Information processing apparatus, information processing method, and program

Abstract

To derive a measure for properly evaluating an affiliated store regarding financing.SOLUTION: An information processing apparatus is configured to: predict future sales based on past sales performance of a store affiliated with an electronic settlement service; derive a measure for specific conditions to determine that the affiliated store may cease to be an affiliated store after a predetermined period or that the affiliated store may be determined to be inappropriate as an affiliated store after the predetermined period, based on information on the affiliated store managed in the electronic settlement service; and derive an evaluation measure for provision conditions to fund the affiliated store based on the future sales and the measure. The apparatus inputs input information to a trained model configured to output a measure according to the input information, and derives a measure based on a result output by the trained model. The input information includes information on at least one of status of use of users using the electronic settlement service in an affiliated store, business type of the affiliated store, prefecture which is a location of the affiliated store, and the number of days in which the store has been registered as an affiliated store.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] BACKGROUND ART A credit margin calculation device has been disclosed that refers to a credit limit table including credit limits for a customer and calculates the credit margin for the customer from the credit limit in the credit limit table (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-179703 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques sometimes fail to provide an appropriate evaluation of funding.

[0005] The present invention has been made in consideration of these circumstances, and one of its objects is to provide an information processing device, an information processing method, and a program that can derive indicators for appropriately evaluating the provision of funds to affiliated stores. [Means for solving the problem]

[0006] One aspect of the present invention is an information processing device comprising: a first processing unit that predicts future sales based on the past sales performance of a member store of an electronic payment service; a second processing unit that derives indicators related to specific conditions under which a member store will cease to be a member store after a predetermined period of time or will be deemed unsuitable as a member store after the predetermined period of time based on information about the member store managed in the electronic payment service; and an integrated processing unit that derives evaluation indicators for provision conditions when providing funds to the member store based on the future sales and the indicators, wherein the second processing unit inputs input information into a trained model that outputs the indicators according to the input information, and derives the indicators based on the results output by the trained model, and the input information includes one or more of the following information: the usage status of users of the electronic payment service at the member store, the industry of the member store, the prefecture in which the member store is located, and the number of days the member store has been registered as a member store. [Effects of the Invention]

[0007] According to one aspect of the present invention, it is possible to provide an information processing device, an information processing method, and a program that can derive an index for appropriately evaluating the provision of funds to a member store. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of an electronic payment system in which an electronic payment service is realized. [Figure 2] This is a sequence diagram (part 1) illustrating the general flow of electronic payment. [Figure 3] This is a sequence diagram (part 2) illustrating the general flow of electronic payment. [Figure 4] FIG. 2 is a configuration diagram of a payment server 100. [Figure 5] FIG. 10 is a diagram showing an example of the contents of user information 172. [Figure 6] FIG. 10 is a diagram showing an example of the contents of affiliated store / store information 176. [Figure 7] FIG. 2 illustrates an example of a functional configuration of a management server 200. [Figure 8] FIG. 10 is a diagram showing an example of proposal information 178. [Figure 9] FIG. 10 is a diagram showing another example of the proposal information 178. [Figure 10] FIG. 10 is a diagram showing an example of an interface screen provided to an interface 72 for affiliated stores. [Figure 11] FIG. 10 is a diagram showing another example of an interface screen provided to the member store interface 72. [Figure 12] 10 is a flowchart showing an example of the flow of processing executed by the payment server 100. [Figure 13] FIG. 10 is a diagram showing an outline of a process for generating proposal information 178. [Figure 14] FIG. 10 is a diagram for explaining a first process. [Figure 15] FIG. 10 is a diagram for explaining a second process. [Figure 16] FIG. 10 is a diagram illustrating an overview of integration processing. [Figure 17] FIG. 10 is a diagram for explaining a simulation. [Figure 18] FIG. 10 is a diagram for explaining a process for calculating an evaluation index. [Figure 19] FIG. 10 is a diagram for explaining a process for calculating an evaluation index. [Figure 20] FIG. 10 is a diagram illustrating correspondence information. [Figure 21] 10 is a flowchart showing an example of the flow of processing executed by the management server 200. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, with reference to the drawings, embodiments of an information processing device, an information processing method, and a program according to the present invention will be described. The various devices and servers described below, which provide services to users and perform internal analysis, may be implemented as a distributed device group, and each device may be operated by a different business. Furthermore, the hardware owner (cloud server provider) and the business that actually operates the device may also be different. An application program and a payment server work together to provide an electronic payment service. In the following description, the application program is referred to as a payment app. An electronic payment service is a service that supports payments for the purchase of goods and services at a store. A store is, for example, a physical store (real-world store) existing in real space, but may also include a virtual store for e-commerce. Virtual stores may also include stores operated by entities other than the operator of the electronic payment service. In such cases, when making a payment for a purchase at a virtual store, the user is controlled to transition to an interface screen for the electronic payment service. In an electronic payment service, a store is treated as belonging to, for example, a member store (brand), and when a purchase is made at a store, processing such as payment is primarily conducted between the user and the member store. Alternatively, processing such as payment may be conducted between the user and the store.

[0010] [Electronic payment service] FIG. 1 shows an example of the configuration of an electronic payment system in which an electronic payment service is realized. The electronic payment service is realized mainly by a payment server 100. An electronic payment system that realizes the electronic payment service includes, for example, one or more user terminal devices 10, one or more first store terminal devices 50, one or more second store terminal devices 70, the payment server 100, and a management server 200. These devices communicate, for example, via a network NW. The network NW includes, for example, the Internet, a LAN (Local Area Network), a wireless base station, a provider device, etc. The payment server 100 or the management server 200 is an example of an "information processing device."

