Information processing device, information processing method, and program

Abstract

To appropriately identify users to whom the service should be recommended. [Solution] An information processing device comprising: an acquisition unit that acquires history information relating to the electronic payment history of a user's electronic payment service; an identification unit that identifies users who meet recommendation criteria, which are estimated to provide a certain level of value to credit card businesses, from the history information; and a provision unit that provides recommendation information recommending credit card application to the terminal device of the identified user.

Description

Technical Field

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

Background Art

[0002] Conventionally, proposals have been made to users regarding credit cards, borrowings, etc. In this regard, there is known an information processing apparatus including a limit estimation unit that estimates a borrowing limit amount of a user based on the price of a target product and the estimated annual income of the user, and a provision unit that presents the estimated borrowing limit amount to the user and makes a borrowing proposal (see Patent Document 1).

Prior Art Documents

Patent Documents

[0003] <关于使用信用卡或借款等向用户提出建议的技术。在此相关的技术中,已知一种信息处理装置,其包括基于目标商品的价格和用户的推定年收入来推定用户借款限额的限额推定部,以及向用户提示推定出的借款限额并进行借款建议的提供部(参见专利文献1)。

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, there are cases where it is not possible to appropriately identify users who recommend using the service.

[0005] The present invention has been made in consideration of such circumstances, and one of its objects is to provide an information processing apparatus, an information processing method, and a program that can appropriately identify users who recommend using the service.

Means for Solving the Problems

[0006] One aspect of the present invention is an information processing device comprising: an acquisition unit that acquires historical information relating to the electronic payment history of a user's electronic payment service; an identification unit that identifies users who meet recommendation criteria from the historical information, where the value provided to the credit card business operator is estimated to be above a predetermined level; and a provision unit that provides recommendation information recommending credit card application to the terminal device of the identified user. [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 appropriately identify users to whom the use of a service is recommended. [Brief explanation of the drawing]

[0008] [Figure 1] This diagram shows an example of a configuration for implementing an electronic payment service. [Figure 2] This is a sequence diagram (part 1) illustrating the general flow of electronic payments. [Figure 3] This is a sequence diagram (part 2) illustrating the general flow of electronic payments. [Figure 4] This is a diagram showing the configuration of payment server 100. [Figure 5] This figure shows an example of the contents of user information 182. [Figure 6] This diagram shows an example of the contents of merchant / store information 186. [Figure 7] This is a diagram to explain feature information 188. [Figure 8] This is a diagram illustrating the early revolving credit model 210. [Figure 9] This is a diagram illustrating the main model 220. [Figure 10] This is a diagram to explain the target group. [Figure 11] This diagram shows the interface screen displayed on the display unit of the user terminal device 10. [Figure 12]This diagram illustrates the process using the LTV model. [Figure 13] This is a diagram illustrating the loan loss score and loan loss model. [Figure 14] This diagram illustrates the churn score and churn model. [Modes for carrying out the invention]

[0009] The following describes embodiments of the information processing apparatus, information processing method, and program of the present invention with reference to the drawings. Various devices used to provide services to users or perform internal analysis, such as the "server," "management device," and "information providing device" described below, may be implemented by a distributed group of devices, and the operators of each device may be different. Furthermore, the owner of the hardware of the devices (the provider of the cloud server) and the operator that actually operates them may also be different. The application program and the payment server work together to provide an electronic payment service. In the following description, the application program will be referred to as the payment app. The electronic payment service is a service that supports payment for the purchase of goods and services at a store. A store is, for example, a physical store (real store) that exists in the real world, but may also include a virtual store for e-commerce. A virtual store may include one provided by an entity different from the operator of the electronic payment service. In that case, when settling a purchase at a virtual store, the user may be directed to the interface screen of the electronic payment service. In the electronic payment service, stores are treated as belonging to, for example, affiliated merchants (brands), and processing such as payment when a purchase is made at a store is mainly carried out between the user and the affiliated merchant. Alternatively, payment and other processing may be conducted between the user and the store.

[0010] [Electronic payment service] Figure 1 shows an example of a configuration for realizing an electronic payment service. The electronic payment service is implemented with a payment server 100 at its core. The payment server 100 communicates with, 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, and a credit server 300 via a network NW. The network NW includes, for example, the internet, a LAN (Local Area Network), a wireless base station, and provider equipment.

[0011] The user terminal device 10 is, for example, a portable terminal device such as a smartphone or tablet. The user terminal device 10 is a computer device having at least optical reading function, communication function, display function, input reception function, and program execution function. In the following description, the components for realizing these functions will be referred to as a camera, communication device, touch panel, CPU (Central Processing Unit), etc. In the user terminal device 10, the payment application 20 is executed by a processor such as the CPU, and it operates in cooperation with the payment server 100 to provide electronic payment services to the user. The payment application 20 is installed on the user terminal device 10, for example, from an application store, and controls the camera, communication device, touch panel, etc.

