JP2018163511A - Information processing apparatus and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To desirably provide a technology that enables support of a businessman by using data managed by an accounting management system.SOLUTION: There is provided an information processing apparatus comprising: an accounting data acquisition part that acquires accounting data related to accounting for a businessman; and a future CF information creation part that creates future CF information indicating the businessman's future cash flow on the basis of the accounting data. There is also provided an information processing apparatus comprising: an accounting data acquisition part that acquires accounting data related to accounting for the businessman; a businessman group specification part that specifies a businessman group corresponding to the businessman; and an information creation part that creates probability information indicating probability that the businessman returns a loan on the basis of the accounting data and characteristic data indicating the characteristics of cash flow of the businessman group specified by the businessman group specification part.SELECTED DRAWING: Figure 1

Description

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

There has been known an accounting management system that supports a business operator's financial accounting work (see, for example, Patent Document 1).
[Prior art documents]
[Patent Literature]
[Patent Document 1] JP 2012-146158 A

  It is desirable to provide technology that enables support for business operators using data managed by an accounting management system.

  According to the 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 relating to the accounting of the business operator. The information processing apparatus may include a future CF information generation unit that generates future CF information indicating the future cash flow of the operator based on the accounting data.

  The information processing apparatus may further include a past CF information generation unit that generates past CF information indicating the past cash flow of the operator based on the accounting data, and the future CF information generation unit includes the past CF information generation unit. The future CF information may be generated based on the CF information. The past CF information generation unit adds or subtracts the transfer details starting from the cash deposit balance on the reference date, using the data of the account items related to the savings and deposits included in the accounting data, so that a predetermined period The future CF information generation unit may generate past CF information indicating a cash balance for each period, and the future CF information generation unit generates future CF information indicating a cash balance for each predetermined period based on the past CF information. May be generated.

  The information processing apparatus may further include a financial institution data acquisition unit that acquires financial institution data including a history of deposits and withdrawals with respect to the financial institution account of the operator, and the future CF information generation unit includes the past CF information and The future CF information may be generated using a difference from the financial institution data. The future CF information generation unit derives at least one of a sales generation pattern, a purchase generation pattern, and an expense generation pattern by analyzing the accounting data, and based on the derived generation pattern, CF information may be generated.

  The future CF information generation unit may generate the future CF information by using data on account items related to increase or decrease in the future deposit and saving balance among account items included in the accounting data. The account item relating to the increase or decrease in the future deposit and saving balance may include at least one of an account item related to trade receivables and an account item related to trade payables. The account item relating to the increase or decrease in the future deposit and saving balance may include at least one of accounts receivable, accounts payable, advance payments and accounts payable.

  The information processing apparatus may further include a transaction data acquisition unit that acquires transaction data related to a transaction by the operator, and the future CF information generation unit obtains the future CF information based on the accounting data and the transaction data. May be generated. The transaction data may include estimate data relating to an estimate by the operator and order data relating to an order received by the operator, and the information processing device may be configured to execute the information on the operator based on the estimate data and the order data. An order rate calculation unit that calculates an order rate indicating the probability of an order for an estimate may be further provided, and the future CF information generation unit may generate the future CF information using the order rate. The transaction data may include billing data related to billing by the business operator and estimation data related to estimation by the business operator, and the information processing device An order rate calculation unit that calculates an order rate indicating the probability of an order for an estimate may be further provided, and the future CF information generation unit may generate the future CF information using the order rate. The transaction data may include billing data related to billing by the business operator, and the information processing apparatus calculates a billing payment rate indicating a probability of payment of the business by the business operator based on the billing data. A deposit rate calculation unit may be further included, and the future CF information generation unit may generate the future CF information using the claimed deposit rate.

  The information processing apparatus may further include a transaction relationship data acquisition unit that identifies a transaction partner of the operator using the transaction data, and acquires transaction relationship data related to a transaction with another operator by the transaction partner, The future CF information generation unit may generate the future CF information based further on the transaction relationship data acquired by the transaction relationship data acquisition unit. The future CF information generation unit may identify the payment status to the other business by the trading partner based on the transaction relationship data acquired by the transaction relationship data acquisition unit, and use the payment status to determine the future CF information may be generated. The future CF information generation unit identifies the trading partner of the trading partner based on the trading relationship data acquired by the trading relationship data acquisition unit, and uses the attribute information of the trading partner of the trading partner to identify the future CF Information may be generated.

  The information processing apparatus determines the probability of loan repayment of the operator based on the application data acquisition unit that acquires the loan application data including the loan application amount by the operator, and the loan application data and the future CF information. And a probability determination unit that performs the above-described process.

  According to the second 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 relating to the accounting of the business operator. The information processing apparatus may include an operator group specifying unit that specifies an operator group corresponding to the operator. The information processing device generates probability information indicating the probability of loan repayment of the operator based on the accounting data and the characteristic data indicating the cash flow characteristics of the operator group specified by the operator group specifying unit. A generation unit may be provided.

  The probability information may be future CF information indicating the future cash flow of the operator. The probability information may be a score indicating the probability of loan repayment by the business operator. The feature data may be generated by analyzing accounting data of a plurality of business operators included in the business operator group. The feature data may be generated based on a sales generation pattern, a purchase generation pattern, and an expense generation pattern derived by analyzing the accounting data. The information processing apparatus may further include a transaction data acquisition unit that acquires transaction data related to a transaction by the operator, and the information generation unit is based on the transaction data, the feature data, and the transaction data. The probability information may be generated.

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

  It should be noted that the above summary of the invention does not enumerate all the necessary features of the present invention. In addition, a sub-combination of these feature groups can also be an invention.

An example of communication environment of loan judging device 100 is shown roughly. An example of past CF information 182 and future CF information 184 is schematically shown. An example of data item 22 of loan application data is shown roughly. An example of the data item 212 of journal data is shown schematically. An example of data item 214 of balance data is shown roughly. An example of data item 222 of billing data is shown roughly. 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 company group data is shown roughly. An example of data item 262 of business relation data is shown roughly. An example of the flow of processing by loan judging device 100 is shown roughly. The other example of the flow of the process by the loan determination apparatus 100 is shown schematically. An example of functional composition of loan judging device 100 is shown roughly. An example of functional composition of account management device 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 schematically.

  Hereinafter, the present invention will be described through embodiments of the invention, but the following embodiments do not limit the invention according to the claims. In addition, not all the combinations of features described in the embodiments are essential for the solving means of the invention.

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

  The loan determination apparatus 100 according to the present embodiment determines whether or not a loan to a business operator is possible. The loan determination apparatus 100 receives, for example, loan application data including a loan application amount, a desired loan date, a loan period, and the like from the communication terminal 20 of the business operator, and determines whether or not the business can be financed. There is a case where a business operator that is a target for which the loan determination device 100 determines whether or not a loan is possible is described as a target business operator.

  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, a mobile phone such as a smartphone, or the like. 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 device 100 may generate probability information indicating a probability of loan repayment by the business operator, and determine whether or not the business can be financed based on the probability information. The probability information is, for example, future CF information indicating the future cash flow (may be described as CF) of the operator. Also, the probability information is, for example, a score indicating the probability of loan repayment by the business operator. The score may be anything as long as it indicates the degree of probability of loan repayment by the business operator.