[0011] Some or all of the functional components included in the electronic payment system may be distributed across multiple devices in any form, or may be integrated into any device. For example, some or all of the functional components of the management server 200 may be included in the configuration of the payment server 100, or some or all of the functional components of the payment server 100 may be included in the management server 200.

[0012] [User terminal device] The user terminal device 10 is, for example, a portable terminal device such as a smartphone or tablet terminal. The user terminal device 10 is a computer device having at least an optical reading function, a communication function, a display function, an input acceptance function, and a program execution function. In the following description, components for realizing these functions are referred to as a camera, a communication device, a touch panel, a CPU (Central Processing Unit), etc. In the user terminal device 10, a processor such as a CPU executes a payment app 20, which operates in cooperation with the payment server 100 to provide electronic payment services to users. The payment app 20 is installed on the user terminal device 10 from, for example, an application store, and controls the camera, communication device, touch panel, etc.

[0013] [First store terminal device] The first store terminal device 50 is installed, for example, in a store. The first store terminal device 50 is a computer device having at least a product price acquisition function, an optical reading function, a program execution function, and a communication function. The first store terminal device 50 includes a so-called POS (Point of Sale) device, and the product price acquisition function and the optical reading function may be realized by the POS device. The store code image 60 is placed in the store and is a code image such as a QR code (registered trademark) printed on a paper or plastic medium. The store code image 60 may be displayed on a display placed in the store (which may be the display of a terminal device such as a smartphone).

[0014] [Second store terminal device] The second store terminal device 70 is used by the operator of the affiliated store. The second store terminal device 70 is a smartphone, tablet terminal, personal computer, etc. An interface for affiliated stores 72 runs on the second store terminal device 70. The interface for affiliated stores 72 may be an app for affiliated stores or a browser. The interface for affiliated stores 72 accepts coupon settings and the like from the operator of the affiliated store and transmits them to the payment server 100. The second store terminal device 70, which is a smartphone, has the function of displaying a code image corresponding to a store code image and reading the code image displayed by the user terminal device 10 by executing the app for affiliated stores.

[0015] The payment server 100 realizes electronic payment based on payment information received from the user terminal device 10 or the first store terminal device 50. The first store terminal device 50 may include a POS device and an affiliated store server, in which case payment information is sent from the POS device via the affiliated store server to the payment server 100. In the following explanation, this distinction will not be made and it is assumed that payment information is sent from the first store terminal device 50.

[0016] 2 and 3 are sequence diagrams illustrating the general flow of electronic payment. There may be two patterns for electronic payment: Pattern 1 and Pattern 2.

[0017] In the case of pattern 1 (hereinafter referred to as user scan) shown in FIG. 2, the user terminal device 10, with the payment application 20 running, decodes the store code image 60 using its optical reading function (S1). The store code image 60 includes store URL (Uniform Resource Locator) information. This store URL is the domain of the electronic payment service to which store identification information has been added, and is associated with an affiliated store ID, store ID, etc. in the payment server 100 (described below). The payment application 20 sends first payment information including the store URL and account ID to the payment server 100 (S2). The payment server 100 searches for store information (described below) using the affiliated store ID and store ID corresponding to the store URL, acquires the affiliated store name and store name information (S3), and sends this to the payment application 20 (S4). The user enters the payment amount into the user terminal device 10 on the screen displaying the affiliated store name and store name (S5). Then, the user terminal device 10 generates second payment information including at least the payment amount and sends it to the payment server 100 (S6). The payment server 100 makes the electronic payment based on the received second payment information (S7). The payment server 100 then sends a payment completion notice (information for displaying a payment completion screen) to the payment app 20 (S8), and the payment app 20 displays the payment completion screen (S9). Note that when the store code image 60 is displayed on a display installed in the store, the store code image 60 may include information on the payment amount in addition to the store URL. In this case, the step of the user inputting the payment amount is omitted, and the payment amount information is included in the first payment information and sent to the payment server 100. Information on the affiliated store name and store name may be included and displayed on the payment completion screen.

[0018] In the case of pattern 2 (hereinafter referred to as store scan) shown in FIG. 3, the payment app 20 sends a request to issue a one-time code to the payment server 100 when the payment app 20 is launched, when a payment operation is performed in the payment app 20, at the automatic update timing (e.g., every minute), and at other timings (S11). The payment server 100 generates a one-time code (S12) and sends it to the payment app 20 (S13). The payment app 20 displays a code image, such as a QR code or barcode, generated based on the one-time code (S14). The user holds (presents) the display surface of the user terminal device 10 over the first in-store terminal device 50, and the first in-store terminal device 50 decodes the code image using its optical reading function and obtains the one-time code, etc. (S15). The first in-store terminal device 50 then generates payment information including the one-time code, payment amount, affiliated store ID, store ID, etc., and sends it to the payment server 100 (S16). The payment amount information is acquired in advance by reading a barcode, manually entering it, etc. Based on the received information, the payment server 100 identifies the user corresponding to the one-time code and performs electronic payment (S17). Then, the payment server 100 sends a payment completion notice to the payment application 20 (S18), and the payment application 20 displays a payment completion screen (S19).

[0019] Note that electronic payment may be performed using only one of the above patterns. Furthermore, the "account ID" described in FIG. 2 may be other information (e.g., a phone number) that can be used as user identification information. Furthermore, issuing a one-time code may be omitted in store scanning, and the payment application 20 may display a code image generated based on the user's account ID. In this case, the payment server 100 identifies the user corresponding to the account ID instead of identifying the user corresponding to the one-time code.

