CN111127193A - Enterprise evaluation system with credit model as core and based on deep learning construction - Google Patents
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Abstract
The invention discloses a method for realizing the maximum effective economic benefit for enterprises; meanwhile, an enterprise evaluation system which takes a credit model as a core can be constructed based on deep learning and can accurately score the enterprise. The enterprise evaluation system which is built based on deep learning and takes a credit model as a core comprises a total scoring server, a basic data scoring server, an industry dominant data scoring server, a public opinion data scoring server, a macroscopic data scoring server and other scoring data servers; and the total scoring server is connected with a total scoring data memory and a total scoring output device. The enterprise evaluation system which is built based on the deep learning and takes the credit model as the core can reflect the credit condition of the enterprise more accurately, so that the credit of the enterprise can be scored accurately; thereby helping the enterprise to realize the maximum effective economic benefit.
Description
Technical Field
The invention relates to an enterprise credit evaluation system, in particular to an enterprise evaluation system which is based on deep learning and takes a credit model as a core.
Background
It is well known that: the credit model is a mathematical model between credit risk control, accumulated historical data are analyzed by utilizing a data mining technology, characteristics and rules of credit risk of enterprises are searched, a corresponding mathematical model is established, and finally credit evaluation is carried out on the enterprises.
The enterprise credit evaluation is helpful for preventing business risks of enterprises and provides good conditions for the construction of enterprise systems. The final aim of transforming the enterprise operation mechanism and establishing a modern enterprise system is to enable the enterprise to become a market competition subject with legal independent operation, self profit and loss, self development and self constraint.
The comprehensive evaluation of the enterprise credit is the basis for determining the loan risk degree and managing the credit asset risk by commercial banks and financial institutions. The enterprise as the main unit of economic activity has close credit relationship with banks and financial institutions, credit is one of the important capital sources for production and development, and whether the conditions of production and operation activities and behaviors are standardized or not is directly related to the quality of credit capital use and benefit. This is directly reflected in the fact that loan companies are reluctant to fulfill or are unable to fully fulfill repayment obligations, and once credit risk has developed, banks and financial institutions will fall asleep at great financial loss due to customer default. This requires banks and financial institutions to give scientific evaluation to the business activities, business results, profitability, and loan-repaying ability of enterprises, etc., to determine the uncertainty of credit asset loss and to prevent loan risks to the maximum extent. At present, with the conversion from nationwide banks to commercial banks, the requirements on the safety and the benefit of credit assets are high.
The existing enterprise data credit rating business system, for example: ZL 201020610651.X discloses a credit rating working subsystem of a credit rating service system, which comprises a client, a network device, a server group, a data bus and a database, wherein the client is connected with the server group through the network device, all servers in the server group are connected through the data bus and connected to the database through the data bus, and the server group comprises a working management server, an information maintenance server, a report making server and an analysis tool server. The invention has the beneficial effects that: under a work management framework, an integrated working platform is provided for users, and rating workers can perform rating operation on rating objects under selected rating items; a plurality of working tools are provided to support rating work, and support is provided for efficient and high-quality rating work. However, the credit rating working subsystem of the credit rating service system has a certain one-sidedness in rating the enterprise, cannot reflect the credit condition of the enterprise in an all-round manner, and is poor in accuracy.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method which is beneficial to enterprises to realize the maximum effective economic benefit; meanwhile, an enterprise evaluation system which takes a credit model as a core can be constructed based on deep learning and can accurately score the enterprise.
The technical scheme adopted by the invention for solving the technical problems is as follows: an enterprise evaluation system taking a credit model as a core is constructed based on deep learning, and comprises a total scoring server, a basic data scoring server, an industry dominant data scoring server, a public opinion data scoring server, a macroscopic data scoring server and other scoring data servers;
the basic data scoring server, the industry dominant data scoring server, the public opinion data scoring server, the macroscopic data scoring server and other scoring data servers are respectively in communication connection with the total scoring server;
the total scoring server is connected with a total scoring data memory and a total scoring output device;
the basic data scoring server is connected with a basic data acquisition terminal for acquiring basic data;
the basic data scoring server is used for carrying a basic data scoring tool to score the basic data acquired by the basic data acquisition terminal;
the industry dominant data scoring server is connected with an industry dominant data acquisition terminal for acquiring industry dominant data;
the industry dominant data scoring server is used for carrying an industry dominant data scoring tool to score the industry dominant data acquired by the industry dominant data acquisition terminal;
the public opinion data scoring server is connected with a public opinion data acquisition terminal for acquiring line public opinion data;
the public opinion data scoring server is used for carrying a public opinion data scoring tool to score the public opinion data collected by the public opinion data collecting terminal;
the macroscopic data scoring server is connected with a macroscopic data acquisition terminal for acquiring macroscopic data;
the macroscopic data scoring server is used for carrying a macroscopic data scoring tool to score the macroscopic data acquired by the macroscopic data acquisition terminal;
the other scoring data server is used for carrying other scoring data scoring tools to score the scoring data;
the total score data memory is used for storing data of total scores; and the total score output device is used for outputting the total score.