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

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

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

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

  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, so-called cloud-type sales management software, and 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, cloud-type accounting software, and related software. The transaction data management device 220 is a transaction provided by a business operator, an accounting office, etc. without going through sales management software, cloud-type sales management software, accounting software, cloud-type accounting software and related software. Data may be managed.

  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 with respect to the financial institution account of the target business. The financial institution account may be an account managed in a financial institution such as a bank and a credit union. The loan judgment device 100 may receive the financial institution data of the target business entity via the network 10 from the financial institution data management device 230 that manages the financial institution data of a plurality of business operators.

  The financial institution data management device 230 may receive and manage financial institution data of a plurality of business operators from a plurality of financial institutions. The financial institution data management device 230 may receive and manage financial institution data that has been certified by a financial institution from each of a plurality of business operators.

  The loan judgment device 100 may manage financial institution data of a plurality of business operators. 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 using, for example, the credit information data of the target business generated by a third party. The loan determination device 100 may receive the credit information data of the target business operator via the network 10 from the credit information management device 240 that manages the credit information data of a plurality of business operators.

  The credit information management device 240 is managed by, for example, a vendor that implements a service that generates and provides credit information data of a plurality of business operators. The method for generating the credit information data by the merchant may be any method.

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

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

  The business operator group is not limited to the business operator group for each business type, and may represent other groups. For example, the operator group may be a group of operators for each series. The business operator group may be a business operator group determined by a person in charge of business operator grouping.

  For example, the business operator group data management device 250 generates feature data by analyzing various data for each of a plurality of business operators. For example, the business operator group data management device 250 analyzes accounting data of a plurality of business operators and generates feature data for each business operator group. The business entity group data management device 250 may independently collect accounting data of a plurality of businesses, and may receive accounting data of a plurality of businesses from the accounting data management device 210. Further, the operator group data management device 250 may manage feature data generated by a person in charge of generating feature data.

  The loan judgment device 100 may manage the characteristic data of a plurality of business operators. That is, the loan determination device 100 may have the function of the business operator group data management device 250.

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

  The business relationship management device 260 manages business relationships between a plurality of business operators. And the transaction relationship management apparatus 260 manages the transaction relationship data regarding the transaction between the business operator and the business partner for each of the plurality of business operators.

  The business relationship between a plurality of business operators includes, for example, a correspondence between a business operator and a business operator that has a business relationship with the business operator. According to the business relationship managed by the business relationship management device 260, it is possible to specify a business operator who has a business relationship with a certain business operator, and furthermore, it is possible to specify a business operator who has a business relationship with a business operator who has the business relationship. It is. As described above, according to the business relationship managed by the business relationship management device 260, it is possible to identify a business operator that is in a business relationship in a chain manner with respect to a certain business operator.

  The loan determination device 100 may manage the transaction relationship and transaction relationship data among 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 apparatus 100 may generate the probability information using the above-described multiple types of data. The loan determination apparatus 100 may determine whether or not a loan to the target business operator is possible using the generated probability information. As described above, according to the loan determination device 100 according to the present embodiment, whether or not a loan to the target business is determined using the probability information generated using a plurality of types of data is used. The probability of occurrence of a situation that cannot be recovered can be reduced. In addition, it is possible to eliminate the need to set an excessively strict standard for whether or not to lend, and to reduce the probability of occurrence of a situation where it is difficult for a business operator who can receive a loan to receive a loan. .

  The account management device 300 manages accounts of a plurality of business operators. The account management apparatus 300 may open an account for the business in response to a request from the business. For example, the account management apparatus 300 manages account balances of a plurality of business operators in one or a plurality of bank accounts. As a specific example, the account management apparatus 300 manages the account balances of N business operators or a larger number of business operators in N bank accounts. The account management device 300 may manage the account balances of a plurality of businesses in the bank account in the name of the administrator of the account management device 300.

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

  The account management apparatus 300 may be able to cooperate with an arbitrary transaction platform, and the transaction content can be uniquely specified between the account management apparatus 300 and the arbitrary transaction platform. Instead of cooperation between the account management apparatus 300 and an arbitrary transaction platform, the account management apparatus 300 may also serve as the transaction platform.

  For example, when the account management apparatus 300 receives a payment request from another company that manages the account to another company, the account management apparatus 300 executes a payment process if the account balance is not insufficient. If the account balance is insufficient, the account management apparatus 300 may determine whether or not to lend to a business operator. If the account management apparatus 300 determines that financing is possible, the account management apparatus 300 may add the loan amount to the account balance.

  The account management apparatus 300 uses the same method as the loan determination apparatus 100 to generate probability information indicating the probability of the loan repayment of the business operator, and determines whether or not it is possible to finance the business operator based on the probability information. Good. For example, the account management apparatus 300 determines whether or not a loan to the operator can be made in the same manner as when receiving loan application data including a loan application amount corresponding to the shortage amount from the operator.

  The account management apparatus 300 may execute a payment process after adding a loan amount equal to or greater than the shortage to the account balance. Further, the account management apparatus 300 may execute the loan repayment process according to a predetermined repayment rule after executing the payment process. For example, when the account balance of the business operator is larger than the loan amount, the account management apparatus 300 executes a loan repayment process that repays the entire loan amount. Further, for example, the account management apparatus 300 executes a loan repayment process for repayment of a part of the loan amount when the account balance of the business operator is smaller than the loan amount. Further, the account management apparatus 300 may execute the interest payment process according to a predetermined interest rate rule when the account balance of the business operator is smaller than the loan amount after the payment process is executed.

  In this way, the account management apparatus 300 manages the accounts of a plurality of business operators, and when a payment request from the business operator to another business operator is accepted, The loan process is executed by automatically determining whether or not the loan is possible. Thus, it is possible to provide a mechanism that allows a business operator to receive a loan and perform a transaction with fewer procedures than before.

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

  The loan judgment device 100 generates past CF information 182 indicating the past cash flow of the target business using the data of the account item related to the deposit and savings included in the accounting data of the target business. The loan determination apparatus 100 may compare the past CF information 182 with the financial institution data of the target business operator and correct the past CF information according to the financial institution data when there is a mismatch.

  The loan determination apparatus 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 past CF information 182 for a plurality of years. The loan determination apparatus 100 may generate the future CF information 184 by averaging the past CF information 182 for a plurality of years or by predicting from the past CF information 182 for a plurality of years. For example, the loan determination apparatus 100 may derive the sales generation pattern, the purchase generation pattern, the expense generation pattern, and the like in the past CF information 182 and generate the future CF information 184 using them.

  The loan determination apparatus 100 may generate the future CF information 184 by further using the data of the account item relating to the increase or decrease of the future deposit and saving balance among the account items included in the accounting data. The loan determination apparatus 100 generates the future CF information 184 using, for example, an account item related to sales receivables and an account item related to purchase payables.

