JP2019067297A - Information processing apparatus and program - Google Patents

Abstract

To desirably provide a technique which enables support of an entrepreneur by using data managed by an accounting management system.SOLUTION: There is provided an information processing apparatus which has an accounting data acquiring unit for acquiring accounting data relating to an account of the entrepreneur, and a future CF information generating unit for generating a plurality of pieces of future category CF information indicated by plural kinds of future cash flow of the entrepreneur to generate future CF information indicating future cash flow of the entrepreneur on the basis of the plurality of pieces of future category CF information.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an information processing apparatus and program.

There has been known an accounting management system that supports financial accounting work of business entities (see, for example, Patent Document 1).
[Prior art document]
[Patent Document]
[Patent Document 1] JP-A-2012-146158

  It would be desirable to provide a technology that allows businesses to be supported using data managed by an accounting management system.

  According to a first aspect of the present invention, an information processing apparatus is provided. The information processing apparatus may include an accounting data acquisition unit that acquires accounting data related to the accounting of the business. The information processing apparatus generates, based on the accounting data, a plurality of futures classified CF information respectively indicating a plurality of future cash flows of the business, and a future of the business based on the plurality of futures classified CF information. And a future CF information generation unit for generating future CF information indicating a cash flow of

  The future CF information generation unit is a future business CF information indicating the future operating cash flow of the business, a future investment CF information indicating the future investment cash flow of the business, and a future financial cash flow of the business. Future financial CF information may be generated. The future CF information generation unit may generate future free CF information indicating the future free cash flow of the business entity.

  The information processing apparatus may further include a past CF information generation unit that generates a plurality of past type-specific CF information respectively indicating the plurality of types of past cash flows of the business operator based on the accounting data, and the future The CF information generation unit may generate the plurality of future type CF information based on the plurality of past type CF information. The above-mentioned past CF information generation unit is past business CF information indicating the past operating cash flow of the business, past investment CF information indicating the past investment cash flow of the business, and past financial cash flow of the business The future CF information generation unit may generate future financial cash of the business based on the past sales CF information, the past investment CF information, and the past financial CF information. Future sales CF information indicating a flow, future investment CF information indicating a future investment cash flow of the business entity, and future financial CF information indicating a future financial cash flow of the business entity may be generated.

  The information processing apparatus may further include a transaction data acquisition unit for acquiring transaction data functioning as a transaction by the business operator, and the future CF information generation unit is configured to execute the future CF based on the accounting data and the transaction data. Information may be generated. The information processing apparatus is a loan to the enterprise based on the application data acquisition unit for acquiring the loan application data including the loan application amount by the enterprise, the above-mentioned loan application data and the plurality of future type CF information The system may further include a loan availability determination unit that determines availability. The information processing apparatus may further include an enterprise group identification unit for identifying an enterprise group corresponding to the enterprise, and the future CF information generation unit is identified by the accounting data and the enterprise group identification unit. The plurality of future type-specific CF information may be generated based on the feature data indicating the characteristics of the cash flow of the business operator group, and the future of the business entity may be generated based on the plurality of future type-specific CF information. Future CF information indicating the cash flow of

  According to a third aspect of the present invention, there is provided a program for causing a computer to function as the information processing apparatus.

  Note that the above summary of the invention does not enumerate all the necessary features of the present invention. In addition, a subcombination of these feature groups can also be an invention.

BRIEF DESCRIPTION OF THE DRAWINGS An example of the communication environment of the loan determination apparatus 100 is shown roughly. An example of past CF information 182 and future CF information 184 is schematically shown. An example of past type-specific CF information 190 and future type-specific CF information 194 is schematically shown. An example of data item 22 of loan application data is shown roughly. An example of data item 212 of journal data is shown roughly. An example of data item 214 of balance data is shown roughly. An example of the data item 222 of billing data is schematically shown. An example of data item 224 of estimate data is shown roughly. An example of data item 232 of financial institution data is shown roughly. An example of data item 242 of credit information data is shown roughly. An example of data item 170 of past history data is shown roughly. An example of data item 252 of enterprise group data is shown roughly. An example of data item 262 of transaction relation data is shown roughly. An example of the flow of the process by the loan determination apparatus 100 is shown roughly. The other example of the flow of the process by the loan determination apparatus 100 is shown roughly. An example of a function structure of the loan determination apparatus 100 is shown roughly. An example of a function structure of the account management apparatus 300 is shown roughly. An example of the hardware constitutions of the computer 1000 which functions as the loan determination apparatus 100 or the account management apparatus 300 is shown roughly.

  Hereinafter, the present invention will be described through the embodiments of the invention, but the following embodiments do not limit the invention according to the claims. Moreover, not all combinations of features described in the embodiments are essential to the solution of the invention.

  FIG. 1 schematically illustrates an example of a communication environment of the loan determination device 100. The loan determination device 100 is an example of an information processing device.

  The loan determination device 100 according to the present embodiment determines whether to lend to a business. The loan determination device 100 receives, for example, loan application data including a loan application amount, a loan request date, a loan period, and the like from the communication terminal 20 of the business entity, and determines whether or not the business can be financed. There may be a case where a company whose target is the loan determination device 100 to determine whether to lend is the target company.

  The communication terminal 20 may be any terminal as long as it has a communication function, and is, for example, a PC (Personal Computer), a tablet terminal, and a mobile phone such as a smartphone. The loan determination device 100 communicates with the communication terminal 20 via the network 10. The network 10 may include the Internet. The network 10 may include a telephone network. The network 10 may include a dedicated network.

  The loan determination apparatus 100 may generate probability information indicating the probability of loan repayment by the business entity, and may determine whether to allow the business entity to finance based on the probability information. The probability information is, for example, future CF information indicating a future cash flow (sometimes described as CF) of the business entity. Also, the probability information is, for example, a score indicating the probability of loan repayment by the business. The score may be anything as long as it indicates the degree of probability of loan repayment by the business.

  The loan determination device 100 generates probability information using, for example, accounting data related to the accounting of the target business. The loan determination device 100 may receive accounting data of a target business entity from the accounting data management device 210 that manages accounting data of a plurality of business entities via the network 10.

  The accounting data management device 210 may manage accounting data input by a business entity, an accounting office or the like, using so-called accounting software and so-called cloud type accounting software and its related software. The accounting data management device 210 may manage accounting data provided by a business entity, an accounting office, etc. without using accounting software, cloud-type accounting software, and related software.

  The loan determination device 100 may manage accounting data of a plurality of business operators. That is, the loan determination device 100 may have the function of the accounting data management device 210.

  The loan determination device 100 generates probability information using, for example, transaction data related to a transaction by the target business entity. The loan determination device 100 may receive transaction data of a target business entity from the transaction data management device 220 that manages transaction data of a plurality of business entities via the network 10.

  The transaction data management device 220 may manage transaction data input by a business operator, an accounting office or the like using so-called sales management software and so-called cloud-based sales management software, and its related software. The transaction data management device 220 may manage transaction data input by a business operator, an accounting office or the like using accounting software and cloud type accounting software, and its related software. The transaction data management device 220 is a transaction provided by a business entity, an accounting office, etc. without using sales management software, cloud-based sales management software, accounting software, cloud-based accounting software and related software. You may manage data.

  The loan determination device 100 may manage transaction data of a plurality of business operators. That is, the loan determination device 100 may have the function of the transaction data management device 220.

  The loan determination device 100 generates probability information using, for example, financial institution data including a history of deposits and withdrawals for a financial institution account of a target business. The financial institution account may be an account managed by a financial institution such as a bank and a credit bank. The loan determination device 100 may receive financial institution data of a target business entity from the financial institution data management device 230 that manages financial institution data of a plurality of business entities via the network 10.

  The financial institution data management device 230 may receive and manage financial institution data of a plurality of businesses from a plurality of financial institutions. The financial institution data management device 230 may receive and manage financial institution data certified by the financial institution from each of a plurality of businesses.

  The loan determination device 100 may manage financial institution data of a plurality of businesses by itself. That is, the loan determination device 100 may have the function of the financial institution data management device 230.

  The loan determination device 100 generates probability information, for example, using the credit information data of the target enterprise generated by a third party. The loan determination device 100 may receive credit information data of a target business entity from the credit information management device 240 that manages credit information data of a plurality of business entities via the network 10.

  The credit information management device 240 is managed, for example, by a provider who carries out a service that generates and provides credit information data of a plurality of businesses. The method of generating credit information data by the vendor may be any method.

  The loan determination device 100 may manage credit information data of a plurality of business operators. That is, the loan determination device 100 may have the function of the credit information management device 240.

  The loan determination device 100 generates probability information, for example, using feature data indicating the feature of the CF of the business operator group corresponding to the target business operator. The loan determination device 100 may receive the feature data of the enterprise group corresponding to the target enterprise via the network 10 from the enterprise group data management apparatus 250 that manages the feature data of a plurality of enterprise groups.

  The business operator group indicates, for example, a group of business operators by industry. For example, when the target company is a construction company, a group of business persons corresponding to the target company may be a building industry. The characteristic of CF in the business group may be, for example, the characteristic of fluctuation of CF in each type of industry, such as the construction industry tending to improve sales in March. The feature data may be, for example, data that embodies the contents of fluctuation, such as increasing sales in March of the building industry by 20%.

  The business operator group may indicate not only the business operator group by industry but also other groups. For example, the business operator group may be a group of business operators for each series. Also, the business operator group may be a group of business operators determined by persons in charge of grouping business operators.

  The business enterprise group data management device 250 generates feature data by analyzing various data for each of a plurality of business operators, for example. The business enterprise group data management device 250 analyzes, for example, accounting data of a plurality of business enterprises, and generates feature data for each business enterprise group. The enterprise group data management device 250 may collect accounting data of a plurality of operators independently, or may receive accounting data of a plurality of operators from the accounting data management device 210. In addition, the business enterprise group data management device 250 may manage feature data generated by a person in charge of generating feature data.

  The loan determination device 100 may manage feature data of a plurality of business operator groups by itself. That is, the loan determination device 100 may have the function of the business group data management device 250.

