a national risk, the final risk value is 8/10 ═ 0.8. And (4) adopting similar modes for policy risk data, market risk data, inefficacy risk data and the like, firstly carrying out long text classification, then obtaining the risk value of the risk data, and finally obtaining the corresponding average risk.

And step S103, acquiring internal risk data of the target exhibitor.

The internal risk data comprises credit risk data, operation risk data, compliance risk data, reputation risk data, personnel risk data and the like. It should be noted that the internal risk may be obtained by crawling from a regular website by using a data collection means such as a crawler.

And step S104, performing second data processing on the internal risk data to obtain a second mapping value.

In a possible implementation manner, as shown in fig. 3, for a specific implementation flowchart of step S104 in fig. 1 of the risk assessment of the exhibitor provided in the embodiment of the present application, the second data processing is performed on the internal risk data to obtain a second mapping value, where the second data processing includes:

and S301, carrying out second classification on the internal risk data.

For example, the internal risk data is classified into credit risk data, operational risk data, compliance risk data, reputation risk data, and personnel risk data using fastText.

And S302, performing second assignment according to the result after the second classification to obtain the second mapping value.

Preferably, if it is determined that the target internal risk data appears in the result after the second classification, the target internal risk data is input into a preset Z value model to obtain a second mapping value.

Wherein the target internal risk data refers to credit risk data. It should be noted that, in the embodiment of the present application, an assignment process of the internal risk data, which is used in the central risk data, is different from that of other internal risk data.

In a specific application, the following is presented according to different types of internal risk data:

performing second assignment on the credit risk data to obtain a second mapping value:

credit risk refers to the possibility that a debtor or counterparty will not normally fulfill obligations or the quality of credit defined by a contract, affecting the value of a financial instrument, thereby causing a loss to the creditor or the holder of the financial instrument, and is also commonly referred to as a default risk.

And constructing a default risk level model of the borrower by adopting the z value model. The method utilizes a discrimination analysis technology in mathematical statistics to select a group of financial ratios which can reflect the financial condition of a borrower most, have the greatest influence on the quality of the borrower and have the most predictive and analytical value, and obtains the importance of each ratio on the analysis of the loan quality by means of regression analysis and the like, thereby evaluating the credit risk of a loan applicant. The specific formula is as follows:

Z＝1.2X₁+1.4X₂+3.3X₃+0.6X₄+1.0X₅

wherein the content of the first and second substances,

through the formula, the obtained Z value can represent the financial condition of the borrower. The larger the Z value is, the better the financial condition of the borrower is, wherein the Z value is the second mapping value.

Performing second assignment on the operation risk data to obtain a second mapping value:

operational risks can be classified into 7 categories according to the occurring rate and the size of the loss:

1. internal fraud. Fraud, theft of assets, law violation, and regulatory behavior of companies that have personnel within the organization involved.

2. External fraud. Third party fraud, theft of assets, law violation.

3. Employment contracts and risk events from work conditions. The compensation requirements caused by failure to fulfill the contract or non-compliance with the labor health and safety regulations.

4. Risk events caused by customer, product, and business activities. Failure to meet a particular customer's needs, either intentionally or unintentionally, or errors due to product properties, design issues.

5. Loss of physical assets. Damage or loss of tangible assets due to catastrophic events or other events.

6. Outages and system errors. Such as software or hardware errors, communication problems, and device aging.

7. Risk events relating to the execution, delivery, and management of transaction processes. For example, transaction failures, partner failures, transaction data entry errors, incomplete legal documents, unauthorized access to customer accounts, and vendor disputes, among others.

Specifically, the fastText is used for classifying the operation risk, and the classification is 7 classes corresponding to the operation risk and other classes. And the numerical value corresponding to each category is 0-7, and the classification result is graded according to corresponding different scores to obtain a second mapping value.

And performing second assignment on the compliance risk, the reputation risk and the personnel risk data to obtain a second mapping value:

in table 1, laws, reputation of enterprises, and personal security correspond to the score conditions of compliance risk, reputation risk, and personal risk, respectively.

TABLE 1

If "there is a slight violation of the regulation" is satisfied, the corresponding compliance risk value is 1, and if there are a plurality of compliance risks, the maximum value is selected. The same reputation risk and personnel risk may also be given with reference to table 1.

And S105, inputting the first mapping value and the second mapping value into a preset processing model to obtain a risk evaluation result.

The preset processing model is a pre-trained processing model, and the preset processing model is an XGboost model.

