CN110046984A - Enterprise credit risk system and evaluation method - Google Patents
Enterprise credit risk system and evaluation method Download PDFInfo
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Abstract
The present invention relates to credit appraisal technical field, in particular to a kind of enterprise credit risk system and evaluation method, the former includes data acquisition module: the enterprise bidding notice information and acceptance of the bid information of each trade center of timing acquisition；Data cleansing module: for being filtered processing to the bulletin of acquisition, index weights determining module: using machine learning random forest (CRF) regression algorithm, constructs model from randomly selecting partial data in historical data and being put into algorithm as training data；Credit scoring computing module: going out corresponding dimension scores according to the weight calculation of each dimension of index, by calculated result storage into relevant database；User searches for display module: searching for for user and is shown search result.The present invention can preferably filter out the high enterprise of those credit ratings and avoid risk as investee, improve benefit for investor.For enterprise, finance costs can be reduced.
Description
Technical field
The present invention relates to credit rating technical field, in particular to a kind of enterprise credit risk system and evaluation method.
Background technique
With the rapid development of society and variation, market economy gradually step into the credit economy epoch.When such a
For under background, no matter for personal or enterprise, credit all accounts for very important status.Personal credit is handling vehicle loan, room
The effect when payment for goods such as loan is it is clear that and business standing also plays in the business activities such as bidding, government procurement to Guan Chong
The effect wanted, personal or enterprise have sincerity that could socially base oneself upon, get the nod and rely on.In modern economy society, letter
With not only a kind of code of ethic, and the valuable source of economic benefit can be brought for enterprise, to a certain extent even
It is more even more important than materials and equipment resources and human resources.
For a long time, the credit rating of medium and small micro- enterprise is ignored by market, becomes its big obstacle for restricting its development.It is right
In some potential, develop good medium-sized and small enterprises, shorter due to setting up the time, shortage of funding often compares when preparing capital
It is more difficult, to limit its fast development.
Therefore it is just to the progress such as the essential information of enterprise and reference information to need to establish a company information evaluation system
Evaluation, solves the problems, such as that the management state of existing medium-sized and small enterprises, development prospect are restricted.
Summary of the invention
For the above technical problems, the object of the present invention is to provide a kind of enterprise credit risk system, to
Solve drawbacks described above.
Technical solution of the present invention first is that being realized by following technical measures: a kind of enterprise credit risk system,
It searches for and shows including data acquisition module, data cleansing module, index weights determining module, credit scoring computing module and user
Module；
Data acquisition module: Scrapy distributed reptile technology, the enterprise bidding of each trade center of timing acquisition are utilized
Notice information and acceptance of the bid information；
Data cleansing module: for being filtered processing to the bulletin of acquisition, by HanLP natural language processing frame,
Entity identification algorithms obtain title and the registered place of enterprise, and the data after parsing and the business datum storage in platform are arrived
In the HDFS distributed file system of Hadoop；
Index weights determining module: machine learning random forest (CRF) regression algorithm is used, is taken out at random from historical data
It takes partial data to be put into algorithm as training data and constructs model；
Credit scoring computing module: spark off-line calculation is used, corresponding demand is read from HDFS distributed file system
Data, corresponding dimension scores are gone out according to the weight calculation of each dimension of index, by calculated result storage to relationship type number
According in library；
User searches for display module: searching for for user and is shown search result.
Here is the further optimization or/and improvement to invention technology described above scheme:
Preferably, credit scoring computing module carries out credit scoring from four dimensions, comprising:
Essential information, including the type of business, establishment time, registration area, enterprise qualification certificate index；
Performance information, including guarantee fund's flowing water, participate in bid number, acceptance of the bid candidate's number, first highest bidder's number, in
Mark number, enterprise procurement conclusion of the business number and enterprise procurement turnover；
Demonstrate,prove letter information, including information of breaking one's promise, punishment information, equity pledge, the change of shareholder's information；
Management state, including enterprise's investments abroad, enterprise security, Corporate finance, enterprise tax arrear information, legal representative couple
Outer investment, equity stability, management level stability and financial information.
Technical solution of the present invention second is that being realized by following technical measures: a kind of enterprise credit risk system
Evaluation method, comprising:
S1: data collection stores the data for crawling data and platform into distributed file system；
S2: credit index scoring weight model is established, including establishes test set and training set；
S3: establishing reasonable data monitoring, monitoring recall rate, accuracy rate and coverage rate, corrected for the model later period provide according to
According to；
S4: calculating the credit total score of each enterprise, determines the grading system and corresponding loan limit of enterprise.