Specifically, the basic data acquisition terminal, the industry dominant data acquisition terminal, the public opinion data acquisition terminal and the macroscopic data acquisition terminal all adopt PC computers.
Specifically, the total score output device adopts electronic equipment with a display screen.
Specifically, the total scoring server employs an IBM server x3650M 510 core E5-2640v 4.
Specifically, the basic data scoring server, the industry dominant data scoring server, the public opinion data scoring server, the macroscopic data scoring server and other scoring data servers all adopt IBM X3650M 4.
The invention has the beneficial effects that: the enterprise evaluation system which takes the credit model as the core is constructed based on the deep learning, parameters of all aspects of an enterprise can be integrated due to the fact that the enterprise evaluation system comprises a basic data scoring server, an industry dominant data scoring server, a public opinion data scoring server, a macroscopic data scoring server and other scoring data servers, and the enterprise can be scored comprehensively, so that the credit condition of the enterprise can be reflected more accurately, and the credit of the enterprise can be scored accurately; thereby helping the enterprise to realize the maximum effective economic benefit.
Drawings
FIG. 1 is a schematic structural diagram of an enterprise evaluation system based on deep learning and with a credit model as a core according to an embodiment of the present invention;
FIG. 2 is a diagram showing the relationship between the average market value of the listed companies and the provinces in each province;
FIG. 3 is a graph of the average market value of a company versus different established times;
the following are marked in the figure: 100-total scoring server, 200-basic data scoring server, 300-industry dominant data scoring server, 400-public sentiment data scoring server, 500-macroscopic data scoring server, 600-other scoring data server, 700-total scoring data memory, 800-and total scoring output device.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
As shown in fig. 1, the enterprise evaluation system based on deep learning and built with a credit model as a core according to the present invention includes a total scoring server 100, a basic data scoring server 200, an industry dominant data scoring server 300, a public opinion data scoring server 400, a macro data scoring server 500, and other scoring data servers 600;
the basic data scoring server 200, the industry dominant data scoring server 300, the public opinion data scoring server 400, the macroscopic data scoring server 500 and the other scoring data servers 600 are respectively in communication connection with the total scoring server 100;
the total scoring server 100 is connected with a total scoring data memory 700 and a total scoring output device 800;
the basic data scoring server 200 is connected with a basic data acquisition terminal 201 for acquiring basic data;
the basic data scoring server 200 is used for carrying a basic data scoring tool to score the basic data acquired by the basic data acquisition terminal 201;
the industry dominant data scoring server 300 is connected with an industry dominant data acquisition terminal 301 for acquiring industry dominant data;
the industry dominant data scoring server 300 is used for carrying an industry dominant data scoring tool to score the industry dominant data acquired by the industry dominant data acquisition terminal 301;
the public opinion data scoring server 400 is connected with a public opinion data acquisition terminal 401 for acquiring public opinion data;
the public opinion data scoring server 400 is used for carrying a public opinion data scoring tool to score the public opinion data collected by the public opinion data collecting terminal 401;
the macroscopic data scoring server 500 is connected with a macroscopic data acquisition terminal 501 for acquiring macroscopic data;
the macroscopic data scoring server 500 is used for carrying a macroscopic data scoring tool to score the macroscopic data collected by the macroscopic data collecting terminal 501;
the other scoring data server 600 is used for carrying other scoring data scoring tools to score the scoring data;
the total score data storage 700 is used for storing data of total scores; the total score output device 800 is used for outputting the total score.
Specifically, the basic data acquisition terminal 201, the industry dominant data acquisition terminal 301, the public opinion data acquisition terminal 401 and the macroscopic data acquisition terminal 501 all adopt PC computers.
Specifically, the total score output device 800 is an electronic device with a display screen, such as a tablet computer, a mobile phone, or a desktop computer.
