Title of Invention: Credit Analysis Support Method, Credit Analysis Support System, and Node
Technical field
[0001]
　The present invention relates to a credit analysis support method, a credit analysis support system, and a node.
Background technology
[0002]
　The so-called keiretsu-type supply chain, which forms a commercial flow associated with purchasing within a corporate group, is declining along with the globalization of the economy. On the other hand, at manufacturing and service sites, there are frequent incidents such as data falsification that undermine the trust of stakeholders in commercial distribution and general consumers.
[0003]
　Therefore, there is a move to determine the trustworthiness of the participants in the supply chain, which may include participants with various backgrounds, and to ensure the reliability of transactions and products in the supply chain.
[0004]
　As a conventional technology related to such credit judgment, for example, in a commercial transaction credit system that enables commercial transactions based on real-time credit judgment between companies on a site built on a computer network, the site is managed. When the computer determines the conditions of participation based on the data of the conditions of participation entered by a person who intends to be newly involved in commercial transactions on the said site as a seller or a buyer, and the said computer satisfies the conditions of participation. a transactor authentication means for causing said computer to authenticate as a transactor to said transactor; is set by the computer, wherein the corporate data is aggregated by a predetermined scoring system from the data collected by the website crawler, and a discriminating means (3960) discriminates whether or not it is corporate information. extracts company information, outputs the result to a relay file (3970), and is based on company information extracted from the data stored in the company information database (30) via the relay file (3970). a rank setting means, a transaction limit setting means for causing the computer to set a transaction limit for the commercial transaction of the trader based on the rank set by the rank setting means, and the set rank bankruptcy probability evaluation means for calculating the bankruptcy probability of the credit recipient company based on the above; and a commercial transaction restriction means characterized by issuing a warning to the seller or the buyer or suspending commercial transactions when the transaction limit is exceeded; wherein the transaction data managed by the transaction data management means is referred to when determining the rank, and the rank is determined according to a predetermined condition.and the sum of the calculated bankruptcy probability, the capital cost rate of the creditee company, and the expense ratio of the creditee company is set as the minimum required commission rate. and a commission rate evaluation means, wherein the transaction data includes complaint information about the transactor regarding whether there is any complaint in the commercial transaction, bankruptcy information of the transactor, and information on dishonored bills involving the transactor. , a minimum required commission rate, information on foreclosures involving the trader, and anxiety information about the trader about the presence or absence of any signs of deterioration in business, wherein the scoring system is specified If there is an item indicator of , it is characterized by determining the specific for each item and adding it, and if it exceeds a predetermined threshold, it is determined that it is a page of corporate information, and using the relay file (3970) A commercial transaction credit system (see Patent Document 1) has been proposed, which is characterized by being able to perform credit management based on always fresh corporate information.
[0005]
　Further, a credit method in a credit system connected to a user terminal via a network includes: a request information receiving step of receiving request information including conditions for credit judgment from the user terminal; a credit information extraction step of extracting credit information from a credit information database storing A credit information transmission step of transmitting credit information to a user terminal has also been proposed (see Patent Document 2).
[0006]
　Further, a storage unit for storing EDI data of each electronic commerce transaction in the EDI system, reading EDI data regarding each company from the storage unit, and applying the information of the read EDI data regarding a predetermined company to an evaluation function held in advance. Then, an information processing device (see Patent Document 3) is also proposed, which includes a calculation unit that specifies the evaluation index of the predetermined company as a trading partner and outputs information on the evaluation index to a predetermined device. It is
prior art documents
patent literature
[0007]
Patent Document 1: JP-A-2012-168984
Patent Document 2: JP-A-2002-157422
Patent Document 3: JP-A-2014-115721
SUMMARY OF THE INVENTION
Problems to be Solved by the Invention
[0008]
　However, due to differences in the positions of participants in the supply chain, the scope of use of such credit-related information (hereafter referred to as credit information) differs. For example, the buyer may have access to the supplier's credit information, but the supplier may not have access to the buyer's credit information.
[0009]
　In this case, the use of credit information is one-sided and cross-referencing is not possible, and the credit analysis method tends to be biased toward the buyer's position, so there is a risk that the credit information itself will not be sufficiently verified. In other words, the reliability of the credit information is easily shaken. These problems are exacerbated if credit analysis does not take into account the hierarchical structure of participants in the supply chain.
[0010]
　SUMMARY OF THE INVENTION Accordingly, it is an object of the present invention to provide a technology that makes it possible to efficiently and widely use highly accurate credit information of supply chain participants.