[0012] 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 may include a so-called POS (Point of Sale) device, and the product price acquisition function and 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 paper or plastic media. The store code image 60 may also be displayed on a display placed in the store (which may be the display of a terminal device such as a smartphone).

[0013] The second store terminal device 70 is used by the operator of the franchise store. The second store terminal device 70 is a smartphone, a tablet terminal, a personal computer, or the like. In the second store terminal device 70, an interface 72 for franchise stores operates. The interface 72 for franchise stores may be an application for franchise stores or a browser. The interface 72 for franchise stores accepts settings of coupons and the like by the operator of the franchise store and transmits them to the payment server 100. The second store terminal device 70 which is a smartphone has functions such as displaying a code image corresponding to the store code image or reading the code image displayed by the user terminal device 10 by executing an application for franchise stores.

[0014] The payment server 100 realizes electronic payment based on the 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 a franchise store server. In that case, the payment information is transmitted from the POS device to the payment server 100 via the franchise store server. In the following description, this will not be particularly distinguished, and it is assumed that the payment information is transmitted from the first store terminal device 50.

[0015] FIG. 2 and FIG. 3 are sequence diagrams illustrating a rough flow of electronic payment. There may be two patterns, pattern 1 and pattern 2, in electronic payment.

[0016] In the case of Pattern 1 shown in Figure 2 (hereinafter referred to as User Scan), 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 contains information about the store URL (Uniform Resource Locator). This store URL is an electronic payment service domain to which information that can identify the store has been added, and is associated with the merchant ID and store ID, etc., at the payment server 100 (described later). The payment application 20 sends the 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 later) from the merchant ID and store ID corresponding to the store URL, obtains the merchant name and store name information (S3), and sends it to the payment application 20 (S4). The user enters the payment amount into the user terminal device 10 on the screen where the merchant name and store name are displayed (S5). The user terminal device 10 then generates second payment information, including at least the payment amount, and sends it to the payment server 100 (S6). The payment server 100 performs electronic payment based on the received second payment information (S7). The payment server 100 then sends a payment completion notification (information for displaying the payment completion screen) to the payment application 20 (S8), and the payment application 20 displays the payment completion screen (S9). If the store code image 60 is displayed on a display placed in the store, the store code image 60 may include payment amount information as well as the store URL. In this case, the procedure for the user to enter the payment amount is omitted, and the payment amount information is included in the first payment information and sent to the payment server 100. Merchant name and store name information may be included and displayed on the payment completion screen.

[0017] In the case of Pattern 2 shown in Figure 3 (hereinafter referred to as Store Scan), when the payment app 20 is launched, when a payment operation is performed in the payment app 20, when it is time for an automatic update (for example, every minute), and at other times, the payment app 20 sends a request to the payment server 100 to issue a one-time code (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 that was generated based on the one-time code (S14). The user holds the display surface of the user terminal device 10 over the first store terminal device 50 (presents it), and the first store terminal device 50 decodes the code image using its optical reading function and obtains the one-time code, etc. (S15). Then, the first store terminal device 50 generates payment information including the one-time code, payment amount, merchant ID, store ID, etc., and sends it to the payment server 100 (S16). The payment amount information is obtained in advance by barcode scanning or manual input. Based on the received information, the payment server 100 identifies the user corresponding to the one-time code and performs the electronic payment (S17). The payment server 100 then sends a payment completion notification to the payment app 20 (S18), and the payment app 20 displays a payment completion screen (S19).

[0018] Furthermore, electronic payment may be performed using only one of the above patterns. Also, the "account ID" and "user ID" explained in Figure 2 may be other information that can be used as user identification information (for example, a phone number). In addition, the issuance of a one-time code may be omitted during store scanning, and the payment app 20 may display a code image generated based on the user's account ID. In that case, the payment server 100 will identify the user corresponding to the account ID instead of identifying the user corresponding to the one-time code.

[0019] [Payment Server] Figure 4 is a diagram of the configuration of the payment server 100. The payment server 100 includes, for example, a communication unit 110, a content provision unit 120, a payment processing unit 130, an information management unit 140, an information processing unit 150, and a storage unit 180. Components other than the communication unit 110 and the storage unit 180 are realized, for example, by 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 LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), and GPU (Graphics Processing Unit), or by the cooperation 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-transient storage medium), or it may be stored in a removable storage medium such as a DVD or CD-ROM (a non-transient storage medium) and installed in the storage device when the storage medium is mounted in a drive device. Note that some of the processing performed by the payment server 100 may be performed by other devices. For example, the process of identifying users, which will be described later, may be performed by other devices, and the information of the identified users may be provided to the payment server 100.

