JP6786321B2 - Judgment device, judgment method, and judgment program - Google Patents

Description

The present invention relates to a determination device, a determination method, and a determination program.

In recent years, with the development of communication networks, various services have been provided via networks. Examples of services via networks include financial services such as lending to companies and individuals and issuing credit cards.

For example, as a technology related to financial services via a network, there is known a system for determining loan conditions based on actual store operation data, which consists of accumulating the latest data generated in store operation.

Japanese Unexamined Patent Publication No. 2014-182572

However, with the above-mentioned conventional technique, it is difficult to accurately determine the creditworthiness of an individual. Specifically, the above-mentioned conventional technique determines the credit rating based on the actual data associated with the operation of the store, and for individuals who have difficulty in acquiring the data corresponding to the actual data associated with the operation of the store. On the other hand, it is difficult to accurately judge the creditworthiness of the individual.

The present application has been made in view of the above, and an object of the present application is to provide a determination device, a determination method, and a determination program capable of accurately determining an individual's creditworthiness.

The determination device according to the present application relates to a predetermined service based on an acquisition unit that acquires total asset information, which is information obtained by arbitrarily combining information on assets owned by the user, and total asset information acquired by the acquisition unit. It is characterized by including a determination unit for determining the creditworthiness of the user.

According to one aspect of the embodiment, there is an effect that the creditworthiness of an individual can be accurately determined.

FIG. 1 is a diagram showing an example of a determination process according to the first embodiment. FIG. 2 is a diagram showing a configuration example of the determination system according to the first embodiment. FIG. 3 is a diagram showing a configuration example of the determination device according to the first embodiment. FIG. 4 is a diagram showing an example of the total asset information storage unit according to the first embodiment. FIG. 5 is a diagram showing an example of a user information storage unit according to the first embodiment. FIG. 6 is a diagram showing an example of a content storage unit according to the first embodiment. FIG. 7 is a flowchart showing a processing procedure according to the first embodiment. FIG. 8 is a diagram showing an example of the determination process according to the second embodiment. FIG. 9 is a diagram showing a configuration example of the determination device according to the second embodiment. FIG. 10 is a diagram showing an example of a terminal information storage unit according to the second embodiment. FIG. 11 is a diagram showing an example of a model storage unit according to the second embodiment. FIG. 12 is a flowchart (1) showing a processing procedure according to the second embodiment. FIG. 13 is a flowchart (2) showing a processing procedure according to the second embodiment. FIG. 14 is a hardware configuration diagram showing an example of a computer that realizes the function of the determination device.

Hereinafter, a determination device, a determination method, and a mode for carrying out the determination program according to the present application (hereinafter, referred to as “the embodiment”) will be described in detail with reference to the drawings. The determination device, determination method, and determination program according to the present application are not limited by this embodiment. In addition, each embodiment can be appropriately combined as long as the processing contents do not contradict each other. Further, in each of the following embodiments, the same parts are designated by the same reference numerals, and duplicate description is omitted.

[1. An example of determination processing according to the first embodiment]
First, an example of the determination process according to the first embodiment will be described with reference to FIG. FIG. 1 is a diagram showing an example of a determination process according to the first embodiment. In FIG. 1, the determination device 100 corresponding to the determination device according to the present application acquires total asset information which is information obtained by arbitrarily combining information on assets owned by the user, and is determined based on the acquired total asset information. An example of the process of determining the creditworthiness of the user regarding the service is shown.

The determination device 100 shown in FIG. 1 is a server device that acquires total asset information from a terminal and determines the creditworthiness of a user based on the acquired information. Further, the determination device 100 performs a process of delivering a predetermined content based on the determined credit rating.

The user terminal 10 shown in FIG. 1 is an information processing terminal used by the user U01. For example, the user terminal 10 is realized by a smartphone, a tablet terminal, or the like. The user terminal 10 registers the total asset information of the user U01 in the determination device 100 according to the operation by the user U01. Further, the user terminal 10 acquires the content distributed from the determination device 100 and displays it on the screen. In the following description, the user terminal 10 may be replaced with the user U01.