  The loan determination apparatus 100 may correct the future CF information 184 using the transaction data of the target company. The loan determination device 100 corrects the future CF information 184 using, for example, estimate data and order data included in the transaction data. The loan determination apparatus 100 may calculate an order rate indicating the probability of an order for the target company from the estimate data and the order data, and correct the future CF information 184 using the order rate. Further, the loan determination device 100 corrects the future CF information 184 using, for example, billing data and estimate data included in the transaction data. The loan determination apparatus 100 may calculate an order rate indicating the probability of an order for the target company from the billing data and the estimate data, and correct the future CF information 184 using the order rate. Moreover, the loan determination apparatus 100 may calculate a billing / payment rate indicating the probability of deposit for the billing of the target company based on the billing data, and correct the future CF information 184 using the billing / payment rate.

  The loan determination apparatus 100 may specify the business partner of the target business operator, and correct the future CF information 184 using the business relationship data related to the business partner's transaction with another business operator. The loan determination apparatus 100 corrects the future CF information 184 using, for example, the payment status of the other party to the business partner. Moreover, the loan determination apparatus 100 corrects the future CF information 184 using, for example, the attribute information of the trading partner. The attribute information includes the scale of the business operator and the creditworthiness of the business operator.

  The loan determination apparatus 100 may correct the future CF information 184 using the characteristic data of the business operator group corresponding to the target business operator. The loan determination apparatus 100 corrects the future CF information 184 using, for example, the sales generation pattern, the purchase generation pattern, and the expense generation pattern indicated by the feature data.

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

  For example, the loan determination apparatus 100 creates a loan repayment schedule from the loan application data received from the target business operator, and determines whether the repayment schedule is feasible based on the future CF information 184, thereby determining whether the loan is possible. You may judge. The loan determination device 100 may further determine whether or not the loan is possible using the credit information data. Moreover, the loan determination apparatus 100 may determine whether or not a loan is possible by further using a loan history for the target business operator.

  FIG. 3 schematically shows an example of the data item 22 of the loan application data. FIG. 3 also shows an example of the data value of each data item 22.

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

  FIG. 4 schematically shows an example of the data item 212 of the journal data included in the accounting data. FIG. 4 also shows an example of the data value of each data item 212.

  The journal data may include a data ID for identifying the journal data. The journal data may include a transaction date. The journal data may include a debit / credit classification. Journal data may include account items. The journal data may include an amount. The journal data may include data items other than these.

  FIG. 5 schematically shows an example of the data item 214 of balance data included in the accounting data. FIG. 5 also shows an example of the data value of each data item 214.

  The balance data may include a data ID for identifying the balance data. The balance data may include a balance date. The balance data may include a debit / credit classification. The balance data may include an account item. The balance data may include an amount. The balance data may include other data items.

  FIG. 6 schematically shows an example of the data item 222 of billing data included in the transaction data. FIG. 6 also shows an example of the data value of each data item 222.

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

  FIG. 7 schematically shows an example of the data item 224 of the estimate data included in the transaction data. In FIG. 7, an example of the data value of each data item 224 is also shown.

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

  FIG. 8 schematically shows an example of the data item 232 of the financial institution data. FIG. 8 also shows an example of the data value of each data item 232.

  The financial institution data may include an account ID that identifies the account. The financial institution data may include a balance date. The financial institution data may include a deposit / withdrawal category. The financial institution data may include an amount. The financial institution data may include data items other than these.

  FIG. 9 schematically shows an example of the data item 242 of the credit information data. FIG. 9 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 subject information including the name of the subject. The credit information data may include a score indicating the trustworthiness of the subject. The credit information data may include data items other than these.

  FIG. 10 schematically shows an example of the data item 170 of the past history data. FIG. 10 also shows an example of the data value of each data item 170.

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

  FIG. 11 schematically shows an example of the data item 252 of the feature data of the business operator group. FIG. 11 also shows an example of the data value of each data item 252.

  The feature data may include a business type ID that identifies a 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 correction. The data value shown in FIG. 11 shows an example in which the sales amount in March is increased by 20% for the group of businesses having the industry type ID 00001. The feature data may include data items other than these.

  FIG. 12 schematically shows an example of the data item 262 of the transaction relation data. FIG. 12 also shows an example of the data value of each data item 262.

  The business relationship data may include a business ID that identifies the business. The business relationship data may include a business name. The business relationship data may include a business partner ID that is a business ID of a business partner of the business identified by the business ID. The business relationship data may include a supplier business name. The transaction relation data may include a collection history for the business operator identified by the business partner business ID by the business operator identified by the business operator ID. The collection history is, for example, a ratio between the number of bills to the business identified by the supplier business ID by the business identified by the business ID and the number of collections of the billed amount. The business relationship data may include data items other than these.

  FIG. 13 schematically illustrates an example of the flow of processing by the loan determination apparatus 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 a result for the loan application data will be described. Each process illustrated in FIG. 13 is executed mainly by a control unit included in the loan determination apparatus 100.

  In step (step may be abbreviated as “S”) 1302, the loan application data transmitted from the communication terminal 20 by the operator wishing to lend is acquired. In S1304, the accounting data of the target company is acquired. In S1306, past CF information is generated based on the accounting data.

  In S1308, various data are acquired. The loan judgment device 100 includes transaction data, financial institution data, credit information data, and past history data of the target business, characteristic data of business groups corresponding to the target business, and business relationships of business partners of the target business Get at least one of the data. In S1310, future CF information is generated using the past CF information generated in S1306 and the various data acquired in S1308.

  In S1312, it is determined whether or not the target company can be financed based on the future CF information generated in S1310. If it is determined that the loan is possible, the process proceeds to S1314. If it is determined that the loan is not permitted, the process proceeds to S1316.

  In S1314, the loan determination apparatus 100 starts a loan procedure for the target business operator. For example, the loan determination apparatus 100 transmits, to the loan application data acquired in S1302, the loan procedure data including the content for notifying that the loan is possible and the content for requesting information necessary for the loan. Send to. Information necessary for financing included in the loan procedure data may be less information than information necessary for normal loan application.

  In S1316, the target company is notified that the loan is rejected. For example, the loan determination apparatus 100 transmits, to the communication terminal 20, notification data for notifying that the loan is rejected for the loan application data acquired in S1302.

  FIG. 14 schematically shows another example of the flow of processing by the loan determination apparatus 100. Here, with respect to the example illustrated in FIG. 13, a flow in the case where the loan determination device 100 executes processing without using the accounting data will be described. Here, differences from the processing flow shown in FIG. 13 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 company is acquired. In S1406, the financial institution data of the target company is acquired.

  In S1408, past CF information is generated using the financial institution data acquired in S1406. In S1410, various data are acquired. The loan judgment device 100 is at least one of the credit information data and past history data of the target business, the characteristic data of the business group corresponding to the target business, and the transaction relation data of the business 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.

  In S1414, it is determined whether or not the target company can be financed based on the future CF information generated in S1412. If it is determined that financing is possible, the process proceeds to S1416. If it is determined that financing is not possible, the process proceeds to S1418. In S1416, a loan procedure is started for the target company. In S1418, the target company is notified that the loan is not accepted.

  FIG. 15 schematically illustrates an example of a functional configuration of the loan determination device 100. The loan judgment 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 rate calculation unit 110, a billed deposit rate calculation unit 112, and a transaction relation data acquisition unit 114. , Operator 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 quality acquisition unit 146, loan processing execution unit 148, A post-financing management unit 150, a repayment information generation unit 152, and a repayment information transmission unit 154 are provided. In addition, it is not necessarily essential that the loan determination apparatus 100 includes all of these configurations.