In order to simplify the system and improve the working efficiency and stability of the system, in particular, the total scoring server 100 adopts an IBM server x3650M 510 and cores E5-2640v 4. The basic data scoring server 200, the industry dominant data scoring server 300, the public opinion data scoring server 400, the macroscopic data scoring server 500 and the other scoring data servers 600 all adopt IBM X3650M 4.
Examples
The enterprise evaluation system which is based on deep learning and takes a credit model as a core is constructed, and the enterprise evaluation system is characterized in that: the system comprises a total scoring server 100, a basic data scoring server 200, an industry dominant data scoring server 300, a public opinion data scoring server 400, a macroscopic data scoring server 500 and other scoring data servers 600;
the basic data scoring server 200, the industry dominant data scoring server 300, the public opinion data scoring server 400, the macroscopic data scoring server 500 and the other scoring data servers 600 are respectively in communication connection with the total scoring server 100;
the total scoring server 100 is connected with a total scoring data memory 700 and a total scoring output device 800; the basic data scoring server 200 is connected with a basic data acquisition terminal 201 for acquiring basic data; the industry dominant data scoring server 300 is connected with an industry dominant data acquisition terminal 301 for acquiring industry dominant data; the public opinion data scoring server 400 is connected with a public opinion data acquisition terminal 401 for acquiring public opinion data; the macroscopic data scoring server 500 is connected with a macroscopic data acquisition terminal 501 for acquiring macroscopic data; the other scoring data server 600 is used for carrying other scoring data scoring tools to score the scoring data;
the total score data storage 700 is used for storing data of total scores; the total score output device 800 is used for outputting the total score. The total score output device 800 is an electronic device with a display screen.
The total scoring server 100 employs an IBM Server x3650M 510 core E5-2640v 4. The basic data scoring server 200, the industry dominant data scoring server 300, the public opinion data scoring server 400, the macroscopic data scoring server 500 and the other scoring data servers 600 all adopt IBM X3650M 4.
In the specific working process:
1. the total scoring server 100, the basic data scoring server 200, the industry dominant data scoring server 300, the public opinion data scoring server 400, the macro data scoring server 500 and the other scoring data servers 600 are respectively provided with corresponding scoring tools. The user can select a corresponding scoring tool, such as weighted average scoring, according to the requirement of the user. Various scoring tools are selected by the user.
2. The basic data acquisition terminal 201, the industry dominant data acquisition terminal 301, the public opinion data acquisition terminal 401 and the macroscopic data acquisition terminal 501 are all PC computers; through network search, collected government department information is combined with internet big data, and useful data are mined through internet data mining.
Specifically, basic data including company registration information, establishment time, registered fund, business scope, and high-master ability analysis are acquired through the basic data acquisition terminal 201. Corresponding data can be obtained through the investigation of the enterprises on the network. The collected data can be input through a PC computer to finish the collection.
Industry advantage data are collected through an industry advantage data collection terminal 301, wherein the industry advantage data comprise business structure analysis, suppliers, customers and financial reports; wherein the business structure analysis includes product structure, industry structure, frequency of purchases and frequency of shipments. The data is provided by the company to be scored and is reviewed by the evaluator. The collected data can be input through a PC computer to finish the collection.
Public opinion data is collected through a public opinion data collecting terminal 401; the public opinion data comprises market performance, social influence, staff and welfare. The data is provided by the company to be scored and is reviewed by the evaluator. The collected data can be input through a PC computer to finish the collection.
Macroscopic data including local economics, demographics, business circles, suppliers, and customers are collected by the macroscopic data collection terminal 501. This data may be obtained from information sent by relevant parts of the local government. The collected data can be input through a PC computer to finish the collection.
3. The collected data are distributed and sent to a basic data scoring server 200, an industry dominant data scoring server 300, a public opinion data scoring server 400 and a macroscopic data scoring server 500; and the grading of each project is realized by using the corresponding grading tool carried by the system.
Moreover, the scoring mechanism may formulate other additional scores according to corresponding scoring criteria, and input the additional scores to other scoring data servers 600; scoring is performed by other scoring data servers 600.
4. Transmitting the scores obtained by each scoring server to a total scoring server 100; the total scoring is implemented within the total scoring server 100 by a total scoring tool.
In this example, total scoring was performed in the following manner;
4.1, establishing a grading model
Wherein,
the total score is expressed as a function of time,
respectively representing the basic score, the industry superiority score, the public opinion score, the local macroscopic data score and other additional scores of the company.
Wherein the artificially set base score is used to provide a conservative estimate of the score for the new customer or supplier.
