JP6704973B2 - Information processing device and program - Google Patents

Description

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

An accounting management system that supports financial accounting work of a business operator has been known (for example, refer to Patent Document 1).
[Prior Art Document]
[Patent Document]
[Patent Document 1] JP 2012-146158 A

It is desirable to provide a technology that enables the support of the business operator by using the data managed by the accounting management system.

According to the first aspect of the present invention, an information processing device is provided. The information processing device may include an accounting data acquisition unit that acquires accounting data regarding accounting of the business operator. The information processing device may include a future CF information generation unit that generates future CF information indicating a future cash flow of the business 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 a past cash flow of the business operator based on the accounting data, and the future CF information generation unit may include the past CF information generation unit. The future CF information may be generated based on the CF information. The past CF information generation unit uses the data of the account items related to deposits and savings included in the above-mentioned accounting data, and adds and subtracts the transfer details from the cash balance of the base date as a starting point, thereby determining a predetermined period. The past CF information indicating the cash balance of each cash may be generated, and the future CF information generation unit generates the future CF information indicating the cash balance of each future 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 to and from a financial institution account of the business operator, and the future CF information generation unit may include the past CF information. The future CF information may be generated using the difference from the financial institution data. The future CF information generation unit derives at least one of a sales occurrence pattern, a purchase occurrence pattern, and an expense occurrence pattern by analyzing the accounting data, and based on the derived occurrence pattern, the future CF information may be generated.

The future CF information generation unit may generate the future CF information by using the data of the account items regarding the future increase/decrease of the deposit/savings balance among the account items included in the accounting data. The account item relating to increase/decrease in the future deposit/savings balance may include at least one of an account item relating to trade receivables and an account item relating to trade payables. The account item relating to the increase/decrease in the future deposit/savings balance may include at least one of accounts receivable, accounts payable, prepayment and unpaid.

The information processing device may further include a transaction data acquisition unit that acquires transaction data regarding a transaction by the business operator, and the future CF information generation unit may obtain the future CF information based on the accounting data and the transaction data. May be generated. The transaction data may include quotation data regarding an estimate by the business operator and order data regarding an order received by the business operator, and the information processing device, based on the quotation data and the order data, the business operator An ordering rate calculation unit that calculates an ordering rate that indicates the probability of receiving an order with respect to the estimate may be further provided, and the future CF information generation unit may generate the future CF information using the ordering rate. The transaction data may include billing data relating to a bill made by the business operator and estimation data relating to a quotation made by the business operator. An ordering rate calculation unit that calculates an ordering rate that indicates the probability of receiving an order with respect to the estimate may be further provided, and the future CF information generation unit may generate the future CF information using the ordering rate. The transaction data may include billing data regarding a bill made by the business operator, and the information processing device calculates a billing deposit rate indicating a probability of depositing the bill of the business enterprise based on the billing data. A deposit rate calculation unit may be further provided, and the future CF information generation unit may generate the future CF information using the billed deposit rate.

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

The information processing apparatus determines the probability of loan repayment by the business operator based on the application data acquisition unit that acquires loan application data including the loan application amount by the business operator, and the loan application data and the future CF information. It may further be provided with a probability determining unit.

According to a second aspect of the present invention, an information processing device is provided. The information processing device may include an accounting data acquisition unit that acquires accounting data regarding accounting of the business operator. The information processing device may include a business entity group identification unit that identifies a business entity group corresponding to the business entity. The information processing apparatus generates probability information indicating the probability of loan repayment by the business operator based on the accounting data and the characteristic data indicating the cash flow characteristics of the business operator group identified by the business entity group specification unit. A generator may be included.

The probability information may be future CF information indicating a future cash flow of the business operator. The probability information may be a score indicating the probability of loan repayment by the business operator. The characteristic data may be generated by analyzing accounting data of a plurality of businesses included in the business group. The characteristic 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 device may further include a transaction data acquisition unit that acquires transaction data related to a transaction by the business operator, the information generation unit, based on the accounting data and the characteristic 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 device.

Note that the above summary of the invention does not enumerate all necessary features of the present invention. Further, a sub-combination of these feature groups can also be an invention.

1 schematically shows an example of a communication environment of loan determination device 100. An example of the past CF information 182 and the future CF information 184 is schematically shown. An example of the data item 22 of loan application data is schematically shown. An example of the data item 212 of journal data is shown roughly. An example of the data item 214 of balance data is schematically shown. An example of the data item 222 of billing data is shown roughly. An example of the data item 224 of quotation data is shown roughly. An example of the data item 232 of financial institution data is shown roughly. An example of the data item 242 of credit information data is shown schematically. An example of the data item 170 of the past history data is schematically shown. An example of the data item 252 of the enterprise group data is schematically shown. An example of the data item 262 of transaction-related data is schematically shown. 1 schematically shows an example of the flow of processing by the loan determination apparatus 100. Another example of the flow of processing by the loan determination apparatus 100 is schematically shown. 1 schematically shows an example of a functional configuration of loan determination device 100. 1 schematically shows an example of the functional configuration of the account management device 300. 1 schematically shows an example of the hardware configuration of a computer 1000 that functions as the loan determination apparatus 100 or the account management apparatus 300.

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. Moreover, not all combinations of the features described in the embodiments are essential to the solving means of the invention.

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

The loan determination device 100 according to the present embodiment determines whether or not to lend to a business. The loan determination device 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, and determines whether or not the business can be loaned. In some cases, the business operator that is the target of the loan determination apparatus 100 to determine whether or not to lend is described as a target business operator.

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

The loan determination apparatus 100 may generate probability information indicating the probability of loan repayment by the business operator, and determine whether or not to lend to the business operator based on the probability information. The probability information is, for example, future CF information indicating a future cash flow (sometimes referred to as CF) of the business operator. Further, the probability information is, for example, a score indicating the probability of the loan repayment by the business operator. The score may be any score as long as it indicates the degree of probability of loan repayment by the business operator.

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

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

The loan determination apparatus 100 may manage the accounting data of a plurality of businesses by itself. That is, the loan determination device 100 may have the function of the accounting data management device 210.

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

The transaction data management device 220 may manage transaction data input by a business operator, an accounting office, etc. using so-called sales management software, so-called cloud-type sales management software, and its related software. The transaction data management device 220 may manage transaction data input by a business operator, an accounting office, or the like by 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 its related software. You may manage the data.

