JP2009245369A - Risk-in-business-partner management device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide information useful for managing the credit administration of a company to be investigated, in consideration of the existence of an associated company and relation via a person. <P>SOLUTION: An information collecting part 20 receives the name of the company to be investigated, and collects a plurality of kinds of document data including the company name via a network. A negative word holding part 28 holds a plurality of negative words which are predetermined as phrases with the risk of deteriorating the reputation of the company by being used together with the company name in the document data. A risk degree holding part 38 holds risk degrees where the degree of deterioration in the reputation is digitized by detecting each of negative words in the document data. A document retrieving part 30 detects the negative words included in the document data. A risk summary part 44 calculates a cumulative risk by totaling the risk degrees imparted to the detected negative words by referring to the risk degree holding part. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a technique for providing information that can be used for credit management of a client company.

  In order for companies to start and continue business with other companies, it is important to manage the credit of the partner companies. This is because problems such as the inability to collect accounts receivable occur if the financial condition of a partner company deteriorates or causes a scandal and the credit of the company decreases. 2. Description of the Related Art Conventionally, it has been widely practiced to collect various data on the business performance and reputation of each business partner company and to provide credit to business partners based on such data.

However, there are a lot of survey items even if a survey is conducted for one customer. Moreover, there are many cases where there are many affiliated companies under one company, or officers of one company are concurrently serving as officers of other companies. It takes a lot of labor and labor. In order to reduce the trouble of such investigation, it is conceivable to use a computer system that provides credit information. For example, in Patent Document 1, in a company information providing system including a database storing company information, each company data in the database is linked with company data having a relationship between the supplier and the sales destination of the company. A system is disclosed. According to this, since a link information is provided to a user when a problem occurs in any company having a link relationship, it is possible to grasp a relationship between suppliers' companies.
JP 2002-74156 A

  However, in the technique of Patent Document 1, although the relationship between companies for which a link has been set in advance can be grasped, there is no substitute for obtaining information on a supplier company and performing credit based on it.

  The present invention has been made in view of such a situation, and its purpose is to provide a technology capable of providing information useful for credit management of a surveyed company in consideration of the existence of affiliated companies and relationships through persons. There is to do.

  One aspect of the present invention is a supplier risk management device. This device receives an investigation company name, collects a plurality of document data including the company name from the outside via a network, and uses the company name in the document data together with the company name. A negative word holding unit that holds a plurality of negative words set in advance as words that may reduce trust, and the degree of risk that quantifies the degree of credit deterioration due to each negative word being detected in the document data The risk level holding unit that holds the negative risk included in the document data, the document search unit that detects negative words contained in the document data, and the risk level holding unit are summed up and the total risk level assigned to the detected negative words A risk counting unit for calculating

  According to this aspect, a negative word that is considered to affect the trust of the survey target company is defined in advance, and the negative word is searched in the document data including the survey target company name collected via the network. Then, a cumulative risk representing the degree of credit deterioration is calculated based on the searched negative word. The user can use this accumulated risk for credit management. In addition, since the cumulative risk is calculated based on document data collected from outside via the network, information updated daily can be reflected in the cumulative risk. Further, since the cumulative risk is calculated based on the presence or absence of negative words in the document data, the user does not need to confirm the contents of the document data.

  You may further provide the outline | summary information acquisition part which acquires the outline | summary information of a survey object company from the database in which the outline | summary information which makes the item the company name or individual name deeply related about a some company or an individual is recorded. In this case, the information collection unit collects document data including each item of the summary information, and the document search unit detects a negative word included in the document data collected for each item of the summary information, and the risk aggregation unit The total risk may be calculated by adding the risk degrees given to the negative words detected for each item of the summary information and adding the total risk to the cumulative risk of the survey target company.

According to this, negative words were also detected from document data including company names or individual names related to the survey target company, and the degree of risk assigned to the detected negative words was calculated for the original survey target company. Added to risk. Accordingly, the cumulative risk is calculated by referring not only to the superficial information of the survey target company but also to information on affiliated companies having a capital relationship or personal relationship. Therefore, the reliability of the cumulative risk in credit management is improved.
Companies that are closely related to the surveyed company include companies that have a capital relationship, such as subsidiaries and affiliated companies, and companies that have a portion of directors and shareholders. Individuals closely related to the surveyed company include company officers and shareholders.