[0020] The storage unit 180 can be an HDD, flash memory, RAM (Random Access Memory), etc. The storage unit 180 may also be a NAS (Network Attached Storage) device that can be accessed by the payment server 100 via the network. The storage unit 180 stores information such as user information 182, content information 184, merchant / store information 186, feature information 188, early revolving model 210, and main model 220. Each piece of information will be described later.

[0021] The communication unit 110 is a communication interface for connecting to a network NW. The communication unit 110 is, for example, a network interface card.

[0022] The content provision unit 120, for example, has the functionality of a web server and provides information (content) for displaying various screens of the electronic payment service to the user terminal device 10, the merchant interface 72, etc. The content provision unit 120 reads the necessary content from the content information 184 as appropriate and provides it to the target device, etc. The user terminal device 10 receives various inputs from the user while the content is being played by the payment application 20 and transmits the aforementioned payment information, etc. to the payment server 100.

[0023] The payment processing unit 130 performs payment processing based on 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 182.

[0024] Figure 5 shows an example of the contents of User Information 182. User Information 182 is an example of user registration information. User Information 182 includes, for example, user URL, account ID, phone number, password, as well as email address, user ID, name, address, date of birth, registration date, charge balance, credit payment settings, credit payment limit, credit payment amount, available credit payment amount, payment method settings, bank account, credit card number, charge history information, and payment history information.

[0025] The user URL is used for processing payments 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 is an ID that the user can optionally set (or not set). Similarly, the email address, name, address, and date of birth (age) are also information that the user can optionally set (or not set). If identity verification has been performed, for example, the address and age will be the address and age confirmed through identity verification. The registration date is the date the user registered for the electronic payment service (the date the account was created). Hereafter, the user instance (electronic payment account) to which this information is associated will be referred to as an account.

[0026] The charge balance is information indicating the balance of electronic money set by the user by sending money to their account in advance. Methods of sending money include sending from an ATM (Automatic Teller Machine) of a designated provider (bank) and sending from a registered bank account. The credit payment setting indicates whether or not the user has completed the settings to enable electronic payments by credit card, and is set to either "Completed" or "Not Completed". The credit payment limit is the monthly limit for credit payments, the credit payment amount is the amount already used for credit payments in the current month, and the available credit payment amount is the amount available for credit payments in the current month, calculated by subtracting the credit payment amount from the credit payment limit. While the diagram shows only one credit payment limit, in reality there are also daily limits, and the lower of these may be set as the credit payment limit. Further details on credit payments will be described later. The payment method setting indicates whether the user will use electronic payment with the charge balance or payment by credit card at that time. The bank account and credit card numbers, respectively, are information (account number, card number) of a bank account or credit card number that can be used to deposit funds into the electronic payment service. In addition to the bank account, user information 182 may also include information such as the name of the bank account holder, the bank name, branch name, bank identification information, and branch identification information (branch number). The charge history information is a record of when the user has previously sent money to the electronic payment service to increase the charge balance. The payment history information is information that shows the details of each payment made by the user (date and time, store ID of the store where the purchase was made, payment amount, payment method, etc.).

[0027] Figure 6 shows an example of the contents of the merchant / store information 186. The merchant / store information 186 includes, for example, a first table 186A in which the merchant ID and store ID are associated with the store URL, a second table 186B in which the merchant name and sales amount (as described above) are associated with the merchant ID, and a third table 186C in which the store name and store address are associated with the store ID. In addition to this information, the merchant / store information 186 may also include information such as the merchant or store category and payment patterns.

[0028] The Information Management Unit 140 acquires various information from the user terminal device 10 or the second store terminal device 70 and manages the acquired information. Based on the information acquired from the user terminal device 10 or the second store terminal device 70, the Information Management Unit 140 manages user information 182 and merchant / store information 186. The Information Management Unit 140 adds, edits, and deletes new records for user information 182 and merchant / store information 186. The Information Management Unit 140 also acquires historical information (payment history information of user information 182) regarding the user's electronic payment service history. The Information Management Unit 140 may also statistically process the user's electronic payment service history information to acquire various indicators (e.g., characteristic information 188).

[0029] [Electronic payment] When the payment processing unit 130 obtains payment information from the user terminal device 10 or the first store terminal device 50, it refers to the user information 182 to obtain the user's "payment method setting". 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, which is managed in association with the user ID, and increasing the value of the merchant's sales proceeds item. The merchant's sales proceeds item value is not used as electronic money itself, for example, but the amount corresponding to the sales proceeds item value is transferred to the bank account in a cycle according to the agreement between the merchant and the electronic payment service. The merchant may receive the sales proceeds as electronic money. In this case, the payment server 100 manages the account (wallet) corresponding to the merchant ID, the electronic money balance of the account, and the sales proceeds history for each payment method in association.