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

  The accounting data acquisition unit 104 acquires accounting data of the target company. The accounting data acquisition unit 104 specifies, for example, the target business operator based on the loan application data acquired by the application data acquisition unit 102, requests the accounting data management device 210 for the accounting data of the target business operator, and transmits the accounting data management device. Receive accounting data from 210. Moreover, when the loan determination apparatus 100 manages the accounting data of a plurality of businesses, the accounting data acquisition unit 104 may read the accounting data of the target business from the managed accounting data. Further, 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 the financial institution data of the target company. For example, the financial institution data acquisition unit 106 identifies a target business operator based on the loan application data acquired by the application data acquisition unit 102, requests the financial institution data of the target business operator from the financial institution data management device 230, The financial institution data is received from the institution data management device 230. In addition, when the loan determination device 100 manages the financial institution data of a plurality of business operators, the financial institution data acquisition unit 106 may read out the financial institution data of the target business operator from the managed financial institution data. The financial institution data acquisition unit 106 may receive financial institution data that has been certified by the financial institution from the communication terminal 20 that has transmitted the loan application data.

  The transaction data acquisition unit 108 acquires transaction data of the target company. For example, the transaction data acquisition unit 108 specifies the target business operator based on the loan application data acquired by the application data acquisition unit 102, requests the transaction data management device 220 for the transaction data of the target business operator, Transaction data is received from 220. Moreover, when the loan determination apparatus 100 manages the transaction data of a plurality of businesses, the transaction data acquisition unit 108 may read the transaction data of the target business from the managed transaction data. Moreover, 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 estimate data as transaction data. Moreover, the transaction data acquisition part 108 may acquire billing data as transaction data. Further, the transaction data acquisition unit 108 may acquire order data as transaction data. The order data may be data indicating that an order is received from another business operator. For example, the order data indicates that an order has been received for the estimate indicated by the estimate data. The order data may include a data ID for identifying the order data. The order data may include an order date. The order data may include ordering source information including the name of the ordering source. The order data may include order contents. The order data may include an amount. The order data may include other data items.

  The order rate calculation unit 110 calculates the order rate of the target business operator based on the transaction data acquired by the transaction data acquisition unit 108. The order rate calculation unit 110 may calculate the order rate using estimated data and order data included in the transaction data. For example, the order receipt rate calculation unit 110 calculates the order receipt rate by dividing the number of estimates by the number of orders received corresponding to the estimate. The order rate calculation unit 110 may calculate the order rate using estimated data and billing data included in the transaction data. For example, the order receipt rate calculation unit 110 calculates the order receipt rate by dividing the number of estimates by the number of requests corresponding to the estimate. For example, when there is no order data corresponding to the estimated data, the order rate calculating unit 110 may calculate the order rate using the estimated data and the billing data.

  The transaction data may include a correspondence relationship between an estimate and an order, and the order rate calculation unit 110 may identify the number of orders corresponding to the estimate by referring to the correspondence relationship. When the transaction data does not include the correspondence between the estimate and the order, the order rate calculation unit 110 calculates the relationship between the estimate date in the estimate data and the order date in the order data, the estimate destination in the estimate data, the estimate contents, and The order corresponding to the estimate may be specified by confirming the amount of money, the ordering source in the order data, the order contents, and the match of the amount. The transaction data may include a correspondence relationship between the estimate and the bill, and the order rate calculation unit 110 may specify the number of bills corresponding to the estimate by referring to the correspondence relationship. When the transaction data does not include the correspondence between the estimate and the bill, the order rate calculation unit 110 calculates the relationship between the estimate date in the estimate data and the charge date in the claim data, the estimate destination in the estimate data, the estimate contents, and The bill corresponding to the estimate may be specified by confirming the amount of money and the match of the billing destination, the billing content, and the amount in the billing data.

  The charge / payment rate calculation unit 112 calculates the charge / payment rate of the target company based on the transaction data acquired by the transaction data acquisition unit 108. The billing rate calculation unit 112 may calculate the billing rate using the billing data included in the transaction data. For example, the charge / payment rate calculation unit 112 calculates the charge / payment rate by dividing the number of charges by the number of charges made.

  The billing data may include information on whether or not the deposit has been made, and the billing rate calculation unit 112 may specify the number of times the deposit has been made by referring to the information. When the billing data does not include the information, the billing rate calculation unit 112 refers to, for example, the financial institution data acquired by the financial institution data acquisition unit 106 to determine whether or not the billing has been received. May be specified.

  The business relationship data acquisition unit 114 acquires business relationship data of the business partner of the target company. The transaction relationship data acquisition unit 114 specifies the transaction partner of the target company using the transaction data acquired by the transaction data acquisition unit 108, requests the transaction relationship data of the specified transaction partner to the transaction relationship management device 260, Transaction relationship data may be received from the transaction relationship management device 260. Moreover, when the loan determination apparatus 100 manages the transaction relationship and transaction relationship data between a plurality of businesses, the transaction relationship data acquisition unit 114 determines the transaction of the target business partner from the managed transaction relationship data. Relevant data may be read.

  The business operator group specifying unit 116 specifies a business operator group corresponding to the target business operator. For example, the business operator group specifying unit 116 refers to a list in which a plurality of business operator groups are registered, and selects a business operator group corresponding to the target business operator from among the business operator groups included in the list. Further, the business operator group specifying unit 116 may specify the business operator group corresponding to the target business operator with reference to a list in which the business operators corresponding to each of the plurality of business operator groups are registered.

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

  The feature data acquisition unit 118 acquires feature data of the business operator group specified by the business operator group specification unit 116. For example, the feature data acquisition unit 118 requests the operator group data management device 250 for feature data of the business operator group specified by the business operator group specification unit 116, and receives the feature data from the business operator group data management device 250. To do. In addition, when the loan determination apparatus 100 manages feature data of a plurality of business operator groups, the feature data acquisition unit 118 selects the business operator group specified by the business operator group specification unit 116 from the managed feature data. Feature data may be read.

  The past CF information generation unit 120 generates past CF information of the target business operator. The past CF information generation unit 120 generates past CF information based on the transaction data acquired by the transaction data acquisition unit 104, for example. The past CF information generation unit 120 adds / subtracts the transfer details starting from the cash deposit balance on the reference date using the data of the account items related to the savings and deposits included in the accounting data, so that each predetermined period The past CF information indicating the current cash balance may be generated. For example, the past CF information generation unit 120 generates past CF information indicating a daily, weekly, or monthly cash deposit balance by adding / subtracting the transfer details starting from the cash deposit balance at the beginning of the year. In addition, although the cash and deposit balance here shows the sum total of cash and deposit savings, the case where cash is 0 may be included. That is, the cash and deposit balance may indicate only the deposit and saving balance.

  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. 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, when the accounting data acquisition unit 104 cannot acquire the accounting data. For example, the past CF information generation unit 120 adds or subtracts the transfer details based on the deposit and attendance included in the financial institution data, starting from the cash deposit balance on the reference date, for example, so that the current CF for each predetermined period is added. Generate past CF information indicating the deposit balance.