  When the cumulative risk is calculated for a plurality of survey target companies, the document search unit may detect a negative word for each item of the summary information in descending order of the cumulative risk. According to this, it is possible to obtain the cumulative risk preferentially for companies with high credit risk.

  You may further provide the investigation range determination part which determines whether the investigation range should be expanded by comparing the total accumulated risk and a predetermined threshold value. When the survey scope decision section determines that the survey scope should be expanded, the summary information acquisition section is a sub-summary that is summary information for each company name or individual name of the survey company's summary information from the database. The information may be acquired, and the document search unit may continue detecting negative words for each item of the sub-summary information. According to this, after searching for negative words for each item in the summary information, the surveyed companies whose cumulative risk is greater than the predetermined value are also negative for affiliated companies and companies and individuals who are closely related to individual names. The presence or absence of a ward is examined and the result is reflected in the cumulative risk. Therefore, it is possible to provide information useful for credit based on a wider range of information about a survey target company having a certain level of risk.

  The negative word holding unit may hold a different set of negative words for each survey target company. According to this, it is possible to prepare an optimal negative word according to the type of business, business content, scale, etc. of the target company.

  The information collecting unit may collect document data from a site on a network designated in advance. According to this, for example, by selecting a site with a certain level of trust in advance, it is possible to avoid calculating the cumulative risk based on document data with low reliability.

  It should be noted that any combination of the above-described components and a representation of the present invention by a method, apparatus, system, recording medium, and computer program are also effective as an aspect of the present invention.

  According to the present invention, it is possible to provide information useful for credit management of a company to be investigated in consideration of the existence of an affiliated company and the relationship through a person.

  One embodiment of the present invention obtains document data including information on a business partner company from a network, and calculates a cumulative risk obtained by quantifying the degree of credit deterioration of the business partner company based on the document data. It is a supplier risk management device that can be used for

  FIG. 1 is an overall schematic diagram of a supplier risk management system 100 according to an embodiment of the present invention. The supplier risk management apparatus 10 is connected to a network 12 such as the Internet or a LAN (Local Area Network), and can access various websites. An operating terminal 18 that is typically a personal computer is connected to the supplier risk management apparatus 10. Using the operation terminal 18, the user can give the supplier risk management device 10 the name of the survey target company and the necessary conditions described later.

  The supplier risk management device 10 accesses the servers 16 of a plurality of websites designated in advance via the network 12. Then, reference is made to news data about the survey target company, document data existing on a website that provides investment information, credit information, etc., and the credit risk of the survey target company is calculated based on them. The calculated risk can be used as data such as credit. The supplier risk management apparatus 10 according to the present embodiment is characterized in that the risk of the survey target company is calculated based on the constantly updated document data provided on the network.

  The supplier risk management apparatus 10 also provides company information that provides, as data, company names or individual names (hereinafter referred to as “summary information”) that are closely related to a plurality of companies or individuals via the network 12. The database 14 is also accessed. Information on affiliated companies, officers, shareholders, etc. of the survey target company is acquired and used for the above-described risk calculation. The company information database 14 may be locally connected to the supplier risk management apparatus 10, but is preferably operated by another entity.

  FIG. 2 is a functional block diagram showing a detailed configuration of the supplier risk management device 10 of FIG. These configurations can be realized in hardware by elements such as a CPU and a memory of a computer, and in terms of software, it is realized by a computer program or the like that has the functions described below and is loaded into a memory. Here, it is depicted as a functional block realized by such cooperation. Therefore, those skilled in the art will understand that these functional blocks can be realized in various forms by hardware only, software only, or a combination thereof.

  The target company setting unit 26 holds the name of the target company of the risk survey input from the user by the operation terminal 18. The negative word holding unit 28 holds a plurality of negative words input from the user by the operation terminal 18. Here, the “negative word” refers to a phrase that may be used together with a company name in document data to reduce the trust of the company. A specific example of the negative word will be described with reference to FIG.