Means to solve problems
[0011]
　According to the credit analysis support method of the present invention for solving the above problems, in a system composed of a plurality of nodes each holding predetermined transaction data associated with transactions in a supply chain in a distributed ledger, the nodes of the parties to the predetermined transaction are: A process of extracting evaluation information of a trading partner in the transaction from predetermined transaction data in a distributed ledger, a process of extracting an evaluation result determined by a predetermined external organization regarding the trading partner from predetermined transaction data in the distributed ledger, and the evaluation and applying the information and the evaluation result to a predetermined rule to generate the credit information of the trading partner.
[0012]
　Further, the credit analysis support system of the present invention is a system composed of a plurality of nodes each holding predetermined transaction data associated with transactions in a supply chain in a distributed ledger, wherein the nodes of the parties to the predetermined transaction are the A process of extracting evaluation information of a trading partner in a transaction from predetermined transaction data in a distributed ledger, a process of extracting an evaluation result determined by a predetermined external organization with respect to the trading partner from predetermined transaction data in the distributed ledger, and the evaluation information and applying the evaluation result to a predetermined rule to execute a process of generating the credit information of the trading partner.
[0013]
　Further, the node of the present invention is a node that holds predetermined transaction data associated with transactions in the supply chain in a distributed ledger, constitutes a distributed ledger system, and stores evaluation information of a trading partner in a predetermined transaction in a predetermined distributed ledger. A process of extracting from transaction data, a process of extracting an evaluation result determined by a predetermined external organization regarding the trading partner from predetermined transaction data in the distributed ledger, and applying the evaluation information and the evaluation result to a predetermined rule. and a processing of generating the credit information of the trading partner.
Effect of the invention
[0014]
　According to the present invention, highly accurate credit information of supply chain participants can be used efficiently and widely.
Brief description of the drawing
[0015]
1 is a network configuration diagram including a credit analysis support system of the present embodiment; FIG.
2 is a diagram showing a hardware configuration example of a node in this embodiment; FIG.
3 is a diagram showing a configuration example of a block chain in this embodiment; FIG.
4 is a diagram showing a data configuration example of order placing/receiving data (transaction) of the embodiment; FIG.
5 is a diagram showing a data configuration example of payment data (transaction) according to the embodiment; FIG.
6 is a diagram showing a data configuration example of supplier evaluation data (transaction) of the present embodiment; FIG.
7 is a diagram showing a data configuration example of credit calculation result data (transaction) of the present embodiment; FIG.
8 is a diagram showing a data configuration example of distribution data (transaction) of the embodiment; FIG.
9 is a diagram showing flow example 1 of a credit analysis support method according to the present embodiment; FIG.
10 is a diagram showing flow example 2 of a credit analysis support method in this embodiment. FIG.
11 is a diagram showing flow example 3 of a credit analysis support method in this embodiment. FIG.
12 is a diagram showing a flow example 4 of a credit analysis support method according to the present embodiment; FIG.
13 is a diagram showing a flow example 5 of a credit analysis support method in this embodiment; FIG.
14 is a diagram showing flow example 6 of a credit analysis support method in the present embodiment; FIG.
15 is a diagram showing screen example 1 in the present embodiment. FIG.
16 is a diagram showing screen example 2 in the present embodiment. FIG.
MODE FOR CARRYING OUT THE INVENTION
[0016]
---Network Configuration---
　Embodiments of the present invention will be described in detail below with reference to the drawings. FIG. 1 is a network configuration diagram including a credit analysis support system 10 of this embodiment. The credit analysis support system 10 shown in FIG. 1 is a computer system that makes it possible to efficiently and widely use highly accurate credit information of supply chain participants.
[0017]
　The credit analysis support system 10 in this embodiment is composed of nodes 100 that are communicably connected to each other via the P2P network 1, for example. This node constitutes a distributed ledger system. In other words, the credit analysis support system 10 of this embodiment constitutes a distributed ledger system.
[0018]
　In the distributed ledger system, on the P2P network 1, a node called a miner judges the validity of the transaction data, that is, the transaction data, and performs confirmation processing by calculating a specific hash value called proof of work. ing. Transaction data that has been finalized and agreed upon in this way is grouped into a single block and recorded on a distributed ledger called a blockchain. This distributed ledger is equally provided in each node, and the distributed ledger is kept synchronized between nodes.