  The loan determination device 100 generates probability information using, for example, transaction relationship data regarding a transaction with a business partner of the target business entity with the other business partner. The transaction relationship data includes, for example, the collection results between the operators and the payment results. The loan determination device 100 may receive, from the transaction relationship management device 260 that manages transaction relationship data between a plurality of business operators, transaction relationship data of a business partner of a target business enterprise via the network 10.

  The transaction relationship management device 260 manages transaction relationships between a plurality of business operators. Then, the transaction relation management device 260 manages, for each of a plurality of business operators, transaction relation data regarding transactions between the business operators and their business partners.

  The business relationship between a plurality of businesses includes, for example, the correspondence between a business and a business having a business relationship with the business. According to the business relationship managed by the business relationship management device 260, it is possible to identify an enterprise having a business relationship with a certain enterprise, and further to identify an enterprise having a business relationship with an enterprise having that business relationship. It is. As described above, according to the transaction relationship managed by the transaction relationship management device 260, it is possible to identify, for a certain enterprise, businesses having a business relationship in a chained manner.

  The loan determination device 100 may manage transaction relationships and transaction relationship data between a plurality of business operators. That is, the loan determination device 100 may have the function of the transaction relationship management device 260.

  The loan determination device 100 may generate probability information using the plurality of types of data described above. The loan determination device 100 may use the generated probability information to determine whether to lend to the target business. As described above, according to the loan determination device 100 according to the present embodiment, the possibility of the loan to the target enterprise is determined using the probability information generated using the plurality of types of data, so although the loan was made It is possible to reduce the probability of occurrence of a situation that can not be recovered. In addition, it is possible to eliminate the need to set an excessively strict standard as to whether to lend or not, and it is possible to reduce the probability of occurrence of a situation where a business that can receive the loan can not readily obtain the loan. .

  The account management device 300 manages accounts of a plurality of businesses. The account management device 300 may open an account for the business in response to a request from the business. The account management device 300 manages account balances of a plurality of businesses, for example, in one or more bank accounts. As a specific example, the account management device 300 manages the account balances of N businesses or more than N businesses in N bank accounts. The account management device 300 may manage account balances of a plurality of business operators within a bank account in the manager's name of the account management device 300.

  The business operator can deposit money from a bank account to the account managed by the account management device 300 or withdraw from the account managed by the account management device 300 to the bank account. Further, the business operator can designate an account managed by the account management device 300 as a settlement account in a transaction with another business company. That is, the business operator can receive payment for the transaction through the account managed by the account management device 300, and can pay.

  The account management device 300 may be able to cooperate with any trading platform, and can uniquely identify transaction content between the account management device 300 and any trading platform. Instead of the account management device 300 and an arbitrary trading platform working together, the account management device 300 may double as the trading platform.

  For example, when a payment request to another provider is received from a provider who manages an account, the account management apparatus 300 executes payment processing if the account balance is not short. The account management apparatus 300 may determine whether to lend to the business if the account balance is insufficient, and may add the loan amount to the account balance if it is determined that the loan is possible.

  The account management device 300 generates probability information indicating the probability of loan repayment of the business using the same method as the loan determination device 100, and determines whether to lend to the business based on the probability information. Good. For example, the account management apparatus 300 determines whether or not to lend to the company, as in the case of receiving the loan application data including the loan application amount corresponding to the shortfall amount from the company.

  The account management device 300 may execute the payment process after adding the loan amount of the shortfall or more to the account balance. Further, after the payment processing is performed, the account management device 300 may execute the loan repayment processing according to a predetermined repayment rule. For example, when the account balance of the business is greater than the loan amount, the account management device 300 executes a loan repayment process to repay the entire loan amount. Also, for example, when the account balance of the business operator is smaller than the loan amount, the account management device 300 executes a loan repayment process to repay part of the loan amount. Further, the account management apparatus 300 may execute interest rate payment processing according to a predetermined interest rate rule when the account balance of the business operator is smaller than the loan amount after the payment processing is performed.

  Thus, when the account management apparatus 300 manages the accounts of a plurality of businesses and receives a payment request from the business to another business, if the account balance is insufficient, the business Automatically determine whether or not to make a loan, and execute the loan process. As a result, it is possible to provide a mechanism in which a business can receive a loan and conduct a transaction with fewer procedures than in the past.

  FIG. 2 schematically illustrates an example of past CF information 182 and future CF information 184. Here, an example of the flow of generation processing of the past CF information 182 and the future CF information 184 by the loan determination device 100 will be described.

  The loan determination apparatus 100 generates the past CF information 182 indicating the past cash flow of the target business using the data of the account item related to deposit and savings included in the accounting data of the target business. The loan determination device 100 may compare the past CF information 182 with the financial institution data of the target business entity and correct the past CF information in accordance with the financial institution data if there is inconsistency.

  The loan determination device 100 generates future CF information 184 based on the past CF information 182. The loan determination device 100 generates, for example, future CF information 184 for one year from the past CF information 182 for multiple years. The loan determination device 100 may generate the CF information 184 in the future by averaging the past CF information 182 for a plurality of years or predicting from the past CF information 182 for a plurality of years. The loan determination device 100 may derive, for example, the generation pattern of sales in the past CF information 182, the generation pattern of purchases, the generation pattern of expenses, etc., and use these to generate CF information 184 in the future.

  The loan determination device 100 may generate CF information 184 in the future by further using data of an account related to a change in future deposit balance among the accounts included in the accounting data. The loan determination device 100 generates the CF information 184 in the future, for example, using an account item for sales credit and an account item for accounts payable.

  The loan determination device 100 may correct the CF information 184 in the future using the transaction data of the target enterprise. The loan determination device 100 corrects the CF information 184 in the future, for example, using the estimate data and the order data included in the transaction data. The loan determination apparatus 100 may calculate an order acceptance rate indicating the probability of the order received for the estimate from the target enterprise from the estimate data and the order acceptance data, and may correct the CF information 184 in the future using the order acceptance rate. In addition, the loan determination device 100 corrects the CF information 184 in the future, for example, using the billing data and the estimate data included in the transaction data. The loan determination apparatus 100 may calculate an order acceptance rate indicating the probability of an order for the estimate of the target business operator from the billing data and the estimate data, and correct the CF information 184 in the future using the order acceptance rate. Further, the loan determination device 100 may calculate a bill payment rate indicating the probability of deposit for a bill of the target business based on the bill data, and correct the CF information 184 in the future using the bill deposit rate.

  The loan determination device 100 may identify the counterparty of the target enterprise and correct the CF information 184 in the future using the transaction relationship data regarding the transaction with the other enterprise by the counterparty. The loan determination device 100 corrects the CF information 184 in the future, for example, using the payment status to the other business by the trading partner. In addition, the loan determination apparatus 100 corrects the CF information 184 in the future, for example, using the attribute information of the trading partner of the trading partner. The attribute information includes the size of the business and the credit of the business.

  The loan determination device 100 may correct the CF information 184 in the future using the feature data of the enterprise group corresponding to the target enterprise. The loan determination device 100 corrects the CF information 184 in the future using, for example, the generation pattern of sales indicated by the feature data, the generation pattern of purchases, the generation pattern of expenses, and the like.

  As described above, the loan determination device 100 generates CF information 184 in the future using a plurality of types of data. Note that to generate CF information 184 in the future using multiple types of data means, as described above, to generate CF information 184 in the future using a part of multiple types of data, and to generate multiple types of future CF information 184 generated The final future CF information 184 is generated by correcting with other data of the data of.

  For example, the loan determination device 100 prepares a loan repayment schedule from the loan application data received from the target business entity, and determines whether the loan is possible by determining whether the repayment schedule can be realized from the CF information 184 in the future. You may judge. The loan determination device 100 may further use the credit information data to determine whether to lend or not. In addition, the loan determination device 100 may further determine the availability of the loan by further using the history of the loan for the target enterprise.

  FIG. 3 schematically illustrates an example of past type-specific CF information 190 and future type-specific CF information 194. Here, an example of the flow of generation processing of the past type-specific CF information 190 and the future type-specific CF information 194 by the financing determination apparatus 100 will be described.

  The past type CF information 190 is past sales CF information 191 indicating the past operating cash flow of the target business, past investment CF information 192 indicating the past investment cash flow of the target business, and past finance of the target business. It may include past financial CF information 193 indicating cash flow.

  The loan determination device 100 may generate the past-type-specific CF information 190 using the accounting data, transaction data, financial institution data, etc. of the target business entity. The loan determination device 100 may generate the past type-specific CF information 190 using the so-called direct method, or may generate the past type-specific CF information 190 using the so-called indirect method. The loan determination device 100 compares, for example, the past type-specific CF information 190 generated using the accounting data of the target business with the financial institution data of the target business, and if there is inconsistency, the financial institution data The past type-specific CF information 190 may be corrected in accordance with the above.

  The loan determination device 100 may generate future type-specific CF information 194 based on the past type-specific CF information 190. Future type CF information 194 represents future operating cash flow of the target business, future operating CF information 195, future investment CF information 196 showing the future investment cash flow of the target business, and future financials of the target business. Future financial CF information 197 indicating cash flow may be included.

  The loan determination device 100 generates, for example, future type CF information 194 for one year from the past type CF information 190 for multiple years. The loan determination device 100 may generate future sales CF information 195 by averaging past sales CF information 191 for a plurality of years, or predicting from the past sales CF information 191 for a plurality of years. The loan determination device 100 may generate future investment CF information 196 by averaging past investment CF information 192 for a plurality of years or predicting from past investment CF information 192 for a plurality of years. The loan determination device 100 may generate future financial CF information 197 by averaging past financial CF information 193 for multiple years or predicting from the past financial CF information 193 for multiple years.

  The loan determination device 100 may correct the business CF information 195 in the future using the transaction data of the target enterprise. The loan determination device 100 corrects the future sales CF information 195, for example, using the estimate data and the order data included in the transaction data. The loan determination apparatus 100 may calculate an order acceptance rate indicating the probability of an order for the estimate from the target enterprise from the estimate data and the order receipt data, and correct the future sales CF information 195 using the order acceptance rate. Further, the loan determination device 100 corrects the future sales CF information 195 using, for example, the billing data and the estimate data included in the transaction data. The loan determination apparatus 100 may calculate an order acceptance rate indicating the probability of an order for the estimate from the target business operator from the billing data and the estimate data, and correct the future sales CF information 195 using the order acceptance rate. In addition, the loan determination device 100 may calculate a bill payment rate indicating the probability of deposit for the bill of the target business based on the bill data, and correct the business CF information 195 in the future using the bill deposit rate. .