Preferably, in S2, the training set: it is used for model construction；The test set: constructing for detection model, surveys
The data of examination collection are used in model testing, the accuracy rate for assessment models.
Beneficial effects of the present invention are as follows:
(1) Scrapy distributed reptile crawls the bid acceptance of the bid bulletin of each website；
(2) it is analyzed using bulletin text of the natural language processing HanLP frame to acquisition, identifies corresponding company name
Claim, province etc..
(3) machine learning random forests algorithm (CRF) regression algorithm is used, determines the weight of each index；
(4) frame is handled using spark big data, data is read from HDFS, and calculated accordingly.Specific mistake
Journey: being to read data from HDFS using spark first, and data are then changed into DataFrame format (the one of spark frame
Kind data format), the score of corresponding index is then calculated using the module that sparkSQL module provides, due to the calculating process
It is memory-based, so calculating speed is all greatly improved relative to other frames.
(5) front and back end that software systems are completed using SpringMVC frame and Ajax, has used distributed file system
The storage of HDFS progress big data quantity.
Detailed description of the invention
Attached drawing 1 is the system structure diagram of the embodiment of the present invention one.
Attached drawing 2 is the method flow diagram of the embodiment of the present invention two.
Specific embodiment
The present invention is not limited by the following example, can be determined according to the technique and scheme of the present invention with actual conditions specific
Embodiment.
Below with reference to examples and drawings, the present invention will be further described:
As shown in Figure 1, a kind of enterprise credit risk system, including data acquisition module, data cleansing module, index weights
Determining module, credit scoring computing module and user search for display module；
Data acquisition module: Scrapy distributed reptile technology, the enterprise bidding of each trade center of timing acquisition are utilized
Notice information and acceptance of the bid information；
From national each province's public resource trade center, government procurement website is adopted for data acquisition module of the invention timing daily
Collect the related call for tender, acceptance of the bid bulletin, comprising:
(1) from the bid acceptance of the bid information issued in acquisition platform in local service database；
(2) enterprise's correlation is obtained from Qi Xinbao third-party platform interface demonstrate,prove letter information；
(3) qualification certificates that enterprise uploads, credit grade certificate and proof.
Data cleansing module: for being filtered processing to the bulletin of acquisition, by HanLP natural language processing frame,
Entity identification algorithms obtain title and the registered place of enterprise, and the data after parsing and the business datum storage in platform are arrived
In the HDFS distributed file system of Hadoop；
Above-mentioned filtering rule is extracted and is announced by regular expression according to the identical bulletin of bulletin title removal title
Title announces issuing time, the information such as acceptance of the bid amount of money.
Index weights determining module: machine learning random forest (CRF) regression algorithm is used, is taken out at random from historical data
It takes partial data to be put into algorithm as training data and constructs model；
Credit scoring computing module: spark off-line calculation is used, corresponding demand is read from HDFS distributed file system
Data, corresponding dimension scores are gone out according to the weight calculation of each dimension of index, by calculated result storage to relationship type number
According in library；
The score of each dimension index is added up and obtains the total score of user, the scoring etc. of enterprise is determined according to total score
Grade, determines corresponding loan limit further according to grade.
User searches for display module: searching for for user and is shown search result.
Above-mentioned index weights determining module obtains the relationship between business standing quality and corresponding index by analysis, together
When the weight of each index is determined in conjunction with corresponding industry experience.
Above-mentioned credit scoring computing module: using spark off-line calculation, corresponding data read from HDFS, according to
The weight calculation of index goes out corresponding score, and calculated result is then stored into relevant database；By each dimension index
Score is cumulative to obtain the total score of user, and the grading system of enterprise is determined according to total score, determines corresponding borrow further according to grade
Amount of money degree.
Above-mentioned user searches for display module: rear end uses SpringMVC frame, and front end uses Ajax technology, and backstage is grasped
Business Name or total score range, grading system, affiliated area can be inputted in corresponding search column by making personnel, check enterprise
All dimensions indication information.
Enterprise credit risk system of the invention by enterprise actual items are participated in platform based on establish air control model
And score-system, efficient, convenient and fast financing services are provided as enterprise.
The present invention realizes following purpose by business standing points-scoring system:
1. can preferably filter out the high enterprise of those credit ratings for investor as investee and evade wind
Danger, improves benefit.
For enterprise, finance costs can be reduced.To the enterprise that the grade that has no credit can refer to, the external world is certainly existed
Relatively high information asymmetry causes enterprise's fund raising relative difficulty.The enterprise of high credit grade is easy to get financial institution
Support, obtain the trust of investor, can expand scale, reduce finance costs.