[0030] The payment processing unit 130 performs electronic payment as follows for users whose "settings information" is set to "credit payment". Credit payment is a payment method that cooperates with a credit card company, which is a separate entity from the operator of the electronic payment service, and allows electronic payment that does not depend on the charge balance within the credit limit. In order to use the credit payment service, it may be required to obtain a credit card provided by the operator of the electronic payment service. The payment processing unit 130 adds the payment amount to the credit payment usage amount in the user information 182 and subtracts the above payment amount from the available credit payment amount. If the payment amount exceeds the available credit payment amount, an error notification is sent back to the payment app 20. The amount used by credit payment is settled, for example, in a lump sum for one month on the payment date of the following month, for example, by direct debit from the bank account. This processing is performed by the operator of the credit card company. If the user has a gift certificate that can be used at the merchant, the payment server 100 performs electronic payment using the gift certificate held. In electronic payment, the amount that is insufficient with the gift certificate is settled by other payment methods.

[0031] The information processing unit 150 identifies users who meet recommendation criteria, which are estimated to provide a certain level of value to credit card companies, based on characteristic information 188 (history information of users who have not yet applied for a credit card). The content provision unit 120 provides recommendation information to the identified user's user terminal device 10, recommending that they apply for a credit card. The value is that the user utilizes a specified service among the credit card services (for example, revolving credit, electronic payments using a credit card, cash advance services, etc.). Another value is that the user prioritizes using the credit card over other credit cards. Details of the information processing unit 150 will be described later.

[0032] [Feature Information] Figure 7 is a diagram illustrating feature information 188. Feature information 188 is information obtained from user information 182. Feature information 188 includes features such as usage scenario, payment method, charging method, and other information (operation method, device model).

[0033] The usage scenario information for feature information 188 includes, for example, the type of business of the merchant where the payment was made, the time of day, the day of the week, the combination of day of the week and time of day, the percentage of the user's total payment amount that was made at convenience stores, the average payment amount, the total payment amount, and other information indicating the degree of payment at convenience stores.

[0034] For example, users who tend to use affiliated stores in specific industries or make payments during specific times and days of the week tend to have higher scores, as will be explained later. For example, users who frequently use convenience stores tend to have higher scores, as will be explained later.

[0035] The payment method information in Feature Information 188 includes the amount, number of transactions, payment type (charge balance, credit payment, points, etc.), percentage of each payment type (payment rate), and percentage of merchants by industry where payments were made for each payment type (information on the industries where payments were made). The payment information in Feature Information 188 also includes, for example, the number of payments made at restaurants and cafes, and information on payments made at restaurants and cafes (for example, average payment amount, frequency of payment use).

[0036] Users who make large payments or frequent transactions tend to have higher scores. Similarly, scores tend to be higher when there are certain trends in payment types (charge balance, credit card payments, points, etc.), the percentage of each payment type, and the percentage of merchants by industry where each payment type was used.

[0037] The charge information in Feature Information 188 includes the charge amount, the number of times (total number of times or number of times within a specified period), the trend of charge types, and the percentage of charges by industry (industry information). Charge types include, for example, charges from a registered bank account, ATM charges, credit cards, charges by adding the charge amount to the payment amount of telephone charges for a designated telephone number that is a partnered communication service, and charges using sales proceeds earned by the user from electronic payment services or related services (sales proceeds from flea markets or auctions). The percentage of charges by industry is the percentage by industry, such as banks and ATMs.

[0038] Users who charge large amounts or make frequent transactions (total number of transactions or number of transactions within a specified period) tend to have higher scores. Similarly, users who charge in a specific way or whose charge distribution by industry tend to follow a predetermined trend also tend to have higher scores.

[0039] Furthermore, the charge information in feature information 188 includes the average balance for each type of electronic money. The types of electronic money include, for example, Type 1 electronic money which can be sent (withdrawn) and Type 2 electronic money which cannot be sent (withdrawn). Type 2 electronic money is, for example, electronic money that has been charged using a credit card. Users with a high average electronic money balance (average balance of specified electronic money) tend to have a high score, for example.

[0040] Other information in feature information 188 includes, for example, the click rate (number of clicks, percentage, etc.) of the notification button (see button B in Figure 11) on the interface screen displayed by the payment app 20, the type of user terminal device 10, and the click rate (number of clicks, percentage, etc.) of the charge button. The notification button is a button used to display notifications from the electronic payment service.

[0041] For example, users who frequently click the notification button or the charge button tend to have higher scores. Similarly, users whose user terminal device 10 is of a specific type (for example, the latest model or a relatively new model) tend to have higher scores.

[0042] The correlation between the above feature information 188 and the score is just one example. There is a correlation between the above feature information 188 and the score, and as will be explained later, an appropriate score can be obtained by inputting the feature information 188 into the model.

[0043] The information processing unit 150 inputs the feature information 188 into the model and identifies the user based on the score output by the model. The model is trained to output a score indicating high value when it receives historical information of a user who is of high value to the credit card business operator. The model is part or all of the early revolving credit model 210, the main credit model 220, and the LTV model described later.