[0019]
　In the above description, confirmation processing by proof of work was given as an example of the validity determination of transaction data, but the method employed in the credit analysis support system 10 of the present embodiment is not limited to this.
[0020]
　The P2P network 1 described above is connected to an appropriate network 5 such as the Internet or a LAN. Each node 100 can communicate data with a client terminal 150 through these networks 1 and 5.
[0021]
　The client terminal 150 accesses the predetermined API 111 in the node 100 via the P2P network 1 and the network 5, obtains, for example, credit information (generated regarding buyers and suppliers) from the node 100, and displays it. etc., and inputs from buyers and suppliers using the client terminal 150, financial institutions, and personnel in charge of external institutions (e.g., designation of buyers and suppliers whose credit information is to be confirmed, evaluation information, input of the evaluation result) and executes distribution processing to the node 100 corresponding to the host device.
[0022]
　In the network configuration of this embodiment, as shown in FIG. 1, four types of nodes are illustrated as nodes 100: a buyer node 200, a supplier node 300, a certification authority node 400, and a financial institution node 500. . It should be noted that when these individual nodes 200 to 500 are collectively referred to, they will be referred to as node 100 (the same applies hereinafter).
[0023]
　Of course, there is no limitation as to the type and number of such nodes. Any node of a stakeholder in a supply chain or a node of various persons receiving information from such a stakeholder can be envisioned.
[0024]
　Among the nodes 100 described above, the buyer node 200 is a node operated by a buyer in the supply chain and is a distributed ledger node. However, this buyer can of course also be a supplier. This point also applies to the supplier who operates the supplier node 300 .
[0025]
　The buyer node 200 issues transaction data related to an order transaction by the buyer with the supplier in the supply chain, and stores the transaction data in its own distributed ledger 110 after consensus building in the distributed ledger system.
[0026]
　The supplier node 300 is a node operated by a supplier in the supply chain and is a distributed ledger node.
[0027]
　The supplier node 300 issues transaction data related to an order transaction between the supplier and the buyer in the supply chain, and stores it in its own distributed ledger 110 after consensus building in the distributed ledger system.
[0028]
　Also, the certification authority node 400 is a node operated by a certification authority (external organization) such as ISO, and is a distributed ledger node. A certification body is a management organization for standards that determine the quality and level of products and services distributed in supply chain transactions.
[0029]
　The certification bodies mentioned above conduct conformity assessments to the standards mentioned above with respect to buyers and suppliers. Therefore, the certification authority node 400 issues transaction data including the result of conformity examination (evaluation result of an external institution), and stores it in its own distributed ledger 110 after consensus building in the distributed ledger system.
[0030]
　A financial institution node 500 is a node operated by a financial institution such as a bank, and is a distributed ledger node. Such financial institutions apply financial services such as financing and settlement related to transactions in the supply chain to the above-mentioned buyers and suppliers. Therefore, the financial institution holds information about the financial situation of buyers and suppliers.
[0031]
　Therefore, the financial institution node 500 issues transaction data including evaluation results of financial conditions such as the repayment status of loans by buyers and suppliers, whether there is a delay in settlement, etc. 110.
[0032]
　As illustrated in FIG. 1, suppliers in a supply chain often have a hierarchical structure. This hierarchical structure consists of a primary supplier that directly receives an order from a buyer, a secondary supplier that procures the parts necessary for manufacturing the product related to the order, and a secondary supplier that manufactures the parts described above. A tertiary supplier that procure materials necessary for As for the depth of the hierarchical structure, a case of three layers (primary to tertiary) has been described here, but it is of course not limited to this.
[0033]
　Recently, various derivative technologies based on the above-mentioned blockchain technology have been proposed and continue to evolve. The main features of the current blockchain are: (1) In transactions between participants on the blockchain network, transactions are finalized by consensus building and approval by (arbitrary or specific) participants, not by a centralized authority. , (2) Collecting multiple transaction data as blocks, recording them on a distributed ledger in a daisy chain, and performing hash calculation on consecutive blocks to make tampering virtually impossible, (3) All participants are the same By sharing ledger data (distributed ledger), it is possible for all participants to confirm transactions.
[0034]
　Based on the above characteristics, blockchain technology is being considered for application in a wide range of fields, such as the financial sector and IoT (Internet of Things), as a mechanism for managing and sharing reliable data and executing and managing transactions based on contracts. It is By using a platform that provides such a blockchain (hereinafter referred to as “blockchain platform”), it is possible to share information and conduct transactions among multiple entities without the need for management by a centralized authority (for example, consortiums and supply chains in specific industries). multiple companies related to the chain, etc.).