The loan determination apparatus 100 may manage transaction data of a plurality of businesses by itself. That is, the loan determination device 100 may have the function of the transaction data management device 220.

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

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

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

The loan determination device 100 generates probability information using, for example, the credit information data of the target business operator generated by a third party organization. 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 trader that implements a service of generating and providing credit information data of a plurality of businesses. The method of generating the credit information data by the trader may be any method.

The loan determination apparatus 100 may manage credit information data of a plurality of businesses by itself. That is, the loan determination device 100 may have the function of the credit information management device 240.

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

The group of businesses indicates, for example, a group of businesses by industry. For example, when the target company is a building company, the group of companies corresponding to the target company may be the building industry. The CF characteristic of the business group may be, for example, a characteristic of the CF variation for each type of industry such that sales tend to increase in the construction industry in March. The characteristic data may be, for example, data embodying the contents of a change such as increasing the sales of the construction industry by 20% in March.

Note that the group of businesses is not limited to a group of businesses by industry, and may indicate other groups. For example, the business group may be a group of businesses for each series. Further, the business group may be a group of businesses determined by a person in charge of grouping the businesses.

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

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

The loan determination apparatus 100 generates probability information using, for example, the transaction relation data regarding the transaction of the target business operator with the other business operator. The transaction-related data includes, for example, collection records and payment records between businesses. The loan determination apparatus 100 may receive, through the network 10, the transaction relationship data of the customer of the target enterprise from the transaction relation management device 260 that manages the transaction relation data between the plurality of enterprises.

The business relationship management device 260 manages business relationships between a plurality of businesses. Then, the transaction relationship management device 260 manages the transaction relationship data regarding the transaction between the business operator and its business partner for each of the plurality of business operators.

The business relationship between a plurality of businesses includes, for example, association of a business with a business having a business relationship with the business. According to the transaction relationship managed by the transaction relationship management device 260, it is possible to specify a business operator having a business relationship with a certain business operator, and further specify a business operator having a business relationship with a business operator having the business transaction relationship. Is. In this way, according to the transaction relationship managed by the transaction relationship management device 260, it is possible to specify the business operators having a chained business relationship with a certain business operator.

The loan determination apparatus 100 may manage transaction relations and transaction relation data between a plurality of businesses by itself. That is, the loan determination device 100 may have the function of the transaction relationship management device 260.

The loan determination device 100 may generate the probability information using the plurality of types of data described above. The loan determining apparatus 100 may use the generated probability information to determine whether to lend to the target business operator. As described above, according to the loan determination device 100 according to the present embodiment, it is determined whether or not a loan can be made to the target business operator by using the probability information generated using a plurality of types of data. It is possible to reduce the probability of occurrence of a situation that cannot be recovered. In addition, it is possible to eliminate the need to set excessively strict criteria regarding whether or not to provide a loan, and reduce the probability that a business that would otherwise be able to receive a loan will not be able to receive a loan easily. ..

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

The business operator can deposit money from a bank account to an account managed by the account management apparatus 300, or withdraw money from an account managed by the account management apparatus 300 to a bank account. Further, the business operator can specify an account managed by the account management device 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 device 300, and can also make payment.

The account management device 300 may be capable of cooperating with any trading platform, and the transaction content can be uniquely specified between the account management device 300 and any trading platform. Instead of linking the account management device 300 and any trading platform, the account management device 300 may also serve as the trading platform.

For example, when the account management device 300 receives a payment request to another company from the company managing the account, if the account balance is not insufficient, the account management apparatus 300 executes the payment process. If the account balance is insufficient, the account management device 300 may determine whether or not to lend to the business operator, and if it is determined to be lending, the account management device 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 loan repayment by the business operator, and determines whether or not to lend to the business operator based on the probability information. Good. For example, the account management device 300 determines whether or not to lend to the business operator, as in the case where the loan application data including the loan application amount corresponding to the shortfall amount is received from the business operator.

The account management device 300 may execute the payment process after adding the loan amount of the shortage or more to the account balance. Further, the account management apparatus 300 may execute the loan repayment process according to a predetermined repayment rule after the payment process is executed. For example, the account management device 300 executes a loan repayment process of repay the entire amount of the loan amount when the account balance of the business operator is larger than the loan amount. Further, for example, when the account balance of the business operator is smaller than the loan amount, the account management device 300 executes a loan repayment process for repay a part of the loan amount. Further, the account management device 300 may execute the interest rate 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 device 300 manages the accounts of a plurality of businesses, and when the business operator receives a payment request from another business to another business, if the account balance is insufficient, the business is concerned. The loan processing is executed by automatically determining whether or not to lend the loan. As a result, it is possible to provide a mechanism by which a business operator can receive a loan and carry out 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 the flow of generation processing of the past CF information 182 and the future CF information 184 by the loan determination device 100 will be described.

The loan determination apparatus 100 uses the data of the account items related to deposits and savings included in the accounting data of the target business operator to generate the past CF information 182 indicating the past cash flow of the target business operator. The loan determination apparatus 100 may compare the past CF information 182 with the financial institution data of the target enterprise, and if there is a mismatch, correct the past CF information according to the financial institution data.

The loan determination device 100 generates future CF information 184 based on the past CF information 182. The loan determination apparatus 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. The loan determination apparatus 100 may derive, for example, a sales occurrence pattern, a purchase occurrence pattern, an expense occurrence pattern, and the like in the past CF information 182, and use these to generate future CF information 184.

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

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

The loan determination apparatus 100 may identify the transaction partner of the target business operator and correct the future CF information 184 using the transaction relation data regarding the transaction with the other business operator by the business transaction partner. The loan determination apparatus 100 corrects the future CF information 184 using, for example, the payment status of the transaction partner to another business. Further, the loan determination device 100 corrects the future CF information 184 using, for example, the attribute information of the trading partner of the trading partner. The attribute information is the scale of the business operator, the creditworthiness of the business operator, and the like.

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

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

The loan determination device 100 creates a loan repayment schedule from, for example, the loan application data received from the target business, and determines whether or not the loan repayment is feasible by the future CF information 184 to determine whether or not the loan is achievable. You may judge. The loan determination device 100 may further determine the availability of the loan by further using the credit information data. Moreover, the loan determination apparatus 100 may determine whether or not the loan can be made by further using the history of the loan to the target enterprise.

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 subject information including the name of the subject. 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 data items other than these.

