JP2016119076A - Generation device, generation method, and generation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a generation device that calculates the credibility of a business operator with high accuracy.SOLUTION: A generation device 100 includes an acquisition unit 131 and a generation unit 132. The acquisition unit obtains information related to a business operator out of information based on a user behavior on a communication network. The generation unit generates a model for predicting an index value indicating the credibility of a second business operator from information related to the second business operator which is obtained by the acquisition unit, on the basis of the correlation between information related to a first business operator out of the information related to the business operator which is obtained by the acquisition unit and an index value indicating the credibility of the first business operator.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a generation device, a generation method, and a generation program.

  In general, when a financial institution such as a bank determines a loan allowance frame, business data provided by a data provider specializing in collection and analysis of company (business) information is referred to. Such business operator data is generated based on, for example, business operators' financial statements (for example, financial statements, profit and loss statements, etc.). That is, the financial institution uses the business data provided by the business in order to calculate credit information for determining whether or not the loan amount to each company is appropriate.

  Here, along with the rapid spread of the Internet in recent years, in addition to information based on business operators' financial statements, etc. as in the past, information users share information about companies on the Internet, and the information that is shared is centralized Technology that enhances the objectivity of business data by integrated management is known.

JP 2010-26602 A

  However, with the above-described conventional technology, it is not always possible to calculate the creditworthiness with respect to the operator with high accuracy. Specifically, the above-mentioned conventional technology only shares information such as financial information of publicly disclosed companies, information on corporate transactions that information users can know, and industry news, and integrates such information. Even so, it is difficult to evaluate the creditworthiness of the operator.

  Further, in the above-described conventional technology, it is difficult to share accurate information for companies where it is difficult for information users to acquire information, such as unlisted companies, small and medium-sized companies, and venture companies. For this reason, it can be said that it is difficult to acquire credit information for determining the loan conditions for unlisted companies, venture companies and the like with the above-described conventional technology.

  The present application has been made in view of the above, and an object of the present invention is to provide a generation device, a generation method, and a generation program that can calculate the reliability of a business operator with high accuracy.

  The generating apparatus according to the present application includes an acquisition unit that acquires information related to a business operator among information based on user behavior on a communication network, and a first of information related to the business operator acquired by the acquisition unit. Based on the correlation between the information related to the business operator and the index value indicating the creditworthiness of the first business operator, the second business is obtained from the information related to the second business operator acquired by the acquisition unit. And a generation unit that generates a model for predicting an index value indicating the creditworthiness of the person.

  According to one aspect of the embodiment, there is an effect that the creditworthiness with respect to the operator can be calculated with high accuracy.

FIG. 1 is a diagram illustrating an example of a generation process according to the embodiment. FIG. 2 is a diagram illustrating an example of processing by the generation system according to the embodiment. FIG. 3 is a diagram illustrating a configuration example of the generation apparatus according to the embodiment. FIG. 4 is a diagram illustrating an example of the business operator data storage unit according to the embodiment. FIG. 5 is a diagram illustrating an example of a search information table according to the embodiment. FIG. 6 is a diagram illustrating an example of the site information table according to the embodiment. FIG. 7 is a diagram illustrating an example of a product information table according to the embodiment. FIG. 8 is a diagram illustrating an example of a social information table according to the embodiment. FIG. 9 is a diagram illustrating an example of a customer information table according to the embodiment. FIG. 10 is a diagram illustrating an example of a model storage unit according to the embodiment. FIG. 11 is a flowchart illustrating a generation processing procedure performed by the generation apparatus according to the embodiment. FIG. 12 is a flowchart illustrating a calculation processing procedure performed by the generation apparatus according to the embodiment. FIG. 13 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the generation apparatus.

  Hereinafter, a mode for carrying out a generation device, a generation method, and a generation program according to the present application (hereinafter referred to as “embodiment”) will be described in detail with reference to the drawings. Note that the generation device, the generation method, and the generation program according to the present application are not limited by this embodiment. In the following embodiments, the same portions are denoted by the same reference numerals, and redundant description is omitted.

[1-1. Example of generation process)
First, an example of the generation process according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of a generation process according to the embodiment. In FIG. 1, a generation process of a calculation formula (model) for outputting an index value (score) indicating the creditworthiness of an operator will be described by the generation device 100 according to the embodiment.

  The generation apparatus 100 illustrated in FIG. 1 acquires information on a business operator via a communication network (for example, the Internet) (not shown), and a score (hereinafter simply referred to as “score”) indicating the creditworthiness of the business operator based on the acquired information. Is a server device that generates a model for outputting. Specifically, the generation device 100 is information related to a business operator, and information based on user behavior on the Internet (for example, product evaluation, word-of-mouth, search site, etc. sent by a user regarding a product provided by each business operator) Search information, etc.). Then, the generation apparatus 100 analyzes the relationship between the acquired information and the score calculated by the provider data provider 50 who is an expert who evaluates the creditworthiness of the provider. Generate a model that can output the scores.

  Here, the provider data provider 50 shown in FIG. 1 is a company specializing in collecting financial information and the like related to the provider and calculating the creditworthiness of the provider based on the collected information. For example, the business operator data provider 50 performs a business of selling business credit information, such as a calculated score, to a financial institution or the like. In addition, the provider data provider 50 calculates the score of the provider based on the financial information of the provider and, in some cases, information on artificial evaluation (e.g., evaluation of management personality and leadership). Have their own model to do. For example, the business operator data provider 50 sets items such as business stability, growth potential, management ability, and openness as items for evaluating the business. Then, the business operator data provider 50 calculates a score corresponding to each item, thereby calculating a comprehensive score indicating the creditworthiness of the business operator.

  Here, as shown in FIG. 1, the business operators are classified into categories such as a giant company, a partly listed company, a large company, a small and medium company, and an individual business owner, depending on the scale and the stock listing status. In the left part of FIG. 1, it is conceptualized and shows whether a business company belongs to a category such as a large company, a large company, or a small and medium company. Of these, large corporations and some large corporations are classified as “with business data”. Here, the operator data is information including financial information for evaluating the creditworthiness of the operator, capital relationship, and the like. In addition, the business operator data includes not only publicly available information but also information obtained by, for example, an investigator who belongs to the business operator data provider 50 independently. In other words, the business operator classified as “with business data” is given a score indicating the credit rating by analyzing the business data by the business data provider 50.

  On the other hand, the business operator classified as “no business data” is not provided with a score indicating the credit rating because the business data analyzed by the business data provider 50 is insufficient. This is because, for example, the financial information is not sufficiently disclosed because the business is not listed, or the business data provider 50 investigator surveys all business operators due to the large number of SMEs. This is because it is practically impossible. In other words, the business operator data provider 50 can calculate the business operator's score only for a relatively small portion of the business operators. For this reason, many operators may not be able to prove creditworthiness, and may suffer from disadvantages such as being unable to receive a loan from a financial institution, or being unable to accurately calculate the loan amount.

  Therefore, the generation apparatus 100 will be described below in order to output a score indicating the creditworthiness of the operator, regardless of acquisition of public financial information or collection of artificial information by an investigator. Perform the process.

  First, the generation apparatus 100 acquires information on a model for the provider data provider 50 to calculate the score of the provider based on the provider data. Here, the company data provider 50 scores the company based on information such as the business history, capital structure, scale, and profit / loss of the company acquired as the company data (step S01). In other words, the provider data provider 50 gives a score for the provider based on the existing model of the provider data provider 50 to the determination target provider. Then, the generation device 100 acquires business data provided (or sold) by the business data provider 50 and including data including the calculated score. At this time, the provider data provider 50 also provides information on the evaluation contents of each item used to give a comprehensive score and information on the score of each item.

  Here, based on the network information, the generating apparatus 100 scores the business operators for the business enterprises to which the business enterprise data provider 50 has already assigned scores (step S02). As will be described in detail later, for example, the generation device 100 associates the capital power, collateral capacity, and the like used by the business data provider 50 for evaluating “stability” with data that can be acquired on the network. Thus, a model that outputs a score corresponding to “stability” is generated. As an example, the generation apparatus 100 uses the number of searches for the company name on the search site, the overall evaluation of product reviews of products sold by the company, and the like. A model capable of outputting a score corresponding to the item of “sex” is generated. The generation apparatus 100 performs such processing for each item such as “growth”, “manager ability”, and “openness”. The generation apparatus 100 acquires the operator's score calculated for the operator using the model generated by the processing.