[0035]
　In addition, blockchain is not only used for simple virtual currency transactions such as Bitcoin, but a mechanism has been created that can be applied to complex transaction conditions and various applications. It is becoming possible to manage not only logic but also logic. This logic is called a smart contract.
[0036]
　The blockchain infrastructure that has the smart contract execution function described above manages the smart contract itself and the input data for the smart contract. In simple terms, the smart contract itself is like a function(s). And the input data is like the smart contract to call, the name of the function, and the arguments given to the function. With smart contract execution functionality, transactions can be executed according to predefined contracts.
[0037]
　Here, smart contracts and their input data are signed and managed in a daisy chain in the blockchain. Therefore, by having a blockchain platform with a smart contract execution function, the registrant of the data and logic becomes clear, and it is possible to always confirm that the registered content has not changed.
[0038]
　It is assumed that the credit analysis support system 10 of this embodiment is operated based on the technical background related to such a distributed ledger.
[0039]
---Hardware Configuration---
　The hardware configuration of the node 100, which mainly constitutes the credit analysis support system 10, is shown in FIG. That is, node 100 comprises storage device 101 , memory 103 , CPU 104 , input device 105 , output device 106 and network interface 107 .
[0040]
　Among them, the storage device 101 is composed of an appropriate non-volatile storage element such as an SSD (Solid State Drive) or a hard disk drive.
[0041]
　Also, the memory 103 is composed of a volatile memory element such as a RAM.
[0042]
　Also, the CPU 104 is an arithmetic unit that reads out the program 102 held in the storage device 101 into the memory 103 and executes it, performs overall control of the device itself, and performs various determinations, calculations, and control processing.
[0043]
　The input device 105 is a device such as a keyboard or mouse that receives key input or voice input from the user.
[0044]
　Also, the output device 106 is a device such as a display or a speaker for displaying data processed by the CPU 104 .
[0045]
　Also, the network interface 107 is a device that connects to the network 1 and performs communication processing with other nodes 100 and the like.
[0046]
　The storage device 101 stores at least a distributed ledger 110 in addition to the program 102 for implementing the necessary functions of the credit analysis support system 10 of this embodiment. The distributed ledger 110 configuration situation is generic and can be assumed, for example, as illustrated in FIG.
[0047]
　The transaction data held by the above-described distributed ledger 110 includes the order receipt/placement data (order receipt/placement data 1104 in FIG. 4), settlement data ((settlement data 1105 in FIG. 5), and customer evaluation data (customer evaluation data in FIG. 6). Evaluation data 1106), credit calculation result data (credit calculation result data 1107 in FIG. 7), distribution data (distribution data 1108 in FIG. 8), and the values ​​of each event that occurs in relation to transactions in the supply chain are included. sell.
[0048]
　Each of the above-mentioned order receiving/ordering data 1104), payment data 1105, customer evaluation data 1106, credit calculation result data 1107), and distribution data 1108 can be transactions constituting a block chain.
[0049]
　Of these, the business partner evaluation data 1106 in FIG. 6 is data obtained by the buyers and suppliers evaluating each other, that is, the business partners in the supply chain. This trading partner evaluation data 1106 is the evaluation of each item such as quality, cost, and delivery date of the product or service to be ordered or ordered, which is input by the person in charge of the buyer or supplier regarding the trading partner. The value is published as transaction data and stored in the distributed ledger 110 after consensus building.
[0050]
　Credit calculation result data 1107 in FIG. 7 corresponds to credit information generated regarding the buyer or supplier to be evaluated. This credit calculation result data 1107 is stored in the distributed ledger 110 after issuing credit information generated by one of the nodes 100 constituting the credit analysis support system 10 as transaction data and forming a consensus.
[0051]
--- Processing associated with order placement ---
　The actual procedure of the credit analysis support method in this embodiment will be described below with reference to the drawings. Various operations corresponding to the credit analysis support method described below are implemented by a program that the nodes 100 and others constituting the credit analysis support system 10 read into a memory or the like and execute. This program is composed of codes for performing various operations described below.
[0052]
　FIG. 9 is a diagram showing flow example 1 of the credit analysis support method in this embodiment. Here, a situation is assumed in which a buyer in a supply chain places an order, for example, in order to procure parts for manufacturing a product. Also, the client terminal 150 of the buyer transmits to the buyer node 200 via the network 5 a client selection request using the buyer-specified conditions regarding the supplier as a key.