FIG. 4 schematically shows an example of the data item 212 of the journal entry data included in the accounting data. In FIG. 4, an example of the data value of each data item 212 is also shown.

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

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

The balance data may include a data ID for identifying the balance data. The balance data may include balance dates. The balance data may include a debit/credit classification. The balance data may include account items. The balance data may include an amount of money. The balance data may include data items other than these.

FIG. 6 schematically shows an example of the data item 222 of the 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. Billing data may include a transaction date. The billing data may include billing address information including the name of the billing address. The billing data may include billing content. The billing data may include an amount of money. The billing data may include data items other than these.

FIG. 7 schematically shows an example of the data item 224 of the quotation data included in the transaction data. FIG. 7 also shows an example of the data value of each data item 224.

The estimate data may include a data ID for identifying the estimate data. The quotation data may include a quotation date. The quotation data may include quotationee information including the name of the quotationee. The estimated data may include estimated contents. The estimate data may include the amount of money. The estimation data may include data items other than these.

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 balance dates. The financial institution data may include deposit/withdrawal classification. The financial institution data may include monetary amounts. 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 creditworthiness of the target person. 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 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 the business type of the business group. The characteristic data may include a correction period indicating a period to be corrected. The characteristic data may include a correction target indicating an item to be corrected. The characteristic data may include correction information indicating the content of correction. The data value shown in FIG. 11 shows an example in which the sales in March are increased by 20% for the group of businesses whose business type ID is 00001. The characteristic data may include data items other than these.

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

The transaction-related data may include a business ID that identifies the business. The transaction-related data may include a business name. The transaction-related data may include a supplier business ID that is a business ID of a business partner of the business identified by the business ID. The transaction-related data may include a business partner name. The transaction-related data may include a collection history by the business operator identified by the business operator ID for the business operator identified by the business partner business operator ID. The collection history is, for example, a ratio of the number of times the business operator identified by the business operator ID charges the business operator identified by the business partner business ID and the number of collections of the billed amount. The transaction-related data may include data items other than these.

FIG. 13 schematically shows an example of the flow of processing by the loan determination apparatus 100. Here, an example of a flow in which the loan determination device 100 acquires loan application data from the communication terminal 20 and notifies the communication terminal 20 of the result of the loan application data will be described. Each process shown in FIG. 13 is executed mainly by the control unit included in the loan determination device 100.

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

In S1308, various data are acquired. The loan determination device 100 includes transaction data of a target business operator, financial institution data, credit information data, and past history data, characteristic data of a business operator group corresponding to the target business operator, and a transaction relationship between the business partners of the target business operator. Get data and/or 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 loan to the target business operator is possible based on the future CF information generated in S1310. When it is determined that the loan is possible, the process proceeds to S1314, and when it is determined that the loan is not permitted, the process proceeds to S1316.

In S1314, loan determination device 100 starts a loan procedure for the target business operator. The loan determination device 100, for example, with respect to the loan application data acquired in S1302, the loan terminal data including the content of notifying that the loan is available and the content of requesting information necessary for the loan, the communication terminal 20. Send to. The information required for the loan included in the loan procedure data may be less information than the information required for a normal loan application.

In S1316, the target business operator is notified that the loan is denied. The loan determination device 100 transmits, to the communication terminal 20, for example, notification data for notifying that the loan is not accepted for the loan application data acquired in S1302.

FIG. 14 schematically shows another example of the flow of processing by the loan determination device 100. Here, with respect to the example shown in FIG. 13, a flow in the case where the loan determination device 100 executes a process without using 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, the transaction data of the target business operator is acquired. In S1406, the financial institution data of the target business operator is acquired.

In S1408, the past CF information is generated using the financial institution data acquired in S1406. In S1410, various data are acquired. The loan determination device 100 is at least one of credit information data and past history data of the target business entity, characteristic data of a business entity group corresponding to the target business entity, and transaction relationship data of the business partner of the target business entity. 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 loan can be made to the target business operator based on the future CF information generated in S1412. When it is determined that the loan is possible, the process proceeds to S1416, and when it is determined that the loan is not permitted, the process proceeds to S1418. In S1416, the financing procedure is started for the target business operator. In S1418, the target business operator is notified that the loan is denied.

FIG. 15 schematically shows an example of the functional configuration of the loan determination device 100. The loan determination device 100 includes an application data acquisition unit 102, an accounting data acquisition unit 104, a financial institution data acquisition unit 106, a transaction data acquisition unit 108, an order rate calculation unit 110, a billing deposit rate calculation unit 112, and a transaction-related data acquisition unit 114. , A business operator group identification unit 116, a characteristic data acquisition unit 118, a past CF information generation unit 120, a probability information generation unit 130, a request data transmission unit 142, a loan approval/disapproval determination unit 144, a credit degree acquisition unit 146, a loan processing execution unit 148, The loan management unit 150, the repayment information generation unit 152, and the repayment information transmission unit 154 are provided. Note that it is not essential that the loan determination apparatus 100 has all of these configurations.

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

The accounting data acquisition unit 104 acquires accounting data of the target business operator. The accounting data acquisition unit 104, for example, identifies the target business operator based on the loan application data acquired by the application data acquisition unit 102, requests the accounting data of the target business operator from the accounting data management device 210, and requests the accounting data management device 210. Receive accounting data from 210. When the loan determination device 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. The accounting data acquisition unit 104 may also receive the accounting data from the communication terminal 20 that has transmitted the loan application data.

The financial institution data acquisition unit 106 acquires financial institution data of the target enterprise. The financial institution data acquisition unit 106, for example, identifies the 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, and performs the financial operation. The financial institution data is received from the institution data management device 230. Further, when the loan determination apparatus 100 manages the financial institution data of a plurality of businesses, the financial institution data acquisition unit 106 may read the financial institution data of the target business from the managed financial institution data. Further, the financial institution data acquisition unit 106 may receive the financial institution data 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 business operator. The transaction data acquisition unit 108, for example, identifies the target business operator based on the loan application data acquired by the application data acquisition unit 102, requests the transaction data of the target business operator from the transaction data management device 220, and then the transaction data management device. Receive transaction data 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. Further, the transaction data acquisition unit 108 may receive the transaction data from the communication terminal 20 that has transmitted the loan application data. The transaction data acquisition unit 108 may acquire quotation data as transaction data. Further, the transaction data acquisition unit 108 may acquire billing data as the 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 has been received from another company or the like. For example, the order data indicates that an order has been received for the quote indicated by the quote data. The order data may include a data ID for identifying the order data. The order data may include the order date. The order data may include orderer information including the name of the orderer. The order data may include order details. The order data may include the amount of money. The order data may include data items other than these.