Can a kind of above-mentioned enterprise credit risk system further be optimized or/and be improved according to actual needs:
As shown in attached drawing 1, table 1, credit scoring computing module carries out credit scoring from four dimensions, comprising:
Essential information, including the type of business, establishment time, registration area, enterprise qualification certificate index；
Performance information, including guarantee fund's flowing water, participate in bid number, acceptance of the bid candidate's number, first highest bidder's number, in
Mark number, enterprise procurement conclusion of the business number and enterprise procurement turnover；
Demonstrate,prove letter information, information of breaking one's promise, punishment information, equity pledge, the change of shareholder's information；
Management state, enterprise's investments abroad, enterprise security, Corporate finance, enterprise's tax arrear information, legal representative externally throw
Money, equity stability (shareholder's stock right transfer), management level stability (industrial and commercial change record) and financial information.
Beneficial effects of the present invention are as follows:
(1) Scrapy distributed reptile crawls the bid acceptance of the bid bulletin of each website；
(2) it is analyzed using bulletin text of the natural language processing HanLP frame to acquisition, identifies corresponding company name
Claim, province etc..
(3) machine learning random forests algorithm (CRF) regression algorithm is used, determines the weight of each index；
(4) frame is handled using spark big data, data is read from HDFS, and calculated accordingly, specific mistake
Journey: being to read data from HDFS using spark first, and data are then changed into DataFrame format (the one of spark frame
Kind data format), the score of corresponding index is then calculated using the module that sparkSQL module provides, due to the calculating process
It is memory-based, so calculating speed is all greatly improved relative to other frames.
(5) front and back end that software systems are completed using SpringMVC frame and Ajax, has used distributed file system
The storage of HDFS progress big data quantity.
Embodiment two: as shown in Fig. 2, a kind of evaluation method of enterprise credit risk system, comprising:
S1: data collection: the data for crawling data and platform are stored into distributed file system；
S2: credit index scoring weight model is established, including establishes test set and training set；
S3: establishing reasonable data monitoring, monitoring recall rate, accuracy rate and coverage rate, corrected for the model later period provide according to
According to；
S4: calculating the credit total score of each enterprise, determines the grading system and corresponding loan limit of enterprise.
As shown in Fig. 2, in S2, the training set: it is used for model construction；The test set: it is used for detection model structure
It builds, the data of test set are used in model testing, the accuracy rate for assessment models.
The preferred embodiment of the present invention above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
Limitations on the scope of the patent of the present invention therefore cannot be interpreted as.It should be pointed out that for those of ordinary skill in the art
For, without departing from the inventive concept of the premise, several deformations can also be made, improves and substitutes, these belong to this hair
Bright protection scope.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
The weight and marking rule list of each dimension index of table 1
Claims (4)
1. a kind of enterprise credit risk system, it is characterised in that true including data acquisition module, data cleansing module, index weights
Cover half block, credit scoring computing module and user search for display module；
Data acquisition module: utilizing Scrapy distributed reptile technology, and the enterprise bidding of each trade center of timing acquisition is announced
Information and acceptance of the bid information；
Data cleansing module: for being filtered processing to the bulletin of acquisition, pass through HanLP natural language processing frame, entity
Recognizer obtains title and the registered place of enterprise, and the data after parsing and the business datum storage in platform are arrived
In the HDFS distributed file system of Hadoop；
Index weights determining module: machine learning random forest regression algorithm is used, part number is randomly selected from historical data
Model is constructed according to being put into algorithm as training data；
Credit scoring computing module: spark off-line calculation is used, the number of corresponding demand is read from HDFS distributed file system
According to going out corresponding dimension scores according to the weight calculation of each dimension of index, by calculated result storage to relevant database
In；
User searches for display module: searching for for user and is shown search result.
2. enterprise credit risk system according to claim 1, which is characterized in that credit scoring computing module is tieed up from four
Degree carries out credit scoring, includes:
Essential information, including the type of business, establishment time, registration area, enterprise qualification certificate index；
Performance information, including guarantee fund's flowing water, participation bid number, acceptance of the bid candidate's number, first highest bidder's number, acceptance of the bid time
Number, enterprise procurement conclusion of the business number and enterprise procurement turnover；
Demonstrate,prove letter information, including information of breaking one's promise, punishment information, equity pledge, the change of shareholder's information；
Management state, including enterprise's investments abroad, enterprise security, Corporate finance, enterprise tax arrear information, legal representative are externally thrown
Money, equity stability, management level stability and financial information.