  The risk level holding unit 38 holds the risk level input from the user by the operation terminal 18. Here, the “risk level” is a numerical value of the degree of credit deterioration due to detection of each negative word in the document data. The degree of risk is preferably set so that the negative word, which is considered to have greater credit concerns for the surveyed company, has a higher value. A specific example of the risk level will also be described with reference to FIG.

The information collecting unit 20 collects a large number of document data including a description about the survey target company from the network. The information collection unit 20 includes a crawling unit 22 and a summary information acquisition unit 24.
The crawling unit 22 receives the survey target company name from the target company setting unit 26 and collects a large number of document data including the company name. “Document data” in this specification refers to data including text information such as text data, HTML, XML, and XHTML files, but the data format is not limited.

The crawling unit 22 may collect document data from an arbitrary website on the network, but can also collect document data only from a site designated in advance by the user. In this case, shorten the time required for information collection and subsequent risk aggregation, and avoid the possibility that the risk level will become inaccurate due to the collection of document data created by individuals and socially unreliable organizations. It is possible to perform a search focusing on the above. The designated site includes a newspaper company site, a news providing site, a site providing credit information, and the like. In addition, since the risk of a supplier changes every day, it is preferable to select a site where data is updated more than a certain frequency. The collected document data is temporarily stored in a memory (not shown).
The summary information acquisition unit 24 acquires summary information of the survey target company from the company information database 14.

  The document search unit 30 detects a negative word included in the document data including the survey target company name accumulated in the information collection unit 20. The document search unit 30 includes a sentence decomposition unit 32 and a matching unit 34.

  The sentence decomposition unit 32 analyzes the document data stored in the information collection unit 20 according to a predetermined rule, and extracts the constituent elements of the sentence. The sentence decomposition unit 32 can use any of the known sentence decomposition algorithms. As an example, the target sentence may be decomposed into morphemes, a morphological analysis for determining a utilization form, original form, and part of speech for each element may be performed, and an element determined as a noun may be compared with a negative word. Since morphological analysis itself is a well-known technique, further explanation is omitted.

The matching unit 34 performs matching between the element that is a noun by the sentence decomposing unit 32 and the negative word in the negative word holding unit 28, and outputs the matched negative word as a search result. The detected negative word is output to the word storage unit 40.
Note that the matching unit 34 may detect a negative word by simply performing a full-text search on document data without using the result of morphological analysis.

  The word accumulation unit 40 accumulates negative words detected from the document data for each survey target company. As will be described later, the word accumulation unit 40 accumulates negative words detected for each item when document data is collected for affiliated companies, officers, shareholders, etc. of the survey target company.

The risk totaling unit 44 refers to the risk degree holding unit 38 and calculates a cumulative risk obtained by adding the risks assigned to the negative words stored in the word storage unit 40. This cumulative risk is calculated for each surveyed company.
In addition, as described later, when a negative word search is conducted for affiliated companies, officers, shareholders, etc. of the surveyed company, the risks assigned to each negative word are aggregated and the total number of surveyed companies is accumulated. Calculate the total cumulative risk added to the risk. Therefore, if the search range for negative words is expanded, such as the affiliated company of the surveyed company and the affiliated company, the cumulative risk increases each time.

  The survey range determination unit 48 compares the total cumulative risk with a predetermined threshold value and determines the survey range of the negative word. Specifically, this will be described with reference to FIG.

  The credit judgment unit 46 receives the total accumulated risk of the survey target company, calculates a credit score reflecting the score for each company and the score of the transaction history with the company, and outputs the credit score to the operation terminal 18. . The user can use this credit score for credit work.

  In the above description, it has been described that the crawling unit 22 first collects document data including the name of the survey target company and searches for negative words from the collected document data. Document data including both the name and any negative word may be collected from the network.

FIG. 3 shows a company-specific data structure 50 provided by the company information database 14. The data structure 50 includes a company name, a parent company name, an affiliated company name, an officer name, a major shareholder name, and a major business partner name. Data other than these, for example, a company address or a representative name may be included in the data. Data is prepared for each company, and necessary information can be obtained using the company name as a key. Further, by executing a search using the officer name as a key, the name of the company in which the person serves as the officer can be searched. Similarly, by executing a search using a shareholder name as a key, it is possible to search for the name of a company in which an individual who is a shareholder serves as an officer, or the name of a company having a company that is a shareholder as an affiliated company.
Needless to say, the company information database 14 may be configured differently from the above. For example, even if company information data, officer data, shareholder data, and business partner data are individually created in a database, the databases are provided in the form of a relational database that is related to each other by company code or person code. Good.