The order acceptance rate calculation unit 110 calculates the order acceptance rate of the target business entity based on the transaction data acquired by the transaction data acquisition unit 108. The order receiving rate calculation unit 110 may calculate the order receiving rate using the estimation data and the order receiving data included in the transaction data. For example, the order rate calculation unit 110 calculates the order rate by dividing the number of times of estimation by the number of times of orders corresponding to the estimation. The order receiving rate calculation unit 110 may calculate the order receiving rate using the estimation data and the billing data included in the transaction data. For example, the order acceptance rate calculation unit 110 calculates the order acceptance rate by dividing the number of times of estimation by the number of times of requests corresponding to the estimation. The order-acceptance rate calculation unit 110 may calculate the order-acceptance rate using the estimate data and the billing data, for example, when there is no order acceptance data corresponding to the estimate 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 received 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 determines the relationship between the estimate date in the estimate data and the order date in the order data, the quoted party in the estimate data, the estimate content, and The order corresponding to the estimate may be specified by confirming the amount of money, the order originator in the order data, the contents of the order, and the amount of money. The transaction data may include a correspondence relationship between the quotation and the bill, and the order rate calculation unit 110 may identify the number of bills corresponding to the quotation by referring to the correspondence relation. When the transaction data does not include the correspondence between the quotation and the bill, the order rate calculation unit 110 determines the relation between the quotation date in the quotation data and the billing date in the billing data, the quoted party in the quotation data, the quotation content, and The bill corresponding to the quotation may be specified by checking the amount, the billing destination in the billing data, the contents of the bill, and the match with the amount.

The billing deposit rate calculation unit 112 calculates the billing deposit rate of the target business entity based on the transaction data acquired by the transaction data acquisition unit 108. The billing deposit rate calculation unit 112 may calculate the billing deposit rate using billing data included in the transaction data. For example, the billing deposit rate calculation unit 112 calculates the billing deposit rate by dividing the number of bills by the number of billing requests.

The billing data may include information as to whether or not a deposit has been made, and the billing/payment rate calculation unit 112 may identify the number of billing requests made by referring to the information. If the billing data does not include the information, the billing deposit rate calculation unit 112 refers to the financial institution data acquired by the financial institution data acquisition unit 106 to determine whether or not the deposit is made to the bill. May be specified.

The transaction-related data acquisition unit 114 acquires transaction-related data of a transaction partner of the target business operator. The transaction relationship data acquisition unit 114 identifies the transaction partner of the target business operator using the transaction data acquired by the transaction data acquisition unit 108, requests the transaction relationship data of the identified transaction partner from the transaction relationship management device 260, The transaction relation data may be received from the transaction relation management device 260. Moreover, when the loan determination apparatus 100 manages the transaction relations and transaction relation data between a plurality of enterprises, the transaction relation data acquisition unit 114 uses the transaction relation data under the management to conduct a transaction with a transaction partner of the target enterprise. The relevant data may be read.

The business group specifying unit 116 specifies a business group corresponding to the target business. The business group identification unit 116 refers to, for example, a list in which a plurality of business groups are registered, and selects a business group corresponding to the target business from the business groups included in the list. Further, the business entity group identification unit 116 may identify the business entity group corresponding to the target business entity by referring to the list in which the businesses corresponding to each of the plurality of business entity groups are registered.

The business entity group identification unit 116 may identify the business entity group corresponding to the target business entity by using the business type information and the scale information of the target business entity. The enterprise group specifying unit 116 may receive the business type information and the scale information of the target enterprise from the communication terminal 20. Further, the business entity group identification unit 116 stores in advance business entity information including business category information and scale information of a plurality of businesses, and acquires the business category information and scale information of the target business entity from the business entity information. May be.

The characteristic data acquisition unit 118 acquires the characteristic data of the business group identified by the business group identification unit 116. The characteristic data acquisition unit 118 requests the characteristic data of the business group identified by the business group identification unit 116 to the business group data management device 250, and receives the feature data from the business group data management device 250, for example. To do. Further, when the loan determination device 100 manages the characteristic data of a plurality of enterprise groups, the characteristic data acquisition unit 118 identifies the enterprise group identified by the enterprise group identification unit 116 from the managed characteristic data. The characteristic 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 accounting data acquired by the accounting data acquisition unit 104, for example. The past CF information generation unit 120 uses the data of the account items related to deposits and savings included in the accounting data to add and subtract the transfer details from the cash balance of the reference date as a starting point, and thus, for each predetermined period. The past CF information indicating the cash deposit balance may be generated. For example, the past CF information generation unit 120 generates past CF information indicating the cash deposit balance for each day, week, or month by adding/subtracting the movement details with the cash deposit balance at the beginning of the year as a starting point. The cash deposit balance here indicates the sum of cash and deposits and savings, but may include the case where cash is zero. That is, the cash and deposit balance may indicate only the deposit and savings 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. The past CF information generation unit 120 adds/subtracts the transfer details based on the deposit and attendance included in the financial institution data, for example, starting from the cash deposit balance on the reference date, and thus the cash for each predetermined period. The past CF information indicating the deposit balance is generated.

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

The future CF information generation unit 132 generates future CF information of the target business operator. The future CF information generation unit 132 may generate future CF information using the accounting data, financial institution data, transaction data, characteristic data, transaction relation data, past CF information, order acceptance rate, and billing deposit rate. The future CF information generation unit 132 may generate future CF information by using all of these, or may generate future CF information by using some of these.

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

The future CF information generation unit 132 may generate future CF information using the data of the account items related to future increase/decrease in the deposit/savings balance among the account items included in the accounting data. For example, the loan determination apparatus 100 generates future CF information by using the account items related to sales receivables and the account items related to trade 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 future CF information, for example, assuming that cash and deposits increase on the scheduled date of collection of accounts receivable.

Further, the future CF information generation unit 132 may generate future CF information using the data of accounts payable. The future CF information generation unit 132 generates future CF information, for example, assuming that the cash on hand decreases on the planned payment date of accounts payable.

Further, the future CF information generation unit 132 may generate future CF information using the data of the unpaid money. The future CF information generation unit 132 generates future CF information, for example, assuming that the cash deposit decreases on the scheduled payment date of the unpaid amount.