  The probability information generation unit 130 generates probability information indicating the probability of loan repayment of the target company. The probability information generation unit 130 includes a future CF information generation unit 132, a score generation unit 134, and a consistency confirmation unit 136. It is not essential that the probability information generation unit 130 has all these configurations.

  The future CF information generation unit 132 generates the future CF information of the target company. 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, order receipt rate, and billing rate. The future CF information generation unit 132 may generate future CF information using all of these, or may generate future CF information using some of these.

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

  The future CF information generation unit 132 may generate the future CF information using the account item data related to the increase or decrease in the future deposit and saving balance among the account items included in the accounting data. For example, the loan determination apparatus 100 generates future CF information using an account item related to sales receivables and an account item related to purchase payables.

  As a specific example, the future CF information generation unit 132 generates future CF information using the data of accounts receivable. The future CF information generation unit 132 generates the future CF information, for example, assuming that the cash deposit increases on the scheduled date of collection of accounts receivable.

  Further, the future CF information generation unit 132 may generate future CF information using the accounts payable data. The future CF information generation unit 132 generates the future CF information on the assumption that the cash and deposits decrease on the scheduled payment date of accounts payable.

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

  Further, the future CF information generation unit 132 may generate future CF information using the advance payment data. For example, if the advance payment data indicates that there is a advance payment, the future CF information generation unit 132 generates the future CF information on the assumption that the cash deposit does not decrease on the scheduled payment date. As a specific example, the future CF information generation unit 132 may generate future CF information on the assumption that the cash deposit does not decrease when the advance payment is greater than the fee. Further, when the advance CF information generation unit 132 has a smaller advance payment than the fee, the future CF information may be generated assuming that the difference between the fee and the advance payment is reduced from the current deposit.

  The future CF information generation unit 132 may generate future CF information using the estimated data and the order rate. For example, the future CF information generation unit 132 generates the future CF information on the basis of the order data received from the estimated data matching the order rate among the plurality of estimated data. For example, when the number of estimated data is 5 and the order rate is 80%, the future CF information is generated on the assumption that 4 out of the 5 estimated data are received and received.

  The future CF information generation unit 132 may generate future CF information using the billing data and the billing rate. For example, the future CF information generation unit 132 generates the future CF information on the billing data corresponding to the billing deposit rate among the plurality of billing data as being deposited. For example, when the number of billing data is 20, and the billing deposit rate is 95%, the future CF information is generated on the assumption that 19 of the 20 estimated data are to be deposited.

  The future CF information generation unit 132 may generate future CF information using the estimated data, billing data, order receipt rate, and billing rate. For example, the future CF information generation unit 132 receives an order for and requests the estimated data that matches the order rate from among the plurality of estimated data, and the estimated data that matches the requested deposit rate among the estimated data that is to be charged. In the future, CF information is generated as a deposit.

  The future CF information generation unit 132 may generate future CF information using the transaction relationship data of the target business partner. For example, the future CF information generation unit 132 uses the transaction relation data to identify the payment status to the other business by the trading partner of the target business, and generates future CF information using the payment status. As a specific example, the future CF information generation unit 132 refers to the transaction relationship data of the trading partner who is the billing destination of the billing data of the target company, specifies the payment status of the trading partner to the other business, It is determined whether or not the billing data is deposited according to the identified deposit status. For example, in the case where the deposit rate to the other business of the trading partner is lower than a predetermined threshold, the CF information is generated in the future assuming that the billing data is not deposited. Thereby, the future CF information can be generated in consideration of the possibility that the trading partner of the target company does not make payment to the target company. Note that the future CF information generation unit 132 may generate future CF information in consideration of the amount of money to be deposited in addition to the deposit rate to the other business partner of the transaction partner.

  Further, for example, the future CF information generation unit 132 uses the transaction relation data to identify the trading partner of the target business partner, and generates future CF information using the identified trading partner attribute information. As a specific example, the future CF information generation unit 132 refers to the transaction relationship data of the transaction partner that is the billing destination of the billing data of the target company, identifies the transaction partner of the transaction partner, and identifies the identified transaction partner. It is determined whether or not payment is made for the billing data according to the attribute information. For example, the threshold value is compared with at least one of the business size and creditworthiness of the specified trading partner, and if it is lower than the threshold value, future CF information is generated assuming that no payment is made for the billing data. Thereby, the future CF information can be generated in consideration of the possibility that the business partner of the target business operator cannot collect from the business partner of the business partner.

  For example, the future CF information generation unit 132 generates future CF information using the difference between the past CF information and the 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. For example, the future CF information generation unit 132 may correct the past CF information so that the difference between the past CF information and the financial institution data approaches the financial institution data.

  The score generation unit 134 generates a score indicating the probability of loan repayment of the target company. The score generation unit 134 may generate a score using the accounting data, financial institution data, transaction data, feature data, transaction relation data, past CF information, order rate, and billing rate. The score generation unit 134 may generate a score using all of these, or may generate a score using some of these. The score generation unit 134 may generate a score using any method.

  The consistency confirmation unit 136 confirms the consistency of the collected various data when determining whether or not the loan to the target company is possible for the loan application data received from the communication terminal 20. For example, the consistency confirmation unit 136 confirms the consistency between the balance data acquired by the transaction data acquisition unit 104 and the financial institution data acquired by the financial institution data acquisition unit 106. For example, the consistency confirmation unit 136 checks the consistency between the billing data acquired by the transaction data acquisition unit 108 and the deposit data in the financial institution data acquired by the financial institution data acquisition unit 106.

  For example, if the consistency confirmation unit 136 detects data inconsistency as a result of confirming consistency, the consistency confirmation unit 136 determines that the data is invalid data. The consistency confirmation unit 136 may generate a score of data validity for each data based on the degree of matching.

  The consistency confirmation unit 136 may notify the request data transmission unit 142 of data determined to be illegal data. The request data transmission unit 142 may transmit request data including at least one of data correction and additional data submission request to the communication terminal 20 for data determined to be invalid data by the consistency confirmation unit 136. . When the consistency confirmation unit 136 accepts the correction contents of the data and the additional data for the request data, the consistency confirmation unit 136 may correct the data using these.

  The consistency confirmation unit 136 may notify the request data transmission unit 142 of data whose data legitimacy score is lower than a predetermined threshold. The request data transmission unit 142 includes at least one of data correction and additional data submission request for data determined by the consistency confirmation unit 136 to have a data validity score lower than a predetermined threshold. The request data may be transmitted to the communication terminal 20. When the consistency confirmation unit 136 accepts the correction contents of the data and the additional data for the request data, the consistency confirmation unit 136 may correct the data using these.

  As described above, since the consistency confirmation unit 136 confirms the consistency of data collected when determining whether or not to loan to the target business operator, an unauthorized application can be detected, for example. In addition, it is possible to prevent a loan from being determined based on erroneous data.

  The loan availability 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 operator. For example, the loan availability determination unit 144 first creates a loan repayment schedule using the loan application amount, the desired loan date, and the loan period in the loan application data. As a specific example, a repayment schedule indicating the monthly repayment amount is created.