[0053]
　In this case, the buyer node 200 receives the client selection request, executes the matching process (FIG. 14) described later, selects the candidate suppliers that match the conditions specified by the buyer, and displays them on the client terminal 150. (s1).
[0054]
　The buyer browses the information about the candidate supplier and specifies a person suitable as a business partner of this time. The client terminal 150 returns to the buyer node 200 the specific content, ie, the information of the supplier determined as the business partner.
[0055]
　In addition, the buyer node 200 acquires the information of the supplier specified by the buyer through the process of s1, and also acquires the details of the order input by the buyer through the client terminal 150 (s2). The contents of the order are such as the order receiving/ordering data 1104 illustrated in FIG.
[0056]
　Subsequently, the buyer node 200 generates transaction data including the order details obtained in s2 and issues it via the network 1 (s3).
[0057]
　On the other hand, other nodes connected to the network 1 receive the transaction data issued by the buyer node 200 at s3, and execute predetermined processing such as consensus building in accordance with the smart contract held in advance together with the above-described buyer node 200. .
[0058]
　The buyer node 200 (and other nodes) stores the transaction data that has undergone consensus building in its own distributed ledger 110 (s4), and ends the process.
[0059]
　The supplier who has received the order from the buyer performs predetermined business procedures, arranges parts and the like in accordance with the contents of the order, and ships and delivers them to the buyer. Of course, it is also possible that the supplier node 300 publishes the transaction data including the processing contents for each procedure along with the business processing on the supplier side, and stores it in the distributed ledger 110 at each node after consensus building etc. .
[0060]
--- Process Accompanied by Acceptance Inspection ---
　FIG. 10 is a diagram showing a flow example 2 of the credit analysis support method in this embodiment. Here, a description will be given of the process under the condition that the above-mentioned buyer receives the delivery from the supplier and inspects it. The buyer performs an acceptance inspection of the deliverables from the supplier regarding predetermined items such as the quantity and specified quality.
[0061]
　In this case, the buyer node 200 acquires the result of the above check from, for example, the buyer's client terminal 150 (s10). This check result is, of course, associated with values ​​such as the order number of the delivery item and the identification information of the buyer who placed the order.
[0062]
　Subsequently, the buyer node 200 extracts, for example, the order number from the check result obtained in s10, and uses this as a key to store the order details (eg, Each value in the "details of order" column) is specified, and the contents of the order are collated with the check result obtained in s10 (s11).
[0063]
　As a result of the above determination, if the delivery does not conform to the order content (s11: No), the buyer node 200 notifies the client terminal 150 of the supplier of the determination result that the acceptance inspection was rejected (s13). At this time, information on acceptance failure is linked to the corresponding order number or transaction data and managed in an appropriate state DB (not shown), or transaction data including information on acceptance failure is issued, It may be stored in the distributed ledger 110 through consensus building or the like.
[0064]
　On the other hand, as a result of the above determination, if the delivery is as per the order content (s11: Yes), the buyer node 200 issues transaction data including the determination result, and after consensus building with other nodes, the distributed ledger 110 (s14), and the process ends. This transaction data shall also include the order amount indicated by the order content (eg, the order quantity multiplied by the order unit price) as an acceptance inspection result.
[0065]
--- Processing Accompanying Settlement ---
　FIG. 11 is a diagram showing a flow example 3 of the credit analysis support method in this embodiment. Here, a description will be given of the processing associated with the settlement business between the buyer and the supplier.
[0066]
　In this case, the buyer node 200 refers to the billing content (issued in response to the order) received from the supplier node 300 and the acceptance inspection result obtained in the acceptance inspection flow (FIG. 10) (s15). , the billed amount matches the ordered amount described in the contents of acceptance inspection (s16).
[0067]
　As a result of the above determination, if the billed amount and the ordered amount do not match (s16: No), the buyer node 200 notifies the client terminal 150 of the relevant supplier of this result (s17), and terminates the process. At this time, the information on the amount discrepancy is linked to the corresponding order number and transaction data and managed in an appropriate state DB (not shown), or the transaction data including the information on the amount discrepancy is issued and an agreement is reached. It may be stored in the distributed ledger 110 through formation and the like.