[0020] [Payment server] 4 is a configuration diagram of the payment server 100. The payment server 100 includes, for example, a communication unit 110, a content providing unit 120, a payment processing unit 130, an information management unit 140, and a storage unit 170. The components other than the communication unit 110 and the storage unit 170 are realized by, for example, a hardware processor such as a CPU executing a program (software). Some or all of these components may be realized by hardware (including circuitry) such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), a GPU (Graphics Processing Unit), or an SOC (System On Chip), or may be realized by a combination of software and hardware. The program may be stored in advance in a storage device such as an HDD (Hard Disk Drive) or flash memory (a storage device with a non-transitory storage medium), or may be stored in a removable storage medium (a non-transitory storage medium) such as a DVD or CD-ROM, and installed in the storage device by inserting the storage medium into a drive device.

[0021] The storage unit 170 is a HDD, flash memory, RAM (Random Access Memory), etc. The storage unit 170 may be a NAS (Network Attached Storage) device that the payment server 100 can access via a network. The storage unit 170 stores information such as user information 172, content information 174, affiliated store / shop information 176, and proposed information 178. Some of this information may be stored in the storage unit of the user terminal device 10. The proposed information 178 is information provided by the management server 200. The proposed information 178 corresponds to the proposed information 326.

[0022] The communication unit 110 is a communication interface for connecting to the network NW, and is, for example, a network interface card.

[0023] The content providing unit 120 has, for example, a web server function, and provides information (content) for displaying various screens for electronic payment services to the user terminal device 10. The content providing unit 120 reads out necessary content from the content information 174 as appropriate and provides it to the user terminal device 10. The user terminal device 10 accepts various inputs from the user while content is being played by the payment application 20, and transmits the above-mentioned payment information and the like to the payment server 100.

[0024] The payment processing unit 130 performs payment processing based on the payment information transmitted by the user terminal device 10 or the first store terminal device 50. The payment processing unit 130 performs payment processing while referring to the user information 172.

[0025] FIG. 5 is a diagram showing an example of the contents of user information 172. User information 172 is an example of user registration information. User information 172 includes, for example, a user URL, account ID, telephone number, and password, as well as associated information such as email address, user ID, name, address, date of birth, registration date, charge balance, deferred payment settings, deferred payment limit, deferred payment usage amount, deferred payment available amount, payment method settings, bank account, credit card number, charge history information, and payment history information. The user URL is used for remittance processing between users. When registering for the electronic payment service, registration of a phone number and password is required. The account ID is issued to the user by the payment server 100, and the user ID can be set by the user (or does not have to be set). The email address, name, address, and date of birth are also information that can be set by the user (or do not have to be set). The registration date is the date on which the user registered for the electronic payment service (the date on which the account was created). Hereinafter, the user's instance (electronic payment account) to which this information is associated will be referred to as an account.

[0026] The charge balance indicates the balance of electronic money set by the user by transferring funds to the account in advance. Transfer methods include transfers from a designated service provider (bank) ATM (Automatic Teller Machine) or from a registered bank account. The deferred payment setting indicates whether the setting for deferred payment electronic payments has been completed and is set to either "completed" or "not completed." The deferred payment limit is the monthly deferred payment limit. The deferred payment usage amount is the amount of deferred payment already used in the current month. The available deferred payment amount is the amount of deferred payment available in the current month, calculated by subtracting the deferred payment usage amount from the deferred payment limit. While the figure shows only one deferred payment limit, in reality, there may also be daily limits, and the lower of these may be set as the deferred payment limit. The payment method setting indicates whether the user will make electronic payments using the charge balance or by deferred payment at that time. The bank account and credit card number are information (account number and card number) on bank accounts or credit cards that can be used to deposit funds into the electronic payment service. Charge history information is a record of the user's previous transfers to the electronic payment service to increase the charge balance. Payment history information is information that shows the details of the user's payments (date and time, store ID of the store where the purchase was made, payment amount, payment method, etc.) for each payment.

[0027] FIG. 6 is a diagram showing an example of the contents of affiliated store / store information 176. The affiliated store / store information 176 includes, for example, a first table 176A in which an affiliated store ID and a store ID are associated with a store URL, a second table 176B in which an affiliated store ID is associated with an affiliated store name, sales amount (described above), and information about the affiliated store (second information 322), and a third table 176C in which a store ID is associated with a store name. In addition to this information, the affiliated store / store information 176 may also include information such as the category of the affiliated store or store, the store's location, and payment patterns. Information held by the payment server 100, such as the user information 172 and affiliated store / store information 176, is provided to the management server 200.

[0028] The information management unit 140 manages the user information 172 and the affiliated store / store information 176 based on information acquired from the user terminal device 10 and the second store terminal device 70. The information management unit 140 adds new records to, edits, deletes, etc. the user information 172 and the affiliated store / store information 176.

[0029] [Electronic Payment] When payment information is acquired from the user terminal device 10 or the first store terminal device 50, the payment processing unit 130 references the user information 172 to acquire the "payment method setting" of the user. For users whose "payment method setting" is set to "charge balance," the payment processing unit 130 performs electronic payment as follows: For example, the payment processing unit 130 performs electronic payment by decreasing the charge balance managed in association with the user ID and increasing the item value of the affiliated store's sales proceeds. The item value of the affiliated store's sales proceeds is not itself used as electronic money, for example, but rather the amount corresponding to the item value of the sales proceeds is transferred to a bank account in a cycle according to an agreement between the affiliated store and the electronic payment service.