  And the production | generation apparatus 100 compares the score calculated by the produced | generated model, and the score which the provider data provider 50 calculated (step S03). As a result, the generation apparatus 100 derives a solution for network information suitable for corresponding to the score of the item of “stability” calculated by the provider data provider 50, and for quantifying the network information, for example. In other words, the generation device 100 learns a model generated by itself using the model of the provider data provider 50 as a correct model. The generation apparatus 100 optimizes the model by repeating for a large number of business operator data (step S04).

  Thereby, the generation device 100 can calculate the score even for a small and medium-sized company in which the provider data provider 50 does not calculate the score. Specifically, the generation apparatus 100 acquires network information related to a small business and inputs the acquired information to the generated model. And the production | generation apparatus 100 scores a provider by the optimized model (step S05). This is because the generation device 100 gives an operator score having a value equivalent to a score calculated by an existing model possessed by a specialist such as the operator data provider 50 to an operator such as a small business. It means to do.

  As described above, the generation apparatus 100 according to the embodiment acquires information related to the business operator from information on the communication network. And the production | generation apparatus 100 is provided with the information relevant to the provider to which the score is provided by the provider data provider 50 among the information related to the acquired provider, and the provider data provider 50. Based on the correlation with the existing score, a model for predicting a score indicating the creditworthiness of the business operator to which no score is assigned by the business enterprise data provider 50 is generated.

  As a result, the generation apparatus 100 according to the embodiment can measure the creditworthiness of the business operator and score it without using the financial information of the company that is generally used to determine the loan conditions. Can be generated. In other words, according to the generating apparatus 100, the operator's score can be calculated regardless of the score given by the operator data provider 50, which is a specialized institution that evaluates the creditworthiness of the operator based on financial information or the like. it can. For this reason, the generation apparatus 100 calculates the operator's score even for a small and medium-sized business, an individual business owner, a venture company, etc., to which the business data provider 50 does not give a score, and uses the score as credit information. Can be provided. As a result, SMEs and the like can receive appropriate loans from financial institutions. In addition, the generation device 100 can acquire information including a large number of samples such as user behavior on the Internet. And the production | generation apparatus 100 can optimize a model by using the score which the provider data provider 50 provides as correct data, and learning many samples. Thereby, since the production | generation apparatus 100 can produce | generate the model which can obtain | require a highly accurate score, it can calculate the reliability with respect to a provider with high precision. Note that the score calculated by the generation device 100 is not necessarily used for the purpose of financial support. For example, various sales activities of an operator (the creditworthiness of an advertiser who distributes an advertisement, a merchant of a shopping site, etc.) Can also be used for creditworthiness, etc.).

[1-2. Generation system)
Next, processing performed by the generation system according to the embodiment will be described with reference to FIG. FIG. 2 is a diagram illustrating an example of processing by the generation system 1 according to the embodiment. In FIG. 2, the generation system 1 is taken as an example, and the flow of the generation process executed by the generation apparatus 100 described in FIG. 1 will be described in detail.

  As illustrated in FIG. 2, the generation system 1 includes a user terminal 10, a financial institution server 30, a web server 40, and a generation apparatus 100. The generation apparatus 100 is connected to the user terminal 10, the financial institution server 30, and the web server 40 through a communication network (for example, the Internet) (not shown) so as to be communicable. Note that the number of user terminals 10, financial institution servers 30, and web servers 40 included in the generation system 1 is not limited to the example illustrated in FIG. 2. For example, the generation system 1 may include a plurality of financial institution servers 30 and a plurality of web servers 40.

  The user terminal 10 is an information processing device used by general users. Specifically, the user terminal 10 is used for a user to browse a web page or post an evaluation of product information on a website. The user terminal 10 is, for example, a mobile terminal such as a smartphone, a tablet terminal, or a PDA (Personal Digital Assistant), a desktop PC (Personal Computer), a notebook PC, or the like. In addition, a general user is a user who does not perform the action with the specific intention with respect to the generation process according to the embodiment. In the embodiment, a person who professionally acquires and analyzes company information such as the provider data provider 50 and a person who provides financing to the provider are excluded from general users. Moreover, although the business operators themselves can be excluded from the general users, the business owners and individual officers of the business operators may be included in the general users.

  The financial institution server 30 is a server device used by a financial institution. Specifically, the financial institution server 30 accepts a loan offer from a business operator or notifies the business operator of the result of the offer. Further, the financial institution server 30 uses the generation device 100 in order to evaluate the creditworthiness of the business in the financing to the business.

  The web server 40 is a server device that provides various web pages when accessed from the user terminal 10. The web server 40 provides various web pages related to, for example, a news site, a weather forecast site, a shopping site, a finance (stock price) site, a route search site, a map providing site, a travel site, a restaurant introduction site, and a web blog.

  The web server 40 stores user behavior on the network. The user behavior information is stored as user information data 42 in the web server 40 or a predetermined storage device. Here, the user behavior on the network means information transmitted from the user terminal 10 in accordance with a user operation when using services provided from various websites. For example, user behavior on the network includes transmission of a search query on a search site, purchase behavior on a shopping site, posting of a review from a user on a product evaluation site, and the like. Further, the user behavior includes, for example, message exchange on an SNS (Social Networking Service) site, follow-up behavior with respect to others, and the like.

  Hereinafter, the generation processing by the generation system 1 and the generation apparatus 100 will be described along the flow with reference to FIG. Note that the description of the matters described in FIG. 1 is omitted.

  First, in the example illustrated in FIG. 2, the generation apparatus 100 receives provision of business data 52 included in the business data provider 50 (Step S <b> 11). In addition to the information disclosed as financial information, the company data 52 includes information investigated by an investigator belonging to the company data provider 50. Further, the business operator data 52 includes information on the business operator score calculated by the business enterprise data provider 50.

  Further, the generation device 100 acquires user information data 42 transmitted from the web server 40 (step S12). The generation apparatus 100 acquires, as the user information data 42, information based on user behavior on the Internet among information on the business operator and information that can be acquired via the Internet. Specifically, the generation device 100 may determine the number of times that the user terminal 10 performs a search action using the name of a business operator or a product name provided by the business operator as a search query, the number of website visits provided by the business operator, Information relating to a review posting for a product provided by the business operator is acquired as user information data 42.

  And the production | generation apparatus 100 analyzes the correlation of the acquired information (step S13). For example, the generation apparatus 100 analyzes the association and correspondence between the score calculated by the business operator data provider 50 and the user information data 42 acquired via the Internet. And the production | generation apparatus 100 produces | generates the reliability model which outputs an operator's score by the method demonstrated in FIG. 1 (step S14).

  And the production | generation apparatus 100 receives the inquiry of the credit information of a predetermined | prescribed provider from the financial institution server 30 (step S15). For example, when the financial institution server 30 receives a loan application from a predetermined operator, the financial institution server 30 inquires the generation apparatus 100 for the credit information in order to acquire the credit information of the predetermined operator. Here, it is assumed that the predetermined company is a small and medium-sized company and a company whose score is not posted in the company data provided by the company data provider 50. In this case, the generation apparatus 100 acquires information on the network of the predetermined operator, and calculates the score of the predetermined operator by inputting the acquired information into the model. In other words, the generation device 100 calculates credit information of a predetermined business operator (step S16).

  And the production | generation apparatus 100 transmits the calculated credit information to the financial institution server 30 (step S17). With such a series of flows, the generation device 100 can provide the credit information of the business operator to the financial institution server 30.

[2. Configuration of the generator
Next, the configuration of the generation apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram illustrating a configuration example of the generation apparatus 100 according to the embodiment. As illustrated in FIG. 3, the generation device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The generation device 100 includes an input unit (for example, a keyboard and a mouse) that receives various operations from an administrator who uses the generation device 100, and a display unit (for example, a liquid crystal display) for displaying various types of information. You may have.

(About the communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card). The communication unit 110 is connected to a communication network by wire or wireless, and transmits / receives information to / from the user terminal 10 or the like via the communication network.