  The loan availability determination unit 144 determines whether the loan is possible by determining whether the repayment schedule is realizable based on the future CF information generated by the future CF information generation unit 132. For example, the loan availability determination unit 144 determines whether or not the cash deposit is insufficient when the monthly repayment amount scheduled for repayment is subtracted from the cash deposit for each month indicated by the future CF information.

  The credit level acquisition unit 146 acquires the credit level of the target company. The credit quality 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 acquisition unit 146 may request the credit information management device 240 for the credit information data of the target company and receive the credit information data from the credit information management device 240. Moreover, when the loan determination apparatus 100 manages the credit information data of a plurality of businesses, the credit quality acquisition unit 146 may read the credit information data of the target business from the managed credit information data. Further, the credit quality acquisition unit 146 may acquire the credit rating information indicating the credit rating of the target company by the financial institution as the credit rating of the target company. The credit acquisition unit 146 may request the credit rating information of the target business from the financial institution and receive the credit rating information from the financial institution. Further, the credit quality acquisition unit 146 may receive and store the credit rating information of a plurality of businesses from a financial institution, and read 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 company using the credit information data of the business partner of the target company. For example, if the score of the business partner of the target company is lower than a predetermined threshold, the credit acquisition unit 146 deducts the score of the target company. Further, for example, when the score of the business partner of the target company is higher than a predetermined threshold, the credit acquisition unit 146 adds points to the score of the target company. Moreover, the credit acquisition part 146 adds the score according to the score of the customer of the target company to the score of the target company. Further, the credit quality acquisition unit 146 may generate the credit quality of the target company using the credit rating information of the business partner of the target business. For example, the credit rating acquisition unit 146 deducts the credit rating of the target company when the credit rating information of the target company's business partner is lower than a predetermined threshold. Further, for example, when the credit rating information of the business partner of the target business is higher than a predetermined threshold, the credit quality acquisition unit 146 adds points to the credit quality of the target business. Further, the credit quality acquisition unit 146 adds a score corresponding to the credit rating information of the target business partner to the credit quality of the target business.

  The creditworthiness acquisition unit 146 may generate the creditworthiness of the target business using the credit information data of the business that has a chain-related business relationship with the target business. For example, the creditworthiness acquisition unit 146 weights and adds the scores of the business operators that are in a business relationship with the target business in a chained manner with respect to the scores of the target business. As the weighting, for example, the weight of the rating of the operator farther from the target operator may be lowered. Moreover, the creditworthiness acquisition unit 146 may generate the creditworthiness of the target business using the credit rating information of the business that has a chained business relationship with the target business. For example, the credit quality acquisition unit 146 weights and adds a value corresponding to the credit rating information of the business operator that is in a business relationship with the target business operator to the target business operator. As the weighting, for example, the weight of the rating of the operator farther from the target operator may be lowered.

  The trustworthiness acquisition unit 146 may generate the trustworthiness of the target operator by combining a plurality of data existing on the Internet. The trustworthiness acquisition unit 146 generates the trustworthiness of the target company using information such as the update frequency of the website operated by the target company and the number of site visitors, for example. Moreover, the creditworthiness acquisition unit 146 may generate the creditworthiness of the target company using evaluation information indicating an evaluation related to a business operated by the target company on a website operated by a third party. Further, the creditworthiness acquisition unit 146 may generate the creditworthiness of the target company using information related to a person related to the target company, such as a representative of the target company. For example, the creditworthiness acquisition unit 146 generates the creditworthiness of the target business using information on the representative of the target business SNS (Social Networking Service).

  The loan availability determination unit 144 may determine the availability of financing for the target business using the credit quality of the target business generated by the credit level acquisition unit 146. For example, the loan availability determination unit 144 determines that the loan is rejected when the creditworthiness of the target business is low, even if there are many cash deposits indicated by future CF information for the repayment schedule. For example, the loan permission / rejection determination unit 144 associates and registers the credit quality determined as the loan rejection for each difference between the repayment schedule and the cash deposit indicated by the future CF information, and the credit level generated by the credit level acquisition unit 146 If the creditworthiness corresponding to the difference is lower, it is determined that the loan is rejected.

  In addition, for example, the loan availability determination unit 144 determines that the loan is possible if the creditworthiness of the target business operator is high even if the cash deposit indicated by the future CF information is small with respect to the repayment schedule. For example, the loan availability determination unit 144 registers the credit level determined to be available for loan for each difference between the repayment schedule and the cash deposit indicated by the future CF information, and the credit level generated by the credit level acquisition unit 146 is registered. If the creditworthiness corresponding to the difference is higher, it is determined that the loan is possible.

  The loan process execution unit 148 executes the loan process according to the determination result by the loan availability determination unit 144. For example, when it is determined that the loan is permitted by the loan permission determination unit 144, the loan processing execution unit 148 starts the loan procedure. The loan processing execution unit 148 sends, to the communication terminal 20, loan procedure data including content for notifying the communication terminal 20 of the target company that the loan is possible and content for requesting information necessary for the loan. May be sent. For example, when it is determined by the loan availability determination unit 144 that the loan is possible, the loan processing execution unit 148 makes a loan offer to the target business operator. The loan processing execution unit 148 sends, to the communication terminal 20, loan offer data including the content for notifying the communication terminal 20 of the target company that the loan is possible and the content for requesting information necessary for the loan. May be sent.

  The loan process execution unit 148 may notify the target company that the loan is not permitted when the loan permission / rejection determination unit 144 determines that the loan is not permitted. The loan processing execution unit 148 transmits, for example, notification data for notifying that the loan is rejected to the communication terminal 20 of the target company.

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

  The post-financing management unit 150 may execute processing such as advancing the reminder time to be transmitted to the target business operator when it is detected that there is a possibility that the loan repayment may be hindered. The post-financing management unit 150 executes various processes in a method that does not depart from the spirit of the Money Lending Business Law.

  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 generate a repayment schedule according to the purpose using the future CF information. For example, the repayment information generation unit 152 generates a repayment schedule when repayment is made with profit, a repayment schedule when repayment is made with funds, and the like.

  The repayment information generation unit 152 may generate a loan repayment schedule using the future CF information when the loan permission determination unit 144 determines that the loan is possible. For example, the repayment information generation unit 152 refers to the future CF information, identifies a month in which the amount of the cash deposit is larger than that of other months, and generates a repayment schedule in which the repayment amount of the month is larger than that of other months. To 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 company. Thereby, a repayment schedule can be flexibly proposed to the target company.

  The repayment information generation unit 152 may generate a repayment schedule that can be lent using the future CF information when the loan permission determination unit 144 determines that the loan is not permitted. 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, the repayment information generation unit 152 generates a repayment schedule when the loan date is changed when it can be determined that the loan is possible by changing the desired loan date included in the loan application data using the future CF information. To do. As a specific example, if the desired loan date included in the loan application data is April 2017 and the loan is not accepted as it is, but the loan date can be changed to July 2017, the loan is permitted. Generate a repayment schedule for the loan date.

  Moreover, the repayment information generation part 152 produces | generates the repayment schedule which changed the loan period, when it can be determined that loan is possible by changing the loan period included in loan application data using future CF information. As a specific example, if the loan period included in the loan application data is June and the loan is rejected as it is, but the loan is allowed by changing the loan period to October, a repayment schedule with the loan period set to October Generate.