[0030] The payment processing unit 130 performs electronic payments for users whose "setting information" is set to "deferred payment" as follows. Deferred payment is set separately from "credit card payment," which is set in cooperation with a credit card company, which is a separate entity from the operator of the electronic payment service. The operator of the electronic payment service acts as the creditor, and allows electronic payments within the deferred payment limit, independent of the remaining balance. To receive the deferred payment service, a user may be required to obtain a credit card provided by the operator of the electronic payment service. The amount used for deferred payment is settled in full on the following month's payment date, for example, by debit from a bank account. In this case, the payment processing unit 130 makes a provisional payment by adding the payment amount to the deferred payment amount and subtracting the same amount from the available deferred payment balance. On the closing date, the payment processing unit 130 performs the process described above to debit the current month's payment on the following month's payment date, or requests the credit card operator to perform this process. If the payment amount exceeds the available deferred payment balance at the time of provisional payment, an error notification is returned to the payment app 20.

[0031] [Administration Server] FIG. 7 is a diagram illustrating an example of the functional configuration of the management server 200. The management server 200 includes, for example, a communication unit 210, a first processing unit 220, a second processing unit 230, an integrated processing unit 240, and a storage unit 300. The components other than the communication unit 210 and the storage unit 300 are implemented by, for example, a hardware processor such as a CPU executing a program (software). Some or all of these components may be implemented by hardware (including circuitry) such as an LSI, ASIC, FPGA, GPU, or SOC, or may be implemented by a combination of software and hardware. The program may be stored in advance in a storage device such as an HDD or flash memory (a storage device with a non-transitory storage medium), or may be stored in a removable storage medium (a non-transitory storage medium) such as a DVD or CD-ROM, and installed in the storage device by inserting the storage medium into a drive.

[0032] The storage unit 300 is a HDD, flash memory, RAM, etc. The storage unit 300 may be a NAS device that the management server 200 can access via a network. The storage unit 300 stores information such as first information 312, a first model 314, second information 322, a second model 324, and proposed information 326.

[0033] The communication unit 210 is a communication interface for connecting to the network NW, and is, for example, a network interface card.

[0034] The first processing unit 220 predicts future sales based on the past sales performance of affiliated stores of the electronic payment service. The second processing unit 230 derives indicators related to specific conditions under which an affiliated store will cease to be an affiliated store after a predetermined period of time or will be deemed inappropriate as an affiliated store after a predetermined period of time, based on information about the affiliated store managed by the electronic payment service. The integrated processing unit 240 derives evaluation indicators for the provision conditions when providing funds to an affiliated store based on the future sales and the indicators.

[0035] [overview] The payment server 100 refers to the proposal information 178 prepared for each member store of the electronic payment service stored in the memory unit 170, identifies the provision conditions for the provision of funds that can be proposed to the member store, and provides the identified provision conditions to the member store interface 72 (the member store's terminal device). The proposal information 178 is an evaluation index determined for each member store, and is information that specifies the provision conditions that can be proposed based on the evaluation index associated with each provision condition.

[0036] 8 and 9 are diagrams showing an example of proposal information 178. Proposal information 178 is information generated for each affiliated store. Proposal information 178 is, for example, information specifying the provision conditions for the provision of funds that can be proposed to affiliated stores. The provision conditions are, for example, a combination of the amount executed, the settlement rate, and the fee. Some of the information among these provision conditions may be omitted or may be fixed. The settlement months in FIGS. 8 and 9 are the number of months for settling the funds procured from the fund-raising service (or provided by the fund-providing service).

[0037] The execution amount is the amount that can be procured from the funding service for the affiliated store (e.g., credit amount, amount that can be provided, amount that can be offered, or amount that the affiliated store receives). The fee is a fee for funding. The settlement ratio is an indicator that shows what proportion of the sales of an affiliated store that uses an electronic payment service is allocated to settlement. For example, an affiliated store receives the amount of payments made at the affiliated store using the electronic payment service at specified intervals (e.g., it is deposited into the electronic wallet of the affiliated store of the electronic payment service). The settlement ratio is the proportion of this deposit that is allocated to settlement.

[0038] Proposable provision conditions are those for which an evaluation index (described in detail below) is equal to or greater than a threshold. As described below, an evaluation index is derived for each provision condition, and those conditions for which the evaluation index is equal to or greater than a threshold are defined as proposable provision conditions. Proposable provision conditions may be those for which the evaluation index is equal to or greater than a threshold and for which the provision conditions are preset and satisfy the conditions.

[0039] 8, if the settlement ratio is 5% and the execution amount is 100,000 yen, 5% of the member store's expected sales (1 million yen), or 50,000 yen, will be allocated to settlement over two months.(1) In the proposal information 178, if the execution amount is 900,000 yen or more and the settlement ratio is 5%, fundraising will not be proposed or approved for the member store.

[0040] In the (2) proposal information 178 of Figure 9, if the execution amount exceeds 150,000 yen and the settlement ratio is 5% or 10%, fundraising will not be proposed to or approved for the affiliated store. The affiliated store to which the (2) proposal information 178 applies is different from the affiliated store to which the (1) proposal information 178 applies.

[0041] As described above, proposal information 178 is prepared for each affiliated store, and the payment server 100 refers to the proposal information 178 corresponding to the target affiliated store to make fundraising proposals or provide fundraising services related to fundraising.