  The loan determination device 100 may identify the counterparty of the target business, and correct the future business CF information 195 using the trade relationship data regarding the business with the other business by the trader. The loan determination device 100 corrects the business CF information 195 in the future, for example, using the payment status to the other business by the trading partner. In addition, the loan determination device 100 corrects the future sales CF information 195 using, for example, the attribute information of the trading partner of the trading partner. The attribute information includes the size of the business and the credit of the business.

  The loan determination device 100 may correct the business CF information 195 in the future using the feature data of the enterprise group corresponding to the target enterprise. The loan determination device 100 corrects the future sales CF information 195 using, for example, the generation pattern of sales indicated by the feature data, the generation pattern of purchases, the generation pattern of expenses, and the like.

  As described above, the loan determination device 100 generates type-specific CF information 194 in the future using a plurality of types of data. Note that generation of future-type CF information 194 using multiple types of data means generation of future-type CF information 194 using a portion of multiple types of data, as described above, and generation by future type The final future CF information 194 is generated by correcting the CF information 194 using other data of multiple types of data.

  The loan determination device 100 may generate future CF information indicating the future cash flow of the target business based on the type-specific CF information 194. The loan determination device 100 generates future CF information by, for example, adding together future business CF information 195, future investment CF information 196, and future financial CF information 197.

  FIG. 4 schematically shows an example of data items 22 of the loan application data. In FIG. 4, an example of data values of each data item 22 is also shown.

  The loan application data may include a subject person ID for identifying a subject person. The loan application data may include target person information including the name of the target person. The loan application data may include the loan application amount. The loan application data may include a loan request date. The loan application data may include a loan period. The loan application data may include other data items.

  FIG. 5 schematically shows an example of data items 212 of journal data included in accounting data. In FIG. 5, an example of the data value of each data item 212 is also shown.

  The journal data may include a data ID for identifying journal data. Journal data may include transaction dates. The journal data may include debit / credit classification. Journal data may include accounts. The journal data may include an amount. Journal data may include other data items.

  FIG. 6 schematically illustrates an example of data items 214 of balance data included in accounting data. In FIG. 6, an example of the data value of each data item 214 is also shown.

  The balance data may include a data ID for identifying the balance data. Balance data may include balance dates. Balance data may include debit / credit classification. Balance data may include accounts. Balance data may include an amount. Balance data may include other data items.

  FIG. 7 schematically illustrates an example of data items 222 of billing data included in transaction data. FIG. 7 also shows an example of the data value of each data item 222.

  The billing data may include a data ID for identifying billing data. The billing data may include the transaction date. The billing data may include billing information including a billing name. The billing data may include billing content. The billing data may include an amount. The billing data may include other data items.

  FIG. 8 schematically illustrates an example of data items 224 of quotation data included in transaction data. FIG. 8 also shows an example of the data value of each data item 224.

  The quotation data may include a data ID for identifying the quotation data. The quotation data may include a quotation date. The quotation data may include quotation destination information including the name of the quotation destination. The quotation data may include the contents of the quotation. The estimate data may include an amount. The estimate data may include other data items.

  FIG. 9 schematically illustrates an example of data items 232 of financial institution data. FIG. 9 also shows an example of the data value of each data item 232.

  The financial institution data may include an account ID identifying the account. The financial institution data may include balance dates. The financial institution data may include a deposit / withdrawal category. The financial institution data may include an amount. The financial institution data may include other data items.

  FIG. 10 schematically shows an example of the data item 242 of the credit information data. FIG. 10 also shows an example of the data value of each data item 242.

  The credit information data may include a data ID for identifying the credit information data. The credit information data may include target person information including the name of the target person. The credit information data may include a score indicating the creditworthiness of the subject. The credit information data may include other data items.

  FIG. 11 schematically illustrates an example of data items 170 of past history data. FIG. 11 also shows an example of the data value of each data item 170.

  The past history data may include a subject person ID for identifying a subject person. The past history data may include target person information including the name of the target person. The past history data may include the loan amount. The past history data may include the date of loan. Past history data may include repayment history. The past history data may include other data items.

  FIG. 12 schematically illustrates an example of data items 252 of feature data of a business group. FIG. 12 also shows an example of the data value of each data item 252.

  The feature data may include a business type ID identifying the business type of the business group. The feature data may include a correction period indicating a period to be corrected. The feature data may include a correction target indicating an item to be corrected. The feature data may include correction information indicating the content of the correction. The data values shown in FIG. 12 show an example in which the sales in March are increased by 20% for the business group having the industry ID of 00001. Feature data may include other data items.

  FIG. 13 schematically illustrates an example of data items 262 of transaction relationship data. FIG. 13 also shows an example of the data value of each data item 262.

  The transaction relationship data may include a business operator ID identifying a business operator. Transaction relationship data may include a business name. The transaction relationship data may include a business partner company ID that is a business operator ID of a business partner of the business entity identified by the business entity ID. Transaction relationship data may include the name of a business partner. The business relationship data may include a collection history for the business identified by the business partner ID by the business identified by the business ID. The collection history is, for example, a ratio of the number of times of billing to the business identified by the business partner ID by the business identified by the business ID and the number of collected bills. Transaction relationship data may include other data items.

  FIG. 14 schematically illustrates an example of the flow of processing by the loan determination device 100. Here, an example of a flow until the loan determination apparatus 100 acquires loan application data from the communication terminal 20 and notifies the communication terminal 20 of the result for the loan application data will be described. Each process shown in FIG. 14 is mainly executed by the control unit included in the loan determination device 100.

  In step (step S may be abbreviated as S.), in step 1302, a provider who desires a loan acquires the loan application data transmitted by the communication terminal 20. In S1304, the accounting data of the target business is acquired. In S1306, past CF information is generated based on the accounting data. The loan determination device 100 may generate past type-specific CF information.

  In S1308, various data are acquired. The loan determination device 100 includes transaction data of a target enterprise, financial institution data, credit information data, and past history data, feature data of a group of operators corresponding to the target enterprise, and transaction relationships of business partners of the target enterprise. Get at least one of the data and In S1310, future CF information is generated using the past CF information generated in S1306 and the various data acquired in S1308. The loan determination device 100 may generate type-specific CF information in the future using past type-specific CF information and the various data.

  In S1312, based on the future CF information generated in S1310, it is determined whether the target business can be financed. The loan determination device 100 may determine whether to lend to the target business based on the type-specific CF information in the future. If it is determined that the loan is possible, the process proceeds to step S1314. If it is determined that the loan is not possible, the process proceeds to step S1316.

  In S1314, the loan determination device 100 starts a loan procedure for the target business. For example, the loan determination device 100 transmits, to the communication terminal 20, the loan procedure data including the content for notifying that the loan is possible for the loan application data acquired in S1302 and the content for requesting the information necessary for the loan. Send to The information required for the loan included in the lending procedure data may be less than the information required for the regular loan application.

  At S1316, the target enterprise is notified that the loan is not accepted. For example, the loan determination device 100 transmits, to the communication terminal 20, notification data for notifying that the loan application data acquired in S1302 is a loan rejection.

  FIG. 15 schematically illustrates another example of the flow of processing by the loan determination device 100. Here, with respect to the example shown in FIG. 14, the flow in the case where the loan determination device 100 executes the process without using the accounting data will be described. Here, points different from the flow of processing shown in FIG. 14 will be mainly described.

  In S1402, the loan application data transmitted by the communication terminal 20 is acquired. In S1404, transaction data of the target business entity is acquired. In S1406, the financial institution data of the target business is acquired.

  In S1408, past CF information is generated using the financial institution data acquired in S1406. The loan determination device 100 may generate past type-specific CF information. In S1410, various data are acquired. The loan determination device 100 includes at least one of credit information data and past history data of a target business, feature data of a business group corresponding to the target business, and transaction relationship data of a business partner of the target business. To get In S1412, future CF information is generated using the past CF information generated in S1408 and the various data acquired in S1410. The loan determination device 100 may generate type-specific CF information in the future, using past type-specific CF information and various data.

  In S1414, based on the future CF information generated in S1412, it is determined whether the target business can be financed. The loan determination device 100 may determine whether to lend to the target business based on the type-specific CF information in the future. If it is determined that the loan is possible, the process proceeds to S1416, and if it is determined that the loan is not possible, the process proceeds to S1418. In S1416, a loan procedure is started for the target business. At S1418, the target enterprise is notified that the loan is not accepted.

  FIG. 16 schematically illustrates an example of a functional configuration of the loan determination device 100. The loan determination device 100 includes an application data acquisition unit 102, an accounting data acquisition unit 104, a financial institution data acquisition unit 106, a transaction data acquisition unit 108, an order acceptance rate calculation unit 110, a bill receipt rate calculation unit 112, and a transaction relationship data acquisition unit 114. A business group identification unit 116, a feature data acquisition unit 118, a past CF information generation unit 120, a probability information generation unit 130, a request data transmission unit 142, a loan availability determination unit 144, a credit acquisition unit 146, a loan processing execution unit 148, The post-financing management unit 150, the repayment information generation unit 152, and the repayment information transmission unit 154 are provided. In addition, it is not necessarily essential for the loan determination device 100 to have all these configurations.

  The application data acquisition unit 102 acquires loan application data. The application data acquisition unit 102 may receive the loan application data from the communication terminal 20. The application data acquisition unit 102 may read out the loan application data received from the communication terminal 20 and stored.

  The accounting data acquisition unit 104 acquires accounting data of a target business. For example, the accounting data acquisition unit 104 identifies the target business entity by the loan application data acquired by the application data acquisition unit 102, requests accounting data of the target business entity from the accounting data management device 210, and executes the accounting data management device Receive accounting data from 210. In addition, when the loan determination device 100 manages accounting data of a plurality of businesses, the accounting data acquisition unit 104 may read accounting data of a target business from accounting data under management. In addition, the accounting data acquisition unit 104 may receive accounting data from the communication terminal 20 that has transmitted the loan application data.