[0068]
　On the other hand, as a result of the above determination, if the billed amount and the ordered amount match (s16: Yes), the buyer node 200 uses the payment service provided on the basis of the supply chain (or the existing payment service provided by the buyer). (s18).
[0069]
　In addition, the buyer node 200 obtains the result of the payment processing processed in s18 from the above-described payment service or the like, issues transaction data including this (eg, payment data in FIG. 5), and forms a consensus with other nodes. are stored in the distributed ledger 110 (s19).
[0070]
　Next, the buyer node 200 acquires evaluation information on each item of quality, cost, and delivery date from the client terminal 150 for the above-mentioned supplier, who is a business partner for whom payment has been completed, and transaction data (eg, : supplier evaluation data in FIG. 6), and stores them in the distributed ledger 110 after consensus building with other nodes (s20).
[0071]
　It should be noted that the evaluation information regarding each of the items described above can be assumed to be, for example, information regarding the number of points out of 5 for each of quality, cost, and delivery date.
[0072]
--- Credit information generation processing ---
　FIG. 12 is a diagram showing a flow example 4 of the credit analysis support method in this embodiment, and FIG. 13 shows a flow example 5 of the credit analysis support method in this embodiment. FIG. 4 is a diagram showing; Here, the flow of generating credit information will be described. It should be noted that any of the nodes 100 may execute the processing in this flow. For example, the buyer node 200 of the buyer who wants to check the credit information of the supplier, the supplier node 300 of the supplier who wants to check the credit information of the buyer, and the financial institution which receives the loan request such as payment funds from the buyer and wants to check the credit information of the buyer. Various situations such as the financial institution node 500 can be assumed.
[0073]
　In this example, a flow corresponding to processing when a certain buyer checks a supplier's credit information will be described.
[0074]
　Therefore, the buyer node 200 accesses its own distributed ledger 110 and selects a certain supplier (eg, designation) is extracted as evaluation data 1106 (s30).
[0075]
　Also, the buyer node 200 acquires each value of quality, cost, and delivery date from the supplier evaluation data obtained in s30 (s31). That is, here, evaluation information evaluated by various buyers and the like is collected with respect to the relevant supplier.
[0076]
　Subsequently, the buyer node 200 also makes s30, 31 and s30, 31 regarding other suppliers (as related business partners) that have a business relationship with the above-mentioned supplier on the supply chain. Similar evaluation information is acquired (s32).
[0077]
　The details of this s32 are shown in the flow of FIG. That is, the buyer node 200 refers to the order receiving/ordering data 1104 in the distributed ledger 110, and identifies the supplier that is the above-mentioned related business partner (s321). For example, if "Company A" is the supplier that received an order from a buyer, if "Company A" is specified as the source of the order request in each order receiving/ordering data 1104, this "Company A" is the buyer. It is possible to specify a lower-level supplier (for example, "Company B") in the hierarchical structure that places an order from the standpoint of Similarly, if the order requester is "Company B" among the orders and orders data 1104, this "Company B" places an order from the buyer's standpoint. supplier (for example, "Company C") can be identified.
[0078]
　Subsequently, the buyer node 200 refers to the supplier evaluation data 1105 regarding each supplier identified in s321 as the related supplier in the distributed ledger 110, and acquires the evaluation information contained therein (s322).
[0079]
　In addition, the buyer node 200 obtains the evaluation information obtained in s322 with respect to the supplier that will be the related business partner, that is, the values ​​of quality, cost, and delivery date. " is the second hierarchy, and "Company C" is the third hierarchy).
[0080]
　For example, in the case of a two-level supply chain, α×QCDme+(1−α)×{α2×Avg. (QCD(2, a), QCD(2, b) . . . )+(1−α2)×Avg. (QCD(3, a), QCD(3, b) . . . ) will be calculated.
[0081]
　Here, α: Weight value for the evaluation value (average value of any of the items of quality, cost, and delivery date) in the first layer supplier “Company A”, α2: Second layer supplier “B company” , QCDme: evaluation value of “Company A”, QCD(n, i): evaluation value of i-th edition supplier of n-th layer, Avg: average or median value.
[0082]
　In addition, the buyer node 200 outputs the processing result of s323, that is, the result value of the weighted average processing obtained with respect to the suppliers who are related trading partners to the memory 103 (s324).
[0083]
　Now, let us return to the description of the flow in FIG. 12 .