4.1.1, registering address factors; the registered address also has significance for the growth of a company, and for the city company as an example, the relationship between the average city value and the province of each province is analyzed as shown in fig. 2 below.
4.1.2, a time-to-establish factor;
the different establishment times of the companies are different from the average market value of the company, as shown in fig. 3 below.
4.2 construction of Credit model
Assume the final model is:
wherein, A represents the maximum value of the score,
representing the corresponding factor
The weight of (c).
Then, the similarity between the subjects a, b:
then score principal a is modified with k principal:
since the difference between scores between similar subjects is guaranteed to be as small as possible, and then the least squares method is used to estimate
And finally solving the parameters in an iterative mode.
4.3, the scores of the enterprise related original data which are required to be subjected to credit evaluation and are obtained by each scoring server are input into a total scoring server 100; and evaluating through a credit model and obtaining an enterprise credit evaluation result.
In summary, the enterprise evaluation system based on deep learning and with a credit model as a core is constructed, and the enterprise evaluation system comprises a basic data scoring server, an industry dominant data scoring server, a public opinion data scoring server, a macroscopic data scoring server and other scoring data servers, wherein collected government department information is fully utilized, and in combination with internet big data, useful data are mined through internet data mining and are fused to the credit model, so that the credit model can comprehensively consider factors of various aspects, and then the method according to a recommendation system is as follows: the score is revised based on collaborative filtering. And analyzes the market reheat point problem, thus reflecting the value of the market reheat point problem.
Therefore, parameters of all aspects of the enterprise can be integrated, and the enterprise can be comprehensively scored, so that the credit condition of the enterprise can be more accurately reflected, and the credit of the enterprise can be accurately scored; thereby helping the enterprise to realize the maximum effective economic benefit.
Claims (5)
1. An enterprise evaluation system based on deep learning and with a credit model as a core is constructed, and is characterized in that: the system comprises a total scoring server (100), a basic data scoring server (200), an industry dominant data scoring server (300), a public opinion data scoring server (400), a macroscopic data scoring server (500) and other scoring data servers (600);
the basic data scoring server (200), the industry dominant data scoring server (300), the public opinion data scoring server (400), the macroscopic data scoring server (500) and the other scoring data servers (600) are respectively in communication connection with the total scoring server (100);
the total scoring server (100) is connected with a total scoring data memory (700) and a total scoring output device (800);
the basic data scoring server (200) is connected with a basic data acquisition terminal (201) for acquiring basic data;
the basic data scoring server (200) is used for carrying a basic data scoring tool to score the basic data acquired by the basic data acquisition terminal (201);
the industry dominant data scoring server (300) is connected with an industry dominant data acquisition terminal (301) for acquiring industry dominant data;
the industry dominant data scoring server (300) is used for carrying an industry dominant data scoring tool to score the industry dominant data collected by the industry dominant data collecting terminal (301);
the public opinion data scoring server (400) is connected with a public opinion data acquisition terminal (401) for acquiring public opinion data;
the public opinion data scoring server (400) is used for carrying a public opinion data scoring tool to score the public opinion data collected by the public opinion data collecting terminal (401);
the macroscopic data scoring server (500) is connected with a macroscopic data acquisition terminal (501) for acquiring macroscopic data;
the macroscopic data scoring server (500) is used for carrying a macroscopic data scoring tool to score the macroscopic data collected by the macroscopic data collecting terminal (501);
the other scoring data server (600) is used for carrying other scoring data scoring tools to score the scoring data;
the total score data memory (700) is used for storing data of total scores; the total score output device (800) is used for outputting the total score.
2. The method for building an enterprise evaluation system with a credit model as a core based on deep learning as claimed in claim 1, wherein: the basic data acquisition terminal (201), the industry dominant data acquisition terminal (301), the public opinion data acquisition terminal (401) and the macroscopic data acquisition terminal (501) are all PC computers.
3. The method for building an enterprise evaluation system with a credit model as a core based on deep learning as claimed in claim 2, wherein: the total score output device (800) adopts electronic equipment with a display screen.
4. The method for building an enterprise evaluation system with a credit model as a core based on deep learning as claimed in claim 3, wherein: the total scoring server (100) employs an IBM server x3650M 510 core E5-2640v 4.
5. The deep learning-based construction credit model-based enterprise valuation system of claim 4, wherein: the basic data scoring server (200), the industry dominant data scoring server (300), the public opinion data scoring server (400), the macroscopic data scoring server (500) and the other scoring data servers (600) all adopt IBM X3650M 4.
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Application publication date: 20200508 |