In the first embodiment, the determination device 100 uses the asset information of the user U01 as information on the bank account contracted by the user U01 and the service points used in the store (the service points are, for example, in the store). Obtain information about (meaning virtual currency that can be used instead of cash), information about securities accounts, information about contracted credit card companies, information about insurance that you have. The total asset information is information that is an arbitrary combination of such information on the assets owned by the user, and can be said to be information on the total assets of the user. The assets owned by the user may include possessions such as automobiles and paintings.

Then, the determination device 100 determines the credit rating of the service provided to the user U01, in other words, the credit rating of the predetermined action when the user U01 tries to use the service, based on the total asset information. The determination device 100 determines the creditworthiness of, for example, an action in which the user U01 attempts to use a predetermined financial service (more specifically, an action in which the user U01 applies for a new issuance of a credit card, etc.) as a predetermined action. So to speak, the determination device 100 determines the suitability of the predetermined service based on the total asset information of the user U01.

Generally, a business operator that provides a financial service measures the creditworthiness of the user U01 based on the information received from the user U01. For example, the business operator accepts the occupation and work place of the user U01, the annual income declared by the user U01, and measures the creditworthiness of the user U01 based on the received information. However, the information provided by the user U01 is only a part of the information of the assets of the user U01. Further, the annual income declared by the user U01 may be uncertain information when the user U01 has a side business or when the user U01 makes a false declaration.

If the business operator can obtain the detailed information of the user U01, it is possible to provide the service according to the assets of the user U01, but it is generally difficult to obtain the detailed information of the user U01. For example, a company that issues a credit card may obtain information about the bank account of user U01 associated with the credit card, but obtains information about other bank accounts and securities accounts contracted by user U01. I can't. Therefore, even if the user U01 is a user having a relatively large amount of assets, the business operator may miss the opportunity to provide services such as raising the status of the credit card and raising the limit amount.

Here, a service called PFM (Personal Financial Management), which integrates and manages information on assets owned by users, is provided. Such a service centrally manages a plurality of bank accounts contracted by users, securities accounts, a service point system provided for each store, insurance, and the like. By registering the user in such a service, the service side can obtain the total asset information which is the comprehensive asset information of the user.

Therefore, the determination device 100 acquires the total asset information of the user by providing the above-mentioned PFM service. Further, the determination device 100 determines the creditworthiness of the user based on the acquired total asset information. Since the determination device 100 determines the creditworthiness based on the total asset information, for example, the information indicating the assets of the user U01 is accurately compared with the information by the user's self-report or the information of only one bank account. The credit rating can be determined based on this. Then, the determination device 100 can provide useful information to the user by distributing the content according to the credit rating determined based on the total asset information. Hereinafter, such a determination process will be described along the flow with reference to FIG. In the example of FIG. 1, for simplification of explanation, the determination device 100 itself provides a service for managing total asset information such as PFM, and the determination device 100 itself is a distribution server that distributes content. It shall be processed as.

First, the determination device 100 acquires the total asset information of the user U01 (step S01). For example, the determination device 100 acquires the total asset information of the user U01 by receiving registration about the information of the financial institution or the like used by the user U01 via the user terminal 10.

In the example shown in FIG. 1, the user U01 has a bank account at the bank 40 or the bank 50. Further, the user U01 uses the point card system issued by the store 60. Further, the user U01 has a securities account at the securities company 70. Further, the user U01 uses a credit card issued by the credit card company 80. In addition, user U01 is insured by the insurance company 90. The user U01 registers this information in the determination device 100 as total asset information.

Then, the determination device 100 determines the creditworthiness of the user U01 regarding the predetermined action based on the total asset information (step S02). Details will be described later, but for example, the determination device 100 determines the creditworthiness of the user U01 regarding an action to use a predetermined financial service (more specifically, an action in which the user U01 applies for a new issuance of a credit card, etc.). To do.