[0084]
　In addition, the buyer node 200 acquires from the distributed ledger 110 evaluation results regarding the above-mentioned suppliers (which may include the concept of suppliers as related business partners) by financial institutions and certification agencies (s33). It is assumed that this evaluation result is registered in the distributed ledger 110 by the authentication institution node 400 and the financial institution node 500 as the supplier evaluation data 1106 .
[0085]
　Next, the buyer node 200 obtains evaluation information (weighted average processing) on ​​each item of quality, cost, and delivery date obtained up to s33 with respect to the above-mentioned suppliers (those in the first layer), financial institution or the evaluation result by the certification body is applied to a predetermined rule to generate credit information (see FIG. 7) (s34), and the process is terminated.
[0086]
　In this case, the buyer node 200 generates credit information by deducting points from 100 points for each item. For example, for each of quality, cost, and delivery date, 1 point is deducted from 100 points for 4 out of 5 points, and 2 points are deducted from 100 points for 3 points.
[0087]
　Note that each item may be further subdivided as illustrated in FIG. In that case, it is assumed that the evaluation information itself is also subdivided.
[0088]
---Matching process---
　FIG. 14 is a diagram showing a flow example 4 of the credit analysis support method in this embodiment, and FIGS. 15 and 16 show examples of output screens. Next, a matching process between participants in the supply chain will be described. Here, a certain company designates desired conditions regarding suppliers, and description will be given assuming a situation in which matching processing is performed accordingly, but the present invention is not limited to this. For example, it is naturally possible to assume a situation in which a certain company designates desired conditions regarding buyers, and matching processing is performed accordingly.
[0089]
　In this case, for example, the buyer node 200 receives search conditions from the client terminal 150 in advance on the screen shown in FIG. Supplier candidates who have been in the past are identified by referring to the order receiving/ordering data 1104 of the distributed ledger 110 (s40).
[0090]
　In addition, the buyer node 200 generates credit information for the supplier candidates identified in s40 in the same manner as in the flows of FIGS. 13 and 14 (s41).
[0091]
　Subsequently, the buyer node 200 uses the above-mentioned credit information as information that serves as the basis of the credit information, for example, the number of times orders have been dealt with under the same conditions (those in s40), etc. is extracted from the order receiving/ordering data 1104 of the distributed ledger 110, or the extracted values ​​are aggregated and generated (s42).
[0092]
　In addition, the buyer node 200 selects companies in descending order of creditworthiness indicated by the credit information (s43), generates a screen displaying this, and causes the client terminal 150 to display the screen (s44). In the screen example shown in FIG. 16, "SupplierB" is at the top, followed by "SupplierC" and "SupplierD".
[0093]
　Buyers can view this screen, confirm the credit information and its grounds, and easily select a company suitable as a business partner.
[0094]
　Although the best mode for carrying out the present invention has been specifically described above, the present invention is not limited to this, and can be variously modified without departing from the scope of the invention.
[0095]
　According to this embodiment, highly accurate credit information of supply chain participants can be used efficiently and widely.
[0096]
　At least the following will be clarified by the description of this specification. That is, in the credit analysis support device of the present embodiment, the node extracts each evaluation value of quality, cost, and delivery date of the transaction object from the transaction data as the evaluation information of the counterparty, and evaluates the counterparty. As a result, the value of the financial status of the counterparty is extracted from the transaction data, and the credit information of the counterparty is generated by applying each of the evaluation values ​​and the value of the financial status to a predetermined rule. good too.
[0097]
　According to this, it is possible to determine the quality, cost, and delivery date of the product or service to be traded, which greatly affect the credit information of the trading partner, and reflect the determination results in the credit information. As a result, more accurate credit information of supply chain participants can be used efficiently and widely.
[0098]
　In addition, in the credit analysis support method of the present embodiment, the node, when extracting the evaluation information and the evaluation result, evaluates not only the transaction counterparty but also an indirect transaction counterparty who is hierarchically connected with the transaction counterparty in a business relationship. extracting information and evaluation results from transaction data relating to the indirect trading partner; applying the evaluation information and evaluation results of the trading partner and the indirect trading partner to a predetermined rule when generating the credit information; It may also generate credit information for the counterparty.
[0099]
　According to this, in the supply chain, based on the chain of stakeholders in a hierarchical structure such as suppliers of parts, suppliers of semi-finished products using the parts, and suppliers of products using the semi-finished products, The evaluation information and evaluation results of the parties concerned can be appropriately reflected in the credit information. As a result, more accurate credit information of supply chain participants can be used efficiently and widely.
