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WO2019095572A1 - Procédé d'évaluation de risque d'investissement d'entreprise, dispositif et support de mémoire - Google Patents

Procédé d'évaluation de risque d'investissement d'entreprise, dispositif et support de mémoire Download PDF

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Publication number
WO2019095572A1
WO2019095572A1 PCT/CN2018/076169 CN2018076169W WO2019095572A1 WO 2019095572 A1 WO2019095572 A1 WO 2019095572A1 CN 2018076169 W CN2018076169 W CN 2018076169W WO 2019095572 A1 WO2019095572 A1 WO 2019095572A1
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Prior art keywords
enterprise
entity
entities
feature vector
enterprise entity
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Chinese (zh)
Inventor
汪伟
罗傲雪
肖京
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities

Definitions

  • the present application relates to the field of computer technology, and in particular, to an enterprise investment risk assessment method, an electronic device, and a computer readable storage medium.
  • Every news site has thousands of news every day, and the news is updated in real time.
  • the big data associated with the investment target enterprise from a large amount of news corpus, such as the internal state of the enterprise: operation, finance, executives, recruitment, website update frequency, etc.
  • the external status of the enterprise such as the status of the affiliated company, as above.
  • the customer, the rating agency's rating of the company, news media related reports and other information will form this information into a network of relationships, analyze and evaluate the risk factor of the investment target enterprise, so that the investor can consider whether the risk can be accepted and decided according to the risk factor. Whether to invest in the company. Therefore, how to extract the information related to the investment target enterprise from the news corpus and use the information to conduct risk assessment is an urgent problem to be solved.
  • the application provides an enterprise investment risk assessment method, an electronic device and a computer readable storage medium, the main purpose of which is to evaluate the risk of the investment target enterprise by analyzing the information disclosed in the news corpus.
  • the present application provides an electronic device including: a memory, a processor, and a memory investment evaluation program run on the processor, the program being used by the processor The following steps are implemented during execution:
  • A1. Crawling news corpus related to the business entity to be assessed, preprocessing the news corpus, and extracting other entities associated with the business entity from the pre-processed news corpus;
  • the name is a node
  • the relationship between the enterprise entity and other entities is an edge
  • a network of relationships between the enterprise entity and other entities is constructed
  • A3. Calculate a vector representation of the enterprise entity according to the relationship network, and generate a first feature vector of the enterprise entity;
  • A6 Input the first feature vector, the second feature vector, and the third feature vector into a predetermined enterprise risk assessment model, and output a risk tag corresponding to the enterprise entity.
  • the present application further provides a method for evaluating enterprise investment risk, which includes:
  • S1 crawling the news corpus related to the enterprise entity to be assessed for risk, pre-processing the news corpus, and extracting other entities associated with the business entity from the pre-processed news corpus;
  • the present application further provides a computer readable storage medium storing a business investment risk assessment program, which is executed by a processor to implement enterprise investment risk assessment as described above. Any step of the method.
  • the present application further provides an enterprise investment risk assessment, the program comprising: an extraction module, a construction module, a first calculation module, a second calculation module, a third calculation module, and an evaluation module, the program is processed Any step of implementing the enterprise investment risk assessment method described above when executed.
  • the enterprise investment risk assessment method, the electronic device and the computer readable storage medium proposed by the application obtain the relationship between the enterprise entity and its associated entity, the internal information of the enterprise entity and the external information from the news corpus, respectively, and obtain the enterprise entity respectively.
  • the first feature vector, the second feature vector, and the third feature vector use the risk assessment model and the first feature vector, the second feature vector, and the third feature vector to perform risk assessment on the investment entity, so that the investor can capture Market investment opportunities to predict investment risks in advance.
  • FIG. 1 is a schematic diagram of an application environment of a preferred embodiment of an enterprise investment risk assessment method according to the present application
  • FIG. 2 is a network diagram of the relationship between the enterprise entity A and other associated entities
  • Figure 3 is a vector representation of the business entity A
  • Figure 4 is a block diagram of the enterprise investment risk assessment procedure of Figure 1;
  • FIG. 5 is a flow chart of a preferred embodiment of an enterprise investment risk assessment method of the present application.
  • the present application provides a method for evaluating enterprise investment risk, which is applied to an electronic device 1.
  • the electronic device 1 may be a PC (Personal Computer), or may be a terminal device such as a smart phone, a tablet computer, an e-book reader, or a portable computer.