[0044] [Early Revolving Credit Model (First Model)] Figure 8 is a diagram illustrating the early revolving credit model 210. The information processing unit 150 inputs the feature information 188 to the early revolving credit model 210 and identifies the user based on the early revolving credit score (first score) output by the early revolving credit model 210. The early revolving credit model 210 is a model that has been trained to output an early revolving credit score indicating a high degree of use of a predetermined service (e.g., revolving credit) when the feature information 188 of a user who is likely to use a predetermined service (e.g., revolving credit) is input. For example, it is a model that predicts whether or not there will be an outstanding balance on revolving credit several months later (e.g., 6 months later).

[0045] For example, the training data for the Early Revolving Credit Model 210 is information that associates information indicating whether or not there is a revolving credit balance six months (a specified number of months) after joining a credit card with characteristic information 188 from the three months immediately preceding joining (a specified number of months). For example, the Early Revolving Credit Model 210 is a model that has learned to output a high score when characteristic information 188 of a user who has a revolving credit balance six months after joining a credit card is input. For example, the Early Revolving Credit Model 210 is a model that has learned to output a low score when characteristic information 188 of a user who does not have a revolving credit balance six months after joining a credit card is input.

[0046] As described above, the information processing unit 150 can use the early revolving credit model 210 and feature information 188 to obtain an early revolving credit score that indicates the likelihood of having an outstanding balance on revolving credit payments several months later.

[0047] [Main model (2nd model)] Figure 9 is a diagram illustrating the mainization model 220. The information processing unit 150 inputs feature information 188 to the mainization model 220 and identifies the user based on the mainization score (second score) output by the mainization model 220. The mainization model 220 is a model that has been trained to output a score indicating a high probability of preferential use when feature information 188 of a user who prefers to use one credit card over another is input.

[0048] For example, the training data for the main card model 220 is information that associates information indicating whether or not a user made the credit card their main card six months (a predetermined period) after joining the credit card program with characteristic information 188 from the three months (a predetermined period) immediately prior to joining. For example, the main card model 220 is a model that has been trained to output a high score when characteristic information 188 of a user who made the credit card their main card after a predetermined period after joining the credit card program is input. For example, the main card model 220 is a model that has been trained to output a low score when characteristic information 188 of a user who did not make the credit card their main card after a predetermined period after joining the credit card program is input.

[0049] The determination of whether a card is a primary card is based on some or all of the following factors: where, how often, and how much the credit card is used. The location criterion is that the credit card is used outside of affiliated services such as those offered by group companies or partner companies associated with the credit card company. For example, while using the credit card at the aforementioned affiliated services may grant benefits from the group company or partner, using it at other locations will not grant these benefits, and therefore the card will be determined to be used as a primary card.

[0050] The frequency criterion is that the credit card is used continuously for a specified number of months. For example, if the credit card is used at least once a month for six consecutive months, it will be determined that the frequency criterion is met.

[0051] The monetary criterion is that the credit card is used for electronic payments exceeding a specified amount. For example, if the credit card is used for electronic payments exceeding a specified amount each month for six consecutive months, the monetary criterion will be met.

[0052] As described above, the information processing unit 150 can use the main card model 220 and the feature information 188 to obtain a main card score that indicates the likelihood of making the credit card the main card.

[0053] Alternatively, instead of the above-mentioned early revolving credit model 210, a model indicating the likelihood of using a specified service may be used. Users who use the specified service are users with a high LTV (Life Time Value). The specified service may be, for example, a cash advance service, credit card payments exceeding a specified amount, using the credit card for credit card payments in the payment app 20, or being a user of multiple services. A user of multiple services is a user who uses the credit card for credit card payments in the payment app 20 and also physically presents the credit card at a physical store. In this case, the training data is information that associates information indicating whether or not the specified service is used six months after joining the credit card (specified months later) with characteristic information 188 for the most recent three months (specified months) prior to joining. For example, the model is trained to output a high score when characteristic information 188 of a user who uses the specified service six months after joining the credit card is input. For example, the model is trained to output a low score when characteristic information 188 of a user who does not use the specified service six months after joining the credit card is input.

[0054] [Recommended for] The information processing unit 150 identifies the target users for recommendations based on the early revolving credit score and the main user score. Figure 10 is a diagram illustrating the target users. The information processing unit 150 designates users (1) and (2) as the target users for recommendations. (1) Users in the top 5% or higher of the early ribo score (above the first threshold) (2) Users whose early ribo score is in the top 5% (below the first threshold), or in the top 20% (above the second threshold which is lower than the first threshold), and whose maining score is in the top 30% (above the third threshold).