  The financial institution data acquisition unit 106 acquires financial institution data of the target business. The financial institution data acquisition unit 106, for example, identifies the target business entity by the loan application data acquired by the application data acquisition section 102, requests the financial institution data management device 230 for the financial institution data of the target business entity, Financial institution data is received from the institution data management device 230. In addition, when the loan determination device 100 manages financial institution data of a plurality of business entities, the financial institution data acquisition unit 106 may read out the financial institution data of the target business entity from the financial institution data under management. Also, the financial institution data acquisition unit 106 may receive, from the communication terminal 20 that has transmitted the loan application data, financial institution data that has been certified by a financial institution.

  The transaction data acquisition unit 108 acquires transaction data of the target business entity. The transaction data acquisition unit 108 specifies, for example, a target business operator by the loan application data acquired by the application data acquisition unit 102, requests the transaction data management device 220 for transaction data of the target business operator, and the transaction data management device Receive transaction data from 220. In addition, when the loan determination device 100 manages transaction data of a plurality of business operators, the transaction data acquisition unit 108 may read transaction data of a target business entity from transaction data under management. Further, the transaction data acquisition unit 108 may receive transaction data from the communication terminal 20 that has transmitted the loan application data. The transaction data acquisition unit 108 may acquire estimated data as transaction data. Further, the transaction data acquisition unit 108 may acquire billing data as transaction data. Further, the transaction data acquisition unit 108 may acquire order data as transaction data. The received order data may be data indicating that an order has been received from another business entity or the like. For example, the order data indicates that an order has been received for the estimate indicated by the estimate data. The order receipt data may include a data ID for identifying the order receipt data. The order receipt data may include an order receipt date. The order reception data may include order source information including the name of the order source. The order receipt data may include the contents of the order receipt. The order data may include an amount. The order receipt data may include other data items.

  The order acceptance rate calculation unit 110 calculates the order acceptance rate of the target business based on the transaction data acquired by the transaction data acquisition unit 108. The order acceptance rate calculation unit 110 may calculate the order acceptance rate using the estimated data and the order acceptance data included in the transaction data. For example, the order acceptance rate calculation unit 110 calculates the order acceptance rate by dividing the number of times of estimation by the number of order acceptances corresponding to the estimation. The order acceptance rate calculation unit 110 may calculate the order acceptance rate using the estimated data and the billing data included in the transaction data. For example, the order acceptance rate calculation unit 110 calculates the order acceptance rate by dividing the number of times of estimation by the number of times of billing corresponding to the estimation. For example, when there is no order data corresponding to the estimate data, the order rate calculator 110 may calculate the order rate using the estimate data and the billing data.

  The transaction data may include the correspondence between the estimate and the order, and the order acceptance rate calculation unit 110 may specify the number of orders corresponding to the estimate by referring to the correspondence. When the transaction data does not include the correspondence between the estimate and the order, the order acceptance rate calculation unit 110 calculates the relationship between the estimate date in the estimate data and the order receipt date in the order data, the estimate destination in the estimate data, the estimate content, The order corresponding to the estimate may be identified by confirming the amount and the order source in the order data, the content of the order, and the match of the amount. The transaction data may include the correspondence between the estimate and the claim, and the order acceptance rate calculation unit 110 may specify the number of claims corresponding to the estimate by referring to the correspondence. When the transaction data does not include the correspondence between the estimate and the request, the order acceptance rate calculation unit 110 determines the relationship between the estimated date in the estimated data and the billing date in the invoice data, the estimate destination in the estimated data, the estimated content, The billing corresponding to the estimate may be identified by confirming the monetary amount and the billing address in the billing data, the billing content, and the correspondence with the monetary amount.

  The bill transfer rate calculation unit 112 calculates the bill transfer rate of the target business based on the transaction data acquired by the transaction data acquisition unit 108. The bill transfer rate calculation unit 112 may calculate the bill transfer rate using the bill data included in the transaction data. For example, the bill transfer rate calculation unit 112 calculates the bill transfer rate by dividing the number of bills by the number of bills on which the deposit is made.

  The billing data may include information as to whether or not the payment has been made, and the billing ratio calculation unit 112 may specify the number of times the bill has been received by referring to the information. When the billing data does not include the information, the billing payment rate calculation unit 112 refers to, for example, the financial institution data acquired by the financial institution data acquisition unit 106 to determine whether the payment has been made to the billing. You may specify.

  The transaction relationship data acquisition unit 114 acquires transaction relationship data of the trading partner of the target business. The transaction relationship data acquisition unit 114 identifies the transaction partner of the target business using the transaction data acquired by the transaction data acquisition unit 108, and requests the transaction relationship management device 260 for transaction relationship data of the identified transaction partner, Transaction relationship data may be received from the transaction relationship management device 260. In addition, when the loan determination device 100 manages the transaction relationship and transaction relationship data between a plurality of business operators, the transaction relationship data acquisition unit 114 determines from the transaction relationship data under management that the transaction of the transaction partner of the target enterprise. Relational data may be read out.

  The enterprise group identification unit 116 identifies an enterprise group corresponding to the target enterprise. For example, with reference to a list in which a plurality of business entity groups are registered, the business enterprise group identification unit 116 selects the business enterprise group corresponding to the target business enterprise among the business enterprise groups included in the list. Further, the business group identification unit 116 may specify the business group corresponding to the target business enterprise by referring to the list in which business companies corresponding to each of the plurality of business enterprise groups are registered.

  The business group identification unit 116 may specify the business group corresponding to the target business using the business type information, size information, and the like of the target business. The business group identification unit 116 may receive the business type information and size information of the target business operator from the communication terminal 20. Further, the business group identification unit 116 stores in advance business entity information including business type information and size information of a plurality of business operators, and acquires business type information and size information of the target business operator from the business operator information. May be

  The feature data acquisition unit 118 acquires feature data of the enterprise group identified by the enterprise group identification unit 116. The feature data acquisition unit 118 requests the enterprise group data management device 250 for the feature data of the enterprise group identified by the enterprise group identification unit 116, for example, and receives the feature data from the enterprise group data management device 250 Do. In addition, when the loan determination device 100 manages feature data of a plurality of business operator groups, the characteristic data acquisition unit 118 determines from the characteristic data under management that the business operator group identification unit 116 identifies. Feature data may be read out.

  The past CF information generation unit 120 generates past CF information of the target business entity. The past CF information generation unit 120 generates past CF information, for example, based on the accounting data acquired by the accounting data acquisition unit 104. The past CF information generation unit 120 uses the data of the account item related to deposits and deposits included in the accounting data, and adds / subtracts the change details starting from the cash deposit balance on the reference date, for each predetermined period. It may generate past CF information indicating the cash balance of the For example, the past CF information generation unit 120 generates past CF information indicating the daily, weekly, or monthly cash balance by adding or subtracting a transfer statement starting from the cash balance at the beginning of the year. Here, the cash balance represents the sum of cash and deposits and savings, but may include the case where cash is zero. That is, the cash balance may indicate only the deposit balance.

  The past CF information generation unit 120 may generate past type-specific CF information of the target business operator. The past CF information generation unit 120 generates past type-specific CF information, for example, based on the accounting data acquired by the accounting data acquisition unit 104.

  The past CF information generation unit 120 generates past sales CF information indicating fluctuations in cash related to sales for each predetermined period, using data of account items related to the sales activities of the target enterprise included in the accounting data. You may Further, the past CF information generation unit 120 generates past investment CF information indicating fluctuation of cash regarding investment for each predetermined period, using data of an account item related to investment activity of a target enterprise included in accounting data. You may In addition, the past CF information generation unit 120 generates past financial CF information indicating the change in cash related to the finance for each predetermined period, using data of the account item related to the financial activity of the target enterprise included in the accounting data. You may

  Further, the past CF information generation unit 120 may generate past free CF information indicating the past free cash flow of the target business. The past CF information generation unit 120 generates, for example, past free CF information from past sales CF information and past investment CF information. The past CF information generation unit 120 may generate past free CF information based on the accounting data.

  The past CF information generation unit 120 may generate past CF information based on the financial institution data acquired by the financial institution data acquisition unit 106. For example, when the accounting data acquisition unit 104 can not acquire accounting data, the past CF information generation unit 120 generates past CF information using the financial institution data acquired by the financial institution data acquisition unit 106. For example, the past CF information generation unit 120 adds and subtracts change details based on payment and attendance included in financial institution data starting from the cash deposit balance on the reference date, for example. Generate past CF information indicating deposit balance.

  The probability information generation unit 130 generates probability information indicating the probability of loan repayment of the target business. The probability information generation unit 130 has a future CF information generation unit 132, a score generation unit 134, and a consistency check unit 136. In addition, it is not necessarily mandatory that the probability information generation unit 130 have all of these configurations.

  The future CF information generation unit 132 generates future CF information of the target business. The future CF information generation unit 132 may generate future CF information using accounting data, financial institution data, transaction data, feature data, transaction relationship data, past CF information, an order acceptance rate, and a bill receipt rate. In the future, the CF information generation unit 132 may generate CF information in the future using all of them, or may generate CF information in the future using a part of these.

  The future CF information generation unit 132 generates future CF information, for example, based on past CF information. The future CF information generation unit 132 may generate future CF information by averaging future CF information for a plurality of years or predicting from the past CF information for a plurality of years. The future CF information generation unit 132 derives, for example, the generation pattern of sales in the past CF information, the generation pattern of purchases, the generation pattern of expenses, etc., and generates CF information in the future using at least one of them. .

  The future CF information generation unit 132 may generate future CF information using data of an account item related to the change in future deposit balance among the account items included in the accounting data. For example, the loan determination apparatus 100 generates CF information in the future using an account item on sales receivables and an account item on accounts payable.

  As a specific example, the future CF information generation unit 132 generates future CF information using data on accounts receivable. The future CF information generation unit 132 generates CF information in the future on the assumption that cash and deposits will increase, for example, on the scheduled collection date of accounts receivable.