(About the storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 according to the embodiment includes a provider data storage unit 121, a network information storage unit 122, and a model storage unit 128. Hereinafter, each storage unit will be described in order.

(About the company data storage unit 121)
The company data storage unit 121 stores information related to company data provided from the company data provider 50. Here, FIG. 4 shows an example of the business operator data storage unit 121 according to the embodiment. As shown in FIG. 4, the business operator data storage unit 121 includes “business operator ID”, “information update date”, “total score”, “industry”, “stability”, “management ability”, “growth potential”. And “openness”. In addition, the number described after each item shows the maximum value of the score which the provider data provider 50 gives.

  “Business operator ID” indicates identification information for identifying the business operator. “Information update date” indicates the date on which information on the business operator was updated. For example, the information update date indicates a date (for example, once a month) on which the score calculated by the business operator data provider 50 for the business operator is updated.

  The “total score” indicates the total score calculated by the provider data provider 50 for the provider. In the example of FIG. 4, the total score is a value obtained by adding the scores of items such as stability, managerial ability, growth potential, and openness. “Industry” indicates the industry or business type to which the business operator belongs.

  “Stability” is one of the items for evaluating a business operator, and is an item for evaluating whether the business can continue stable management, such as business continuity of the business operator. The items of “stability” include small items such as “business history”, “financial status”, and “business relationship”. In each small item, a score given by the business operator data provider 50 is described. For example, the business data provider 50 determines the superiority or inferiority of the business history of the business as the business history, and gives a score using a unique model. Or business enterprise data provider 50 may give a score from an artificial viewpoint such as an investigator. Further, the item of “stability” is not limited to the small items shown in FIG. 4, and may include small items such as the capital adequacy ratio of the operator, the collateral capacity, and the settlement status.

  “Management ability” is one of the items for evaluating the business operator, and is an item for evaluating the ability as a business manager, such as the career and personality of the business operator. The item “management ability” includes small items such as “assets collateral”, “management attitude”, and “business experience”. In each of the small items, a score calculated by the business operator data provider 50 using a model and a score given from an artificial viewpoint are described as in the “stability” item. Further, the item of “management ability” is not limited to the small items shown in FIG. 4, and may include small items such as the health status of the business operator, the presence / absence of a successor, and human network.

  “Growth” is one of the items for evaluating a business operator, and is an item for evaluating the business operator's expected value for future management development. The “growth” item includes small items such as “profit growth” and “industry growth”. In each of the small items, a score calculated by the business operator data provider 50 using a model and a score given from an artificial viewpoint are described as in the “stability” item. In addition, the item of “growth” is not limited to the small items shown in FIG. 4, and may include small items such as the merchandise marketability of the merchandise provided by the business operator and the vitality of the company.

  “Openness” is one of the items for evaluating a business operator, and is an item for evaluating the business operator's attitude toward information disclosure. The items of “openness” include small items such as “public status” and “general public opinion”. In each of the small items, a score calculated by the business operator data provider 50 using a model and a score given from an artificial viewpoint are described as in the “stability” item. For example, the business data provider 50 is high because it is assumed that the more financial information, stock information, etc. are disclosed to business partners and shareholders, the higher the soundness of management of the business. Give a score. Further, the item of “openness” is not limited to the small items shown in FIG. 4, and may include small items such as the merchandise marketability of the merchandise provided by the business operator and the vitality of the company.

  That is, in FIG. 4, as an example of stored information, information updated to “November 1, 2014” for the business entity identified by the business entity ID “A01”, and the overall score is “80”. The industry is “Manufacturing (Electric)”, the score of each item is “5”, the current financial status is “15”, the business relationship is “8”, the asset The security is “5”, the management attitude is “4”, the business experience is “5”, the profit growth is “8”, the industry growth is “3”, and the disclosure status is The example is “4” and the overall reputation is “3”.

  In the following, the identification information stored in the “operator ID” shown in FIG. 4 may be used as a reference symbol. For example, in the following, the business operator identified by the business operator ID “A01” may be referred to as “business operator A01”.

(About the network information storage unit 122)
The network information storage unit 122 stores user information acquired via a communication network. Specifically, the network information storage unit 122 stores information related to the provider among information on the communication network. As illustrated in FIG. 3, the network information storage unit 122 includes a search information table 123, a site information table 124, a product information table 125, a social information table 126, and a customer information table 127. Hereinafter, the data table will be described.

(Regarding the search information table 123)
The search information table 123 stores information related to user search behavior on the Internet. Here, FIG. 5 shows an example of the search information table 123 according to the embodiment. As shown in FIG. 5, the search information table 123 includes items such as “operator ID”, “total period”, “number of searches”, “degree of increase”, “search ranking”, and “target word”.

  “Business operator ID” indicates identification information for identifying the business operator. “Aggregation period” indicates a period during which the search actions performed by the user terminal 10 are aggregated. In the example of FIG. 5, the aggregation period is in units of one week, but the aggregation period may be a different period. For example, when the aggregation period is set to one month, the generation apparatus 100 can easily grasp the search tendency over a longer period.

  “Number of searches” indicates, for example, the number of times that a search engine has been searched by a search engine on a predetermined search site. As will be described later, the search query counted as the number of searches is not limited to the name of the business operator itself, and may include the name of a product provided by the business operator, the name of the business operator of the business operator, and the like.

  The “degree of increase” indicates an increase / decrease value of the number of searches with respect to the number of searches in the immediately preceding aggregation period. “Search ranking” indicates a ranking of the number of searches in a predetermined search site. Although illustration is omitted, the search ranking item may store not only the ranking based on the number of searches but also the ranking based on the degree of increase.

  The “target word” indicates a word that is counted as the number of searches related to the operator when the described word is transmitted as a search query. For example, when the name of a product provided by a business is higher than the name of the business, the user may perform a search action using the product name. In this case, even if a search is performed using the product name as a search query, if the product name is set as the target word, the number of searches for the business provider that provides the product is counted. The target word may be artificially set by an application of the administrator of the generation apparatus 100 or a business operator, or may be automatically set by analyzing a link of a website based on a search result, for example. Good. Specifically, when searching for “product BBB”, if a large number of websites related to “business name A01” are displayed as search results, “product BBB” is displayed together with the business name A01. Is automatically set to the target word.

  That is, in FIG. 5, as an example of the data, the business operator identified by the business operator ID “A01” is “30000 times” during the aggregation period “November 15, 2014 to November 21, 2014”. The degree of increase from the number of previous searches is “1000 times”, and the search ranking is “12000”. In addition, it is shown that “enterprise name A01”, “product BBB”, “manager CCC”, and the like are set in the enterprise A01 as target words in the search.

  As another example, the operator A11 has been searched “200000 times” during the aggregation period “November 30, 2014 to December 6, 2014”, and the number of searches “195500 times” It shows that it is rising. This is presumed that an event in which the name recognition of the business operator A11 rapidly increased occurred. In such a case, the search information table 123 may store, for example, a mark for recognizing the business operator A11 as a notable company according to the rapid rise. Such a mark may be used, for example, for generating a model to be described later.

(About the site information table 124)
The site information table 124 stores information related to websites operated or managed by the business operator. Here, FIG. 6 shows an example of the site information table 124 according to the embodiment. As shown in FIG. 6, the site information table 124 includes items such as “operator ID”, “total period”, “PV”, “UU”, and “CVR”.

  The “company ID” and “total period” correspond to the same items stored in the search information table 123. “PV” indicates a page view on the website, that is, the number of browsing.

  “UU” indicates the number of unique users. The unique user indicates the number of visitors to the website. Even if the same user visits the same website many times, the UU number is counted as “1”.

  “CVR” indicates a conversion rate. CVR indicates the percentage of conversions to the number of browsing websites. Conversion means the final result you can get on your website. For example, the conversion is purchase of a product on an online shopping site, member registration on an information providing site or a community site, request for materials, and the like. The CVR may be a conversion rate for the number of browsing, or a conversion rate for a unique user may be adopted.

  That is, in FIG. 6, as an example of data, the website provided by the operator A01 is viewed “11000 times” during the aggregation period “November 15, 2014 to November 21, 2014”. The number of UUs viewed is “3000”, indicating that conversion has been performed by “1%” of the number of browsing.