  • the electronic device 1 includes a memory 11, a processor 12, a communication bus 13, and a network interface 14.
  • the memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like.
  • the memory 11 may be an internal storage unit of the electronic device 1, such as a hard disk of the electronic device 1, in some embodiments.
  • the memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in hard disk equipped on the electronic device 1, a smart memory card (SMC), and a secure digital (Secure Digital) , SD) card, flash card (Flash Card), etc.
  • the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device.
  • the memory 11 can be used not only for storing application software and various types of data installed in the electronic device 1, such as the enterprise investment risk assessment program 10, but also for temporarily storing data that has been output or will be output.
  • the processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor or other data processing chip for running program code or processing stored in the memory 11. Data, such as the corporate investment risk assessment process10.
  • CPU Central Processing Unit
  • controller microcontroller
  • microprocessor or other data processing chip for running program code or processing stored in the memory 11.
  • Data such as the corporate investment risk assessment process10.
  • Communication bus 13 is used to implement connection communication between these components.
  • the network interface 14 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), and is typically used to establish a communication connection between the device and other electronic devices.
  • a standard wired interface such as a WI-FI interface
  • Figure 1 shows only the electronic device 1 with components 11-14, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead.
  • the electronic device 1 may further include a user interface
  • the user interface may include a display, an input unit such as a keyboard, and the optional user interface may further include a standard wired interface and a wireless interface.
  • the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch sensor, or the like.
  • the display may also be referred to as a display screen or display unit for displaying information processed in the electronic device 1 and a user interface for displaying visualizations.
  • the enterprise investment risk assessment program 10 is stored in the memory 11; when the processor 12 executes the enterprise investment risk assessment program 10 stored in the memory 11, the following steps are implemented:
  • A1. Crawling news corpus related to the business entity to be assessed, preprocessing the news corpus, and extracting other entities associated with the business entity from the pre-processed news corpus;
  • the name is a node
  • the relationship between the enterprise entity and other entities is an edge
  • a network of relationships between the enterprise entity and other entities is constructed
  • A3. Calculate a vector representation of the enterprise entity according to the relationship network, and generate a first feature vector of the enterprise entity;
  • A6 Input the first feature vector, the second feature vector, and the third feature vector into a predetermined enterprise risk assessment model, and output a risk tag corresponding to the enterprise entity.
  • the corpus refers to a plurality of different fields.
  • This embodiment uses a news corpus as an example to describe a specific solution of the present application, but is not limited to the field of news.
  • the web crawler When the investor needs to know the current news to obtain the internal data and external data associated with the investment target enterprise, use the web crawler to crawl the online news from the Internet, for example, crawling online news of Sina, Baidu, Tencent, etc. through crawlers. . Understandably, each company operates differently in different time periods. Therefore, in order to enable investors to more accurately understand the information of the investment target company, the crawled network news is filtered in the time dimension. Set the preset time interval and only crawl the online news of the time period, for example, only crawling online news for nearly half a year.
  • the source of news corpus is diverse, there are many types of corpus in the corpus.
  • the news corpus needs to be preprocessed, and the news corpus text data is obtained to form a news corpus text set.
  • the pre-processing may unify the format of the news corpus into a text format, remove advertising noise from the news corpus and filter one or more of dirty words and sensitive words.
  • the format of the news corpus is unified into a text format, the content that cannot be converted into a text format by the current technology can be filtered out.
  • all the enterprise names are extracted from the pre-processed news corpus, and then the associated enterprise data of the enterprise entity (ie, the investment target enterprise) according to the risk to be assessed is used. , filter out other entities associated with the business entity to be assessed, and build the enterprise entity and other entities into a network of relationships.
  • the associated enterprise data can be obtained through third party data. It can be understood that it may be many to extract other entities associated with the enterprise entity from the news corpus. To construct all the related entities in the relational network is unreasonable, therefore, before extracting the relational network, the extracted and the enterprise entity Other related entities are filtered and filtered.
  • other entities associated with the enterprise entity retained by the filtering and screening step include: a shareholder company of the business entity, other entities that have money with the business entity, suppliers, Customer, credit rating structure, etc.
  • B1 is The rating agency that gives the enterprise entity A a credit rating can learn from the historical rating data that B1 gives the enterprise entity A a credit rating of BBB
  • B2 is a supplier that supplies the enterprise entity A with raw materials or goods
  • the amount owed is 300,000
  • B3 is the customer of enterprise entity A
  • enterprise entity A has defaulted to B3 twice.