  Also, in the future, the CF information generation unit 132 may generate CF information in the future using data on payables. The future CF information generation unit 132 generates future CF information on the assumption that cash and deposits will decrease on the scheduled payment of payables, for example.

  Further, the future CF information generation unit 132 may generate future CF information using data on unpaid money. The future CF information generation unit 132 generates future CF information, for example, on the assumption that cash and deposits will decrease on the scheduled payment date of the unpaid money.

  Further, the future CF information generation unit 132 may generate future CF information using data on advance payments. The future CF information generation unit 132 generates future CF information on the assumption that cash and deposits will not decrease on the scheduled payment date, for example, when it is indicated that there is a down payment by data on the down payment. As a specific example, in the future, the CF information generation unit 132 may generate CF information in the future assuming that cash and deposits are not reduced if the amount of advance payment is larger than the fee. Further, in the future, when the amount of advance payment is smaller than the charge, the CF information generation unit 132 may generate CF information from the cash and deposits as the difference between the charge and the advance payment decreases.

  The future CF information generation unit 132 may generate future CF information using estimated data and an order acceptance rate. For example, the future CF information generation unit 132 generates future CF information on the assumption that the estimate data meeting the order acceptance rate among the plurality of estimate data is received and received. For example, in the case where the number of estimated data is five and the order acceptance rate is 80%, CF information is generated in the future on the assumption that four of the five estimated data are accepted and received.

  The future CF information generation unit 132 may generate future CF information using the billing data and the billing payment rate. For example, the future CF information generation unit 132 generates future CF information on the assumption that the billing data meeting the billing payment rate among the plurality of billing data is to be deposited. For example, if the number of billing data is 20 and the billing payment rate is 95%, CF information will be generated in the future on the assumption that 19 of the 20 estimation data will be deposited.

  The future CF information generation unit 132 may generate future CF information using estimated data, billing data, an order acceptance rate, and a bill receipt rate. For example, in the future, the CF information generation unit 132 receives an order from the plurality of estimated data, and estimates the estimated data corresponding to the order receiving rate, and estimates the estimated data corresponding to the bill payment rate among the estimated data to be charged. Will generate future CF information as being deposited.

  The future CF information generation unit 132 may generate CF information in the future using the transaction relationship data of the trading partner of the target business entity. For example, the future CF information generation unit 132 uses the transaction relationship data to specify the payment status of the trading partner of the target company to other businesses, and generates CF information in the future using the payment status. As a specific example, the CF information generation unit 132 in the future refers to the transaction relationship data of the trading partner who is the billing destination of the billing data of the target business entity, and identifies the payment status to the other business partner. It is determined whether or not payment for the billing data is made according to the identified payment status. For example, if the deposit rate to the other business partner of the transaction partner is lower than a predetermined threshold, CF information is generated in the future, assuming that the deposit to the billing data is not made. In this way, it is possible to generate CF information in the future, taking into consideration the possibility that the business partner of the target business entity will not pay to the target business enterprise. The future CF information generation unit 132 may generate future CF information in consideration of the amount of money to be received, in addition to the payment rate to the other business partner of the trading partner.

  Also, for example, the future CF information generation unit 132 specifies the correspondent of the business partner of the target business using the transaction relationship data, and generates the future CF information using the specified attribute information of the correspondent. As a specific example, the CF information generation unit 132 in the future refers to the transaction relationship data of the trading partner who is the billing destination of the billing data of the target business entity, identifies the trading partner of the trading partner, and identifies the trading partner identified. In accordance with the attribute information, it is determined whether payment for the billing data is made. For example, the threshold value is compared with at least one of the business size and the credit rating of the identified trading partner, and if lower than the threshold value, future CF information is generated as that no payment is made for the billing data. In this way, it is possible to generate CF information in the future in consideration of the possibility that the trading partner of the target company can not collect from the trading partner of the trading partner.

  Also, for example, the future CF information generation unit 132 generates future CF information using the difference between past CF information and financial institution data. For example, the future CF information generation unit 132 corrects the past CF information based on the difference between the past CF information and the financial institution data, and generates the future CF information using the corrected past CF information. The future CF information generation unit 132 may correct, for example, the past CF information so that the difference between the past CF information and the financial institution data approaches the financial institution data.

  The future CF information generation unit 132 may generate future type CF information of the target business. The future CF information generation unit 132 may generate future type-specific CF information using accounting data, financial institution data, transaction data, feature data, transaction relationship data, past CF information, an order acceptance rate, and a bill receipt rate. In the future, the CF information generation unit 132 may generate CF information on a type basis in the future using all of them, or may generate CF information on a type basis in the future using a part of these.

  The future CF information generation unit 132 generates future type-specific CF information based on, for example, past type-specific CF information. The future CF information generation unit 132 may generate future type-specific CF information by averaging the past type-specific CF information for a plurality of years or predicting it from the plurality of years of past type-specific CF information.

  The future CF information generation unit 132 may generate future sales CF information by averaging past sales CF information for a plurality of years or predicting from the sales CF information for a plurality of years. The future CF information generation unit 132 may generate future investment CF information by averaging past investment CF information for a plurality of years or predicting from past investment CF information for a plurality of years. The future CF information generation unit 132 may generate future financial CF information by averaging past financial CF information for a plurality of years or predicting from the past financial CF information for a plurality of years.

  The future CF information generation unit 132 may generate the future business CF information, the future investment CF information, and the future financial CF information by the same method. For example, the future CF information generation unit 132 applies the same averaging method and the same prediction method to each of past business CF information, past investment CF information, and past financial CF information for a plurality of years. Generate future sales CF information, future investment CF information, and future financial CF information.

  Also, the future CF information generation unit 132 may generate the future sales CF information, the future investment CF information, and the future financial CF information by different methods. For example, the future CF information generation unit 132 applies a different averaging method and a different forecasting method to each of past sales CF information, past investment CF information, and past financial CF information for a plurality of years to perform future sales. Generate CF information, future investment CF information, and future financial CF information.

  When the future CF information generation unit 132 generates one of the type-specific CF information, it may use another of the type-specific CF information in the future. For example, the future CF information generation unit 132 generates future sales CF information based on past sales CF information, and generates future investment CF information based on past investment CF information and future sales CF information. Also, for example, the future CF information generation unit 132 generates future sales CF information based on past sales CF information, and generates future financial CF information based on past financial CF information and future sales CF information.

  The future CF information generation unit 132 derives, for example, the occurrence pattern of sales in the past sales CF information, the occurrence pattern of purchases, the occurrence pattern of expenses, etc., and generates future sales CF information using these as well. Good. The future CF information generation unit 132 may generate future sales CF information using data of an account item related to a change in future deposit balance among account items included in the accounting data. The future CF information generation unit 132 generates future sales CF information using, for example, an account item on sales receivables and an account item on accounts payable.

  As a specific example, the future CF information generation unit 132 generates future sales CF information using data on accounts receivable. The future CF information generation unit 132 generates future sales CF information on the assumption that cash and deposits will increase, for example, on the scheduled collection date of accounts receivable.

  Also, in the future, the CF information generation unit 132 may generate business CF information in the future using data on payables. The future CF information generation unit 132 generates future sales CF information on the assumption that cash and deposits will decrease on the scheduled payment of payables, for example.

  Also, the future CF information generation unit 132 may generate future sales CF information using data on unpaid money. The future CF information generation unit 132 generates future sales CF information on the assumption that cash and deposits will decrease, for example, on the scheduled payment date of the unpaid money.

  Further, the future CF information generation unit 132 may generate future sales CF information using data on advance payments. The future CF information generation unit 132 generates future sales CF information on the assumption that cash will not decrease on the scheduled payment date, for example, when it is indicated that there is a down payment by data on the down payment. As a specific example, the future CF information generation unit 132 may generate future business CF information on the assumption that cash and deposits are not reduced if the amount of prepayment is greater than the fee. In the future, the CF information generation unit 132 may generate business CF information in the future on the assumption that the difference between the charge and the advance payment is reduced from the cash and deposits if the advance payment is smaller than the charge.

  The future CF information generation unit 132 may generate future sales CF information using the estimated data and the order acceptance rate. For example, the future CF information generation unit 132 generates future sales CF information on the assumption that the estimate data meeting the order receiving rate among the plurality of estimate data is received and received. For example, in the case where the number of estimated data is five and the order acceptance rate is 80%, future sales CF information is generated on the assumption that an order is received for four of the five estimated data.

  The future CF information generation unit 132 may generate future sales CF information using the billing data and the billing payment rate. For example, the future CF information generation unit 132 generates future sales CF information on the assumption that the billing data meeting the billing payment rate among the plurality of billing data is to be deposited. For example, in the case where the number of billing data is 20 and the billing payment rate is 95%, future sales CF information is generated on the assumption that 19 of the 20 pieces of estimate data are to be deposited.

  The future CF information generation unit 132 may generate future sales CF information using estimated data, billing data, an order acceptance rate, and a bill receipt rate. For example, in the future, the CF information generation unit 132 receives an order from the plurality of estimated data, and estimates the estimated data corresponding to the order receiving rate, and estimates the estimated data corresponding to the bill payment rate among the estimated data to be charged. , Generate future sales CF information as being to be deposited.

  The future CF information generation unit 132 may generate sales CF information in the future, using the transaction relationship data of the trading partner of the target business. For example, the future CF information generation unit 132 specifies the payment status of the business partner of the target company to the other business using the transaction relationship data, and generates business CF information in the future using the payment status. As a specific example, the CF information generation unit 132 in the future refers to the transaction relationship data of the trading partner who is the billing destination of the billing data of the target business entity, and identifies the payment status to the other business partner. It is determined whether or not payment for the billing data is made according to the identified payment status. For example, if the payment rate to the other business partner of the transaction partner is lower than a predetermined threshold value, future business CF information is generated on the assumption that payment for the billing data is not made. In this way, it is possible to generate sales CF information in the future, taking into consideration the possibility that the business partner of the target company will not pay to the target business company. In the future, the CF information generation unit 132 may generate business CF information in the future, in consideration of the amount of money to be received, in addition to the deposit rate to the other business partner of the trading partner.