  FIG. 4 is a table 60 showing an example of negative words and risk levels. The negative word 62 and the risk degree 64 are set by the user inputting words and numerical values in a dedicated format displayed on the operation terminal 18. The negative word 62 includes, for example, words and phrases related to financial risks such as bad loans, words and phrases related to crimes such as financial settlement, bribery and arrest, and words related to reputations such as allegations and impersonation. There is no limit to the number of negative words that can be set.

  The risk degree 64 is determined by the user in the range of 1 to 5 as an example. In the example of FIG. 4, a negative word related to crimes such as powder settlement, bribery, and arrest is assigned a risk level of “5”, and other negative words are assigned a risk level of 1 or 2. .

  The negative word and risk level may be the same for all surveyed companies, different for each industry type, or different for each company. Good.

FIG. 5 is a diagram for explaining the relationship between a certain survey target company and summary information about the company, developed according to the database of FIG.
In FIG. 5, it is assumed that the survey target company is company A. With reference to the above-described company information database, information on the affiliated company 74, the officers 76, and the shareholders 78 can be acquired for A company. Therefore, it can be said that each data of the affiliated company 74, the officer 76, and the shareholder 78 is data located in a lower hierarchy than the company A. In the present embodiment, these data directly derived from the survey target company are referred to as first layer data.

Focusing on the “a1” company among the affiliated companies 74, information on the affiliated company 80, the officers 82, and the shareholders 84 can be acquired by referring to the company information database for the a1 company. In this way, data derived from the data of the first hierarchy is called data of the second hierarchy. For the other affiliated companies a2 and a3, the affiliated company, officers and shareholders can be similarly expanded as the second hierarchy data. As for the first-level officers 76, another company name in which the person serves as an officer and the company name in which the person is a shareholder can be expanded as second-tier data. Further, for the first-tier shareholders 78, if the shareholders are individuals, the second-tier data includes the name of another company in which the person serves as an officer and the name of the company in which the person is a shareholder. Can be deployed as When the shareholder is a corporation, the affiliated company, officer, and shareholder of the company can be developed as data in the second hierarchy.
For affiliated companies, officers, and shareholders in the second hierarchy, data can be expanded to a lower hierarchy.
Thus, it can be seen that the company name, officer name, and shareholder name related to the survey target company increase rapidly each time the hierarchy is traced down.

  FIG. 6 is a diagram illustrating an example of negative word search and risk tabulation results for a survey target company. The column 92 represents the name of the survey target company. A column 94 represents negative words retrieved from document data including the company name in the column 92. Column 96 represents the level assigned to the negative word in column 94. A column 98 represents the cumulative risk obtained by adding the risk level of the negative word searched for each survey target company. In FIG. 6, it can be seen that the cumulative risk for company A is “15” and the cumulative risk calculated for company B is “35”.

  In the example of FIG. 6, no matter how many times the same negative word is detected in the document data, the degree of risk assigned to that word is one negative word. As another embodiment, an appearance frequency column may be provided in the table 90, the number of each negative word appearing in the document data may be counted, and the risk degree may be multiplied by the count number.

  FIG. 7 is a diagram illustrating a reference example 100 for the investigation range determination unit 48 to determine whether or not to further expand the risk investigation range to a lower hierarchy. A column 102 represents a range in which a negative word search should be executed by the information collection unit 20 and the document search unit 30. Column 104 represents threshold values for the cumulative risk up to the first hierarchy. A column 106 represents a threshold value for the cumulative risk calculated at the end of the investigation up to the second hierarchy. Note that the threshold value is also determined for each layer 3 and below.