  • the enterprise entity A, B1, B2, and B3 are used as nodes, and the relationship between B1, B2, and B3 and A is used as the edge, and a network diagram of the relationship between the enterprise entity and other entities as shown in FIG. 2 is constructed.
  • the vector representation of the enterprise entity A is calculated.
  • This embodiment adopts the Skip-Gram method because the vector representation of the enterprise entity A in the relational network represents the vector representation of the entities B1, B2, B3 associated with it. There is a management relationship between them.
  • the Skip-Gram method uses the current enterprise entity to predict surrounding entities, as shown in Figure 3. An1, An2, An3, and An4 in Fig. 3 are unordered and are represented as adjacent entities of the enterprise entity A.
  • a fixed prediction length L is set to predict L neighboring entities around the enterprise entity A. If the real neighboring entity is less than L, the output is NULL.
  • the vector representation embedding (E1), embedding (E2), ... of the enterprise entity A can be obtained, and the vector is represented as the first feature vector of the enterprise entity A.
  • each reference factor in the internal information of the enterprise is converted into a number to be quantified.
  • the value in the financial information can be converted into a characteristic value.
  • the net profit is 300,000 yuan
  • 30 is a corresponding feature.
  • the value, the frequency of website updates, and the number of hiring people in the most recent year are also numerical values, and can also be based on preset conversion rules. In other embodiments, it is also possible to convert 300,000 yuan into other values according to a preset conversion ratio. After each reference factor in the internal information of the enterprise entity A is quantized, a second feature vector of the enterprise entity A is generated.
  • the external information includes the upstream and downstream relationship of the enterprise entity A. For example, suppliers, customers, whether the company has defaulted or owed money to other entities in the upstream and downstream relationship, if any, the number of defaults and the period of arrears.
  • the external information of the enterprise entity A also includes the rating agency's rating on the business entity A (rating level 3A, 2A is good, A is good, BBB is general, etc.), the news media positive/negative reports on the business entity A, etc. .
  • each reference factor in the internal information of the enterprise is converted into a number for quantization.
  • the number of defaults can be quantized into three values, no default-0, mild default-1, and severe default- 2; arrears can be quantified into 2 values, no arrears-0, with arrears-1; ratings can be quantified into multiple values, rating level 3A-6, rating level 2A-5, rating level A-4, rating Level BBB-3, rating level BB-2, rating level B-1.
  • the external information is quantified, the number of defaults -1, the arrears -1, the rating -3, and the third feature vector of the enterprise entity A is generated according to the quantized information.
  • the risk enterprise A can be assessed next.
  • the name of the enterprise entity A and the first feature vector, the second feature vector and the third feature vector of the enterprise entity A are input into a predetermined risk assessment model for risk assessment, and the risk assessment result is output.
  • the training step of the predetermined risk assessment model includes: acquiring the first feature vector, the second feature vector, and the third feature vector of the plurality of enterprise entities by using the foregoing steps A1-A5, and the specific implementation manner is consistent with the foregoing steps , no longer repeat them here.
  • the second feature vector, the third feature vector, and the corresponding risk tag are used as sample data. Extracting, from the sample data, the first feature vector, the second feature vector, the third feature vector of the first proportion (for example, 60%) of the enterprise entity, and the risk tag corresponding to the first entity (for example, 60%) of the enterprise entity As a training set, the first feature vector, the second feature vector, the third feature vector, and the second ratio (eg, 50%) of the second entity (eg, 50%) of the business entity are randomly extracted from the remaining sample set.
  • the risk label corresponding to the enterprise entity is used as a verification set, that is, 20% of the sample data of the sample data is extracted as a verification set; the support vector machine is trained by using the 50% sample data, and the model parameters of the risk assessment model are determined.
  • the model output result is 0, it indicates that the investment enterprise entity A is substantially risk-free, and if the model output result is 1 , it means that the investment enterprise entity A has a greater risk.
  • the electronic device 1 proposed by the foregoing embodiment obtains the first feature vector, the second feature vector, and the third feature vector of the enterprise entity by understanding the relationship between the enterprise entity and its associated entity, the internal information of the enterprise entity, and the external information.
  • the risk assessment model and the first feature vector, the second feature vector and the third feature vector are used to perform risk assessment on the investment entity, so that the investor can capture market investment opportunities.