[0055] The users described above are likely to provide businesses with a high LTV. For example, by adding (2) to (1) as described above, it is possible to secure a sufficient number of users to be targeted by the campaign, and further improve accuracy by capturing users that are not captured by (1) with (2). For example, by defining (2) as users who meet the criteria for an early revolving credit score with a high LTV and a main credit score with a relatively high LTV, it is possible to identify potential users that are not captured by (1) as described above.

[0056] As described above, it is possible to identify users with high LTV (Lifetime Value). For these identified users, after a predetermined period of membership, the proportion of outstanding balances on revolving credit and the LTV increased to a predetermined multiple compared to the average user. Furthermore, it was confirmed that defaults did not increase beyond a predetermined level.

[0057] The target audience for recommendations may be either (1) or (2). Furthermore, each threshold may be determined arbitrarily. For example, the threshold may be determined by the number of people to whom recommendations are made. For instance, if there is a predetermined number of people to whom recommendations are made, the threshold may be adjusted so that the predetermined number of users satisfy both (1) and (2).

[0058] Furthermore, the information processing unit 150 may statistically process the early revolving credit score and the maining score to obtain an integrated score, and users whose integrated score is above a threshold may be selected as recommendation targets. For example, in the statistical processing, the weight of the early revolving credit score may be set to be greater than the weight of the maining score.

[0059] The information processing unit 150 may, for example, determine a predetermined number of people with high early revolving credit scores as the target of recommendations, or it may determine a predetermined number of people with high mainning scores as the target of recommendations.

[0060] Furthermore, while the above example describes a case where both the early revolving credit model 210 and the maining model 220 are used, it is also possible to use only one of the models and identify the target users for recommendations based on the score output by that model. In addition to one or both of the early revolving credit score and the maining score, scores from other models may also be used. For example, a score derived from a model that indicates the likelihood of using a particular service (service score) may be used. For example, users whose service score meets the criteria may be identified as target users, or users whose service score and other scores meet the respective criteria may be identified as target users.

[0061] [Interface screen] Figure 11 shows an interface screen displayed on the display unit of the user terminal device 10. The interface screen is, for example, the home screen of the payment application 20. The content provision unit 120 displays, for example, information about a credit card enrollment campaign in the AR area of ​​the home screen of the payment application 20 on the user terminal device 10 of the identified user. For example, it displays information indicating that the user will receive ○○○ points as a reward for enrolling in the credit card. Note that this campaign display is not limited to the home screen; it may also be displayed in push notifications or on other screens.

[0062] As described above, the payment server 100 can efficiently recommend credit card applications to users who are likely to provide a high LTV (Lifetime Value).

[0063] [LTV Model] In the above example, the early revolving credit model 210 and the main credit model 220 were used as models to identify users with high LTV, but an LTV model may be used instead.

[0064] Figure 12 is a diagram illustrating the process using the LTV model. The LTV model, upon input of feature information 188, outputs a high score for users who are likely to provide a high LTV.

[0065] For example, the training data for an LTV model is information that associates information indicating whether a customer provides (or does not provide) a high LTV after a predetermined period from the time of credit card enrollment with characteristic information 188 for the three months immediately preceding enrollment. For example, an LTV model is trained to output a high score when it is input with characteristic information 188 for users who provided a high LTV after a predetermined period from the time of credit card enrollment. For example, an LTV model is trained to output a low score when it is input with characteristic information 188 for users who did not provide a high LTV after a predetermined period from the time of credit card enrollment.

[0066] A user who has provided a high LTV is a user whose credit card service usage from the time of enrollment to a specified period meets the specified criteria. For example, this includes some or all of the following: the total amount spent is above a threshold, there is an outstanding balance on revolving credit, the credit card has been made the primary card, the credit card has been used at specified merchants, the frequency of use of the credit card is above a specified level, the credit card has been used for credit card payments in the payment app 20 (credit payment usage), and combined usage has occurred. In addition, the total amount spent, the outstanding balance on revolving credit, the credit card being made the primary card, the credit card being used at specified merchants, the frequency of use of the credit card, credit payment usage, and combined usage may be scored, and a high LTV may be determined if the total score is above a specified level.

[0067] The information processing unit 150 may input the feature information 188 into the LTV model and identify users whose LTV score output by the LTV model is above a threshold as target users for recommendation.

[0068] As described above, the payment server 100 can efficiently recommend credit card applications to users who are likely to provide a high LTV (Lifetime Value).

[0069] [We will change the incentive details] The information processing unit 150 may change the benefits (points and coupons) in the credit card enrollment campaign depending on some or all of the scores of the Early Revolving Credit Score, Main Account Score, and LTV Score, and the user's characteristic information 188. For example, users with relatively high Early Revolving Credit Score, Main Account Score, and LTV Score will be offered benefits with a higher degree of reward. In addition, users who meet certain conditions among the characteristic information 188 will be offered benefits with a higher degree of reward. These conditions include, for example, users whose payment amount, payment frequency, average charge balance, frequency of use at convenience stores, amount spent at convenience stores, frequency of clicking notification buttons, number of payments at restaurants and cafes, payment amount, and charge frequency are above a certain level, and who may be offered benefits with a higher degree of reward than other users. In addition, users whose user terminal device 10 is of a certain model may be offered benefits with a relatively higher degree of reward.