  For example, if the cumulative risk up to the first hierarchy is 50 or more, the document search unit 30 performs a survey on the lower hierarchy. That is, a negative word search is performed on document data including companies, officers, and shareholders related to each affiliated company, officer, and shareholder in the first hierarchy in FIG. When the cumulative risk up to the first hierarchy is 21 or more and 49 or less, the document search unit 30 performs a survey on a part of the lower hierarchy. That is, a negative word search is performed on document data including companies, officers, and shareholders related to the related companies in the first hierarchy in FIG. Searches are not performed for subordinates of officers and shareholders. If the cumulative risk up to the first level is 20 or less, the survey is ended up to the first level.

For the cumulative risk up to the second level, the survey range is determined based on the table 100 in the same manner as described above. In addition, it is preferable that the threshold value for determining the investigation range is a different value for each hierarchy. The reason for this is as follows. In other words, if negative word surveys are executed up to the lower hierarchy, a large number of negative words are inevitably detected, and the cumulative risk is higher than that of the upper hierarchy. Therefore, if the same value is used without changing the threshold value of the lower hierarchy in accordance with it, the threshold value is always exceeded after a certain hierarchy, and the investigation is not completed.
Note that the threshold value as described above may not be set, and it may be set so that the investigation is always executed up to a predetermined hierarchy.

FIG. 8 and FIG. 9 are flowcharts for explaining the flow of executing risk judgment of a business partner company according to the present embodiment.
First, the information collecting unit 20 receives a survey target company name from the operation terminal 18, and the document search unit 30 receives a negative word from the negative word holding unit 28 (S10). The crawling unit 22 collects document data including the survey target company name from the server 16 of the designated site via the network (S12). The document search unit 30 searches the collected document data for negative words (S14), and the word storage unit 40 stores the searched negative words for each survey target company (S16). The risk totaling unit 44 acquires the risk level assigned to the accumulated negative word from the risk level holding unit 38, and calculates a cumulative risk obtained by adding them (S18).

Moving to FIG. 9, the information collection unit 20 and the document search unit 30 proceed to the investigation on the first hierarchy. First, the summary information acquisition unit 24 in the information collection unit 20 accesses the company information database 14 and acquires summary information regarding the survey target company, specifically, the affiliated company name, officer name, and shareholder name (S20). . The crawling unit 22 collects document data including the acquired affiliate name, officer name, or shareholder name, and temporarily stores them (S22).
The document search unit 30 performs a negative word search for the document data for each item of the affiliated company name, officer name, and shareholder name accumulated in the information collecting unit 20 (S24), and the detected negative word Are stored in the word storage unit 40 by item (S26). The risk totaling unit 44 acquires the risk level assigned to the negative word from the risk level holding unit 38, and totals the negative word risk accumulated for each item (S28). Furthermore, the risk totaling unit 44 adds the total risk for each item of the first layer summary information to the previously calculated cumulative risk of the survey target company (S30). The total accumulated risk thus obtained is passed to the survey range determination unit 48.

  The survey range determination unit 48 compares the total accumulated risk with the threshold value in the column 104 shown in FIG. 7 (S32). If the total cumulative risk is equal to or greater than a predetermined threshold, for example, “21” (Y in S32), the information collection unit 20 and the document search unit 30 will further expand the investigation range to a lower hierarchy. (S34), S20 to S30 are repeated. If the total cumulative risk is less than the predetermined threshold (N in S32), the negative word survey is completed up to the current level, and the total cumulative risk is output to the credit determination unit 46 (S36).

FIG. 10 is a conceptual diagram for explaining the situation when risk aggregation is performed by the supplier risk management device 10 for the three companies of Company A, Company B, and Company C.
Assume that the cumulative risks of Company A, Company B, and Company C are “15”, “35”, and “17”, respectively, based on negative word search results for document data collected for Company A, Company B, and Company C. Regardless of the total risk level, the first word summary information directly derived from the database of the target company is searched for negative words by the information collection unit 20 and the document search unit 30.

  The box 118 in the figure indicates that, for the company A, the summary information acquisition unit 24 has acquired two affiliated companies, two officers, and one shareholder. A box 120 indicates that Company B has been acquired by the summary information acquisition unit 24 as two affiliated companies, two officers, two shareholders, and a negative word survey target. A box 122 represents that Company C has been acquired by the summary information acquisition unit 24 as one of the affiliated companies, one officer, two shareholders, and the negative word survey target.