Further, the future CF information generation unit 132 may generate future CF information using the data of the advance payment. The future CF information generation unit 132, for example, when the advance payment data indicates that there is an advance payment, generates future CF information on the assumption that the cash will not decrease on the scheduled payment date. As a specific example, the future CF information generation unit 132 may generate the future CF information assuming that the cash deposit does not decrease when the prepayment amount is larger than the charge amount. Further, when the advance payment is smaller than the charge, the future CF information generation unit 132 may generate the future CF information assuming that the difference between the charge and the advance payment is reduced from the cash deposit.

The future CF information generation unit 132 may generate future CF information using the estimated data and the order acceptance rate. For example, the future CF information generation unit 132 generates future CF information regarding the quotation data corresponding to the order rate among the plurality of quotation data, as the data to be ordered and received. For example, when the number of pieces of estimate data is 5 and the order rate is 80%, future CF information is generated assuming that 4 pieces of the 5 pieces of estimate data will be ordered and received.

The future CF information generation unit 132 may generate future CF information using the billing data and the bill deposit rate. For example, the future CF information generation unit 132 generates future CF information regarding the billing data corresponding to the billing deposit rate, out of 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 for 19 of the 20 estimated data to be deposited.

The future CF information generation unit 132 may generate future CF information using the estimate data, the billing data, the order rate, and the billing deposit rate. For example, the future CF information generation unit 132 is assumed to receive an order for and invoice the quotation data matching the order rate among the plurality of quotation data, and the quotation data corresponding to the billing deposit rate among the estimated data to be billed. Will be generated as CF information in the future.

The future CF information generation unit 132 may generate future CF information by using the transaction relationship data of the transaction partner of the target company. For example, the future CF information generation unit 132 uses the transaction-related data to identify the payment status of the target business operator to the other business operator, and generates future CF information using the payment status. As a specific example, the future CF information generation unit 132 refers to the transaction-related data of the trading partner who is the billing destination of the billing data of the target business operator, identifies the payment status of the business partner to another business operator, Depending on the identified deposit status, it is determined whether or not the billing data is deposited. For example, if the payment rate to the other business operator of the transaction partner is lower than a predetermined threshold value, it is determined that the payment to the billing data will not be made, and future CF information is generated. Accordingly, it is possible to generate CF information in the future in consideration of the possibility that the transaction partner of the target business operator will not make a payment to the target business operator. The future CF information generation unit 132 may generate the future CF information by further considering the amount of money to be deposited in addition to the rate of payment to the other business partner of the trading partner.

Further, for example, the future CF information generation unit 132 identifies the transaction partner of the transaction partner of the target business entity using the transaction relation data, and generates the future CF information using the attribute information of the identified transaction partner. As a specific example, the future CF information generation unit 132 refers to the transaction relationship data of the transaction partner who is the billing destination of the billing data of the target business operator, identifies the transaction partner of the transaction partner, and identifies the identified transaction partner. According to the attribute information, it is determined whether or not the payment for the billing data is made. For example, at least any one of the business scale and creditworthiness of the identified trading partner is compared with a threshold value, and if the value is lower than the threshold value, future CF information is generated assuming that payment to the billing data is not made. Accordingly, the CF information can be generated in the future in consideration of the possibility that the trading partner of the target business operator cannot collect the trading partner from the trading partner of the trading partner.

Further, for example, the future CF information generation unit 132 uses the difference between the past CF information and the financial institution data to generate future CF information. 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 uses the corrected past CF information to generate the future CF information. 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 becomes closer to the financial institution data.

The score generation unit 134 generates a score indicating the probability of loan repayment by the target business operator. The score generation unit 134 may generate a score using the accounting data, financial institution data, transaction data, characteristic data, transaction relationship data, past CF information, order acceptance rate, and billing receipt rate. The score generation unit 134 may generate the score using all of these, or may generate the score using some of these. The score generator 134 may generate the score using any method.

The consistency check unit 136 checks the consistency of the various data collected when determining whether or not a loan to the target business operator is possible with respect to the loan application data received from the communication terminal 20. For example, the consistency check unit 136 checks the consistency between the balance data acquired by the accounting data acquisition unit 104 and the financial institution data acquired by the financial institution data acquisition unit 106. Further, for example, the consistency check 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 check unit 136 detects a mismatch of data as a result of checking the consistency, the data is determined to be invalid data. The consistency checker 136 may generate a data validity score for each data based on the degree of matching.

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

The consistency checker 136 may notify the request data transmitter 142 of data whose data validity score is lower than a predetermined threshold value. The request data transmission unit 142 includes at least one of a data correction and a data additional submission request for the data for which the consistency check unit 136 determines that the data validity score is lower than a predetermined threshold value. The request data may be transmitted to the communication terminal 20. When accepting the correction content of the data and the additional data with respect to the requested data, the consistency check unit 136 may correct the data using these.

As described above, since the consistency check unit 136 checks the consistency of the data collected in determining whether or not to lend a loan to the target business operator, for example, an unauthorized application can be detected. In addition, it is possible to prevent whether or not a loan can be made based on incorrect data.

The loan approval/disapproval 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 enterprise. For example, the loan approval/disapproval 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 showing a monthly repayment amount is created.

Then, the loan approval/disapproval determination unit 144 determines the loan approval/disapproval by determining whether or not the repayment schedule is feasible based on the future CF information generated by the future CF information generation unit 132. For example, the loan approval/disapproval determination unit 144 determines whether or not there is a shortage of cash when the monthly repayment amount scheduled for repayment is subtracted from the cash in each month indicated by the future CF information.

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

The credibility acquisition unit 146 may generate the credibility of the target enterprise by using the credit information data of the business partner of the target enterprise. For example, the credibility acquisition unit 146 deducts the score of the target business operator when the score of the business partner of the target business operator is lower than a predetermined threshold value. Further, for example, when the rating of the supplier of the target business operator is higher than a predetermined threshold value, the credibility acquisition unit 146 adds the score to the target business operator. Further, the credibility acquisition unit 146 adds points according to the scores of the business partners of the target business operator to the scores of the target business operator. Further, the credibility acquisition unit 146 may generate the credibility of the target enterprise using the credit rating information of the business partner of the target enterprise. For example, the credit rating acquisition unit 146 deducts the credit rating of the target business operator when the credit rating information of the business partner of the target business operator is lower than a predetermined threshold value. Further, for example, when the credit rating information of the business partner of the target business operator is higher than a predetermined threshold value, the credit rating acquisition unit 146 adds points to the credit rating of the target business operator. Further, the credit rating acquisition unit 146 adds a score according to the credit rating information of the business partner of the target business operator to the credit rating of the target business operator.