[0070] [Loan default score] The information processing unit 150 may use the default score to identify the target customers for recommendations. Figure 13 is a diagram illustrating the default score and the default model. The default score is a score that indicates the risk of difficulty in recovering the amount spent on credit cards. The default model is a model that outputs a default score when the characteristic information 188 described above is input.

[0071] For example, the training data for a bad debt model is information that associates information indicating whether a bad debt will occur (or not occur) six months after credit card issuance (a specified period of time) with characteristic information 188 from the three months immediately preceding issuance (a specified period of time). For example, a bad debt model is trained to output a high score when it is input with characteristic information 188 of a user who defaulted after a specified period of time after credit card issuance. For example, a bad debt model is trained to output a low score when it is input with characteristic information 188 of a user who did not default after a specified period of time after credit card issuance.

[0072] The information processing unit 150 obtains the default score of the person to be recommended and identifies users whose default score is below a threshold among the recommended users as the person to be recommended. The threshold may be variable depending on the level of the early revolving credit score, main credit score, and LTV score. For example, the higher the early revolving credit score, main credit score, and LTV score, the higher or lower the default score threshold may be set. This degree of variability or threshold is set for each early revolving credit score, main credit score, combination of early revolving credit score and main credit score, and LTV score.

[0073] As described above, the payment server 100 can efficiently recommend credit card applications to users with a low risk of default. [Cancellation Score] The information processing unit 150 may use the churn score to identify target users for recommendations. Figure 14 is a diagram illustrating the churn score and the churn model. The churn score is a score indicating the risk of canceling a credit card after joining. The churn model is a model that outputs a churn score when the characteristic information 188 described above is input.

[0074] For example, the training data for the cancellation model is information that associates information indicating whether or not a user will cancel their credit card six months (a specified number of months) after joining with characteristic information 188 from the three months immediately preceding joining (a specified number of months). For example, the cancellation model is trained to output a high score when it is input with characteristic information 188 of a user who canceled their credit card after a specified period after joining. For example, the cancellation model is trained to output a low score when it is input with characteristic information 188 of a user who did not cancel their credit card after a specified period after joining.

[0075] The information processing unit 150 obtains the churn score of the target users for recommendation and identifies users whose churn score is below a threshold among the target users for recommendation as the actual target users for recommendation.

[0076] As described above, the payment server 100 can efficiently recommend credit card applications to users with a low risk of cancellation. This process is particularly effective when running promotional campaigns for credit cards that have annual fees, for example.

[0077] For example, credit card sign-up campaigns were not conducted from an LTV (Lifetime Value) perspective, or it was difficult to identify users based on LTV. For instance, it was difficult to run campaigns only for users with high LTV, and it was necessary to advertise campaigns to both high-LTV users and other users at a cost. As described above, it was difficult to acquire users with high LTV efficiently and effectively.

[0078] In contrast, in this embodiment, by easily identifying users who are expected to have a high LTV and inviting them to join, costs such as advertising expenses and incentive expenses can be reduced, encouraging high-LTV users to join, and acquiring high-LTV users efficiently and effectively.

[0079] According to the embodiments described above, the payment server 100 can appropriately identify users to whom the use of the service is recommended by identifying users who meet recommendation criteria that are estimated to provide a certain level of value to credit card businesses, based on the history information of the user's electronic payment history for the electronic payment service, and by providing recommendation information that recommends credit card application to the identified user's terminal device.

[0080] Although embodiments for carrying out the present invention have been described above using examples, the present invention is not limited in any way to these embodiments, and various modifications and substitutions can be made without departing from the spirit of the present invention. [Explanation of Symbols]

[0081] 1. Electronic payment system 10. User terminal device 20 Payment Apps 50. First store terminal device 72 Merchant Interface 100 Payment Servers 140 Information Management Department 150 Information Processing Unit 188 Feature Information 210 Early Revolving Model 220 Main Model 300 credit server