  The repayment information transmission unit 154 transmits the repayment information to the communication terminal 20 of the target company, so that the target company can be informed that the loan can be made by changing the condition of the loan application data. For example, if the business of the target business is agriculture, it is difficult to repay the loan amount evenly every month, but it may be possible to repay it in the harvesting period. The characteristics of repayment capacity vary depending on the business. According to the repayment information generation unit 152 according to the present embodiment, a repayment schedule suitable for the cash flow indicated by the target operator's future CF information can be generated, and thus the repayment schedule is flexibly provided according to the situation of the target operator. be able to.

  FIG. 16 schematically shows an example of the functional configuration of the account management apparatus 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 processing execution unit 310, a loan availability determination unit 312, a loan repayment execution unit 314, and an interest rate processing execution. A unit 316 and an instruction receiving unit 318. Note that it is not essential that the account management apparatus 300 has all of these configurations.

  The account management unit 302 manages account balances of a plurality of business operators. The account management unit 302 may open an account for the business in response to a request from the business. For example, the account management unit 302 manages accounts of a plurality of business operators in one or a plurality of bank accounts. As a specific example, the account management unit 302 manages the account balances of N business operators or a larger number of business operators in N bank accounts. For example, the account management unit 302 manages accounts of a plurality of businesses in a bank account in the name of the administrator 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, order receipt rate, and billing receipt rate. The data acquisition unit 304 may acquire all of these, or may acquire some of them.

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

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

  The payment processing execution unit 310 executes payment processing according to the payment request acquired by the payment request acquisition unit 308. The payment process execution unit 310 may execute the payment process when the account balance of the target business is larger than the payment amount of the payment request. For example, the payment processing execution unit 310 remits the payment amount to the account number of the payment destination of the payment request. The payment processing execution unit 310 may present an instruction to transfer the payment amount to the account number of the payment destination of the payment request to the operator of the account management apparatus 300 or the like. The payment processing execution unit 310 subtracts the payment amount from the account balance along with payment. When the account balance of the target business is less than the payment amount of the payment request, the payment processing execution unit 310 causes the loan propriety determination unit 312 to determine whether or not the target business can be financed.

  The loan availability determination unit 312 determines whether or not a loan to the target business operator is possible. The loan availability determination unit 312 may determine whether or not a loan to the target company can be made based on the future CF information of the target company generated by the future CF information generation unit 306. The loan availability determination unit 312 may determine the availability of the loan by executing the same processing as the loan availability determination unit 144.

  The loan availability determination unit 312 may determine whether or not the loan to the target company is possible in response to a request from the payment processing execution unit 310. For example, the loan availability determination unit 312 determines whether it is possible to finance a loan amount equal to or greater than the difference between the payment amount of the payment request and the account balance of the target business operator.

  For example, the loan availability determination unit 312 determines whether it is possible to finance a difference between the payment amount of the payment request and the account balance of the target business operator. Further, the loan availability determination unit 312 may determine whether it is possible to finance 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 operator. The predetermined amount may be determined by the administrator of the account management apparatus 300, the target company, and the like as the minimum amount required for the account balance of the target company.

  The account management unit 302 may add the loan amount to the account balance of the target business operator when the loan availability determination unit 312 determines that the loan is possible. 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 company to the account balance of the target company. 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 company to the account balance of the target company.

  The loan permission / inhibition determination unit 312 may periodically determine whether or not a loan to a business entity that manages the account is permitted. For example, the loan availability determination unit 312 periodically determines a loan allowance frame for a business entity that manages an account. The loan availability determination unit 312 may notify the account management unit 302 of the loanable amount. The account management unit 302 may present the lending available frame determined by the lending availability determination unit 312 to the business operator managing the account. Thereby, the business operator can grasp how much financing can be received at the present time.

  The loan repayment execution unit 314 monitors the account balance of the target business operator after the loan amount is added to the account balance by the account management unit 302 and the payment process is executed by the payment process execution unit 310. Then, the loan repayment execution unit 314 executes a repayment process from the account balance according to a predetermined condition. For example, the loan repayment execution unit 314 subtracts the loan amount from the account balance when the account balance of the target company is greater than the loan amount. The subtracted loan amount is used to repay the loan. Further, the loan repayment execution unit 314 may execute a repayment process in order to repay the loan amount in stages. For example, the loan repayment execution unit 314 subtracts the difference between the account balance and a predetermined threshold from the account balance when the account balance of the target business is less than the loan amount. 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 the difference 200 between the account balance and the threshold to 200. Subtract from balance. The subtracted amount will be used to repay the loan.

  The interest rate processing execution unit 316 monitors the account balance of the target company after the loan amount is added to the account balance by the account management unit 302 and the payment processing is executed by the payment processing execution unit 310. Then, the interest rate processing execution unit 316 may subtract the interest amount determined according to a predetermined interest rate rule from the account balance when the account balance of the target business is smaller than the loan amount. The subtracted interest amount is used to pay interest on 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 business operator. For example, even if the interest amount is 50, and the target company's account balance is in the state of -300 with 300 loans remaining, if the timing comes, 316 subtracts 50 to calculate the account balance -350. It should be noted that as long as it is within the range of the credit limit, the actual repayment may be made when the account balance exceeds a predetermined threshold.

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

  FIG. 17 schematically illustrates an example of a hardware configuration of a computer 1000 that functions as the loan determination apparatus 100 or the account management apparatus 300. The computer 1000 according to this embodiment includes a CPU peripheral unit including a CPU 1010, a RAM 1030, and a graphic controller 1085 that are connected to each other by a host controller 1092; a ROM 1020 that is connected to the host controller 1092 by an input / output controller 1094; An input / output unit having F1040, a hard disk drive 1050, a DVD drive 1070, and an input / output chip 1080 is provided.

  The CPU 1010 operates based on programs stored in the ROM 1020 and the RAM 1030 and controls each unit. 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 displays the image data on the display 1090. Alternatively, the graphic controller 1085 may include a frame buffer that stores 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 wireless. The communication I / F 1040 functions as hardware that performs 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 via the RAM 1030.

  The ROM 1020 stores a boot program executed when the computer 1000 is started 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.