  Further, for example, the future CF information generation unit 132 specifies the correspondent of the business partner of the target business using the transaction relationship data, and generates future sales CF information using the specified attribute information of the correspondent. As a specific example, the CF information generation unit 132 in the future refers to the transaction relationship data of the trading partner who is the billing destination of the billing data of the target business entity, identifies the trading partner of the trading partner, and identifies the trading partner identified. In accordance with the attribute information, it is determined whether payment for the billing data is made. For example, it compares the threshold value with at least one of the business size and the credit rating of the identified trading partner, and if lower than the threshold value, it generates future sales CF information as not being paid for the billing data. In this way, it is possible to generate sales CF information in the future, taking into consideration the possibility that the trading partner of the target enterprise can not collect from the trading partner of the trading partner.

  Further, the future CF information generation unit 132 derives, for example, a sale pattern of fixed assets, a purchase pattern of fixed assets, a sale pattern of securities, a purchase pattern of securities, etc. in past investment CF information, and uses them. Future investment CF information may be generated. Further, in the future, the CF information generation unit 132 may derive, for example, an increase pattern of borrowing and a repayment pattern of borrowing in past financial CF information, and may generate future financial CF information using these.

  Also, for example, the future CF information generation unit 132 generates future type-specific CF information using the difference between past type-specific CF information and financial institution data. For example, the future CF information generation unit 132 corrects the past type CF information based on the difference between the past type CF information and the financial institution data, and uses the corrected past type CF information to calculate the future type CF information. Generate The future CF information generation unit 132 may correct, for example, the past type-specific CF information so that the difference between the past type-specific CF information and the financial institution data approaches the financial institution data.

  The future CF information generation unit 132 may generate future free CF information indicating the future free cash flow of the target business. The future CF information generation unit 132 generates future free CF information from, for example, future sales CF information and future investment CF information.

  The score generation unit 134 generates a score indicating the probability of loan repayment of the target business. The score generation unit 134 may generate a score using accounting data, financial institution data, transaction data, feature data, transaction relationship data, past CF information, an order acceptance rate, and a bill receipt rate. The score generation unit 134 may generate a score using all of these, or may generate a score using a part of these. The score generation unit 134 may generate a score using any method.

  The consistency check unit 136 checks the consistency of the collected various data in determining whether the loan to the target enterprise is possible for the loan application data received from the communication terminal 20. For example, the consistency check unit 136 checks the consistency between the balance data acquired by the accounting data acquisition unit 104 and the financial institution data acquired by the financial institution data acquisition unit 106. Also, for example, the consistency check unit 136 checks the consistency between the billing data acquired by the transaction data acquisition unit 108 and the payment data in the financial institution data acquired by the financial institution data acquisition unit 106.

  The consistency check unit 136 determines that the data is invalid data, for example, when the data mismatch is detected as a result of checking the consistency. The consistency check unit 136 may generate a data legitimacy score for each piece of data based on the degree of match.

  The consistency check unit 136 may notify the request data transmission unit 142 of the data determined to be invalid data. The request data transmitting unit 142 may transmit, to the communication terminal 20, request data including at least one of data correction and data addition submission request for the data determined to be incorrect data by the consistency check unit 136. . When the consistency check unit 136 receives the correction content of the data and the additional data with respect to the request data, the consistency check unit 136 may correct the data using these.

  The consistency check unit 136 may notify the request data transmission unit 142 of data whose rating of validity of data is lower than a predetermined threshold. The request data transmitting unit 142 includes at least one of data correction and additional data submission request for data whose integrity is determined by the integrity checking unit 136 to be lower than a predetermined threshold. The request data may be transmitted to the communication terminal 20. When the consistency check unit 136 receives the correction content of the data and the additional data with respect to the request data, the consistency check unit 136 may correct the data using these.

  As described above, since the consistency check unit 136 checks the consistency of the collected data when determining whether the target business is financed or not, it is possible to detect, for example, a fraudulent application. In addition, it is possible to prevent the determination of the loan availability based on the erroneous data.

  The loanability determination unit 144 uses the probability information generated by the probability information generation unit 130 to determine whether the loan application data acquired by the application data acquisition unit 102 can be loaned to the target business. For example, the loan determination unit 144 first creates a loan repayment schedule using the loan application amount, the loan request date, and the loan period in the loan application data. As a specific example, a repayment schedule indicating monthly repayment amount is created.

  Then, the loanability determination unit 144 determines whether the repayment plan can be realized or not by the future CF information generated by the CF information generation unit 132 in the future, thereby determining the loanability. For example, when the amount of repayment for each month to be repayed is subtracted from the cash deposits of each month indicated by the CF information in the future, the loanability determination unit 144 determines whether there is a shortage of cash deposits.

  The loanability determination unit 144 may further use the future type-specific CF information generated by the CF information generation unit 132 to determine whether to allow or not. The loanability determination unit 144 determines the CF pattern of the target business operator based on, for example, future business CF information, future investment CF information, and future financial CF information, and determines whether to use the CF pattern. As an example of CF pattern, future sales CF information is positive, future investment CF information is negative, future financial CF information is negative pattern (denoted as pattern A), future sales CF information is positive, future investment CF information is negative , Future financial CF information has a positive pattern (described as pattern B), Future operating CF information is positive, Future investment CF information is positive, Future financial CF information has a negative pattern (pattern C), Future operating CF Negative information, future investment CF information negative, future financial CF information positive pattern (denoted as pattern D), future business CF information negative, future investment CF information positive, future financial CF information negative pattern ( Pattern E), and future sales CF information is negative, future investment CF information plus is future goods CF information and the like have a positive pattern (referred to as pattern F).

  The loanability determination unit 144 may determine whether or not the creditability is low in the order of the patterns A, B, C, D, E, and F. For example, even if there are few cash deposits indicated by CF information in the future with respect to the repayment schedule, the loanability judgment unit 144 can be loaned if the CF pattern of the target company is any of patterns A, B, and C. It is determined that If the difference between the repayment schedule and the cash and deposits indicated by the CF information in the future is equal to or less than the first difference, the loan possibility determining unit 144 may determine that the loan is possible for any of the patterns A, B, and C. If the difference is less than or equal to a second difference greater than the first difference, then it may be determined that the loan is acceptable if it is pattern A or pattern B, and the difference is less than or equal to a third difference greater than the second difference If there is a pattern A, it may be determined that the loan is possible.

  Further, for example, even if the credit possibility determination unit 144 has a large amount of cash and deposits indicated by CF information in the future with respect to the repayment schedule, if the CF pattern of the target business is one of patterns D, E, and F. Judge as no loan. If the difference between the repayment schedule and the cash and deposits indicated by the CF information in the future is equal to or less than the first difference, the loanability judgment unit 144 may judge that the loan is not even for any of the patterns D, E, and F. If the difference is less than or equal to a second difference greater than the first difference, it may be determined that the loan is not a pattern E or a pattern F, and the difference may be less than or equal to a third difference greater than the second difference If there is a pattern F, it may be determined that the loan is not made.

  The credit acquisition unit 146 acquires the credit of the target business. The credit acquisition unit 146 acquires, for example, a score in the credit information data of the target business as the credit of the target business. The credit degree acquisition unit 146 may request the credit information management device 240 for the credit information data of the target business entity, and may receive the credit information data from the credit information management device 240. In addition, when the loan determination device 100 manages credit information data of a plurality of businesses, the credit degree acquisition unit 146 may read out the credit information data of the target business from the managed credit information data. In addition, the credit rating acquisition unit 146 may acquire, as the credit rating of the target enterprise, credit rating information indicating the credit rating of the target enterprise by the financial institution. The credit rating acquisition unit 146 may request the credit rating information of the target business from the financial institution, and may receive the credit rating information from the financial institution. In addition, the credit rating acquisition unit 146 may receive and store credit rating information of a plurality of businesses from a financial institution, and read out the credit rating information of the target business from the stored credit rating information.

  The credit acquisition unit 146 may generate the credit of the target business using the credit information data of the business partner of the target business. For example, the credibility acquisition unit 146 deducts the score of the target business if the score of the business partner of the target business is lower than a predetermined threshold. In addition, for example, when the rating of the business partner of the target business is higher than a predetermined threshold, the credit degree acquisition unit 146 adds a score to the target business. Further, the credit degree acquisition unit 146 adds a score corresponding to the score of the business partner of the target business to the score of the target business. Further, the credit degree acquisition unit 146 may generate the credit degree of the target business using the credit rating information of the business partner of the target business. For example, when the credit rating information of the business partner of the target business is lower than a predetermined threshold, the credit acquisition unit 146 deducts the credit of the target business. Also, for example, when the credit rating information of the business partner of the target business is higher than a predetermined threshold, the credit acquisition unit 146 adds the credit of the business of the target. Further, the credit degree acquisition unit 146 adds a score according to the credit rating information of the business partner of the target business to the credit of the target business.

  The credit acquisition unit 146 may generate, for the target business, the credit of the target business using the credit information data of the business having a business relationship in a chained manner. For example, the credit degree acquisition unit 146 adds a weight to the score of a business entity that has a business relationship in a chain relationship with the business of the target business, with respect to the score of the business target. As weighting, for example, the weight of the score of the business operator whose chain relation is farther from the target business operator may be lowered. In addition, the credit acquisition unit 146 may generate, for the target business, the credit of the target business using the credit rating information of the business having a business relationship in a chained relationship. For example, the credit degree acquisition unit 146 adds a weight to a value corresponding to the credit rating information of a business entity who has a business relationship in a chain relationship with the target business entity, with respect to the creditworthiness of the target business entity. As weighting, for example, the weight of the score of the business operator whose chain relation is farther from the target business operator may be lowered.

  The credit acquisition unit 146 may generate the credit of the target business by combining a plurality of data existing on the Internet. The credit acquisition unit 146 generates, for example, the credit of the target business using information such as the update frequency of the website operated by the target business and the number of site visitors. In addition, the credit degree acquisition unit 146 may generate the credit degree of the target business using the evaluation information indicating the evaluation regarding the business operated by the target business on the website operated by the third party. In addition, the credit acquisition unit 146 may generate the credit of the target business using information on a person related to the target business, such as a representative of the target business. For example, the credibility acquisition unit 146 generates the credibility of the target business using the information of SNS (Social Networking Service) of the representative of the target business.