Claims

1. An acquisition unit that acquires information regarding the user's use of electronic payment services, An identification unit inputs the aforementioned usage information into a model and identifies the target user based on the score output by the model, The system includes a provisioning unit that provides recommendation information recommending credit card application to the terminal device of the identified target user, The aforementioned model is a model that has been trained to output a higher score when information regarding the usage of the target user is input than when information regarding the usage of a user who is not the target user is input. The aforementioned target users are those who have used the revolving payment option of the aforementioned credit card, or those whose use of the aforementioned credit card meets the prescribed criteria. The aforementioned prescribed standards are: (1) The credit card was used for a service different from that of a company related to the credit card issuer and a company that partners with the credit card issuer. (2) The credit card has been used continuously at a predetermined frequency, (3) The credit card was used for electronic payments exceeding a specified amount, and at least one of these conditions is met. The aforementioned information regarding use is, The type of business, time of day, day of the week, and frequency of payments at convenience stores for the aforementioned electronic payments, The amount of the electronic payment, the number of electronic payments, information on the type of business of the store where the electronic payment was made, and information on the payment of the electronic payment at a restaurant, including a coffee shop, Information indicating whether the type of payment for the electronic payment is a charge balance, a payment using a credit card registered with the electronic payment service, or points from the electronic payment service, The amount charged to the wallet of the aforementioned electronic payment service, the number of charges, the type of charge, information on the industry in which the charge was made, and the average amount of the charge balance, This includes at least one of the following pieces of information: the degree to which the notification button in the payment app of the electronic payment service is operated, and the degree to which a charge operation is performed in the payment app. Information processing device.

2. The information relating to the use is, The type of business, time of day, day of the week, and frequency of payments at convenience stores for the aforementioned electronic payments, The amount of the electronic payment, the number of electronic payments, information on the type of business of the store where the electronic payment was made, and information on the payment of the electronic payment at a restaurant, including a coffee shop, Information indicating whether the type of payment for the electronic payment is a charge balance, a payment using a credit card registered with the electronic payment service, or points from the electronic payment service, The amount charged to the wallet of the aforementioned electronic payment service, the number of charges, the type of charge, information on the industry in which the charge was made, and the average amount of the charge balance, This includes the degree to which the notification button in the payment app is operated, and the degree to which a charge operation is performed in the payment app. The information processing apparatus according to claim 1.

3. Computers We obtain information regarding the user's use of electronic payment services. The usage information is input into the model, and the target user is identified based on the score output by the model. The system provides recommendation information to the terminal device of the identified target user, recommending that they apply for a credit card. The aforementioned model is a model that has been trained to output a higher score when information regarding the usage of the target user is input than when information regarding the usage of a user who is not the target user is input. The aforementioned target users are those who have used the revolving payment option of the aforementioned credit card, or those whose use of the aforementioned credit card meets the prescribed criteria. The aforementioned prescribed standards are: (1) The credit card was used for a service different from that of a company related to the credit card issuer and a company that partners with the credit card issuer. (2) The credit card has been used continuously at a predetermined frequency, (3) The credit card was used for electronic payments exceeding a specified amount, and at least one of these conditions is met. The aforementioned information regarding use is, The type of business, time of day, day of the week, and frequency of payments at convenience stores for the aforementioned electronic payments, The amount of the electronic payment, the number of electronic payments, information on the type of business of the store where the electronic payment was made, and information on the payment of the electronic payment at a restaurant, including a coffee shop, Information indicating whether the type of payment for the electronic payment is a charge balance, a payment using a credit card registered with the electronic payment service, or points from the electronic payment service, The amount charged to the wallet of the aforementioned electronic payment service, the number of charges, the type of charge, information on the industry in which the charge was made, and the average amount of the charge balance, This includes at least one of the following pieces of information: the degree to which the notification button in the payment app of the electronic payment service is operated, and the degree to which a charge operation is performed in the payment app. Information processing methods.

4. On the computer, We obtain information regarding the user's use of electronic payment services. The information regarding the aforementioned usage is input into the model, and the target user is identified based on the score output by the model. The terminal device of the identified target user is provided with recommendation information recommending credit card application. The aforementioned model is a model that has been trained to output a higher score when information regarding the usage of the target user is input than when information regarding the usage of a user who is not the target user is input. The aforementioned target users are those who have used the revolving payment option of the aforementioned credit card, or those whose use of the aforementioned credit card meets the prescribed criteria. The aforementioned prescribed standards are: (1) The credit card was used for a service different from that of a company related to the credit card issuer and a company that partners with the credit card issuer. (2) The credit card has been used continuously at a predetermined frequency, (3) The credit card was used for electronic payments exceeding a specified amount, and at least one of these conditions is met. The aforementioned information regarding use is, The type of business, time of day, day of the week, and frequency of payments at convenience stores for the aforementioned electronic payments, The amount of the electronic payment, the number of electronic payments, information on the type of business of the store where the electronic payment was made, and information on the payment of the electronic payment at a restaurant, including a coffee shop, Information indicating whether the type of payment for the electronic payment is a charge balance, a payment using a credit card registered with the electronic payment service, or points from the electronic payment service, The amount charged to the wallet of the aforementioned electronic payment service, the number of charges, the type of charge, information on the industry in which the charge was made, and the average amount of the charge balance, This includes at least one of the following pieces of information: the degree to which the notification button in the payment app of the electronic payment service is operated, and the degree to which a charge operation is performed in the payment app. program.

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