[0100]
　Further, in the credit analysis support method of the present embodiment, when the node extracts the evaluation information and the evaluation result, only transaction data from the present to a predetermined period ago is subject to extraction processing. good too.
[0101]
　According to this, for example, it is possible to avoid using old evaluation information and evaluation results for generating credit information, and reflect so-called fresh evaluation information and evaluation results in credit information. As a result, more accurate credit information of supply chain participants can be used efficiently and widely.
[0102]
　Further, in the credit analysis support method of the present embodiment, each of the plurality of nodes issues transaction data including evaluation information of a counterparty in a predetermined transaction, and the distributed ledger of the transaction data through consensus building among the nodes. and further executing consensus building between nodes and storing transaction data including the evaluation result issued by the node of the external institution in the distributed ledger.
[0103]
　According to this, various information related to transactions in the supply chain can be held in a distributed ledger and utilized for generating credit information. As a result, more accurate credit information of supply chain participants can be used efficiently and widely.
Code explanation
[0104]
1, 5 Network
10 Credit analysis support system
100 Node
101 Storage device
102 Program
103 Memory
104 Arithmetic device
105 Input device
106 Output device
107 Communication device
110 Distributed ledger
111 API
150 Client terminal
200 Buyer
node 300 Supplier node
400 Certification authority node
500 Financial institution node
The scope of the claims
[Claim 1]
　In a system composed of multiple nodes that each hold predetermined transaction data associated with transactions in the supply chain in a distributed ledger, a
　node of a party to a predetermined transaction stores evaluation information of the counterparty in the transaction on a predetermined distributed ledger. A process of extracting from transaction data, a process of extracting an evaluation result determined by a predetermined external organization with respect to the trading partner from predetermined transaction data in the distributed ledger, and applying the evaluation information and the evaluation result to a predetermined rule. , a process of generating the credit information of the transaction partner
　.
[Claim 2]
　The node
　extracts each evaluation value of quality, cost, and delivery date of the transaction object from the transaction data as the evaluation information of the trading partner, and extracts the value of the financial situation of the trading partner as the evaluation result of the trading partner. 2.
　The credit analysis according to claim 1 , wherein said credit information is generated by extracting from said transaction data and applying each said evaluation value and said financial condition value to a predetermined rule to generate said counterparty's credit information. how to help.
[Claim 3]
　When the said node
　extracts the said evaluation information and the said evaluation result, in addition to the said trading partner, the evaluation information and the evaluation result regarding the indirect trading partner hierarchically linked in the business relationship with the said trading partner extracting from the data,
　and applying evaluation information and evaluation results of the trading partner and the indirect trading partner to a predetermined rule to generate the credit information of the trading partner when generating the credit information
　; The credit analysis support method according to claim 1.
[Claim 4]
　2. The credit analysis according to claim 1 , wherein 　said node, when
　extracting said evaluation information and said evaluation results, processes only transaction data from the present to a predetermined period ago as the object of extraction processing.
how to help.
[Claim 5]
　Each of the plurality of nodes
　issues transaction data including evaluation information of a trading partner in a predetermined transaction, stores the transaction data after consensus building between nodes in the distributed ledger, and issues the transaction data from the node of the external institution.
　2. The credit analysis support method according to claim 1, further comprising forming a consensus among nodes and storing the transaction data including the evaluation result obtained by the evaluation in the distributed ledger .
[Claim 6]
　A system composed of multiple nodes that respectively hold predetermined transaction data associated with transactions in the supply chain in a distributed ledger, in which a
　node of a party to a predetermined transaction stores evaluation information of the counterparty in the transaction in the distributed ledger A process of extracting from predetermined transaction data, a process of extracting evaluation results determined by a predetermined external organization regarding the trading partner from predetermined transaction data in the distributed ledger, and applying the evaluation information and the evaluation results to a predetermined rule.
　A credit analysis support system, characterized in that it applies processing to generate the credit information of the trading partner .
[Claim 7]
　A process of holding predetermined transaction data associated with transactions in a supply chain in a distributed ledger, being a node constituting a distributed ledger system,
　and extracting evaluation information of a trading partner in a predetermined transaction from predetermined transaction data in the distributed ledger; A process of extracting an evaluation result determined by a predetermined external organization with respect to a trading partner from predetermined transaction data in the distributed ledger, and applying the evaluation information and the evaluation result to a predetermined rule to determine the credit information of the trading partner.
　A node characterized by having an arithmetic unit for executing a process to generate .