3. a kind of evaluation method of enterprise credit risk system, characterized by comprising:
S1: data collection stores the data for crawling data and platform into distributed file system；
S2: credit index scoring weight model is established, including establishes test set and training set；
S3: establishing reasonable data monitoring, and monitoring recall rate, accuracy rate and coverage rate provide foundation for the amendment of model later period；
S4: calculating the credit total score of each enterprise, determines the grading system and corresponding loan limit of enterprise.
4. the evaluation method of enterprise credit risk system according to claim 3, it is characterised in that in S2, the training
Collection: it is used for model construction；The test set: constructing for detection model, and the data of test set are used in model testing, uses
In the accuracy rate of assessment models.
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Cited By (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110400088A (en) * | 2019-07-31 | 2019-11-01 | 山东浪潮商用系统有限公司 | A kind of taxpaying credit evaluation method based on relevant database |
| CN110400215A (en) * | 2019-07-31 | 2019-11-01 | 浪潮软件集团有限公司 | Small micro- Enterprise Credit Rating Model construction method and system towards family, enterprise |
| CN110728418A (en) * | 2019-08-26 | 2020-01-24 | 成都市互联互通大数据科技有限公司 | Method for counting waste standard rate |
| CN111143339A (en) * | 2019-12-20 | 2020-05-12 | 东方微银科技(北京)有限公司 | Method, device, equipment and storage medium for distributing service resources |
| CN111144778A (en) * | 2019-12-30 | 2020-05-12 | 智慧神州(北京)科技有限公司 | Management and evaluation method and system based on data account |
| CN111383115A (en) * | 2020-03-16 | 2020-07-07 | 中科天玑数据科技股份有限公司 | Transaction place abnormal behavior monitoring and analyzing method and system |
| CN111415067A (en) * | 2019-12-30 | 2020-07-14 | 北京澎湃信用管理有限公司 | Enterprise and personal credit rating system |
| CN111428980A (en) * | 2020-03-18 | 2020-07-17 | 西安通软软件科技有限公司 | Real estate upstream and downstream enterprise credit analysis and statistics method |
| CN111429285A (en) * | 2020-03-18 | 2020-07-17 | 贵阳块数据城市建设有限公司 | Real-time credit evaluation system and method based on enterprise data |
| CN111460260A (en) * | 2020-03-31 | 2020-07-28 | 上海智芝全智能科技有限公司 | Data processing system, method and medium for multi-type data |
| CN111709669A (en) * | 2020-07-08 | 2020-09-25 | 天元大数据信用管理有限公司 | Enterprise credit scoring system and method |
| CN111709639A (en) * | 2020-06-14 | 2020-09-25 | 湖南三湘银行股份有限公司 | Grading method suitable for heavy machinery manufacturing enterprises of bank system |
| CN111967985A (en) * | 2020-10-23 | 2020-11-20 | 四川川投云链科技有限公司 | Block chain-based acceptance draft management method |
| CN112288564A (en) * | 2020-09-29 | 2021-01-29 | 北京农业信息技术研究中心 | Method and system for generating credit level of agricultural social service main body |
| CN112381407A (en) * | 2020-11-16 | 2021-02-19 | 浪潮软件股份有限公司 | Credit weighting double-random supervision method based on random algorithm |
| CN112686553A (en) * | 2020-12-31 | 2021-04-20 | 民生科技有限责任公司 | Enterprise automatic rating management system, method, server and readable storage device |
| CN112734568A (en) * | 2021-01-29 | 2021-04-30 | 深圳前海微众银行股份有限公司 | Credit scoring card model construction method, device, equipment and readable storage medium |
| CN112801779A (en) * | 2021-03-02 | 2021-05-14 | 成都高投盈创动力投资发展有限公司 | Enterprise financing credit evaluation system and method |
| CN113298640A (en) * | 2021-05-17 | 2021-08-24 | 浙江惠瀜网络科技有限公司 | Data processing method and device for vehicle credit admission evaluation |
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| CN114219277A (en) * | 2021-12-14 | 2022-03-22 | 中国建设银行股份有限公司 | Credit report generation method and system based on crawler and model scoring |
| TWI759759B (en) * | 2020-06-09 | 2022-04-01 | 台北富邦商業銀行股份有限公司 | Enterprise Loan Evaluation System |
| CN114358908A (en) * | 2022-01-07 | 2022-04-15 | 电子科技大学 | AI-driven collaborative enterprise multidimensional credit feature extraction and evaluation method |
| CN114611972A (en) * | 2022-03-21 | 2022-06-10 | 广东贤能数字科技有限公司 | Merchant credit rating system and method based on artificial intelligence |