By way of example and not limitation, inputting the first mapping value and the second mapping value into a preset processing model to obtain a risk assessment result, including:

obtaining a risk assessment result according to the following formula:

It is understood that x ═ is (country risk value, policy risk value, market risk value, inequality risk value, credit risk value, operation risk value, compliance risk value, reputation risk value and personnel risk value), y ═ is output as a mapping value corresponding to the risk assessment result, i.e. exhibitor risk assessment value, the exhibitor risk assessment value is larger, and when the exhibitor risk assessment value is larger than the assessment threshold value, attention of exhibitor audience needs to be reminded.

In the embodiment of the application, the internal risk data and the external risk data of the target exhibitor are acquired, and the internal risk data and the external risk data are directly input into the preset processing model for risk assessment, so that the effect of comprehensively assessing the risk of the exhibitor is achieved.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

The following describes an example of a risk assessment device for exhibitors provided by the embodiments of the present application. The exhibitor risk assessment device of the embodiment corresponds to the exhibitor risk assessment method.

Fig. 4 is a schematic structural diagram of an apparatus for risk assessment of exhibitor provided in an embodiment of the present application, where the apparatus may be specifically integrated with the apparatus, and the apparatus may include:

a first obtaining module 41, configured to obtain external risk data of a target exhibitor;

the first processing module 42 is configured to perform first numerical processing on the external risk data to obtain a first mapping value;

a second obtaining module 43, configured to obtain internal risk data of the target exhibitor;

a second processing module 44, configured to perform second data processing on the internal risk data to obtain a second mapping value;

and the generating module 45 is configured to input the first mapping value and the second mapping value to a preset processing model to obtain a risk assessment result.

In one possible implementation, the first processing module includes:

In one possible implementation, the second processing module includes:

In a possible implementation manner, the second assignment unit includes:

the generation module comprises:

obtaining a risk assessment result according to the following formula:

It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.

Fig. 5 is a schematic diagram of a server 5 provided in an embodiment of the present application. As shown in fig. 5, the server 5 of this embodiment includes: a processor 50, a memory 51 and a computer program 52, such as a push message program, stored in said memory 51 and operable on said processor 50. The steps in the various extraction method embodiments described above are implemented when the computer program 52 is executed by the processor 50. Alternatively, the processor 50 implements the functions of the modules/units in the above-described device embodiments when executing the computer program 52.

Illustratively, the computer program 52 may be partitioned into one or more modules/units, which are stored in the memory 51 and executed by the processor 50 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 52 in the server 5.

The server 5 may be a computing device such as a cloud server. The server 5 may include, but is not limited to, a processor 50, a memory 51. Those skilled in the art will appreciate that fig. 5 is merely an example of a server 5 and does not constitute a limitation of the server 5 and may include more or fewer components than shown, or some components in combination, or different components, e.g., the server 5 may also include input output devices, network access devices, buses, etc.

The Processor 50 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The storage 51 may be an internal storage unit of the server 5, such as a hard disk or a memory of the server 5. The memory 51 may also be an external storage device of the server 5, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like provided on the server 5. Further, the memory 51 may also include both an internal storage unit and an external storage device of the server 5. The memory 51 is used for storing the computer program and other programs and data required by the server 5. The memory 51 may also be used to temporarily store data that has been output or is to be output.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed terminal device and method may be implemented in other ways. For example, the above-described terminal device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical function division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be 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 through some interfaces, devices or units, 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 place, or may be distributed on a plurality of 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 application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims

1. A method for risk assessment of a exhibitor, the method comprising:

acquiring external risk data of a target exhibitor;

acquiring internal risk data of the target exhibitor;

2. The method of claim 1, wherein the performing a first data process on the external risk data to obtain a first mapping value comprises:

performing a first classification on the external risk data;

3. The method of claim 1, wherein performing a second data process on the internal risk data to obtain a second mapping value comprises:

performing a second classification on the internal risk data;

4. The method of claim 3, wherein performing a second assignment according to the second classified result to obtain a second mapping value comprises:

5. The method for risk assessment of exhibitors according to any of claims 1 to 4, wherein said pre-set processing model is the XGboost model;

obtaining a risk assessment result according to the following formula:

6. An exhibitor risk assessment apparatus, the apparatus comprising:

7. Server comprising a memory, a processor, a camera device and a computer program stored in said memory and executable on said processor, characterized in that said processor implements the method according to any of claims 1 to 5 when executing said computer program.

8. Readable storage medium, in 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 claims 1 to 5.