  The loanability determination unit 144 may use the credit of the target enterprise generated by the credit acquisition unit 146 to determine whether to loan the target enterprise. For example, even if there are many cash deposits indicated by CF information in the future with respect to the repayment schedule, the loanability judgment unit 144 judges that the credit is not given if the credit of the target company is low. For example, for each difference between the repayment schedule and the cash and deposits indicated by the CF information in the future, the creditability determining unit 144 associates and registers the credit rating that is determined to be the lending status, and the credit rating generated by the credit rating acquisition unit 146 If it is lower than the credit corresponding to the difference, it is judged that the loan is not made.

  Further, for example, even if the cash deposit indicated by the CF information is small in the future with respect to the repayment schedule, the loanability determination unit 144 determines that the credit is possible if the creditworthiness of the target enterprise is high. For example, for each difference between the repayment schedule and the cash and deposits indicated by the CF information in the future, the creditability determining unit 144 associates and registers the creditworthiness to be judged as the creditable, and the creditworthiness generated by the creditworthiness acquisition unit 146 is If it is higher than the credit corresponding to the difference, it is judged that the loan is possible.

  The loan processing execution unit 148 executes the loan processing in accordance with the determination result by the loan availability determination unit 144. For example, when it is determined that the loan is determined to be possible by the loan determination unit 144, the loan processing execution unit 148 starts a loan procedure. The loan processing execution unit 148 sends, to the communication terminal 20, the financing procedure data including the contents for notifying the communication terminal 20 of the target business of the fact that the loan is possible and the contents for requesting the information necessary for the loan. May be sent. Also, for example, when it is determined that the loan is possible by the loan possibility determination unit 144, the loan processing execution unit 148 makes a loan offer to the target enterprise. The loan processing execution unit 148 sends, to the communication terminal 20, the loan offer data including the content for notifying the communication terminal 20 of the target business that the loan is possible and the content for requesting the information necessary for the loan. May be sent.

  The loan processing execution unit 148 may notify the target enterprise of the fact that the loan is not accepted if the loan possibility determination unit 144 determines that the loan is not made. For example, the loan processing execution unit 148 transmits, to the communication terminal 20 of the target business, notification data notifying that the loan is not made.

  The post-financing management unit 150 manages the target business entity after the loan is made. For example, after the loan is made, the post-financing management unit 150 causes the CF information generation unit 132 to continuously generate future CF information of the target enterprise. Then, when it is detected based on the CF information that there may be a problem in the loan repayment, the post-financing management unit 150 notifies that to a person in charge of collecting the loan. For example, if the cash deposit of each month of the newly generated future CF information is smaller than the monthly repayment amount, the post-financing management unit 150 determines that there is a possibility that the loan repayment may be interrupted.

  The post-financing management unit 150 may execute processing such as advancing the timing of reminding to be transmitted to the target enterprise, when it is detected that there may be a problem in repayment of the loan. The post-financing management unit 150 executes various processes in a manner that does not deviate from the spirit of the Money Lending Business Act.

  The repayment information generation unit 152 generates repayment information. The repayment information is, for example, a loan repayment schedule. The repayment information generation unit 152 may use the CF information in the future to generate a repayment schedule according to the purpose. For example, the repayment information generation unit 152 generates a repayment schedule for repayment by profit, a repayment schedule for repayment by cashing, and the like.

  The repayment information generation unit 152 may generate a repayment schedule of a loan using CF information in the future, when it is determined that the loan is determined to be possible by the loan determination unit 144. For example, the repayment information generation unit 152 refers to the CF information in the future, specifies a month in which the amount of cash is greater than the other months, and generates a repayment schedule in which the amount of repayment in that month is increased than in the other months Do.

  The repayment information transmission unit 154 may transmit the repayment information generated by the repayment information generation unit 152 to the communication terminal 20 of the target enterprise. Thus, it is possible to flexibly propose a repayment schedule to the target enterprise.

  The repayment information generation unit 152 may generate a repayment-possible repayment schedule by using CF information in the future, when it is determined by the loanability determination unit 144 that the loan is not made. For example, the repayment information generation unit 152 generates a repayment schedule in which at least a part of the loan application data is changed.

  For example, if it can be determined that the loan can be made by changing the loan request date included in the loan application data in the future using the CF information, the repayment information generation unit 152 generates a repayment schedule when the loan date is changed. Do. As a specific example, when the loan request date included in the loan application data is April 2017, and the loan is not accepted as it is, if it becomes possible to obtain a loan by changing the loan date to July 2017, July 2017 Generate a repayment schedule to be a loan date.

  Further, when it can be determined that the loan is possible by changing the loan period included in the loan application data in the future, the repayment information generation unit 152 generates a repayment schedule in which the loan period is changed, using the CF information. As a specific example, if the loan period included in the loan application data is June, and the loan is not accepted as it is, but it becomes possible to loan by changing the loan period to October, the repayment schedule with the loan period as October Generate

  The repayment information transmission unit 154 transmits the repayment information to the communication terminal 20 of the target company, whereby it is possible to inform the target company that a loan can be made by changing the conditions of the loan application data. For example, when the target company's business is agriculture, it is difficult to equally distribute the loan amount and repay it each month, but it may be possible to repay it collectively in the harvest period, etc. Characteristics of repayment ability differ depending on According to the repayment information generation unit 152 according to the present embodiment, a repayment schedule suitable for the cash flow indicated by the future CF information of the target business can be generated, so that the repayment schedule can be flexibly provided according to the situation of the target business. be able to.

  FIG. 17 schematically illustrates an example of a functional configuration of the account management device 300. The account management apparatus 300 includes an account management unit 302, a data acquisition unit 304, a future CF information generation unit 306, a payment request acquisition unit 308, a payment process execution unit 310, a loan availability determination unit 312, a loan repayment execution unit 314, and interest rate processing execution. And an instruction receiving unit 318. In addition, it is not necessarily required for the account management device 300 to have all these configurations.

  The account management unit 302 manages account balances of a plurality of businesses. The account management unit 302 may open an account for the business in response to a request from the business. The account management unit 302 manages accounts of a plurality of business operators, for example, in one or more bank accounts. As a specific example, the account management unit 302 manages the account balances of N businesses or more than N businesses in N bank accounts. The account management unit 302 manages, for example, accounts of a plurality of businesses in a bank account in the manager's name of the account management apparatus 300.

  The data acquisition unit 304 acquires various data. The data acquisition unit 304 may acquire accounting data, financial institution data, transaction data, feature data, transaction relationship data, past CF information, an order acceptance rate, and a bill payment rate. The data acquisition unit 304 may acquire all of these, or may acquire some of these.

  The future CF information generation unit 306 uses the data acquired by the data acquisition unit 304 to generate future CF information of the business operator managing the account. The future CF information generation unit 306 may generate future CF information of the business operator based on at least one of accounting data and transaction data. The future CF information generation unit 306 may generate future CF information by executing the same process as the future CF information generation unit 132. The future CF information generation unit 306 may generate future type-specific CF information by executing processing similar to that of the CF information generation unit 132 in the future.

  The payment request acquisition unit 308 acquires a payment request for a business partner from a business entity that manages an account. The payment request includes, for example, the payee's account number and the payment amount. The payment request acquisition unit 308 receives, for example, the payment request transmitted by the communication terminal 20. An entity that has made a payment request may be referred to as a target entity.

  The payment processing execution unit 310 executes payment processing according to the payment request acquired by the payment request acquisition unit 308. The payment processing execution unit 310 may execute payment processing if the account balance of the target business is greater than the payment amount of the payment request. The payment processing execution unit 310, for example, transfers the payment amount to the account number of the payee of the payment request. The payment processing execution unit 310 may present, to the operator or the like of the account management apparatus 300, an instruction to remit the payment amount to the account number of the payment destination of the payment request. The payment processing execution unit 310 subtracts the payment amount from the account balance upon payment. If the account balance of the target business is smaller than the payment amount of the payment request, the payment processing execution unit 310 causes the loan availability determination unit 312 to determine whether the target business can be financed.

  The loan determination unit 312 determines whether to lend to the target business. Based on the future CF information of the target enterprise generated by the CF information generation unit 306 in the future, the loan availability determination unit 312 may determine the availability of the loan to the target enterprise. Based on the future type-specific CF information of the target business operator generated by the CF information generation unit 306 in the future, it may be determined whether to lend the target business operator. The loanability determination unit 312 may determine whether to lend or not by executing the same process as the loanability determination unit 144.

  In response to the request from the payment process execution unit 310, the loan availability determination unit 312 may determine whether the target business is financed. For example, the loan possibility determination unit 312 determines whether to lend a loan amount equal to or more than the difference between the payment amount of the payment request and the account balance of the target business.

  For example, the loan possibility determination unit 312 determines whether to lend the difference between the payment amount of the payment request and the account balance of the target business. In addition, the loan possibility determination unit 312 may determine whether to lend an amount obtained by adding a predetermined amount to the difference between the payment amount of the payment request and the account balance of the target business. The predetermined amount of money may be determined by the manager of the account management apparatus 300, the target business operator, or the like as the minimum necessary amount of money for the account balance of the target business company.

  The account management unit 302 may add the loan amount to the account balance of the target business if it is determined that the loan is possible by the loan determination unit 312. For example, the account management unit 302 adds the difference between the payment amount of the payment request and the account balance of the target business to the account balance of the target business. Further, the account management unit 302 adds, for example, an amount obtained by adding a predetermined amount to the difference between the payment amount of the payment request and the account balance of the target business to the account balance of the target business.

  The loanability determination unit 312 may periodically determine whether to lend to a business that manages an account. The loan determination unit 312 periodically determines, for example, a limit of available funds for a business that manages an account. The loan possibility determination unit 312 may notify the account management unit 302 of the possible loan amount. The account management unit 302 may present, to the business that manages the account, the available financial limits determined by the loan availability determination unit 312. This enables the business operator to grasp how much financing can be received at this time.