For example, the determination device 100 refers to the balance in the accounts of the banks 40 and 50, and the monthly income and expenditures in the total asset information. Then, the determination device 100 determines whether the total balance in the accounts of the bank 40 or the bank 50 exceeds a predetermined threshold value. In addition, the determination device 100 determines whether the amount obtained by subtracting the expenditure from the monthly income exceeds a predetermined threshold value. Then, when these values exceed a predetermined threshold value, the determination device 100 determines the creditworthiness of the user U01 to be high with respect to the behavior of the user U01 trying to use the predetermined financial service.

Further, the determination device 100 determines whether or not the amount of monthly use of the point service of the store 60 by the user U01 exceeds a predetermined threshold value, whether or not the balance in the securities account or the monthly transaction amount exceeds a predetermined threshold value, and credit. Based on various information in the total asset information, such as whether the monthly usage amount of the card exceeds a predetermined threshold value and whether the monthly premium for insurance exceeds a predetermined threshold value, the user U01 provides a predetermined financial service. You may determine the creditworthiness of the user regarding the behavior of trying to use.

Then, the determination device 100 sets the targeting according to the determined credit rating to the user U01 (step S03). In the above example, the determination device 100 shows an example in which the determination device 100 determines the creditworthiness of the user regarding the behavior in which the user tries to use a predetermined financial service, but the determination device 100 similarly determines the creditworthiness of the user with respect to other behaviors. Is judged. For example, the determination device 100 determines the credit rating when the user U01 tries to purchase a high-priced product (for example, real estate, automobile, etc.), the credit rating when applying for a golf member, and the credit rating when the user U01 changes jobs. To do.

Then, the determination device 100 determines the creditworthiness of the user U01 for each action, and sets the targeting according to the determined creditworthiness. For example, the determination device 100 sets the targeting for the user U01 so that the content according to the credit rating when the user U01 applies for the new issuance of the credit card is delivered to the user U01.

For example, it is assumed that the determination device 100 determines the credit rating in the case where the user U01 applies for the new issuance of a credit card to be relatively high. In this case, the credit card company is motivated to encourage the user U01 to apply for the issuance of a new credit card. Therefore, the determination device 100 sets the targeting as the content so that the advertisement related to the credit card application is delivered to the user terminal 10. Specifically, the determination device 100 adjusts the setting so that the category to which the advertisement related to the credit card application belongs is a category having a high priority among the categories of the advertisement delivered to the user terminal 10. On the other hand, the determination device 100 targets the user terminal 10 so that the advertisement related to the credit card application is difficult to be delivered when the credit rating is determined to be relatively low when the user U01 applies for the new issuance of the credit card. To set.

Then, the determination device 100 distributes the targeted information (content) to the user terminal 10 at a predetermined distribution opportunity (step S04). For example, when the web page including the advertising space is displayed on the user terminal 10, the determination device 100 delivers the targeted content as an advertisement to be displayed in the advertising space. Alternatively, the determination device 100 may distribute the content by using an opportunity such as a recommendation of a shopping site or a push notification for access to a web page.

As described above, the determination device 100 according to the first embodiment acquires the total asset information which is the information regarding the comprehensive assets of the user U01. Then, the determination device 100 determines the creditworthiness of the user U01 regarding the predetermined service based on the acquired total asset information.

That is, the determination device 100 according to the first embodiment determines the creditworthiness of the user U01 based on the total asset information of the user U01, not the information declared by the user U01 or the information of a certain bank account. To do. As a result, the determination device 100 can include asset information such as securities accounts other than bank accounts and points for each store, which are not normally acquired by businesses such as financial institutions, as determination elements. Based on detailed asset information, the creditworthiness of an individual can be accurately determined.