[0042] [Information provided to affiliated stores] The payment server 100 provides the interface for member stores 72 with offer conditions whose evaluation index is equal to or greater than a threshold as offer conditions that can be proposed. If there are no offer conditions whose evaluation index is equal to or greater than a threshold, the payment server 100 determines that there are no offer conditions that can be proposed and does not provide information related to the provision of funds to the interface for member stores 72. The proposal information 178 is updated at predetermined intervals (for example, once a month) according to the sales performance of the member stores. The payment server 100 determines whether there are offer conditions that can be proposed based on the updated proposal information 178, and provides the offer conditions to the interface for member stores 72 according to the result of the determination. Offer conditions that can be proposed are, for example, offer conditions whose evaluation index is equal to or greater than a threshold and whose offer conditions are preset and satisfy the conditions (the executed amount, settlement ratio, or fee satisfy the conditions).

[0043] The payment server 100 obtains information on the amount selected by the member store from the member store interface 72, out of the amounts that can be proposed to the member store, and provides information on the settlement ratio that can be proposed to the member store interface 72 based on the amount selected by the member store and the evaluation index. The payment server 100 determines a commission rate based on the settlement ratio selected by the member store from the settlement ratios that can be proposed and the credit amount selected by the member store, and provides information on the determined commission rate to the member store interface 72. For example, the amount to be received by the member store is determined, the settlement ratio is determined, and a settlement plan including fees and the like is determined and presented as follows:

[0044] [Interface screen (1)] FIG. 10 is a diagram showing an example of an interface screen provided to the affiliated store interface 72. The payment server 100 references the proposal information 178 and provides the interface screen IF1 to the affiliated store interface 72. The interface screen IF1 includes an area for inputting the application amount for the service related to the provision of funds, a recommended application amount, a scheduled transfer date for the application amount, the percentage to be debited from the deposit (settlement ratio), and a confirmation button. The recommended application amount is an amount that is specified as being available for proposal in the proposal information 178. The percentage to be debited from the deposit is also an amount that is specified as being available for proposal in the proposal information 178. The percentage to be debited from the deposit is a percentage determined based on the application amount and the proposal information 178.

[0045] As described above, the payment server 100 can provide information according to the affiliated store by referring to the proposal information 178. For example, the payment server 100 can propose information such as a recommended application amount according to the affiliated store and a percentage to be deducted from the deposit.

[0046] [Interface screen (2)] 11 is a diagram showing another example of an interface screen provided to the affiliated store interface 72. This screen is displayed when the user inputs the application amount and the percentage to be deducted from the deposit on interface screen IF2 and presses the confirmation button. Interface screen IF2 includes information such as the application amount, usage fee, total amount, monthly deposit amount (settlement amount), transaction amount, settlement amount, transfer amount, estimated next settlement amount, and settlement schedule.

[0047] As described above, the payment server 100 provides the affiliated store with information such as usage fees and settlement amounts in accordance with the information entered by the affiliated store, thereby enabling the affiliated store to easily understand the details of the fund raising service.

[0048] The payment server 100 may provide each of the provision conditions to the member store interface 72 by referring to the evaluation index for each provision condition (see FIG. 20 , described later). The payment server 100 may preferentially provide to the member store interface 72 provision conditions (amount, settlement ratio, usage fee, or a combination thereof) whose evaluation index falls within a predetermined upper range and whose provision conditions satisfy predetermined conditions. The evaluation index falling within a predetermined upper range refers to the highest predetermined number of evaluation indexes. For example, the payment server 100 may preferentially propose an application amount with a higher evaluation index, and may preferentially propose a settlement ratio with a higher evaluation index for the application amount selected by the member store. Furthermore, the payment server 100 may provide to the member store interface 72 the usage fee rate with the highest evaluation index among the application amount and settlement ratio selected by the member store. This enables the payment server 100 to propose provision conditions that are more suitable for the service provider to the member store.

[0049] [Flowchart (1)] FIG. 12 is a flowchart showing an example of the flow of processing executed by the payment server 100. First, the payment server 100 determines whether or not there is access from the member store interface 72 (S100). If there is access from the member store interface 72, the payment server 100 acquires the member store's identification information and identifies the member store (S102). The payment server 100 acquires, for example, the member store's identification information input to the member store interface 72, and identifies the member store based on the acquired identification information (S104). Next, the payment server 100 identifies proposed information 178 corresponding to the identified member store (S106). For example, proposed information 178 is prepared for each member store, so the payment server 100 identifies proposed information 178 corresponding to the identified member store.

[0050] Next, the payment server 100 refers to the proposal information 178 and determines whether or not a fundraising service can be proposed to the affiliated store (S106). For example, depending on the affiliated store (proposal information 178), a fundraising service may not be proposed. For example, the proposal information 178 may specify that proposals cannot be made for all amounts, settlement rates, etc. In this case, it is determined that a fundraising service cannot be proposed, and the processing of S108 is skipped.

[0051] If a fundraising service can be offered, the payment server 100 refers to the offer information 178 corresponding to the affiliated store and provides information about the fundraising service to the affiliated store interface 72 (S108). For example, the above-mentioned interface screen IF1 or interface screen IF2 is provided. This completes the processing of one routine of this flowchart. Note that the information about the fundraising service may be notified by push notification.

[0052] [Generate Proposal Information] The following describes the process for generating the proposed information 178. Fig. 13 is a diagram showing an overview of the process for generating the proposed information 178. An integration process is performed to integrate the first process (the processing result using the first model 314) and the second process (the processing result using the second model 324), and the proposed information 178 is generated.

[0053] [First process] The first processing unit 220 predicts future sales of the affiliated store using the first model 314. FIG. 14 is a diagram for explaining the first processing. The first model 314 is a model that outputs future sales of the affiliated store when the first information 312 is input. The first model 314 is, for example, a statistical prediction model of time-series data.