  After the loan amount is added to the account balance by the account management unit 302 and the payment process execution unit 310 executes the payment process, the loan repayment execution unit 314 monitors the account balance of the target business. Then, the loan repayment execution unit 314 executes repayment processing from the account balance in accordance with predetermined conditions. For example, when the account balance of the target company is greater than the loan amount, the loan repayment execution unit 314 subtracts the loan amount from the account balance. The loan amount deducted is used for loan repayment. In addition, the loan repayment execution unit 314 may execute repayment processing in order to repay the loan amount in stages. For example, when the account balance of the target enterprise is smaller than the loan amount, the loan repayment execution unit 314 subtracts the difference between the account balance and a predetermined threshold from the account balance. As a specific example, when the loan amount is 1,000, the account balance is 500, and the predetermined threshold is 300, the loan repayment execution unit 314 sets 200, which is the difference between the account balance and the threshold. Subtract from the balance. The deducted amount is used to repay the loan.

  The interest rate processing execution unit 316 monitors the account balance of the target business after the account management unit 302 adds the loan amount to the account balance and the payment processing execution unit 310 executes the payment process. Then, when the account balance of the target company is smaller than the loan amount, the interest rate processing execution unit 316 may subtract from the account balance the interest rate amount determined according to a predetermined interest rate rule. The deducted interest rate amount is used to pay the interest rate for the loan amount. The interest rate processing execution unit 316 subtracts the amount of interest from the account balance according to a predetermined timing regardless of the amount of the account balance of the target company. For example, even if the amount of interest is 50 and the account balance of the target company is 300 with the balance of 300 loans, when the timing comes, 316 subtracts 50 and sets the account balance It is set to -350. In addition, as long as the credit line is within the range, what is actually repaid may be when the account balance exceeds a predetermined threshold.

  The instruction receiving unit 318 receives an instruction for the managed account from the communication terminal 20 of the enterprise. The instruction receiving unit 318, for example, withdraws from a managed account to a bank account designated by the business operator.

  FIG. 18 schematically illustrates an example of a hardware configuration of a computer 1000 functioning as the loan determination device 100 or the account management device 300. A computer 1000 according to this embodiment includes a CPU peripheral unit having a CPU 1010, a RAM 1030, and a graphic controller 1085 mutually connected by a host controller 1092, a ROM 1020 connected to a host controller 1092 by an input / output controller 1094, and a communication I / O. It comprises an input / output unit having an F 1040, a hard disk drive 1050, a DVD drive 1070 and an input / output chip 1080.

  The CPU 1010 operates based on programs stored in the ROM 1020 and the RAM 1030 to control each part. The graphic controller 1085 acquires image data generated by the CPU 1010 or the like on a frame buffer provided in the RAM 1030 and causes the display 1090 to display the image data. Alternatively, the graphic controller 1085 may internally include a frame buffer for storing image data generated by the CPU 1010 or the like.

  The communication I / F 1040 communicates with other devices via a network by wire or wirelessly. The communication I / F 1040 also functions as hardware for performing communication. The hard disk drive 1050 stores programs and data used by the CPU 1010. The DVD drive 1070 reads a program or data from the DVD-ROM 1072 and provides it to the hard disk drive 1050 through the RAM 1030.

  The ROM 1020 stores a boot program executed when the computer 1000 starts up, a program depending on the hardware of the computer 1000, and the like. The input / output chip 1080 connects various input / output devices to the input / output controller 1094 via, for example, a parallel port, a serial port, a keyboard port, a mouse port, and the like.

  The program provided to the hard disk drive 1050 via the RAM 1030 is stored in a recording medium such as the DVD-ROM 1072 or an IC card and provided by the user. The program is read from the recording medium, installed in the hard disk drive 1050 via the RAM 1030, and executed by the CPU 1010.

  A program installed in the computer 1000 and causing the computer 1000 to function as the loan determination device 100 or the account management device 300 works on the CPU 1010 or the like to cause the computer 1000 to function as each part of the loan determination device 100 or the account management device 300. Good. The information processing described in these programs is a specific means in which software and the various hardware resources described above cooperated by being read into the computer 1000, and the application data acquisition unit 102, the accounting data acquisition unit 104, financial institution data acquisition unit 106, transaction data acquisition unit 108, order acceptance rate calculation unit 110, bill receipt rate calculation unit 112, transaction relationship data acquisition unit 114, business group identification unit 116, feature data acquisition unit 118, past CF Information generation unit 120, probability information generation unit 130, request data transmission unit 142, loan availability determination unit 144, credit degree acquisition unit 146, loan processing execution unit 148, post loan management unit 150, repayment information generation unit 152, and repayment information transmission It functions as the unit 154. In addition, the information processing described in these programs is read by the computer 1000, and the account management unit 302 and the data acquisition unit are specific means in which software and the various hardware resources described above cooperated. 304 functions as a future CF information generation unit 306, a payment request acquisition unit 308, a payment processing execution unit 310, a loan availability determination unit 312, a loan repayment execution unit 314, an interest rate processing execution unit 316, and an instruction reception unit 318. Then, by realizing calculation or processing of information according to the purpose of use of the computer 1000 in this embodiment by these specific means, the loan determination device 100 or the account management device 300 specific to the purpose of use is constructed. Be done.

  As mentioned above, although this invention was demonstrated using embodiment, the technical scope of this invention is not limited to the range as described in the said embodiment. It is apparent to those skilled in the art that various changes or modifications can be added to the above embodiment. It is also apparent from the scope of the claims that the embodiments added with such alterations or improvements can be included in the technical scope of the present invention.

  The order of execution of each process such as operations, procedures, steps and steps in the devices, systems, programs and methods shown in the claims, the specification and the drawings is as follows. It is to be noted that “it can be realized in an arbitrary order unless it is explicitly stated as“ etc. ”and the output of the previous process is not used in the later process. With regard to the flow of operations in the claims, the specification and the drawings, even if it is described using “first,” “next,” etc. for the sake of convenience, it means that it is essential to carry out in this order. It is not a thing.

10 Network, 20 Communication Terminal, 22 Data Items, 100 Financing Determination Device, 102 Application Data Acquisition Unit, 104 Accounting Data Acquisition Unit, 106 Financial Institution Data Acquisition Unit, 108 Transaction Data Acquisition Unit, 110 Order Acceptance Rate Calculation Unit, 112 Billing Rate calculation unit 114 Transaction relationship data acquisition unit 116 Enterprise group identification unit 118 Characteristic data acquisition unit 120 Past CF information generation unit 130 Probability information generation unit 132 Future CF information generation unit 134 Score generation unit 136 Consistency check unit, 142 request data transmission unit, 144 loan possibility judgment unit, 146 credit degree acquisition unit, 148 loan processing execution unit, 150 post loan management unit, 152 repayment information generation unit, 154 repayment information transmission unit, 170 data item, 182 Past CF Information, 184 Future CF Information, 190 By Past Type CF information, 191 past sales CF information, 192 past investment CF information, 193 past financial CF information, 194 future type CF information, 195 future sales CF information, 196 future investment CF information, 197 future financial CF information, 210 accounting data management Equipment, 212 data items, 214 data items, 220 transaction data management devices, 222 data items, 224 data items, 230 financial institution data management devices, 232 data items, 240 credit information management devices, 242 data items, 250 business group data Management device, 252 data item, 260 transaction relationship management device, 262 data item, 300 account management device, 302 account management unit, 304 data acquisition unit, 306 future CF information generation unit, 308 payment request acquisition unit, 310 payment processing execution unit , 312 Financing decision Division, 314 loan repayment execution unit, 316 interest rate processing execution unit, 318 instruction reception unit, 1000 computer, 1010 CPU, 1020 ROM, 1030 RAM, 1040 communication I / F, 1050 hard disk drive, 1070 DVD drive, 1072 DVD-ROM, 1080 I / O chip, 1085 graphics controller, 1090 display, 1092 host controller, 1094 I / O controller

Claims (9)

Accounting data acquisition department which acquires accounting data on accounting of business,
Based on the accounting data, a plurality of future type-specific CF information indicating the plurality of types of future cash flows of the business entity are generated, and based on the plurality of future type-specific CF information, the future of the business entity An information processing apparatus comprising: a future CF information generation unit that generates future CF information indicating a cash flow.   The future CF information generation unit is a future sales CF information indicating a future operating cash flow of the business, a future investment CF information indicating a future investment cash flow of the business, and a future financial cash flow of the business. The information processing apparatus according to claim 1, generating future financial CF information indicating.   The information processing apparatus according to claim 1, wherein the future CF information generation unit generates future free CF information indicating a future free cash flow of the business entity. The system further comprises: a past CF information generation unit that generates a plurality of past type-specific CF information respectively indicating the plurality of types of past cash flows of the business operator based on the accounting data.
The information processing apparatus according to claim 1, wherein the future CF information generation unit generates the plurality of future type CF information based on the plurality of past type CF information. The past CF information generation unit includes past business CF information indicating a past operating cash flow of the business, past investment CF information indicating a past investment cash flow of the business, and past financial cash flow of the business Generate past financial CF information indicating
The future CF information generation unit, based on the past sales CF information, the past investment CF information, and the past financial CF information, indicates future business CF information indicating the future business cash flow of the business, the business The information processing apparatus according to claim 4, generating future investment CF information indicating future investment cash flow and future financial CF information indicating the future financial cash flow of the business. And a transaction data acquisition unit that acquires transaction data that functions as a transaction by the business entity.
The information processing apparatus according to any one of claims 1 to 5, wherein the future CF information generation unit generates the future CF information based on the accounting data and the transaction data. An application data acquisition unit that acquires a loan application data including a loan application amount by the company;
The information according to any one of claims 1 to 6, further comprising: a loan availability determination unit that determines whether to lend to the business based on the loan application data and the plurality of future type CF information. Processing unit. It further comprises a business group identification unit for specifying a business group corresponding to the business,
The future CF information generation unit generates the plurality of future type CF information based on the accounting data and the feature data indicating the features of the cash flow of the business group identified by the business group identification unit. The information processing apparatus according to any one of claims 1 to 7, wherein future CF information indicating a future cash flow of the business entity is generated based on the plurality of future type CF information.   A program for causing a computer to function as the information processing apparatus according to any one of claims 1 to 8.

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