The credibility acquisition unit 146 may generate the credibility of the target enterprise by using the credit information data of the enterprise having a business relationship in a chain with the target enterprise. For example, the credibility acquisition unit 146 weights and adds the score of the target business operator to the score of the business operator having a chained business relationship with the target business operator. As the weighting, for example, the weight of the score of the business operator whose chain relationship is farther from the target business operator may be lowered. Further, the credibility acquisition unit 146 may generate the credibility of the target enterprise by using the credit rating information of the enterprise having a business relationship in a chain with the target enterprise. For example, the credit rating acquisition unit 146 weights and adds to the credit rating of the target business operator, a value corresponding to the credit rating information of the business operators having a chained business relationship with the target business operator. As the weighting, for example, the weight of the score of the business operator whose chain relationship is farther from the target business operator may be lowered.

The credibility acquisition unit 146 may generate the credibility of the target enterprise by combining a plurality of data existing on the Internet. The credibility acquisition unit 146 generates the credibility 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. Further, the credibility acquisition unit 146 may generate the credibility of the target operator using the evaluation information indicating the evaluation of the business operated by the target operator on the website operated by the third party. In addition, the credibility acquisition unit 146 may generate the credibility of the target enterprise using information about a person related to the target enterprise such as a representative of the target enterprise. For example, the credibility acquisition unit 146 generates the credibility of the target business operator by using the information of SNS (Social Networking Service) of the representative of the target business operator.

The loan approval/disapproval determination unit 144 may use the creditworthiness of the target business operator generated by the creditworthiness acquisition unit 146 to determine the loan approval/disapproval of the target business operator. For example, the loan approval/disapproval determination unit 144 determines that the loan is unacceptable when the repayment schedule has a large amount of cash and deposits indicated by the CF information in the future but the creditworthiness of the target enterprise is low. For example, the loan approval/disapproval determination unit 144 associates and registers the creditworthiness determined to be loan refusal for each difference between the repayment schedule and the cash and deposits indicated by the future CF information, and the creditworthiness generated by the creditworthiness acquisition unit 146 is If the credit level is lower than the credit level corresponding to the difference, it is determined that the loan is not accepted.

Further, for example, the loan approval/disapproval determination unit 144 determines that the loan is acceptable when the repayment schedule has a small amount of cash and deposits indicated by the CF information in the future, but the credit credibility of the target business operator is high. For example, the loan approval/disapproval determination unit 144 associates and registers the creditworthiness determined to be loanable for each difference between the repayment schedule and the cash and deposits indicated by the future CF information, and the creditworthiness generated by the creditworthiness acquisition unit 146 is If it is higher than the credit corresponding to the difference, it is determined that the loan is available.

The loan processing execution unit 148 executes the loan processing according to the determination result by the loan approval/disapproval determination unit 144. For example, when the loan approval/disapproval determination unit 144 determines that the loan is available, 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 enterprise that the loan is available and content for requesting information necessary for the loan. You may send it. Further, for example, when the loan approval/disapproval determination unit 144 determines that the loan is available, the loan processing execution unit 148 makes a loan offer to the target business operator. The loan processing execution unit 148 provides the communication terminal 20 with loan offer data including content for notifying the communication terminal 20 of the target enterprise that the loan is available and content for requesting information necessary for the loan. You may send it.

When the loan approval/disapproval determination unit 144 determines that the loan is not permitted, the loan process execution unit 148 may notify the target enterprise that the loan is not permitted. The loan processing execution unit 148 transmits, for example, to the communication terminal 20 of the target business operator, notification data notifying that the loan is not accepted.

The post-financing management unit 150 manages the target business operator after the loan is provided. The post-financing management unit 150 causes the future CF information generation unit 132 to continuously generate future CF information of the target business operator, for example, after the loan is provided. Then, when it is detected that the loan repayment may be hindered based on the CF information in the future, the post-finance management unit 150 notifies the person in charge of loan collection or the like to that effect. The post-financing management unit 150 determines that the loan repayment may be hindered, for example, when the cash deposit of each month of the newly generated future CF information is smaller than the monthly repayment amount.

The post-finance management unit 150 may execute processing such as accelerating the reminding period to be transmitted to the target business operator when it is detected that the loan repayment may be hindered. The post-financing management unit 150 executes various processes by a method that does not deviate 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 by using the future CF information. For example, the repayment information generation unit 152 generates a repayment schedule for repayment with profit, a repayment schedule for repayment with cash flow, and the like.

The repayment information generation unit 152 may generate a repayment schedule for the loan using the future CF information when the loan approval/disapproval determination unit 144 determines that the loan is available. For example, the repayment information generation unit 152 refers to future CF information, identifies a month in which the amount of cash and deposits is larger than 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 business operator. Thereby, the repayment schedule can be flexibly proposed to the target business operator.

When the loan approval/disapproval determination unit 144 determines that the loan is not available, the repayment information generation unit 152 may use the future CF information to generate a loanable repayment schedule. 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 uses the future CF information to generate a repayment schedule when the loan date is changed, when it is possible to determine that the loan is acceptable by changing the desired loan date included in the loan application data. To do. As a specific example, when 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 is permitted by changing the loan date to July 2017, the loan is set to July 2017. Generate a repayment schedule for the loan date.

Further, the repayment information generation unit 152 uses the future CF information to generate a repayment schedule with a changed loan period when it can be determined that the loan is possible by changing the loan period included in the loan application data. As a specific example, when the loan period included in the loan application data is June and the loan is not accepted as it is, but the loan is permitted by changing the loan period to October, a repayment schedule with the loan period as October is set. To generate.

By the repayment information transmitting unit 154 transmitting the repayment information to the communication terminal 20 of the target business operator, the target business operator can be notified that the loan can be provided by changing the condition of the loan application data. For example, if the business of the target company is agriculture, it is difficult to distribute the loan amount evenly in each month and to repay it, but in some cases it may be possible to repay it collectively during the harvest season. The characteristics of the repayment ability differ depending on the business. Since the repayment information generation unit 152 according to the present embodiment can generate a repayment schedule suitable for the cash flow indicated by the future CF information of the target business operator, the repayment schedule is flexibly provided according to the situation of the target business operator. be able to.