In the example of FIG. 1 above, the determination device 100 shows an example of acquiring total asset information by receiving registration from the user terminal 10. However, the determination device 100 may acquire information such as a bank account from a financial institution such as a bank 40 or a bank 50 after being registered once from the user terminal 10. That is, the user terminal 10 may register only the information capable of logging in to the bank account online in the determination device 100, instead of transmitting the actual account information or the like to the determination device 100. In this case, the determination device 100 can perform processing such as acquisition of account information provided for each financial institution by incorporating an API (Application Programming Interface) into the PFM service, for example. Further, the determination device 100 may continuously acquire total asset information via a program installed in the user terminal 10 (for example, an application dedicated to a smartphone).

Further, in FIG. 1, an example of a process in which the determination device 100 determines the creditworthiness of the user U01 with respect to a predetermined service is shown, but the determination device 100 relates to the user U01 when determining the creditworthiness based on the total asset information. You may perform a process of estimating various information. For example, the determination device 100 calculates the monthly income amount of the user U01 (for example, the total amount transferred to each account in the name of "salary" or "reward") based on the total asset information, thereby causing the user to use the determination device 100. You may estimate your annual income. Further, the determination device 100 may estimate the amount of public money (tax, utility cost, etc.) paid by the user U01 annually based on the information regarding the expenditure of the total asset information. In this case, the determination device 100 may estimate the annual income of the user U01 based on the estimated amount of public money. Further, the determination device 100 may estimate the disposable income of the user U01 based on the estimated annual income. In this way, the determination device 100 may estimate information about the assets of the user U01 based on the total asset information of the user U01, determine the creditworthiness from the estimated information, and use the determined creditworthiness for targeting.

[2. Judgment system configuration]
Next, the configuration of the determination system 1 including the determination device 100 according to the first embodiment will be described with reference to FIG. FIG. 2 is a diagram showing a configuration example of the determination system 1 according to the first embodiment. As illustrated in FIG. 2, the determination system 1 according to the first embodiment includes a user terminal 10, a financial institution server 45, a store server 65, and a determination device 100. These various devices are connected via network N so as to be communicable by wire or wirelessly. Further, the number of each device included in the determination system 1 shown in FIG. 2 is not limited to that shown in the figure. For example, the determination system 1 may include a plurality of user terminals 10, a plurality of financial institution servers 45, and a plurality of store servers 65.

As described above, the user terminal 10 is an information processing terminal such as a mobile phone including a smartphone, a tablet terminal, a desktop PC (Personal Computer), a notebook PC, or a PDA (Personal Digital Assistant). The user terminal 10 also includes a wearable device, which is a glasses-type or watch-type information processing terminal. Further, the user terminal 10 may include various smart devices having an information processing function for registering the total asset information in the determination device 100. For example, the user terminal 10 may include smart home appliances such as TVs (Televisions), refrigerators, and vacuum cleaners, smart vehicles such as automobiles, drones, and domestic robots.

The financial institution server 45 is a server device managed by a financial institution. The financial institution server 45 manages account information and the like at the financial institution. For example, the financial institution server 45 is managed by a bank 40 or a bank 50.

The store server 65 is a server device managed by the store 60. The store server 65 manages the point card system and the like in the store 60.

Although not shown in FIG. 2, the determination system 1 relates to total asset information such as a server managed by a securities company, a server managed by a credit card company, and a server managed by an insurance company. Various servers that provide and manage information may be included. Further, the determination system 1 may include a web server that distributes a service page (web page) included in a website that provides various services to the user terminal 10, an advertisement distribution server that distributes an advertisement to the web page, and the like. Good.

As described above, the determination device 100 is a server device that determines the creditworthiness of the user regarding a predetermined action based on the total asset information of the user terminal 10.

[3. Configuration of the determination device according to the first embodiment]
Next, the configuration of the determination device 100 according to the first embodiment will be described with reference to FIG. FIG. 3 is a diagram showing a configuration example of the determination device 100 according to the first embodiment. As shown in FIG. 3, the determination device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The determination device 100 includes an input unit (for example, a keyboard, a mouse, etc.) that receives various operations from an administrator or the like who uses the determination device 100, and a display unit (for example, a liquid crystal display, etc.) for displaying various information. You may have.