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| CN110400088A (en) * | 2019-07-31 | 2019-11-01 | 山东浪潮商用系统有限公司 | A kind of taxpaying credit evaluation method based on relevant database |
| CN110400215A (en) * | 2019-07-31 | 2019-11-01 | 浪潮软件集团有限公司 | Small micro- Enterprise Credit Rating Model construction method and system towards family, enterprise |
| CN110400215B (en) * | 2019-07-31 | 2023-11-03 | 浪潮软件集团有限公司 | Method and system for constructing enterprise family-oriented small micro enterprise credit assessment model |
| CN110728418A (en) * | 2019-08-26 | 2020-01-24 | 成都市互联互通大数据科技有限公司 | Method for counting waste standard rate |
| CN111143339A (en) * | 2019-12-20 | 2020-05-12 | 东方微银科技(北京)有限公司 | Method, device, equipment and storage medium for distributing service resources |
| CN111144778A (en) * | 2019-12-30 | 2020-05-12 | 智慧神州(北京)科技有限公司 | Management and evaluation method and system based on data account |
| CN111415067A (en) * | 2019-12-30 | 2020-07-14 | 北京澎湃信用管理有限公司 | Enterprise and personal credit rating system |
| CN111383115A (en) * | 2020-03-16 | 2020-07-07 | 中科天玑数据科技股份有限公司 | Transaction place abnormal behavior monitoring and analyzing method and system |
| CN111429285A (en) * | 2020-03-18 | 2020-07-17 | 贵阳块数据城市建设有限公司 | Real-time credit evaluation system and method based on enterprise data |
| CN111428980A (en) * | 2020-03-18 | 2020-07-17 | 西安通软软件科技有限公司 | Real estate upstream and downstream enterprise credit analysis and statistics method |
| CN111460260A (en) * | 2020-03-31 | 2020-07-28 | 上海智芝全智能科技有限公司 | Data processing system, method and medium for multi-type data |
| TWI759759B (en) * | 2020-06-09 | 2022-04-01 | 台北富邦商業銀行股份有限公司 | Enterprise Loan Evaluation System |
| CN111709639A (en) * | 2020-06-14 | 2020-09-25 | 湖南三湘银行股份有限公司 | Grading method suitable for heavy machinery manufacturing enterprises of bank system |
| CN111709669A (en) * | 2020-07-08 | 2020-09-25 | 天元大数据信用管理有限公司 | Enterprise credit scoring system and method |
| CN112288564A (en) * | 2020-09-29 | 2021-01-29 | 北京农业信息技术研究中心 | Method and system for generating credit level of agricultural social service main body |
| CN111967985A (en) * | 2020-10-23 | 2020-11-20 | 四川川投云链科技有限公司 | Block chain-based acceptance draft management method |
| CN112381407A (en) * | 2020-11-16 | 2021-02-19 | 浪潮软件股份有限公司 | Credit weighting double-random supervision method based on random algorithm |
| CN112686553A (en) * | 2020-12-31 | 2021-04-20 | 民生科技有限责任公司 | Enterprise automatic rating management system, method, server and readable storage device |
| CN112734568A (en) * | 2021-01-29 | 2021-04-30 | 深圳前海微众银行股份有限公司 | Credit scoring card model construction method, device, equipment and readable storage medium |
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| CN113298640A (en) * | 2021-05-17 | 2021-08-24 | 浙江惠瀜网络科技有限公司 | Data processing method and device for vehicle credit admission evaluation |
| CN114219277A (en) * | 2021-12-14 | 2022-03-22 | 中国建设银行股份有限公司 | Credit report generation method and system based on crawler and model scoring |
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| CN113934751A (en) * | 2021-12-16 | 2022-01-14 | 深圳市明源云科技有限公司 | Index value measuring method, equipment and computer readable storage medium |
| CN114358908B (en) * | 2022-01-07 | 2023-04-07 | 电子科技大学 | AI-driven collaborative enterprise multidimensional credit feature extraction and evaluation method |
| CN114358908A (en) * | 2022-01-07 | 2022-04-15 | 电子科技大学 | AI-driven collaborative enterprise multidimensional credit feature extraction and evaluation method |
| KR20230116385A (en) * | 2022-01-28 | 2023-08-04 | 김재열 | Credit rating system based on e-bidding information |
| KR102644940B1 (en) * | 2022-01-28 | 2024-03-06 | 김재열 | Credit rating system based on e-bidding information |
| CN114611972B (en) * | 2022-03-21 | 2023-01-10 | 广东贤能数字科技有限公司 | Merchant credit rating system and method based on artificial intelligence |
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| CN115796457A (en) * | 2023-02-03 | 2023-03-14 | 山东铁路投资控股集团有限公司 | Personnel and enterprise rating method and system based on multidimensional data |
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