  Note that the aggregation period in FIG. 6 is an example, and PV and the like may be aggregated in units of one day or one month instead of one week. In addition, for each item such as PV, not only the numerical value as an absolute number but also the number of fluctuations from the immediately preceding aggregation period may be aggregated.

(About product information table 125)
The product information table 125 stores information related to products provided by the business operator. Here, FIG. 7 shows an example of the product information table 125 according to the embodiment. As illustrated in FIG. 7, the product information table 125 includes items such as “enterprise ID”, “product”, “user evaluation”, “number of reviews”, and “store ranking”.

  “Business operator ID” corresponds to the same item stored in the search information table 123. “Product” indicates the name of the product provided by the operator.

  “User evaluation” indicates an evaluation value indicated by a general user in a product evaluation site on the Internet. The product evaluation site is a community site that receives word-of-mouth information such as product reviews and evaluations from general users. In addition, when a product provided by a business operator is an application for a terminal, a site (referred to as an application store or the like) that provides an application download service may also serve as a product evaluation site. Here, user evaluation shall be shown by the average value of the numerical value from "0" transmitted from a user to "5".

  “Number of reviews” indicates the number of reviews posted by the user at the product evaluation site on the Internet. “Store ranking” indicates a ranking among similar products handled in the product evaluation site. The store ranking may be ranked according to the numerical value of the user evaluation, or may be ranked according to the number of products sold. As described above, when the product evaluation site is an app store, the store ranking may be the number of downloads of the app.

  That is, in FIG. 7, as an example of data, the product “BBB” provided by the operator A01 has a user evaluation of “4 points”, the number of reviews posted is “4500”, and the store ranking is “ "10th place".

(About the social information table 126)
The social information table 126 stores an index value that can be used to evaluate a business's social reputation and personality. Specifically, the social information table 126 stores information acquired from an SNS site used by business owners and officers. Here, FIG. 8 shows an example of the social information table 126 according to the embodiment. As illustrated in FIG. 8, the social information table 126 includes items such as “enterprise ID”, “investigation subject”, and “number of SNS connections”.

  “Business operator ID” corresponds to the same item stored in the search information table 123. “Survey object person” indicates an individual name to be subjected to social analysis. For example, the survey target is a manager of the business operator, an executive such as a president or a director.

  The “SNS connection number” indicates a numerical value connected to another person on the SNS when the survey target person uses the SNS. For example, the number of followers on the SNS corresponds. Note that the number of SNS connections does not include the number of general users, and the number of connections between managers and officers of different operators may be counted. Thereby, the number of SNS connections can be a more reliable numerical value as an index value indicating the human pulsation force in the survey subject.

  That is, in FIG. 8, as an example of data, the survey target person in the operator A01 is “CCC” or “HHH”, the number of connections in the SNS used by “CCC” is “120”, and “HHH” This indicates that the number of connections in the SNS used by is “50”.

(Regarding customer information table 127)
The customer information table 127 stores information related to business customers. Here, FIG. 9 shows an example of the customer information table 127 according to the embodiment. As shown in FIG. 9, the customer information table 127 includes items such as “enterprise ID”, “number of product users”, “continuous usage rate”, and “customer unit price”.

  “Business operator ID” corresponds to the same item stored in the search information table 123. “Number of product users” indicates the number of customers who use the products provided by the business. For example, when the product provided by the business is an application, the total number of downloads of the application provided by the business corresponds.

  “Continuous usage rate” indicates the continuous usage rate of the customer with respect to the operator. For example, when a business operator operates a shopping site on the Internet, the ratio of the number of users who regularly use the site corresponds to the total number of users browsing the site. Further, the continuous usage rate may be the number of user terminals 10 whose use is continuously confirmed with respect to the total number of downloads when the provider provides an application. In this case, the continuous usage rate is stored as an operation rate of the application (for example, a value obtained by dividing the number of usage users per predetermined period by the number of download users).

  “Customer unit price” indicates a unit price per customer. For example, when a business provider provides a shopping site, the customer unit price corresponds to the purchase amount per user in a predetermined period. The unit price of the customer can also be calculated based on the download sales amount of the application, the cost for continuous use, and the like when the provider provides the application.

  That is, in FIG. 9, as an example of data, the product user provided by the operator A01 is “300000”, the continuous usage rate is “0.25”, and the customer unit price is “8000 yen”. Is shown.

(About the model storage unit 128)
The model storage unit 128 stores information related to the model generated by the generation device 100. Here, FIG. 10 shows an example of the model storage unit 128 according to the embodiment. As illustrated in FIG. 10, the model storage unit 128 includes items such as “model ID”, “information update date”, and “industry”.

  “Model ID” indicates identification information for identifying a model. “Information update date” indicates the date on which the model was updated. “Industry” indicates the industry to which the business operator whose score is calculated belongs. This means that models are generated for each industry of operators. That is, the model is generated by using common business data of a predetermined industry. This is because it is easier to find the commonality and similarity of numerical values in the items to be compared when using business data of the same industry when generating the model.

  That is, in FIG. 10, as an example of data, the model M001 has information updated on “December 13, 2014”, and the industry to which the model M001 is applied is “manufacturing (electric)”. ing.

(About the control unit 130)
The control unit 130 is configured such that, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like stores various programs (corresponding to an example of a search program) stored in a storage device inside the generation device 100 as a work area. This is realized by being executed as The control unit 130 is realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  As illustrated in FIG. 3, the control unit 130 according to the embodiment includes an acquisition unit 131, a generation unit 132, a reception unit 133, a calculation unit 134, and a notification unit 135, and information processing described below Realize or execute the functions and operations of Note that the internal configuration of the control unit 130 is not limited to the configuration illustrated in FIG. 3, and may be another configuration as long as the information processing described below is performed. Further, the connection relationship between the processing units included in the control unit 130 is not limited to the connection relationship illustrated in FIG. 3, and may be another connection relationship.

(About the acquisition unit 131)
The acquisition unit 131 acquires information related to a provider from information on a communication network (for example, the Internet). For example, the acquisition unit 131 acquires information related to a business operator and based on user behavior on the Internet. Specifically, the acquisition unit 131 according to the embodiment specifies a business operator serving as a model generation sample, and searches the Internet for information regarding the specified business operator. And the acquisition part 131 is the service provided from various websites as information based on the user action on the Internet, Comprising: The information transmitted from the user at the time of use of the service which a provider relates is acquired from the web server 40. To do. Note that information based on user behavior on the Internet includes, for example, transmission of a search query performed by a user on a search site, review of a product performed by a user on a product evaluation site, and release of information on a SNS by a user. It means information generated when a user uses a service on various websites. The service related to the business is not limited to a service such as a shopping site directly provided by the business. For example, a service provided by a search site capable of searching for the business or evaluation of the business of the business. This includes services from evaluation sites that can be used.

  For example, the acquisition unit 131 acquires search information regarding a business operator at a predetermined search site. Specifically, the acquisition unit 131 acquires search information such as how many times the business has been searched by the user, using a search query related to the business as a search target word. Then, the acquisition unit 131 stores the acquired information in the search information table 123.

  In addition, the acquisition unit 131 acquires site information on a website provided by a business operator that is an acquisition target of information. Specifically, the acquisition unit 131 acquires information such as the number of PVs, the number of UUs, and CVR of the website provided by the business operator. Then, the acquisition unit 131 stores the acquired information in the site information table 124.

  In addition, the acquisition unit 131 acquires information related to a product provided by a business operator from which information is acquired. Specifically, the acquisition unit 131 acquires information such as user evaluation, the number of reviews, and store ranking on the Internet of products provided by the business operator. Note that the acquisition unit 131 may acquire information (that is, a change rate) regarding a trend of each information such as a change in user evaluation and a change in store ranking. In addition, when the product provided by the provider is a program product such as an application, the acquisition unit 131 displays the number of downloaded applications, the number of users, the average usage time per user, and the applications in a predetermined period. Get the index value such as the utilization rate of. Then, the acquisition unit 131 stores the acquired information in the product information table 125.