  • the enterprise investment risk assessment program 10 may also be divided into one or more modules, one or more modules being stored in the memory 11 and being processed by one or more processors (this Embodiments are executed by processor 12) to accomplish the present application, and a module referred to herein refers to a series of computer program instructions that are capable of performing a particular function.
  • FIG. 4 it is a schematic diagram of a module of the enterprise investment risk assessment program 10 in FIG. 1.
  • the program may be divided into an extraction module 110, a construction module 120, a first calculation module 130, and a second.
  • the calculation module 140, the third calculation module 150, and the evaluation module 160, the functions or operation steps implemented by the modules 110-160 are similar to the above, and are not described in detail herein, for example, where:
  • the extracting module 110 is configured to crawl the news corpus related to the enterprise entity to be evaluated for risk, pre-process the news corpus, and extract other entities associated with the business entity from the pre-processed news corpus;
  • the building module 120 is configured to construct a relationship network between the enterprise entity and other entities by using the name as a node, the relationship between the enterprise entity and other entities as an edge;
  • the first calculating module 130 is configured to calculate a vector representation of the enterprise entity according to the relationship network, and generate a first feature vector of the enterprise entity;
  • the second calculating module 140 is configured to quantize the internal information of the enterprise entity according to the first preset rule to generate a second feature vector
  • the third calculating module 150 is configured to extract external information of the enterprise entity from the news corpus, and quantize the external information of the enterprise entity according to the second preset rule to generate a third feature vector of the enterprise entity;
  • the evaluation module 160 is configured to input the first feature vector, the second feature vector, and the third feature vector into a predetermined enterprise risk assessment model, and output a risk tag corresponding to the enterprise entity.
  • the application also provides a method for assessing enterprise investment risk.
  • FIG. 5 it is a flowchart of a preferred embodiment of the enterprise investment risk assessment method of the present application. The method can be performed by a device that can be implemented by software and/or hardware.
  • the enterprise investment risk assessment method includes:
  • S1 crawling the news corpus related to the enterprise entity to be assessed for risk, pre-processing the news corpus, and extracting other entities associated with the business entity from the pre-processed news corpus;
  • the corpus refers to a plurality of different fields.
  • This embodiment uses a news corpus as an example to describe a specific solution of the present application, but is not limited to the field of news.
  • the web crawler When the investor needs to know the current news to obtain the internal data and external data associated with the investment target enterprise, use the web crawler to crawl the online news from the Internet, for example, crawling online news of Sina, Baidu, Tencent, etc. through crawlers. . Understandably, each company operates differently in different time periods. Therefore, in order to enable investors to more accurately understand the information of the investment target company, the crawled network news is filtered in the time dimension. Set the preset time interval and only crawl the online news of the time period, for example, only crawling online news for nearly half a year.
  • the sources of news corpus are diverse, there are many types of corpus in the corpus.
  • the news corpus needs to be preprocessed, and the news corpus text data is obtained to form a news corpus text set.
  • the pre-processing may unify the format of the news corpus into a text format, remove advertising noise from the news corpus and filter one or more of dirty words and sensitive words.
  • the format of the news corpus is unified into a text format, the content that cannot be converted into a text format by the current technology can be filtered out.
  • all the enterprise names are extracted from the pre-processed news corpus, and then the associated enterprise data of the enterprise entity (ie, the investment target enterprise) according to the risk to be assessed is used. , filter out other entities associated with the business entity to be assessed, and build the enterprise entity and other entities into a network of relationships.
  • the associated enterprise data can be obtained through third party data. It can be understood that it may be many to extract other entities associated with the enterprise entity from the news corpus. To construct all the related entities in the relational network is unreasonable, therefore, before extracting the relational network, the extracted and the enterprise entity Other related entities are filtered and filtered.
  • other entities associated with the enterprise entity retained by the filtering and screening step include: a shareholder company of the business entity, other entities that have money with the business entity, suppliers, Customer, credit rating structure, etc.
  • B1 is The rating agency that gives the enterprise entity A a credit rating can learn from the historical rating data that B1 gives the enterprise entity A a credit rating of BBB
  • B2 is a supplier that supplies the enterprise entity A with raw materials or goods
  • the amount owed is 300,000
  • B3 is the customer of enterprise entity A
  • enterprise entity A has defaulted to B3 twice.