FIG. 16 schematically shows an example of the functional configuration of the account management device 300. The account management device 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 approval/disapproval determination unit 312, a loan repayment execution unit 314, and an interest rate processing execution. The unit 316 and the instruction receiving unit 318 are provided. Note that it is not essential that the account management device 300 has all of these configurations.

The account management unit 302 manages account balances of a plurality of businesses. The account management unit 302 may open an account for the business operator in response to a request from the business operator. The account management unit 302 manages the accounts of a plurality of businesses within one or a plurality of bank accounts, for example. As a specific example, the account management unit 302 manages the account balances of N businesses or a number of businesses greater than N in N bank accounts. The account management unit 302 manages the accounts of a plurality of businesses, for example, within the 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, characteristic data, transaction-related data, past CF information, order acceptance rate, and billing deposit rate. The data acquisition unit 304 may acquire all of them, 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 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 business operator based on at least one of the accounting data and the transaction data. The future CF information generation unit 306 may generate the future CF information by performing the same processing as the future CF information generation unit 132.

The payment request acquisition unit 308 acquires a payment request to a customer from a business operator who manages an account. The payment request includes, for example, the account number of the payee and the payment amount. The payment request acquisition unit 308 receives, for example, the payment request transmitted by the communication terminal 20. Businesses that have requested payment may be described as target businesses.

The payment process execution unit 310 executes the payment process according to the payment request acquired by the payment request acquisition unit 308. The payment processing execution unit 310 may execute the payment processing when the account balance of the target business operator is larger than the payment amount of the payment request. The payment processing execution unit 310, for example, sends the payment amount to the account number of the payee of the payment request. The payment processing execution unit 310 may present an instruction to send the payment amount to the account number of the payee 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 with the payment. When the account balance of the target business operator is smaller than the payment amount of the payment request, the payment processing execution module 310 causes the loan approval/disapproval determination module 312 to determine whether or not to lend to the target business operator.

The loan approval/disapproval determination unit 312 determines whether or not to provide a loan to the target business operator. The loan approval/disapproval determination unit 312 may determine the approval/disapproval of a loan to the target business operator based on the future CF information of the target business operator generated by the future CF information generation unit 306. The loan approval/disapproval determination unit 312 may determine the loan approval/disapproval by performing the same processing as the loan approval/disapproval determination unit 144.

The loan approval/disapproval determination unit 312 may determine the approval/disapproval of a loan to the target business operator in response to a request from the payment processing execution unit 310. For example, the loan approval/disapproval determination unit 312 determines whether to provide a loan amount equal to or larger than the difference between the payment amount of the payment request and the account balance of the target enterprise.

The loan approval/disapproval determination unit 312 determines, for example, whether or not the difference between the payment amount of the payment request and the account balance of the target enterprise can be loaned. In addition, the loan approval/disapproval determination unit 312 may determine whether or not it is possible to provide an amount of money 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 of money may be determined by the manager of the account management apparatus 300, the target business operator, etc. as the minimum amount of money required for the account balance of the target business operator.

The account management unit 302 may add the loan amount to the account balance of the target enterprise when the loan approval/disapproval determination unit 312 determines that the loan is available. The account management unit 302 adds, for example, the difference between the payment amount of the payment request and the account balance of the target business operator to the account balance of the target business operator. Further, the account management unit 302 adds, for example, an amount obtained by adding a predetermined amount to the difference between the payment amount of the payment request and the account balance of the target business operator to the account balance of the target business operator.

The loan approval/disapproval determination unit 312 may periodically determine whether or not to provide a loan to a business operator who manages an account. The loan approval/disapproval determination unit 312, for example, periodically determines a loan available line for a business operator who manages an account. The loan approval/disapproval determination unit 312 may notify the account management unit 302 of the available loan amount. The account management unit 302 may present the lending allowance frame determined by the loan approval/disapproval determination unit 312 to the enterprise managing the account. Thereby, the business operator can grasp how much loan can be obtained 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 processing is executed by the payment processing execution unit 310. Then, the loan repayment executing unit 314 executes a repayment process from the account balance according to a predetermined condition. For example, the loan repayment executing unit 314 subtracts the loan amount from the account balance when the account balance of the target business operator is larger than the loan amount. The subtracted loan amount is used for repayment of the loan. Further, the loan repayment execution unit 314 may execute a repayment process to repay the loan amount in stages. For example, the loan repayment executing unit 314 subtracts the difference between the account balance and a predetermined threshold value from the account balance when the account balance of the target business operator is smaller than the loan amount. As a specific example, when the loan amount is 1,000, the account balance is 500, and the predetermined threshold value is 300, the loan repayment execution unit 314 sets the account difference of 200 to 200 as the difference. Subtract from balance. The deducted amount is used to repay the loan.

The interest rate processing execution unit 316 monitors the account balance of the target business operator 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, when the account balance of the target business operator is smaller than the loan amount, the interest rate processing executing unit 316 may subtract the interest rate amount determined according to a predetermined interest rate rule from the account balance. The subtracted interest amount is used for payment of interest on the loan amount. The interest rate processing execution unit 316 subtracts the interest rate amount 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 account balance of the target business operator is in the state of -300 with a loan balance of 300, if the timing comes, 316 subtracts 50 to calculate the account balance. -350. In addition, as long as it is within the range of the credit line, the actual repayment may be made when the account balance exceeds a predetermined threshold value.

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

FIG. 17 schematically shows an example of the hardware configuration of a computer 1000 that functions as the loan determination device 100 or the account management device 300. The computer 1000 according to the present embodiment includes a CPU peripheral unit having a CPU 1010, a RAM 1030, and a graphic controller 1085 that are mutually connected by a host controller 1092, a ROM 1020 that is connected to the host controller 1092 by an input/output controller 1094, and a communication I/O. The F1040, the hard disk drive 1050, the DVD drive 1070, and the input/output unit having the input/output chip 1080 are 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 it on the display 1090. Alternatively, the graphic controller 1085 may internally 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 wirelessly. Further, 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 by the computer 1000 at startup and a program dependent on the hardware of the computer 1000. The input/output chip 1080 connects various input/output devices to the input/output controller 1094 via, for example, a parallel port, a serial port, a keyboard port, a mouse port, and the like.