  Moreover, the acquisition part 131 acquires the information which can evaluate the social reputation and attractiveness of the provider from which information is acquired. Specifically, the acquisition unit 131 acquires information such as the number of consolidated SNSs of business managers and officers. The acquisition unit 131 may acquire the trends of individual managers on the Internet as index values that can evaluate the social reputation and attractiveness of the business. For example, the acquisition unit 131 acquires information on a person who is personally connected to the manager on the SNS (for example, name recognition, the size of the company to be managed). That is, the acquisition unit 131 acquires information or the like that is assumed to indicate the human network power of the manager. In addition to the above-mentioned number of SNS connections, the acquisition unit 131 determines the number of accesses from general users and the number of followers in the SNS of the business owners and executives, Or information on reputation. Then, the acquisition unit 131 stores the acquired information in the social information table 126.

  Moreover, the acquisition part 131 acquires the information regarding the customer of the provider who becomes the acquisition target of information. Specifically, the acquisition unit 131 acquires information such as the number of users of a product provided by the business operator, the continuous usage rate of the user, and the customer unit price. For example, for a provider that provides an application, the acquisition unit 131 can acquire the number of product users by acquiring the number of downloaded applications from the application store. In addition, for a business operator who operates a shopping site, the acquisition unit 131 acquires information on a continuous usage rate and a customer unit price based on information such as a visit interval of a user's site and a purchase amount. Can do. Then, the acquisition unit 131 stores the acquired information in the customer information table 127.

  Note that the acquisition unit 131 may randomly acquire information on various operators without specifying the operator from which the information is acquired. For example, the acquisition unit 131 may acquire information related to the operator or update the acquired information at any time by using a program such as a search robot used for a search engine or the like to crawl on the Internet. it can.

(About the generator 132)
The generation unit 132 generates a model for calculating the creditworthiness of the business based on various information. Specifically, the generation unit 132 according to the embodiment performs the business based on the network information acquired by the acquisition unit 131, the existing model of the provider data provider 50, and the score calculated by the existing model. A model that outputs the creditworthiness of the person as a score is generated. In the following description, the business operators A01 to A03 or the business operator A11 shown in FIGS.

  For example, among the information related to the business acquired by the acquisition unit 131, the generation unit 132 includes information related to the business A 01 and the score of the business A 01 given by the business data provider 50. Based on the correlation, a score indicating the creditworthiness of the business operator A11 is predicted from information related to the business operator A11 that is not given a score by the business enterprise data provider 50 among the information acquired by the acquisition unit 132. Generate a model for In addition, the generation unit 132 determines the correlation between the score of each item constituting the score assigned by the business operator data provider 50 and the information associated with each item among the information related to the business operator A01. Based on this, a model is generated. Here, “corresponding to each item” means that the “stability” item includes the search information shown in FIG. 5, the site information shown in FIG. 6, the product information shown in FIG. Among them, information related to the operator A01 is associated. Specifically, the generation unit 132 determines whether or not a certain number of business operators A01 have been searched from the user over a predetermined period as information for measuring the stability of the business operators A01, and a certain number of websites provided by the business operators A01. Whether or not browsing has been made and information such as the rate of increase / decrease of these numerical values are associated.

  Note that the generation unit 132 may receive an artificial input such as an administrator of the generation apparatus 100 regarding the association of information as described above. For example, the administrator of the generation apparatus 100 associates search information and the like with the item “stability” as described above. Further, the administrator of the generation apparatus 100 may arbitrarily associate search information, customer information, and the like with the item “growth”. Then, the generation unit 132 adopts information having an appropriate correspondence in a model generation process described below, and changes information having an inappropriate correspondence so that a useful score cannot be obtained. Thereby, the generation unit 132 can optimize the generated model.

  Here, the generation unit 132 is information associated with each item among the score of each item constituting the score assigned by the business operator data provider 50 and information related to the business operator A01. A model is generated by performing regression analysis on the converted information.

  That is, the generation unit 132 has a value (for example, a value indicated by a variable obtained by quantifying the information acquired by the acquisition unit 131 and the score of each item constituting the score given by the provider data provider 50 which is correct data) By generating regression analysis on the number of searches, product evaluation rankings, number of product users, etc., which has been digitized so that it can be compared with correct data), a model that outputs the operator's score is generated. To do. As an example, the generation unit 132 obtains a coefficient for calculating the score of each item using a linear expression such as the following expression (1).

    y = α · x + β (1)

  In the above formula (1), “y” indicates a score given by the business data provider 50 which is correct data. “X” indicates a variable obtained by digitizing the information acquired by the acquisition unit 131. “Α” indicates a coefficient of “x”, and “β” is a number that complements the relationship between “y” and “α · x”. Here, “y” is a numerical value indicating the item of “stability” of the operator A01, and this number is assumed to be “25”. “X” is network information of the operator A01, for example, information obtained by digitizing the number of searches. Correspondence and digitization of such information types are optimized as the regression analysis progresses. For example, the generation unit 132 first sets an arbitrary conditional expression for the number of searches “30000” in the predetermined period of the business operator A01, and gives an arbitrary numerical value such as “10”. Then, the generation unit 132 appropriately changes the first conditional expression in the process of calculating the correlation between the arbitrarily given numerical value and the correct answer data. Thereby, the production | generation part 132 can obtain the optimized numerical value.

  The generation unit 132 assigns a numerical value to “y” and “x” in the above equation (1) to establish one equation. The generation unit 132 performs the same process for the business operator A02 and the business operator A03. By repeating such processing, the generation unit 132 determines “α” and “β”. In this process, “α” or “β” may be approximated to an optimal solution by using a least square method or the like. The variable “x” is not limited to one. For example, the generation unit 132 can approximate the score of the provider data provider 50 by variously combining the provider information on the network. For example, the generation unit 132 may use an expression such as the following expression (2).

y = α ₁ · x ₁ + α ₂ · x ₂ + α ₃ · x ₃ + ... + α _n · x _n + β (2)

As in the above equation (2), the generation unit 132 uses variables “x _{1 to} x _n (n is an arbitrary number)” to approximate the score of the provider data provider 50 that is correct data and The coefficients “α _{1 to} α _n ” may be obtained.

  Further, the generation unit 132 may change the type of information related to the provider A01 associated with each item constituting the score assigned by the provider data provider 50 based on the result of the regression analysis. Good. That is, the generation unit 132 allows the information associated with the “stability” item to more easily approximate the other types of information to the correct data than the information obtained by quantifying the “number of searches” of the operator A01. If possible, use other types of information. Thereby, the production | generation part 132 can optimize the kind of network information used as a model.

  In the above description, the generation unit 132 has described the generation of a model that outputs a score for the item “stability”. The generation unit 132 generates a model for comprehensively calculating the operator's score by performing such processing on each item of “manager ability”, “growth”, and “openness”.

  The generation unit 132 may perform the information used for generating the model by appropriately adjusting a predetermined period. For example, the number of searches and the number of PVs on websites fluctuate rapidly due to the influence of positive materials (such as when news is reported in the development of hot products) and negative materials (such as news reporting of fraudulent detection). It is possible. In this case, the generation unit 132 can suppress such influence by using information such as the number of searches over a longer period than usual. Such determination of the period may be manually determined by the administrator of the generation apparatus 100 or may be automatically determined by analyzing a word of the news article.

  In addition to the number of searches described above, the generation unit 132 can appropriately use information stored in the network information storage unit 122. For example, the generation unit 132 can use product information analysis, analysis based on the social relationship of the business operator, customer analysis, and the like. For example, the generation unit 132 associates information providing many products highly evaluated by the user with items of business continuity and growth. Specifically, the generation unit 132 adjusts the value of the variable to be digitized based on the user evaluation in the product information table 125, the number of reviews, and the magnitude of the value stored in the store ranking item. In addition, the generation unit 132 may use the number of application downloads, the number of users, the average usage time per user, the operation rate in a predetermined period, or the fluctuation rate of these values in a predetermined period.

  In addition, the generation unit 132 may generate a model using information on business operators belonging to a common industry. For example, as for information such as product reviews, it is assumed that more appropriate information can be obtained by comparing peers than by comparing products of different industries. Therefore, the generation unit 132 classifies the business operators for each industry and generates a model for each industry. In this case, the calculation unit 134, which will be described later, calculates a score using an industry model corresponding to the business target to be processed. FIG. 10 illustrates an example in which the generation unit 132 generates a model for each industry and stores the model in the model storage unit 128.