  • the enterprise entity A, B1, B2, and B3 are used as nodes, and the relationship between B1, B2, and B3 and A is used as the edge, and a network diagram of the relationship between the enterprise entity and other entities as shown in FIG. 2 is constructed.
  • the vector representation of the enterprise entity A is calculated.
  • This embodiment adopts the Skip-Gram method because the vector representation of the enterprise entity A in the relational network represents the vector representation of the entities B1, B2, B3 associated with it. There is a management relationship between them.
  • the Skip-Gram method uses the current enterprise entity to predict surrounding entities, as shown in Figure 3. An1, An2, An3, and An4 in Fig. 3 are unordered and are represented as adjacent entities of the enterprise entity A.
  • a fixed prediction length L is set to predict L neighboring entities around the enterprise entity A. If the real neighboring entity is less than L, the output is NULL.
  • the vector representation embedding (E1), embedding (E2), ... of the enterprise entity A can be obtained, and the vector is represented as the first feature vector of the enterprise entity A.
  • each reference factor in the internal information of the enterprise is converted into a number to be quantified.
  • the value in the financial information can be converted into a characteristic value.
  • the net profit is 300,000 yuan
  • 30 is a corresponding feature.
  • the value, the frequency of website updates, and the number of hiring people in the most recent year are also numerical values, and can also be based on preset conversion rules. In other embodiments, it is also possible to convert 300,000 yuan into other values according to a preset conversion ratio. After each reference factor in the internal information of the enterprise entity A is quantized, a second feature vector of the enterprise entity A is generated.
  • the external information includes the upstream and downstream relationship of the enterprise entity A. For example, suppliers, customers, whether the company has defaulted or owed money to other entities in the upstream and downstream relationship, if any, the number of defaults and the period of arrears.
  • the external information of the enterprise entity A also includes the rating agency's rating on the business entity A (rating level 3A, 2A is good, A is good, BBB is general, etc.), the news media positive/negative reports on the business entity A, etc. .
  • each reference factor in the internal information of the enterprise is converted into a number for quantization.
  • the number of defaults can be quantized into three values, no default-0, mild default-1, and severe default- 2; arrears can be quantified into 2 values, no arrears-0, with arrears-1; ratings can be quantified into multiple values, rating level 3A-6, rating level 2A-5, rating level A-4, rating Level BBB-3, rating level BB-2, rating level B-1.
  • the external information is quantified, the number of defaults -1, the arrears -1, the rating -3, and the third feature vector of the enterprise entity A is generated according to the quantized information.
  • the risk enterprise A can be assessed next.
  • the name of the enterprise entity A and the first feature vector, the second feature vector and the third feature vector of the enterprise entity A are input into a predetermined risk assessment model for risk assessment, and the risk assessment result is output.
  • the training step of the predetermined risk assessment model includes: acquiring the first feature vector, the second feature vector, and the third feature vector of the plurality of enterprise entities by using the foregoing steps S1-S5, and the specific implementation manner is consistent with the foregoing steps , no longer repeat them here.
  • the second feature vector, the third feature vector, and the corresponding risk tag are used as sample data. Extracting, from the sample data, the first feature vector, the second feature vector, the third feature vector of the first proportion (for example, 60%) of the enterprise entity, and the risk tag corresponding to the first entity (for example, 60%) of the enterprise entity As a training set, the first feature vector, the second feature vector, the third feature vector, and the second ratio (eg, 50%) of the second entity (eg, 50%) of the business entity are randomly extracted from the remaining sample set.
  • the risk label corresponding to the enterprise entity is used as a verification set, that is, 20% of the sample data of the sample data is extracted as a verification set; the support vector machine is trained by using the 50% sample data, and the model parameters of the risk assessment model are determined.
  • the model output result is 0, it indicates that the investment enterprise entity A is substantially risk-free, and if the model output result is 1 , it means that the investment enterprise entity A has a greater risk.
  • the enterprise investment risk assessment method proposed by the above embodiment obtains the first feature vector, the second feature vector and the third of the enterprise entity by understanding the relationship between the enterprise entity and its associated entity, the internal information of the enterprise entity and the external information.
  • the feature vector utilizes the risk assessment model and the first feature vector, the second feature vector, and the third feature vector to perform risk assessment on the investment enterprise entity, so that the investor can capture market investment opportunities.