  Each box 118-122 also shows the risk level of the negative word detected for each of the summary information. For example, the box 120 indicates that the risk level of the negative word detected for the affiliated company b1 is 2, and the risk level of the negative word detected for the affiliated company b2 is 5. The risk totaling unit 44 adds the calculated risk degree to the cumulative risk obtained for the upper hierarchy, that is, the original survey target company. As a result, the total accumulated risks of Company A, Company B, and Company C are “17”, “52”, and “20”, respectively.

  The survey range determination unit 48 compares the total cumulative risk with a predetermined threshold value and determines whether or not to further expand the negative word survey range. Here, it is compared with the threshold value defined in the column 104 of FIG. As a result, for Company A and Company C, the total cumulative risk is 20 or less, so the negative word survey is stopped here. For Company B, the total cumulative risk is 50 or more, so the negative word survey is expanded further down the hierarchy.

  The summary information acquisition unit 24 acquires summary information about affiliated companies, officers, and shareholders in the box 120. The result is shown in box 124 in the figure. That is, for the affiliated company b1, one affiliated company, one officer, and one shareholder are acquired. For the shareholder b5, one affiliated company and one officer have been acquired. The box 124 also indicates the risk level of the negative word detected for each of the summary information.

The risk tabulation unit 44 adds the degree of risk given to each summary information of the second hierarchy, and further adds to the total accumulated risk of the original survey target company. As a result, it is assumed that the total cumulative risk up to the second hierarchy is 70. The survey range determination unit 48 compares the threshold value set in the column 106 in FIG. 7 with the total accumulated risk. Since the total accumulated risk is 70 or more, the negative word survey is expanded to the third level below for Company B.
In this way, the negative word search is executed until the total cumulative risk obtained in the survey up to each level falls below a predetermined threshold value or until the predetermined upper limit number of surveys is completed. .

  In a survey target company (for example, Company B in FIG. 10) whose survey scope has been expanded to a lower hierarchy, the total cumulative risk increases as the survey progresses. As described above, in this embodiment, the risk investigation target can be automatically expanded to a wider range for a company with a high risk. It should be noted that when the total cumulative risk of the survey target company reaches a predetermined upper limit value, it is possible to set a mechanism such as that the subsequent search is terminated because the company is not appropriate as a target for transactions.

  As described above, according to the present embodiment, a negative word that is considered to affect the trust of the survey target company is defined in advance, and the document data including the survey target company name collected via the network is included in the document data. To search for negative words. Then, a cumulative risk representing the degree of credit deterioration is calculated based on the searched negative word. The user can use this accumulated risk for credit management.

  In addition, since the cumulative risk is calculated based on document data collected from outside via the network, information updated daily can be reflected in the cumulative risk. Further, since the cumulative risk is calculated based on the presence or absence of negative words in the document data, the user does not need to confirm the contents of the document data.

  In addition, negative words are detected from document data including affiliated companies, officer names, shareholder names, etc. related to the surveyed company, and the degree of risk assigned to the detected negative word is calculated for the original surveyed company. Added to the cumulative risk. Accordingly, the cumulative risk is calculated by referring not only to the superficial information of the survey target company but also to information on affiliated companies having a capital relationship or personal relationship. Therefore, the reliability of the cumulative risk in credit management is improved.

  The present invention has been described based on some embodiments. It is understood by those skilled in the art that these embodiments are exemplifications, and that various modifications can be made to combinations of the respective constituent elements and processing processes, and such modifications are also within the scope of the present invention. By the way.

  It should also be understood by those skilled in the art that the functions to be fulfilled by the constituent elements described in the claims are realized by the individual functional blocks shown in the present embodiment or their linkage.

  The crawling unit 22 may refer to the newness of the document data when collecting the document data from the network. For example, the crawling unit 22 may collect only document data created within a predetermined period (such as one week).