(About the reception unit 133)
The accepting unit 133 accepts a request regarding the creditworthiness of the business operator. Specifically, the accepting unit 133 according to the embodiment accepts an inquiry regarding the credit information of the operator to be used for setting the financing conditions from the financial institution server 30. In this case, the reception unit 133 may receive information on the business operator along with the offer. For example, the accepting unit 133 accepts the name of the business operator, product information provided by the business operator, the business type of the business operator, information on the manager and officers, and the like. Then, the reception unit 133 sends the received information to the calculation unit 134, which will be described later, thereby calculating the operator's score. Note that the reception unit 133 does not receive product information provided by the business operator or information on managers and officers and the financial institution server 30 does not receive such information from the financial institution server 30, or network information. When such information is not stored in the storage unit 122, information may be newly acquired from an external information processing apparatus (for example, the web server 40). Then, the reception unit 133 sends the received information to the calculation unit 134.

(About the calculation unit 134)
The calculation unit 134 calculates a credit rating for the business operator. Specifically, the calculation unit 134 according to the embodiment outputs the score of the operator that is the processing target by inputting information on the operator accepted by the accepting unit 133 into the model generated by the generating unit 132. And the calculation part 134 calculates a provider's reliability based on the output score. Note that the calculation unit 134 may use the output score itself as the creditworthiness of the operator.

  For example, when the reception unit 133 receives a request regarding the creditworthiness of the business operator A11, the calculation unit 134 acquires the network information of the business operator A11. Then, the calculation unit 134 inputs the network information of the business operator A11 into the model generated by the generation unit 132. And the calculation part 134 acquires the score of provider A11 as an output. In addition, when the model corresponding to the industry to which the business operator A11 belongs exists in the model storage unit 128, the calculation unit 134 may calculate the score by using the model corresponding to the industry preferentially. .

(About the notification unit 135)
The notification unit 135 notifies a response to the received request. Specifically, the notification unit 135 according to the embodiment notifies the credit (credit information) such as the score of the business subject to evaluation in response to the request of the financial institution server 30 received by the reception unit 133.

[3. Processing procedure)
Next, a generation process procedure performed by the generation apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 11 is a flowchart illustrating a generation processing procedure performed by the generation apparatus 100 according to the embodiment.

  As illustrated in FIG. 11, the acquisition unit 131 according to the generation device 100 acquires business data including business operator scores provided by the business data provider 50 from the business data provider 50 (step S101). ). Moreover, the acquisition part 131 acquires the network information regarding a provider via the internet (step S102).

  Then, the generation unit 132 associates the acquired information (step S103). Specifically, the generation unit 132 associates information on items constituting the company data with information related to the company on the Internet.

  And the production | generation part 132 produces | generates the model which outputs an operator's score, when the information relevant to the operator on the internet is input (step S104). The generation unit 132 stores the generated model in the model storage unit 128 and ends the generation process.

  Next, a calculation processing procedure performed by the generation apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 12 is a flowchart illustrating a calculation processing procedure performed by the generation apparatus 100 according to the embodiment.

  As illustrated in FIG. 12, the reception unit 133 according to the generation device 100 determines whether or not a request related to the credit rating has been received from, for example, the financial institution server 30 (step S201). If the request is not received, the reception unit 133 waits until it is received (step S201; No).

  On the other hand, when a request is received (step S201; Yes), the reception unit 133 acquires network information of a business operator that is a target of calculation processing (step S202). Then, the calculation unit 134 inputs the network information regarding the business acquired by the reception unit 133 into the model (Step S203).

  And the calculation part 134 outputs a provider's score by performing the calculation process by a model (step S204). The calculating unit 134 calculates the creditworthiness of the business based on the output score (step S205). And the notification part 135 notifies the calculated result to the financial institution server 30 (step S206), and complete | finishes a calculation process.

[4. (Modification)
The generation apparatus 100 according to the above-described embodiment may be implemented in various different forms other than the above-described embodiment. Therefore, in the following, another embodiment of the generation device 100 will be described.

[4-1. Use of related party information)
In the said embodiment, the production | generation apparatus 100 showed the example which produces | generates a model using the information regarding a business manager's manager, a well-known degree, a personal network, etc. of an officer. Here, the generation apparatus 100 may further acquire information from, for example, an SNS used by a business manager or an individual officer, and use the information for generating a model.

  For example, the acquisition unit 131 acquires information on purchasing behaviors of business owners and officers from the SNS. In this case, the acquisition unit 131 is that the management or the individual of the executive or the individual of the officer has purchased a relatively expensive product, etc., or has performed frequent investment activities. Get information such as being. And the production | generation part 132 uses the acquired management information of a management or an officer individual as information which digitized the network information of the said provider for the production | generation of a model. For example, the generation unit 132 determines that the management state of the business operator is better as the frequency of purchasing activities or investment behaviors of the manager or individual officer increases, and determines a higher numerical value for the information.

  In addition, the acquisition unit 131 may acquire information on an individual (an individual who is connected in the SNS) that is assumed to have a connection in an SNS that is used by a business manager or an individual officer. For example, the acquisition unit 131 may provide information such as the status of individuals who are connected to the managers and officers of the business operator, the size and management status of the company with which the individual is involved, the name and personality of the individual, and the status of the individual's previous job. To get. The generation unit 132 determines the personality of the business manager and the individual officer based on the number of persons connected to the business manager and the individual officer, and the above-described information on each person. And the production | generation part 132 digitizes network information based on the magnitude of the network power of the manager of the business operator who determined, and the officer individual. As described above, the generation apparatus 100 can increase the business operator's growth potential by determining the human network power of the business operator, and can generate a model based on the growth potential.

  Moreover, the acquisition part 131 may acquire the information regarding an operator's personnel trend among the information transmitted in SNS which the manager of a company and an individual officer use. For example, the acquisition unit 131 acquires information related to job offers and job separations at business operators. And the production | generation part 132 determines with the business continuity of a business operator being opaque when the information regarding the job offer and the job separation in the business is frequently transmitted, and reduces the numerical value of the information. On the other hand, the generation unit 132 improves the numerical value of such information as promising in the growth potential of the operator when the information on the job offer and the expansion of the operator's scale are observed in the long term.

  In addition, for the information transmitted on the Internet, such as SNS, the acquisition unit 131 registers, for example, a word that is assumed to be an evaluation index in advance and automatically collects information from the SNS. Can do. In addition, the acquisition unit 131 can acquire information that can be used as an evaluation index with high accuracy by updating a word registered in advance by machine learning.

[4-2. Profession〕
In the above-described embodiment, the generation apparatus 100 may generate a model using information related to product trends of peers. For example, the generating unit 132 determines that the scale of the industry as a whole is increasing, the needs of consumers are increasing, and the like from information related to the management status of the same company. Specifically, the generation unit 132 determines that the attention level of the entire industry has improved due to an increase in the number of searches related to peers, an increase in the number of browsing websites, and the like. In this case, the generation unit 132 sets the variables in the regression analysis, digitizes the information based on the improvement in the number of searches related to the same company, the improvement in the number of browsing of the website, and the like. A model that improves the score of a person can be generated.

[4-3. Amount of information)
In the above embodiment, the generation apparatus 100 has shown an example in which a model is generated based on various information that can be acquired on a network. Here, the generation apparatus 100 may perform processing using only information acquired from general users who use various websites that exceeds a certain threshold.

  For example, reviews regarding products on a product evaluation site, user evaluations, and the like may show a biased tendency if they are not based on a certain number or more of data. In this case, the generation apparatus 100 may generate a model that cannot accurately calculate the creditworthiness of the business operator due to the influence of data with a tendency biased toward the regression equation. For this reason, the generation apparatus 100 can use only data that has received a number of reviews or user evaluations transmitted from the user as data handled in the model generation process. Thereby, the generation device 100 can generate a model for calculating a highly reliable score.

[4-4. weight〕
The generation apparatus 100 may weight specific information for the acquired network information. For example, the generation apparatus 100 determines that a website or the like where a specialist in a specific field reviews a product has higher reliability than an evaluation site or the like that accepts posts from general users. That is, the generation device 100 weights the information related to product reviews and user evaluations on websites where specialists criticize products more than information that can be acquired from other general sites, and generates a model. Can be used. Thereby, the generation device 100 can generate a model for calculating a highly reliable score.