  • the embodiment of the present application further provides a computer readable storage medium, where the enterprise investment risk assessment program is stored, and the enterprise investment risk assessment program is executed by the processor to:
  • A1. Crawling news corpus related to the business entity to be assessed, preprocessing the news corpus, and extracting other entities associated with the business entity from the pre-processed news corpus;
  • the name is a node
  • the relationship between the enterprise entity and other entities is an edge
  • a network of relationships between the enterprise entity and other entities is constructed
  • A3. Calculate a vector representation of the enterprise entity according to the relationship network, and generate a first feature vector of the enterprise entity;
  • A6 Input the first feature vector, the second feature vector, and the third feature vector into a predetermined enterprise risk assessment model, and output a risk tag corresponding to the enterprise entity.
  • the technical solution of the present application which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM as described above). , a disk, an optical disk, including a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the methods described in the various embodiments of the present application.
  • a terminal device which may be a mobile phone, a computer, a server, or a network device, etc.

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Abstract

La présente invention concerne un procédé d'évaluation de risque d'investissement d'entreprise. Le procédé consiste : à explorer des corpus d'actualités associés à une entité d'entreprise cible d'investissement et à extraire d'autres entités associées à l'entité d'entreprise ; à construire un réseau de relations en utilisant des noms en tant que nœuds et des relations d'association entre l'entité d'entreprise et d'autres entités en tant que bords ; à calculer la représentation vectorielle de l'entité d'entreprise et à générer un premier vecteur propre de l'entité d'entreprise ; à quantifier des informations internes de l'entité d'entreprise selon une première règle préétablie et à générer un deuxième vecteur propre ; à quantifier des informations externes de l'entité d'entreprise selon une seconde règle préétablie et à générer un troisième vecteur propre ; à entrer le premier vecteur propre, le deuxième vecteur propre et le troisième vecteur propre dans un modèle d'évaluation de risque d'entreprise, à obtenir et à émettre une étiquette de risque correspondant à l'entité d'entreprise. La présente invention concerne également un appareil électronique et un support de mémoire lisible par ordinateur. Au moyen de la présente invention, par analyse des informations révélées dans des corpus d'actualités, des risques d'investissement dans des entreprises cibles peuvent être évalués.
PCT/CN2018/076169 2017-11-17 2018-02-10 Procédé d'évaluation de risque d'investissement d'entreprise, dispositif et support de mémoire Ceased WO2019095572A1 (fr)

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CN201711141730.3 2017-11-17

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Cited By (29)

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Families Citing this family (37)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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CN114168757B (zh) * 2022-02-11 2022-04-29 子长科技(北京)有限公司 公司事件风险预测方法、装置、存储介质及电子设备
CN117194932A (zh) * 2022-05-26 2023-12-08 腾讯科技（深圳）有限公司 实体的评价方法、装置、计算机可读介质及电子设备