  A program provided to the hard disk drive 1050 via the RAM 1030 is stored in a recording medium such as a DVD-ROM 1072 or an IC card and provided by a 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 that is installed in the computer 1000 and causes the computer 1000 to function as the loan determination apparatus 100 or the account management apparatus 300 works on the CPU 1010 or the like to cause the computer 1000 to function as each part of the loan determination apparatus 100 or the account management apparatus 300. Good. The information processing described in these programs is read by the computer 1000, whereby the application data acquisition unit 102, the accounting data acquisition unit, which are specific means in which the software and the various hardware resources described above cooperate. 104, financial institution data acquisition unit 106, transaction data acquisition unit 108, order rate calculation unit 110, invoiced deposit rate calculation unit 112, transaction relation data acquisition unit 114, operator 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 quality acquisition unit 146, loan processing execution unit 148, post-financing management unit 150, repayment information generation unit 152, and repayment information transmission It functions as the unit 154. The information processing described in these programs is read by the computer 1000, whereby the account management unit 302, the data acquisition unit, which are specific means in which the software and the various hardware resources described above cooperate. 304, 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. And by these specific means, the calculation or processing of information according to the purpose of use of the computer 1000 in this embodiment is realized, so that a specific loan judgment device 100 or account management device 300 according to the purpose of use is constructed. Is 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 will be apparent to those skilled in the art that various modifications or improvements can be added to the above-described embodiment. It is apparent from the scope of the claims that the embodiments added with such changes 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 stages in the apparatus, system, program, and method shown in the claims, the description, and the drawings is particularly “before” or “prior to”. It should be noted that it can be realized in any order unless the output of the previous process is used in the subsequent process. Regarding the operation flow in the claims, the description, and the drawings, even if it is described using “first,” “next,” etc. for 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 item, 100 loan judgment device, 102 application data acquisition unit, 104 accounting data acquisition unit, 106 financial institution data acquisition unit, 108 transaction data acquisition unit, 110 order rate calculation unit, 112 Rate calculation unit, 114 transaction relation data acquisition unit, 116 business operator group identification unit, 118 feature 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 Confirmation Unit, 142 Request Data Transmitting Unit, 144 Loan Acceptability Determining Unit, 146 Credit 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, 210 Accounting data Management Device, 212 Data Item, 214 Data Item, 220 Transaction Data Management Device, 222 Data Item, 224 Data Item, 230 Financial Institution Data Management Device, 232 Data Item, 240 Credit Information Management Device, 242 Data Item, 250 Operator 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 execution of payment processing , 312 loan acceptability determination unit, 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, 107 0 DVD drive, 1072 DVD-ROM, 1080 input / output chip, 1085 graphic controller, 1090 display, 1092 host controller, 1094 input / output controller

Claims (23)

An accounting data acquisition unit for acquiring accounting data relating to the accounting of the operator;
A future CF information generation unit that generates future CF information indicating a future cash flow of the operator based on the accounting data;
An information processing apparatus comprising: A past CF information generation unit that generates past CF information indicating the past cash flow of the operator based on the accounting data;
The information processing apparatus according to claim 1, wherein the future CF information generation unit generates the future CF information based on the past CF information. The past CF information generation unit adds or subtracts the transfer details starting from the cash deposit balance on the reference date using the data of the account items related to the savings and deposits included in the accounting data, thereby setting a predetermined period Generate past CF information showing the balance of each cash deposit,
The information processing apparatus according to claim 2, wherein the future CF information generation unit generates future CF information indicating a cash balance for each predetermined period in the future based on the past CF information. A financial institution data acquisition unit for acquiring financial institution data including a history of deposits and withdrawals with respect to the financial institution account of the operator;
The information processing apparatus according to claim 2 or 3, wherein the future CF information generation unit generates the future CF information using a difference between the past CF information and the financial institution data.   The future CF information generation unit derives at least one of sales generation pattern, purchase generation pattern, and expense generation pattern by analyzing the accounting data, and based on the derived generation pattern The information processing apparatus according to claim 1, which generates CF information.   The future CF information generation unit generates the future CF information by using data on account items related to increase / decrease in future deposits and savings balances among account items included in the accounting data. The information processing apparatus according to one item.   The information processing apparatus according to claim 6, wherein the account item related to the increase or decrease in the future deposit and saving balance includes at least one of an account item related to sales receivables and an account item related to trade payables.   The information processing apparatus according to claim 6 or 7, wherein the account item relating to the increase or decrease in the future deposit and saving balance includes at least one of accounts receivable, accounts payable, advance payments and accounts payable. A transaction data acquisition unit for acquiring transaction data related to transactions by the business operator;
The information processing apparatus according to any one of claims 1 to 8, wherein the future CF information generation unit generates the future CF information based on the transaction data and the transaction data. The transaction data includes estimate data relating to an estimate by the operator, and order data relating to an order received by the operator,
The information processing apparatus includes:
An order rate calculation unit that calculates an order rate indicating the probability of an order with respect to the estimate of the operator based on the estimate data and the order data, further comprising:
The information processing apparatus according to claim 9, wherein the future CF information generation unit generates the future CF information using the order reception rate. The transaction data includes billing data relating to billing by the operator, and estimate data relating to an estimate by the operator,
The information processing apparatus includes:
Based on the billing data and the estimate data, the system further comprises an order rate calculation unit for calculating an order rate indicating a probability of an order for the estimate of the operator,
The information processing apparatus according to claim 9, wherein the future CF information generation unit generates the future CF information using the order reception rate. The transaction data includes billing data related to billing by the operator,
The information processing apparatus includes:
A billing deposit rate calculation unit that calculates a billing deposit rate that indicates the probability of depositing the bill of the business based on the billing data,
The information processing apparatus according to any one of claims 9 to 11, wherein the future CF information generation unit generates the future CF information using the billing rate. A transaction relationship data acquisition unit that identifies a transaction partner of the operator using the transaction data and acquires transaction relationship data related to a transaction with another operator by the transaction partner,
The information processing according to any one of claims 9 to 12, wherein the future CF information generation unit generates the future CF information based further on the transaction relationship data acquired by the transaction relationship data acquisition unit. apparatus.   The future CF information generation unit specifies a payment status to the other business by the trading partner based on the transaction relationship data acquired by the transaction relationship data acquisition unit, and uses the payment status to determine the future CF information. The information processing apparatus according to claim 13, which generates   The future CF information generation unit identifies the trading partner of the trading partner based on the trading relationship data acquired by the trading relationship data acquisition unit, and uses the attribute information of the trading partner of the trading partner to identify the future CF The information processing apparatus according to claim 13, which generates information. An application data acquisition unit for acquiring loan application data including a loan application amount by the operator;
The information processing apparatus according to any one of claims 1 to 15, further comprising a probability determination unit that determines a probability of loan repayment of the operator based on the loan application data and the future CF information. An accounting data acquisition unit for acquiring accounting data relating to the accounting of the operator;
A business operator group identifying unit that identifies a business operator group corresponding to the business operator;
An information generating unit that generates probability information indicating a probability of loan repayment of the business operator based on the accounting data and feature data indicating a cash flow characteristic of the business operator group specified by the business operator group specifying unit An information processing apparatus comprising:   The information processing apparatus according to claim 17, wherein the probability information is future CF information indicating a future cash flow of the operator.   The information processing apparatus according to claim 17, wherein the probability information is a score indicating a probability of loan repayment of the business operator.   The information processing apparatus according to any one of claims 17 to 19, wherein the feature data is generated by analyzing accounting data of a plurality of business operators included in the business operator group.   The information processing apparatus according to claim 20, wherein the characteristic data is generated based on a sales generation pattern, a purchase generation pattern, and an expense generation pattern derived by analyzing the accounting data. A transaction data acquisition unit for acquiring transaction data relating to the transaction by the operator;
Further comprising
The information processing apparatus according to any one of claims 17 to 21, wherein the information generation unit generates the probability information based on the transaction data, the feature data, and the transaction data.   A program for causing a computer to function as the information processing apparatus according to any one of claims 1 to 22.

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