[4-5. correction〕
The generation apparatus 100 may generate a model that can correct the output score according to the actual economic state. For example, the generation device 100 classifies a business operator whose business condition is improved when the yen is strong, a business person who is not affected, and a business person whose business condition is deteriorated. Then, when generating the operator's score, the generation apparatus 100 inputs the trend of the value of the yen in a predetermined period to the model, and outputs the operator's score corrected by taking the trend of the circle into account. Generate the model to be executed. Such correction can be reflected in a model to be generated by, for example, acquiring business data in the long term and accumulating data related to the trend of the value of the yen.

[4-6. Information on communication network)
In the above embodiment, the generation apparatus 100 has been described in detail with respect to an example of acquiring information mainly based on user behavior among information on a communication network and related to a business operator. However, the information on the communication network acquired by the generation apparatus 100 is not limited to the above example.

  For example, the generation apparatus 100 may acquire information that does not involve the actions of general users who use the communication network as information on the communication network related to the business operator. For example, the generation apparatus 100 may acquire information related to natural phenomena such as weather information. As an example, the generation device 100 includes, among weather information and disaster information that can be acquired on the network, weather information and disaster information of a region where the business operator is located, or weather information of a region included in the name of the business operator, Obtain disaster information as information related to the business. This is because there is a possibility that the business condition of the business operator will fluctuate in the future depending on the weather situation or disaster situation of the area where the business operator is located. The generation apparatus 100 can generate a model that calculates the evaluation of the creditworthiness of the operator with higher accuracy by including elements derived from the weather information in this way.

  In addition, the generating apparatus 100 is not limited to information transmitted from the user himself / herself, but information (so-called mono-things) that a communicable device located around the user uploads the state of the device on a communication network using a sensor or the like. Information that does not involve the user, such as information on the Internet (Internet of Things), may be acquired. As an example, the generation apparatus 100 is information transmitted from a product provided by a predetermined provider among information that can be acquired on a network, and a large number of products are always operating across a wide area. Get information that This is because the penetration rate and occupancy rate of products provided by a business can be an indicator of the business stability of that business. The generation device 100 is not limited to the information transmitted from the user as described above, and includes a model for calculating the creditworthiness of the operator with higher accuracy by including elements of information transmitted from various objects. Can do. As described above, the generation apparatus 100 is not limited to information directly or indirectly related to the user, and generates a model based on various information existing on the communication network. It is possible to generate a highly versatile model applicable to the above.

[4-7. Others]
In addition, among the processes described in the above embodiment, all or part of the processes described as being automatically performed can be performed manually, or the processes described as being performed manually can be performed. All or a part can be automatically performed by a known method. In addition, the processing procedures, specific names, and information including various data and parameters shown in the document and drawings can be arbitrarily changed unless otherwise specified.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured.

  For example, the information in the storage unit 120 illustrated in FIG. 3 may be stored in an external storage server or the like without the generation apparatus 100. In this case, the generation apparatus 100 acquires various pieces of stored information by accessing the storage server.

  In addition, for example, the generation device 100 described above receives a proposal regarding the credit information of the operator or notifies the credit information of the operator, for example, a front-end server side that mainly executes an exchange with an external device, an Internet It may be distributed to the back-end server side that acquires the above information or executes generation processing or the like. In this case, for example, the front-end server includes at least a reception unit 133 and a notification unit 135. Further, the back-end server has at least a generation unit 132.

[5. Hardware configuration)
Further, the generation apparatus 100 according to the embodiment described above is realized by a computer 1000 having a configuration as shown in FIG. 13, for example. FIG. 13 is a hardware configuration diagram illustrating an example of a computer 1000 that implements the functions of the generation apparatus 100. The computer 1000 includes a CPU 1100, RAM 1200, ROM 1300, HDD 1400, communication interface (I / F) 1500, input / output interface (I / F) 1600, and media interface (I / F) 1700.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400 and controls each unit. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, a program depending on the hardware of the computer 1000, and the like.

  The HDD 1400 stores a program executed by the CPU 1100, data used by the program, and the like. The communication interface 1500 receives data from other devices via the communication network 500 (corresponding to the communication network according to the embodiment) and sends the data to the CPU 1100, and other data generated by the CPU 1100 via the communication network 500. To the device.

  The CPU 1100 controls an output device such as a display and a printer and an input device such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. Further, the CPU 1100 outputs the data generated via the input / output interface 1600 to the output device.

  The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads such a program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. Etc.

  For example, when the computer 1000 functions as the generation apparatus 100, the CPU 1100 of the computer 1000 implements the function of the control unit 130 by executing a program loaded on the RAM 1200. In addition, each data in the storage unit 120 is stored in the HDD 1400. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them, but as another example, these programs may be acquired from other devices via the communication network 500.

[6. effect〕
As described above, the generation apparatus 100 according to the embodiment includes the acquisition unit 131 that acquires network information that is information related to a provider out of information on the communication network, and the provider acquired by the acquisition unit 131. Of the information related to, related to the business (herein referred to as “first business”) to which an index value (score) indicating credit is given by the business data provider 50, which is a third party. Out of the information acquired by the acquisition unit 132 based on the correlation between the information to be acquired and the score of the first provider assigned by the provider data provider 50, the score is set by the provider data provider 50. Generating unit that generates a model for predicting a score indicating the creditworthiness of the second operator from information related to the operator that is not granted (here, “second operator”) With 32 and, the.

  As described above, the generation apparatus 100 according to the embodiment can calculate the operator's score based on the user behavior in the network, unlike the financial information of a company generally used in a financial institution or the like. Generate a model. For this reason, according to the generating apparatus 100, it is possible to calculate the reliability with high accuracy even for a small and medium-sized company such as a venture company in which financial information or the like is not sufficiently accumulated, which cannot normally be evaluated. .

  Moreover, the production | generation part 132 is based on the correlation of the score of each item which comprises the comprehensive score provided by the provider data provider 50, and the information matched with each item among the information relevant to a provider. To generate a model.

  In this way, the generation apparatus 100 generates a model for determining the creditworthiness with respect to the business operator by classifying the creditworthiness for each item. As a result, the generation device 100 generates a model that can calculate an accurate reliability without biasing to a specific element.

  Further, the generation unit 132 is information associated with each item of the score of each item constituting the total score given by the provider data provider 50 and information related to the provider, and is quantified. A model is generated by performing regression analysis on the obtained information.

  As described above, the generation apparatus 100 performs approximation of the network information and the data provided by the provider data provider 50 by performing regression analysis. Thereby, the production | generation apparatus 100 can produce | generate the model which calculates the score which has a value which the expert of corporate data like the provider data provider 50 calculates using network information.

  Moreover, the production | generation part 132 changes the kind of information relevant to the provider matched with each item which comprises the comprehensive score provided by the provider data provider 50 based on the result of regression analysis.

  In this way, the generation apparatus 100 optimizes information used for model generation by selecting network information used for model generation. Thereby, the production | generation apparatus 100 can produce | generate the model which can calculate a highly reliable reliability.

  In addition, the generation unit 132 includes, as each item constituting the total score given by the business operator data provider 50, at least one item of the business operator's stability, managerial ability, growth potential, and openness. A model is generated by associating information related to the business operator.

  As described above, the generation device 100 generates a model using network information corresponding to the evaluation items classified for the business operator. Thereby, the production | generation apparatus 100 can produce | generate the model which can calculate a highly reliable reliability.

  Moreover, the acquisition part 131 acquires at least any one of the number of searches by the search query relevant to a provider, a search ranking, and the fluctuation rate of the number of searches for every predetermined period as information based on user behavior on the Internet. Then, the generation unit 132 associates information obtained by quantifying at least one of the number of searches acquired by the acquisition unit 131, the search ranking, and the variation rate of the number of searches for each predetermined period, with the item score. To generate a model.

  As described above, the generation apparatus 100 can determine the degree of attention from the general user with respect to the evaluation target business operator by analyzing the search information. Thereby, the production | generation apparatus 100 can produce | generate the model with high precision which made the business continuity, growth potential, etc. of the business operator one of the determination factors.

  Moreover, the acquisition part 131 acquires at least any one of the browsing number in the website which a provider provides, the number of viewers, and a conversion rate as information based on the user action in the internet. And the production | generation part 132 produces | generates a model by making the score of an item respond | correspond with the information which digitized at least any one of the browsing number acquired by the acquisition part 131, the number of viewers, and a conversion rate. .