CN116433022A (zh) * 2023-04-07 2023-07-14 重庆文诗奇源科技有限公司 一种基于信息网络嵌入的中小型企业风险评估方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150149247A1 (en) * 2013-05-02 2015-05-28 The Dun & Bradstreet Corporation System and method using multi-dimensional rating to determine an entity's future commercical viability
CN105740335A (zh) * 2016-01-22 2016-07-06 山东合天智汇信息技术有限公司 一种基于titan的企业信息分析平台及其构建方法
CN106203808A (zh) * 2016-07-01 2016-12-07 中国民生银行股份有限公司 企业信用风险评估方法和装置
CN107229756A (zh) * 2017-06-30 2017-10-03 山东合天智汇信息技术有限公司 一种直观展现企业关系图谱的设计方法及系统

Family Cites Families (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040215551A1 (en) * 2001-11-28 2004-10-28 Eder Jeff S. Value and risk management system for multi-enterprise organization
EP1479024A4 (fr) * 2002-01-31 2007-01-03 Seabury Analytic Llc Procede et modele d'evaluation de risque dans une entreprise
US20070208600A1 (en) * 2006-03-01 2007-09-06 Babus Steven A Method and apparatus for pre-emptive operational risk management and risk discovery
JP6009864B2 (ja) * 2011-09-21 2016-10-19 典秀 野田 企業評価システム、企業評価方法及び企業評価プログラム
US9087088B1 (en) * 2012-11-13 2015-07-21 American Express Travel Related Services Company, Inc. Systems and methods for dynamic construction of entity graphs
EP2973282A4 (fr) * 2013-03-13 2016-11-16 Guardian Analytics Inc Détection et analyse de fraude
CN103942718A (zh) * 2014-04-14 2014-07-23 中国人民银行征信中心 企业信贷信息采集整合方法
CN105528465A (zh) * 2016-02-03 2016-04-27 天弘基金管理有限公司 信用状况评估方法及装置
CN105975491A (zh) * 2016-04-26 2016-09-28 重庆誉存企业信用管理有限公司 企业新闻分析方法及系统
CN105913195A (zh) * 2016-04-29 2016-08-31 浙江汇信科技有限公司 基于全行业数据的企业金融风险评分方法
CN106447066A (zh) * 2016-06-01 2017-02-22 上海坤士合生信息科技有限公司 一种大数据的特征提取方法和装置
CN106445988A (zh) * 2016-06-01 2017-02-22 上海坤士合生信息科技有限公司 一种大数据的智能处理方法和系统
CN106126614A (zh) * 2016-06-21 2016-11-16 山东合天智汇信息技术有限公司 一种追溯两个企业多层级关联路径的方法及系统
CN106934712A (zh) * 2017-03-16 2017-07-07 深圳微众税银信息服务有限公司 一种企业画像数据处理方法及系统
CN107133732A (zh) * 2017-04-27 2017-09-05 青岛格兰德信用管理咨询有限公司 基于大数据分析的关系圈挖掘方法及其应用
CN107239882A (zh) * 2017-05-10 2017-10-10 平安科技（深圳）有限公司 风险评估方法、装置、计算机设备及存储介质
CN107301493A (zh) * 2017-05-19 2017-10-27 四川新网银行股份有限公司 一种基于深度神经网络的互金企业评级模型
CN107220237A (zh) * 2017-05-24 2017-09-29 南京大学 一种基于卷积神经网络的企业实体关系抽取的方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150149247A1 (en) * 2013-05-02 2015-05-28 The Dun & Bradstreet Corporation System and method using multi-dimensional rating to determine an entity's future commercical viability
CN105740335A (zh) * 2016-01-22 2016-07-06 山东合天智汇信息技术有限公司 一种基于titan的企业信息分析平台及其构建方法
CN106203808A (zh) * 2016-07-01 2016-12-07 中国民生银行股份有限公司 企业信用风险评估方法和装置
CN107229756A (zh) * 2017-06-30 2017-10-03 山东合天智汇信息技术有限公司 一种直观展现企业关系图谱的设计方法及系统

Cited By (39)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110443459A (zh) * 2019-07-05 2019-11-12 深圳壹账通智能科技有限公司 预警信息推送方法、装置、计算机设备和存储介质
CN110414806B (zh) * 2019-07-10 2024-05-14 平安科技（深圳）有限公司 员工风险预警方法及相关装置
CN110414806A (zh) * 2019-07-10 2019-11-05 平安科技（深圳）有限公司 员工风险预警方法及相关装置
CN110532357A (zh) * 2019-09-04 2019-12-03 深圳前海微众银行股份有限公司 Esg评分体系的生成方法、装置、设备及可读存储介质
CN110532357B (zh) * 2019-09-04 2024-03-12 深圳前海微众银行股份有限公司 Esg评分体系的生成方法、装置、设备及可读存储介质
CN113643035A (zh) * 2020-05-11 2021-11-12 阿里巴巴集团控股有限公司 信息处理方法、信息展示方法、装置、设备及存储介质
CN111951079A (zh) * 2020-08-14 2020-11-17 国网电子商务有限公司 一种基于知识图谱的信用评级方法、装置及电子设备
CN111951079B (zh) * 2020-08-14 2024-04-02 国网数字科技控股有限公司 一种基于知识图谱的信用评级方法、装置及电子设备
CN113743111A (zh) * 2020-08-25 2021-12-03 国家计算机网络与信息安全管理中心 基于文本预训练和多任务学习的金融风险预测方法及装置
CN113743111B (zh) * 2020-08-25 2024-06-04 国家计算机网络与信息安全管理中心 基于文本预训练和多任务学习的金融风险预测方法及装置
CN112418320B (zh) * 2020-11-24 2024-01-19 杭州未名信科科技有限公司 一种企业关联关系识别方法、装置及存储介质
CN112418320A (zh) * 2020-11-24 2021-02-26 杭州未名信科科技有限公司 一种企业关联关系识别方法、装置及存储介质
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