[0054] The first information 312 includes information indicating the affiliated store's daily sales (sales record), the day of the week (Monday, Saturday, etc.) for each day, and which days are holidays. In addition to the above information, the first information 312 may also include other information.

[0055] When the first information 312 is input, the first model 314 predicts future sales. For example, it outputs time-series sales information (e.g., trend line L1) shown in FIG. 14. The first processing unit 220 generates sales information (e.g., trend line L2) by correcting the output sales information by applying a predetermined function, algorithm, or the like. For example, if sales are on an increasing trend, the first processing unit 220 conservatively adjusts sales to suppress the increasing trend. Note that if sales are on a decreasing trend, no adjustment may be made.

[0056] Furthermore, the first processing unit 220 sets a fluctuation range for the sales. The fluctuation range is a preset fluctuation range. The fluctuation range may vary depending on the amount of sales, the sales trend (tendency to increase or decrease), the day of the week, the type of day of the week (weekday, holiday, etc.), or a combination of these. In this way, future sales and the fluctuation range are identified and used in the integration process.

[0057] The first model 314 is a model that models each of the trend g(t), periodicities s(t), h(t), and e(t) as a function of time t, as shown in the following equation (1), and calculates the output y by adding the derived results of these functions. The first model 314 is a model for each affiliated store that is tuned according to, for example, the amount of past sales, sales trends (tendencies of increase or decrease), the day of the week, the type of day of the week (weekday, holiday, etc.), or a combination of these. For example, the first model 314 is a model that is updated monthly according to monthly sales, etc. This model is one example of the first model 314, and naturally, other models may also be applied. y=g(t)+s(t)+h(t)+e(t)...(1)

[0058] The first model 314 is not limited to the above, and may be a model that learns learning information and outputs future time-series sales when input information is input. The learning information is information that associates future time-series sales with input information for learning (sales amount, sales trend (tendency to increase or decrease), day of the week, type of day of the week (weekday, holiday, etc.), or a combination of these).

[0059] [Second process] The second processing unit 230 predicts the future probability of an affiliated store closing using the second model 324. FIG. 15 is a diagram for explaining the second processing. The second model 324 is a model that outputs the future probability of an affiliated store closing when the second information 322 is input. The second model 324 is, for example, a trained model such as LightGBM.

[0060] The second information 322 includes, for example, past sales, the merchant's industry, the prefecture where the merchant is located, the number of days since the merchant was registered as a merchant, the payment unit price at the merchant, the number of payment users, the time of payment, bankruptcy probability, the number of inquiries, and the type of inquiry. Some of this information may be omitted. The payment unit price is, for example, a statistically processed unit price such as an average unit price or a median. The payment time may be, for example, each time a payment was made, or one or more representative times obtained by statistically processing each time. The bankruptcy probability is the bankruptcy probability of the merchant provided by a research company or the like. The number of inquiries is the number of inquiries about the merchant made to the customer center of the electronic payment service. The type of inquiry is the type of inquiry about the merchant made to the customer center of the electronic payment service (such as complaints or other inquiries).

[0061] In addition to or instead of the above, the second information 322 may be information managed by the electronic payment service (e.g., payment server 100). The second information 322 may include, for example, user information 172 (e.g., characteristics (attributes) of main users of affiliated stores derived by analyzing information managed by payment server 100). The second information 322 may also include information on the owner of the affiliated store. For example, this may include the owner's personality and characteristics analyzed based on information managed by the electronic payment service, the history of use of the electronic payment service, and characteristics of how the electronic payment service is used. Alternatively, the second information may be information held by an affiliate partner that is affiliated with the operator of the electronic payment service.

[0062] When the second information 322 is input, the second model 324 derives and predicts the store closure probability within a predetermined month. Instead of the store closure probability, the second model 324 may derive the probability that sales will be equal to or below a threshold. In this way, the store closure probability is derived and used in the integration process.

[0063] The second model 324 is a model that, for example, learns learning information and outputs a store closure probability (or a probability that sales will reach a threshold) when the second information 322 is input. The learning information is information in which the store closure probability, which is correct answer data, is associated with the second information 322 for learning.

[0064] In the second model 324, for example, in addition to sales, prefecture, number of inquiries, type of inquiry, etc. are variables that affect store closure. Although it is possible to derive the store closure probability even if these variables are excluded, it is preferable to input one or more pieces of information from the prefecture, number of inquiries, type of inquiry, etc. in addition to sales, etc. into the second model 324.

[0065] [Merge Processing] The integration processing unit 240 performs integration processing that utilizes the results of the first processing and the second processing. Fig. 16 is a diagram for explaining an overview of the integration processing. (1) The integrated processing unit 240 performs a sales simulation. The integrated processing unit 240 simulates sales forecasts for a predetermined period (for example, any period such as 12 months, 24 months, or 36 months) a predetermined number of times for each affiliated store. (2) The integration processing unit 240 calculates the risk-return evaluation index for each combination of "usage amount," "settlement ratio," and "fee" based on the prediction in (1) above. (3) The integration processing unit 240 narrows down the conditions by referring to the evaluation index. The integration processing unit 240 uses the evaluation index to exclude high-risk combinations and identify the optimal combination of conditions (usage amount, settlement ratio, and fee).

[0066] The above (1) and (2) are examples of processing in which the integrated processing unit 240 "generates a plurality of sales simulation patterns of future sales that take into account the probability of satisfying the specific condition, based on the future sales and the probability of satisfying the specific condition, which is the indicator; generates a plurality of provision conditions with different contents; and derives the evaluation indicator based on the results of applying each of the provision conditions to each of the sales simulation patterns."