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

A program installed in the computer 1000 and causing the computer 1000 to function as the loan determination apparatus 100 or the account management apparatus 300 causes the CPU 1010 or the like to cause the computer 1000 to function as each unit 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, so that the application data acquisition unit 102 and the accounting data acquisition unit, which are specific means in which software and the various hardware resources described above cooperate with each other. 104, financial institution data acquisition unit 106, transaction data acquisition unit 108, order rate calculation unit 110, billing/deposit rate calculation unit 112, transaction related data acquisition unit 114, business group identification unit 116, characteristic data acquisition unit 118, past CF Information generation unit 120, probability information generation unit 130, request data transmission unit 142, loan approval/disapproval determination unit 144, credit degree 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. Further, the information processing described in these programs is read by the computer 1000, and the account management unit 302 and the data acquisition unit, which are concrete means in which software and the various hardware resources described above cooperate with each other. 304, future CF information generation unit 306, payment request acquisition unit 308, payment process execution unit 310, loan approval/disapproval determination unit 312, loan repayment execution unit 314, interest rate process execution unit 316, and instruction reception unit 318. Then, by realizing the calculation or processing of information according to the purpose of use of the computer 1000 in the present embodiment by these specific means, the specific loan determination device 100 or the account management device 300 according to the purpose of use is constructed. To be done.

Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is apparent to those skilled in the art that various changes 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 devices, systems, programs, and methods shown in the claims, the specification, and the drawings is "preceding" or "prior to". It should be noted that the output of the previous process can be realized in any order unless the output of the previous process is used in the subsequent process. Even if the operation flow in the claims, the description, and the drawings is described by using “first”, “next”, and the like for convenience, it means that it is essential to carry out in this order. Not a thing.

10 network, 20 communication terminal, 22 data item, 100 loan determination device, 102 application data acquisition unit, 104 accounting data acquisition unit, 106 financial institution data acquisition unit, 108 transaction data acquisition unit, 110 order rate calculation unit, 112 bill payment Rate calculation unit, 114 transaction-related data acquisition unit, 116 enterprise group identification unit, 118 characteristic data acquisition unit, 120 past CF information generation unit, 130 probability information generation unit, 132 future CF information generation unit, 134 score generation unit, 136 Consistency confirmation unit, 142 request data transmission unit, 144 loan approval/disapproval determination unit, 146 credit rating acquisition unit, 148 loan processing execution unit, 150 post-loan management unit, 152 repayment information generation unit, 154 repayment information transmission unit, 170 data items, 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 enterprise group data management device, 252 data item, 260 transaction relationship management device, 262 data item, 300 account management device, 302 account management unit, 304 data acquisition unit, 306 future CF information Generation unit, 308 payment request acquisition unit, 310 payment processing execution unit, 312 loan approval/disapproval determination unit, 314 loan repayment execution unit, 316 interest rate processing execution unit, 318 instruction receiving unit, 1000 computer, 1010 CPU, 1020 ROM, 1030 RAM, 1040 communication I/F, 1050 hard disk drive, 1070 DVD drive, 1072 DVD-ROM, 1080 input/output chip, 1085 graphic controller, 1090 display, 1092 host controller, 1094 input/output controller

Claims (9)

An accounting data acquisition unit that acquires accounting data related to the accounting of the business operator,
A transaction data acquisition unit that acquires transaction data relating to transactions by the business operator;
A transaction relationship data acquisition unit that identifies a transaction partner of the business operator using the transaction data, and acquires transaction relationship data regarding a transaction by the business partner with another business operator,
By analyzing the accounting data, a sales occurrence pattern, a purchase occurrence pattern, and an expense occurrence pattern are derived, and future CF information indicating a future cash flow of the business operator based on the derived occurrence pattern. Is a future CF information generation unit that identifies a trading partner of the trading partner based on the trading relationship data acquired by the trading relationship data acquisition unit, and according to attribute information of the trading partner of the trading partner. A future CF information generation unit that determines whether or not the billing data for the transaction partner is deposited, and uses the determination result to generate the future CF information .
The attribute information is the scale and creditworthiness of the business operator,
The future CF information generation unit compares at least one of the business scale and credit of the trading partner of the trading partner with a threshold value, and if the value is lower than the threshold value, the future CF information is generated assuming that the billing data is not deposited. To do
Information processing device. An application data acquisition unit for acquiring loan application data including a loan application amount by the business operator,
A credibility acquisition unit that acquires the credibility of the entrepreneur by using the credibility of the entrepreneur and credit information data of the entrepreneur who has a transactional relationship with the entrepreneur,
The loan approval/disapproval determination unit that determines whether or not the loan application data can be loaned to the business operator based on the loan application data, the creditworthiness of the business operator, and the future CF information. Information processing device. The creditworthiness acquisition unit assigns a weight to a score of a business operator that is farther from the business operator having a chain relationship with respect to a score of the business operator, to a score of a business operator that has a chained business relationship with the business operator. The information processing apparatus according to claim 2 , wherein the creditworthiness of the business operator is generated by weighting to be lowered and adding. Further comprising a past CF information generation unit that generates past CF information indicating a past cash flow of the business operator based on the accounting data,
The future CF information generation unit, wherein generating the future CF information based on past CF information processing apparatus according to any one of claims 1 3. The past CF information generation unit uses the data of the account items related to deposits and savings included in the accounting data to add and subtract the transfer details from the cash balance of the reference date as a starting point, thereby determining a predetermined period. Generating past CF information showing cash balance for each
The information processing apparatus according to claim 4 , wherein the future CF information generation unit generates, based on the past CF information, future CF information indicating a cash deposit balance for each predetermined future period. A financial institution data acquisition unit that acquires financial institution data including a history of deposits and withdrawals to and from the financial institution's account of the business operator;
The future CF information generating unit, by using the difference between the past CF information and the financial institution data, wherein generating the future CF information processing apparatus according to claim 4 or 5. The future CF information generation unit uses data of account items relating to future increase/decrease in deposit/savings balance, including at least one of accounts receivable, payable, prepayment and accrual, among account items included in the accounting data. The information processing apparatus according to any one of claims 1 to 6 , which generates the future CF information. An application data acquisition unit for acquiring loan application data including a loan application amount by the business operator,
The information processing apparatus according to any one of claims 1 to 7 , further comprising: a probability determining unit that determines a probability of loan repayment of the business entity based on the loan application data and the future CF information. A program for causing a computer to function as the information processing apparatus according to any one of claims 1 to 8.

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