  As described above, the generation apparatus 100 can determine the interest from the general user with respect to the evaluation target business operator by analyzing the information on the website provided by the business operator. Thereby, the production | generation apparatus 100 can produce | generate the model with high precision which made the business continuity, growth potential, etc. of the business operator one of the determination factors.

  Moreover, the acquisition part 131 acquires at least any one of the evaluation value from the user in the product which a provider provides, the number of users of a product, and the number of review posts about a product as information based on user behavior on the Internet. . And the production | generation part 132 makes numerically the at least any one of the evaluation value from the user in the product which the provider provides, the number of users of a product, and the number of review contributions about a product. The model is generated by associating the obtained information with the item score.

  As described above, the generation apparatus 100 acquires information related to the evaluation of the business operator (or the product provided) from the general user by analyzing the information related to the product provided by the business operator. Thereby, the production | generation apparatus 100 can determine a business continuity, growth potential, etc. of a business operator. Moreover, since the evaluation from a general user is immediately reflected in the site etc. regarding the evaluation of a product, the production | generation apparatus 100 can acquire the reaction with respect to a provider from a general user more directly. For this reason, the production | generation apparatus 100 can produce | generate the model which can calculate the score which the user's evaluation reflected more.

  In addition, when the product provided by the provider is a program product such as an application, the acquisition unit 131 displays the number of application downloads, the number of users, the average usage time per user, or the operation rate for a predetermined period. Get at least one of them. Then, when the product provided by the operator acquired by the acquisition unit 131 is a program product such as an application, the generation unit 132 downloads the application, the number of users, and the average usage time per user. Alternatively, a model is generated by associating information obtained by quantifying at least one of the operating rates in a predetermined period with the item score.

  Thereby, the production | generation apparatus 100 can determine a business continuity, growth potential, etc. of a business operator. In other words, the generation device 100 can generate a model in which a response from a real-time user sent to an application store or the like is added to a determination element.

  In addition, the acquisition unit 131 acquires at least one of the number of business customers, the continuous usage rate of customers, and the unit price of customers as information based on user behavior on the Internet. Then, the generating unit 132 associates the information obtained by quantifying at least one of the number of customers acquired by the acquiring unit 131, the continuous usage rate of the customer, and the unit price of the customer with the score of the item, and thereby creates a model. Generate.

  In this way, the generation apparatus 100 can determine the business status of the business operator by analyzing the information related to the business customer. Thereby, since the production | generation apparatus 100 can determine an operator's bankruptcy probability, business continuity, etc., it can produce | generate the model which calculates a more highly accurate score.

  In addition, the generation unit 132 generates a model using information on business operators belonging to a common industry. Thereby, since the production | generation apparatus 100 can produce | generate the model which considered the similarity of the numbers etc. which are used in the industry, it can calculate a provider's creditworthiness more accurately.

  As described above, some of the embodiments of the present application have been described in detail with reference to the drawings. However, these are merely examples, and various modifications, including the aspects described in the disclosure section of the invention, based on the knowledge of those skilled in the art, It is possible to implement the present invention in other forms with improvements.

  Further, the generation apparatus 100 described above may be realized by a plurality of server computers, and depending on functions, an external platform or the like may be realized by calling an API (Application Programming Interface), network computing, or the like. Can be changed flexibly.

  Further, “section (module, unit)” described in the claims can be read as “means”, “circuit”, and the like. For example, the generation unit can be read as generation means or a generation circuit.

DESCRIPTION OF SYMBOLS 1 Generation system 10 User terminal 30 Financial institution server 40 Web server 50 Provider data provider 100 Generation apparatus 110 Communication unit 120 Storage unit 121 Provider data storage unit 122 Network information storage unit 123 Search information table 124 Site information table 125 Product information Table 126 Social information table 127 Customer information table 128 Model storage unit 130 Control unit 131 Acquisition unit 132 Generation unit 133 Reception unit 134 Calculation unit 135 Notification unit

Claims (13)

Of the information based on user behavior on the communication network, an acquisition unit that acquires information related to the operator,
Based on the correlation between the information related to the first operator out of the information related to the operator acquired by the acquisition unit and the index value indicating the creditworthiness of the first operator, the acquisition unit A generating unit that generates a model for predicting an index value indicating the creditworthiness of the second operator from information related to the acquired second operator;
A generating apparatus comprising: The acquisition unit
As information based on user behavior on the communication network, acquiring information based on user behavior for a web service related to a business operator,
The generating apparatus according to claim 1, wherein: The generator is
Based on the correlation between the index value of each item constituting the index value indicating the creditworthiness of the first operator and the information associated with each item among the information related to the first operator, Generating the model,
The generating apparatus according to claim 1 or 2, wherein The generator is
The index value of each item constituting the index value indicating the credit rating of the first business operator and the information associated with each item among the information related to the first business operator, which is quantified Generating the model by regression analysis of the information;
The generating apparatus according to claim 3. The generator is
As each item constituting the index value indicating the creditworthiness of the first operator, at least one item of stability, managerial ability, growth potential and openness of the first operator, the first The model is generated by associating information related to the operator of
The generating apparatus according to claim 3 or 4, wherein The acquisition unit
As information based on user behavior on the communication network, obtain at least one of the number of searches by a search query related to the provider, a search ranking, and the rate of change in the number of searches per predetermined period,
The generator is
By associating information obtained by quantifying at least one of the number of searches acquired by the acquisition unit, the search ranking, and the rate of change of the number of searches for each predetermined period, with the index value of the item, Generate a model,
The generating apparatus according to claim 5, wherein: The acquisition unit
As information based on user behavior on the communication network, obtain at least one of the number of browsing on the website provided by the operator, the number of viewers, the conversion rate,
The generator is
The model is generated by associating information obtained by quantifying at least one of the number of viewers, the number of viewers, and the conversion rate acquired by the acquisition unit with the index value of the item,
The generating apparatus according to claim 5 or 6, wherein The acquisition unit
As information based on user behavior on the communication network, obtain at least one of an evaluation value from a user in a product provided by the operator, the number of users of the product, and the number of reviews posted about the product
The generator is
Information obtained by quantifying at least one of an evaluation value from a user in a product provided by the provider, the number of users of the product, and the number of reviews posted about the product, acquired by the acquisition unit, and the item The model is generated by associating with the index value of
The generation device according to claim 5, wherein the generation device is a device. The acquisition unit
If the product provided by the operator is a program product, obtain at least one of the number of downloads of the program product, the number of users, the average usage time per user, or the operation rate for a specified period And
The generator is
Information obtained by quantifying at least one of the number of downloads of the program product acquired by the acquisition unit, the number of users, the average usage time per user, or the operation rate in a predetermined period, and the item Generating the model by associating with the index value;
The generation device according to claim 5, wherein the generation device is a device. The acquisition unit
As information based on user behavior on the communication network, obtain at least one of the number of customers of the business operator, the continuous usage rate of customers, and the unit price of customers,
The generator is
The model is obtained by associating information obtained by quantifying at least one of the number of customers of the business operator, the continuous usage rate of the customer, and the unit price of the customer acquired by the acquisition unit with the index value of the item. Generate
The generation device according to claim 5, wherein the generation device is a device. The generator is
Generating the model using information about the first operator belonging to a common industry;
The generation device according to claim 1, wherein: A generation method executed by a computer,
Of the information based on user behavior on the communication network, an acquisition step of acquiring information related to the operator,
Based on the correlation between the information related to the first operator among the information related to the operator acquired by the acquisition step and the index value indicating the creditworthiness of the first operator, the acquisition step A generation step of generating a model for predicting an index value indicating the creditworthiness of the second operator from the information related to the acquired second operator;
The generation method characterized by including. Among the information based on user behavior on the communication network, an acquisition procedure for acquiring information related to the operator,
Based on the correlation between the information related to the first operator out of the information related to the operator acquired by the acquisition procedure and the index value indicating the creditworthiness of the first operator, the acquisition procedure A generation procedure for generating a model for predicting an index value indicating the creditworthiness of the second operator from the information related to the acquired second operator;
A program for causing a computer to execute.

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