[0067] [(1) Simulation] FIG. 17 is a diagram illustrating a simulation. The integration processing unit 240 generates multiple future sales simulation patterns, assuming that future sales will fluctuate randomly within a predetermined fluctuation range. The integration processing unit 240 uses the sales prediction obtained in the first process and random numbers to predict future sales for each day of a predetermined period, reflecting the fluctuation range of sales. The integration processing unit 240, for example, randomly sets future sales so that each day's sales fall within the fluctuation range for each day. The fluctuation range may be a fluctuation range that is determined in advance based on information such as the day of the week and sales increase / decrease trends. Alternatively, sales simulation may be performed so that an error occurs within the fluctuation range according to a probability distribution of errors depending on the day of the week and sales increase / decrease trends. In this way, a predetermined number of sales simulations are performed.

[0068] The integrated processing unit 240 determines that the affiliated store will satisfy the specific condition after a predetermined period of time according to the probability of satisfying the specific condition in the sales simulation pattern (store closure probability, or the probability that sales will be below a threshold), sets the date on which the specific condition is satisfied, and sets the sales of the affiliated store to zero after the date on which the specific condition is satisfied. The integrated processing unit 240 causes a store closure according to the store closure probability obtained in the second process, and sets sales to "0" after the store closure. For example, the integrated processing unit 240 sets a store closure for a predetermined simulation based on the results of a predetermined number of simulations so that a store closure occurs according to the store closure probability.

[0069] (2) Calculation of evaluation indicators The integration processing unit 240 calculates an evaluation index. The integration processing unit 240 applies each of the provision conditions to each of the sales simulation patterns, and calculates a return and a risk measure using a profit distribution based on multiple simulation results. The return is, for example, an index indicating the return for each provision condition. The risk measure is, for example, an index indicating the risk for each provision condition. An example of the return is the expected profit, and an example of the risk measure is the standard deviation or CVaR (Condition Value at Risk). The integration processing unit 240 generates an evaluation index based on the return and the risk measure. The evaluation index is, for example, an index generated by statistically processing the return and the risk measure. In this way, the integration processing unit 240 derives an evaluation index for each provision condition.

[0070] 18 and 19 are diagrams for explaining the process of calculating the evaluation index. The integration processing unit 240 generates predetermined patterns of combinations of the usage amount, the settlement ratio, and the commission rate, as shown in Fig. 18. For example, a first predetermined pattern is generated for the usage amount, a second predetermined pattern for the settlement ratio, and a third predetermined pattern for the commission rate, and a predetermined pattern is generated by combining these.

[0071] The integration processing unit 240 applies each of the predetermined patterns to each of the simulations in (1) above, and calculates the return, risk measure, and evaluation index for each of the predetermined patterns shown in Fig. 19. In this way, the return, risk measure, and evaluation index for each of the predetermined patterns are calculated.

[0072] The return is, for example, the average profit obtained, which is the average of the settlement amount (the amount settled by the merchant for the merchant's funding) minus the amount used. The risk measure is, for example, the average of a specified percentage of the worst case, which is the average of the lower specified percentage of the settlement amount minus the amount used. The evaluation index is a risk-return evaluation index, which is an index obtained by dividing the return by the risk measure.

[0073] For example, a certain pattern combination (usage amount, settlement ratio, and commission rate) is applied to a predetermined simulation to calculate the return, risk measure, and evaluation index. This is performed for each of the predetermined pattern combinations to calculate the return, risk measure, and evaluation index for each pattern.

[0074] As a result, the integrated processing unit 240 generates correspondence information in which an evaluation index is associated with each combination of usage amount, settlement ratio, and commission rate, as shown in FIG. 20. That is, the integrated processing unit 240 generates correspondence information in which each of a plurality of provision conditions for each affiliated store is associated with an evaluation index. The larger the evaluation index, the higher the evaluation. For example, the evaluation index tends to be large in the following cases: The commission rate is high. Sales are high. Sales fluctuations are small. The probability of store closure is low. The usage amount is low. The settlement ratio is high.

[0075] [(3) Narrowing down the conditions] The integration processing unit 240 generates proposal information 326 (proposal information 178) by referring to the above evaluation indexes. For example, the integration processing unit 240 sets the conditions for which a proposal can be made to the affiliated store as a usage amount, settlement ratio, or commission rate for which the evaluation index is equal to or greater than a threshold. Furthermore, the integration processing unit 240 may set the conditions for which a proposal can be made as a usage amount, settlement ratio, or commission rate that is equal to or less than a threshold. In this way, the integration processing unit 240 generates the proposal information 326 described above.

[0076] [flowchart] 21 is a flowchart showing an example of the flow of processing executed by the management server 200. First, the management server 200 determines whether a predetermined timing has arrived (S200). The predetermined timing may be, for example, once a month, when sales for each month are finalized.

[0077] When a predetermined timing arrives, the management server 200 acquires the results of the first processing using the first model 314 (S202). Next, the management server 200 acquires the results of the second processing using the second model 324 (S204). Next, the management server 200 executes a simulation using the results of the first processing (S206), and acquires the results of the third processing in which the results of the second processing are applied to the simulation (S208). For details, see FIG. 17 described above.

[0078] Next, the management server 200 calculates an evaluation index for each combination of "usage amount," "settlement ratio," and "fee rate" (S210). Next, the management server 200 references the evaluation index to generate proposal information 326 (S212). This completes the processing of one routine of this flowchart.

[0079] As described above, the integration processing unit 240 can derive more accurate evaluation indices for the provision conditions when providing funds to member stores based on future sales and indices.