1 is an overall schematic diagram of a supplier risk management system according to an embodiment of the present invention. It is a functional block diagram which shows the detailed structure of the supplier risk management apparatus of FIG. It is a figure which shows the data structure according to company provided with a company information database. It is a figure which shows an example of a negative word and a risk degree. It is a figure explaining the relationship between a certain investigation object company and each item of the outline information of the company developed according to the database of FIG. It is a figure which shows an example of the result of having searched the negative word about the investigation object company, and totaling risk. It is a figure which shows the example of a reference | standard for an investigation range determination part determining whether the range of a risk investigation is extended further to a lower hierarchy. It is a flowchart explaining the flow which implements risk determination of a supplier company according to one Embodiment. It is a flowchart explaining the flow which implements risk determination of a supplier company according to one Embodiment. It is a conceptual diagram explaining a mode when risk totalization is implemented with a supplier risk management apparatus about three companies, A company, B company, and C company.

Explanation of symbols

  10 supplier risk management device, 12 network, 14 company information database, 16 designated site server, 18 operation terminal, 20 information collection unit, 22 crawling unit, 24 summary information acquisition unit, 26 target company setting unit, 28 negative word holding unit , 30 document search unit, 32 sentence decomposition unit, 34 matching unit, 38 risk degree holding unit, 40 word storage unit, 44 risk aggregation unit, 46 credit judgment unit, 48 survey range determination unit.

Claims (8)

An information collection unit that receives the name of the company to be investigated and collects a plurality of document data including the company name from the outside via a network;
A negative word holding unit that holds a plurality of negative words set in advance as words that may decrease the trust of the company by being used together with the company name in the document data;
A risk degree holding unit for holding a risk degree obtained by quantifying the degree of credit deterioration due to detection of each of the negative words in the document data;
A document search unit for detecting the negative word included in the document data;
Referring to the risk level holding unit, a risk totaling unit for calculating the cumulative risk by summing the risk levels given to the detected negative words;
A supplier risk management device characterized by comprising: It further comprises a summary information acquisition unit that acquires summary information of the surveyed company from a database in which summary information having a company name or personal name that is closely related to a plurality of companies or individuals is recorded,
The information collection unit collects document data including each item of the summary information;
The document search unit detects a negative word included in document data collected for each item of the summary information,
The risk totaling unit calculates the total cumulative risk obtained by adding the risk degrees given to the negative words detected for each item of the summary information and adding to the cumulative risk of the survey target company. Item 1. The supplier risk management device according to Item 1.   The said document search part performs the detection of a negative word about each item of the said summary information in the order with the said largest risk, when the said cumulative risk is calculated about several investigation object company, The said search part is characterized by the above-mentioned. The supplier risk management device described. A survey range determination unit that determines whether or not the survey range should be expanded by comparing the total cumulative risk with a predetermined threshold;
When it is determined by the survey range determination unit that the survey range should be expanded, the summary information acquisition unit obtains summary information for the company name or individual name that is each item of the survey target company's summary information from the database 4. The supplier risk management device according to claim 2, wherein the sub-summary information is acquired, and the document search unit continues to detect a negative word for each item of the sub-summary information.   The supplier risk management device according to claim 1, wherein the negative word holding unit holds a set of different negative words for each survey target company.   6. The supplier risk management apparatus according to claim 1, wherein the information collection unit collects the document data from a site on a network designated in advance. A program for managing the risks associated with transactions with a company,
A function for receiving a surveyed company name and collecting a plurality of document data including the company name from outside via a network;
A function of storing in memory a plurality of negative words set in advance as words that may decrease the trust of the company by being used together with the company name in the document data;
A function for storing in a memory a risk degree obtained by quantifying a degree of deterioration of trust due to detection of each of the negative words in document data;
A function of detecting the negative word included in the document data;
A function of calculating a cumulative risk by summing up the degree of risk given to the detected negative word with reference to the memory;
Risk management program characterized by causing a computer to execute. A function of acquiring summary information of the surveyed company from a database in which summary information having items of company names or personal names closely related to a plurality of companies or individuals is recorded;
A function of collecting document data including each item of the summary information;
A function of detecting a negative word included in document data collected for each item of the summary information;
A function of calculating the total cumulative risk by adding the risk degrees given to the negative words detected for each item of the summary information and adding the cumulative risk of the survey target company;
The supplier risk management program according to claim 7, further comprising:

Images