[0080] Note that instead of the above processing, the integration processing unit 240 may derive an evaluation index for the provision conditions using a predetermined algorithm, correspondence information, a trained model, etc. from the future sales that are the result of the first processing and the store closure probability (or the probability that sales will be equal to or less than a threshold) that are the result of the second processing. For example, the predetermined algorithm is an algorithm that uses a function with future sales and store closure probability as parameters. The correspondence information is information in which an evaluation index is associated with a combination of sales and store closure probability. The trained model is a model that has learned learning information in which an evaluation index is associated with a combination of sales and store closure probability. For example, the trained model is a model that, when information on a combination of sales and store closure probability is input, outputs an evaluation index according to the input information.

[0081] In addition, future sales and store closure probability (or the probability that sales will be below a threshold) may be calculated using a model different from the above-mentioned model, such as a linear model or a specified function, instead of the above-mentioned model.

[0082] Furthermore, although the processing of this embodiment has been described as being applied to a fundraising service (or a fund provision service), it may instead (or additionally) be applied to a fund lending service. In this case, "fundraising" or "fund provision" in the embodiment of this application should be read as "loan," "settlement" as "repayment," and "receive" as "borrowing."

[0083] According to the embodiment described above, the management server 200 derives evaluation indexes for the provision conditions when providing funds to a member store based on future sales and indexes related to specific conditions under which the member store will cease to be a member store after a predetermined period of time or will be determined to be inappropriate as a member store after the predetermined period of time. This enables the management server 200 to provide more appropriate provision conditions for the provision of funds according to the member store.

[0084] According to the embodiment described above, the payment server 100 refers to the proposal information prepared for each affiliated store of the electronic payment service stored in the memory unit, identifies the provision conditions for the provision of funds that can be proposed to the affiliated store, and provides the identified provision conditions to the affiliated store's terminal device, thereby being able to provide more appropriate provision conditions for the provision of funds depending on the affiliated store.

[0085] The above describes the form for carrying out the present invention using an embodiment, but the present invention is not limited to such an embodiment, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present invention. [Explanation of symbols]

[0086] 10 User terminal device 20. Payment App 100 Payment Server 120 Contents Provider 130 Payment processing unit 140 Information Management Department 150 Information Processing Department 200 Management Server 220 First Processing Section 230 Second Processing Section 240 Integrated Processing Unit 312 1st information 314 1st model 322 Second information 324 2nd Model 326 Proposal Information

Claims

1. a first processing unit that predicts future sales based on past sales performance of affiliated stores of the electronic payment service; a second processing unit that derives an index relating to a specific condition under which a member store will cease to be a member store after a predetermined period of time, or is determined to be inappropriate as a member store after the predetermined period of time, based on information about the member store managed in the electronic payment service; an integrated processing unit that derives evaluation indices for provision conditions when providing funds to the member store based on the future sales and the indices; The second processing unit inputs input information to a trained model that outputs the index according to the input information, and derives the index based on a result output by the trained model; The input information includes one or more of the following: the usage status of the user of the electronic payment service at the affiliated store; the business type of the affiliated store; the prefecture in which the affiliated store is located; and the number of days since the affiliated store was registered. Information processing device.

2. The input information further includes sales of the affiliated store. The information processing device according to claim 1 .

3. The input information includes a usage status of the user of the electronic payment service, The usage status of the user of the electronic payment service includes one or more of the following information: information on the unit price of electronic payment of the electronic payment service at the affiliated store; the number of users involved in electronic payment of the electronic payment service; and information on the time when the electronic payment was made.

3. The information processing device according to claim 1.

4. The information about the unit price is information obtained by statistically processing the unit price. The information processing device according to claim 3 .

5. The information about the time is information obtained by statistically processing the time. The information processing device according to claim 3 .

6. The input information further includes information of the user who used the electronic payment service at the affiliated store.

3. The information processing device according to claim 1.

7. The user information includes characteristic information of the user. The information processing device according to claim 6 .

8. The input information includes the usage status of the user of the electronic payment service at the affiliated store, the business type of the affiliated store, the prefecture in which the affiliated store is located, the number of days since the affiliated store was registered, and the sales of the affiliated store. The information processing device according to claim 1 .

9. The computer Based on the past sales performance of the merchants of the electronic payment service, future sales are predicted. deriving an index relating to a specific condition under which a member store will cease to be a member store after a predetermined period of time or will be deemed inappropriate as a member store after the predetermined period of time based on information about the member store managed in the electronic payment service; deriving an evaluation index for the provision conditions when providing funds to the member store based on the future sales and the index; Inputting input information into a trained model that outputs the index according to the input information, and deriving the index based on the result output by the trained model; The input information includes one or more of the following: the usage status of the user of the electronic payment service at the affiliated store; the business type of the affiliated store; the prefecture in which the affiliated store is located; and the number of days since the affiliated store was registered. Information processing methods.

10. On the computer, Based on the past sales performance of the electronic payment service member stores, future sales are predicted. deriving an index relating to a specific condition under which a member store will cease to be a member store after a predetermined period of time or will be deemed inappropriate as a member store after the predetermined period of time based on information about the member store managed in the electronic payment service; deriving an evaluation index for the provision conditions when providing funds to the member store based on the future sales and the index; Input information is input to a trained model that outputs the index according to the input information, and the index is derived based on the result output by the trained model; The input information includes one or more of the following: the usage status of the user of the electronic payment service at the affiliated store; the business type of the affiliated store; the prefecture in which the affiliated store is located; and the number of days since the affiliated store was registered. program.

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