(About communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. The communication unit 110 is connected to the network N by wire or wirelessly, and transmits / receives information to / from the user terminal 10, the financial institution server 45, and the store server 65 via the network N.

(About storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory (Flash Memory), or a storage device such as a hard disk or an optical disk. The storage unit 120 includes a total asset information storage unit 121, a user information storage unit 122, and a content storage unit 123.

(About total asset information storage unit 121)
The total asset information storage unit 121 stores information related to total asset information. Here, FIG. 4 shows an example of the total asset information storage unit 121 according to the first embodiment. FIG. 4 is a diagram showing an example of the total asset information storage unit 121 according to the first embodiment. In the example shown in FIG. 4, the total asset information storage unit 121 has "total asset information ID", "user ID", "terminal ID", "individual asset information", "customer", "type", and "asset". It has items such as "data".

The "total asset information ID" indicates identification information that identifies the total asset information. The "user ID" indicates identification information that identifies the user. The "terminal ID" indicates identification information that identifies the user terminal 10. In the embodiment, the identification information shall match the reference code used for the explanation. For example, the terminal identified by the terminal ID "10" indicates the user terminal 10. Further, the user identified by the user ID "U01" indicates the user U01.

“Individual asset information” indicates individual asset information that constitutes total asset information. “Business partner” refers to a business partner who provides financial services to users, such as a depositor or contractor of assets. "Type" indicates the type of asset.

“Asset data” indicates asset data for each individual asset. In the example shown in FIG. 4, the asset data is shown by the concept of "X40", but in reality, the asset data includes balances deposited in a bank account, information on income and expenditure, liabilities, and the like. It includes various data for each individual asset, such as loan limits and insurance contract details.

That is, in FIG. 4, the total asset information identified by the total asset information ID "A01" is the total asset information of the user U01 identified by the user ID "U01", and the user U01 is the terminal ID "10". It indicates that the user terminal 10 to be identified is being used. Further, as the individual asset information constituting the total asset information, for example, the individual asset information such that the business partner is "bank 40", the type is "bank account", and the asset data is "X40". It shows that there is.

The total asset information does not necessarily have to be composed of a plurality of individual asset information. For example, if the user is a user who uses only one bank account, he / she will have total asset information composed of one individual asset information.

(About user information storage unit 122)
The user information storage unit 122 stores information about the user. Here, FIG. 5 shows an example of the user information storage unit 122 according to the first embodiment. FIG. 5 is a diagram showing an example of the user information storage unit 122 according to the first embodiment. As shown in FIG. 5, the user information storage unit 122 includes "user ID", "total asset information ID", "annual income estimation", "public money payment estimation", "disposable income estimation", "credit", and "comprehensive". It has items such as "rank", "service", and "individual rank".

The "user ID" and "total asset information ID" correspond to the items shown in FIG. "Annual income estimation" indicates the annual income of the user estimated based on the total asset information. "Public payment estimation" indicates the user's public payment amount estimated based on the total asset information. "Disposable income estimation" indicates the amount of disposable income of the user estimated based on the total asset information. In the example shown in FIG. 5, items such as annual income estimation are shown by the concept of "C01", but in reality, items such as annual income estimation are estimated from the amount of income in the total asset information. The actual amount etc. is stored.

"Credit" indicates the credit of the user. "Overall rank" indicates the overall rank of the user with respect to a predetermined service. For example, the credit rating rank is indicated by a numerical value in 10 steps with 1 being the minimum and 10 being the maximum. “Service” indicates the content of each service when the credit rating is determined for each service by the determination device 100. The "individual rank" indicates the rank of the credit rating for each service when the credit rating is determined for each service by the determination device 100.

That is, FIG. 5 shows an example in which the user U01 indicated by the user ID “U01” registers the total asset information A01 indicated by the total asset information ID “A01”. Then, it is shown that the annual income estimated based on the total asset information A01 is "C01", the estimated public money payment amount is "D01", and the estimated disposable income amount is "E01". ing. In addition, the overall rank of the credit rating of the user U01 determined based on the total asset information A01 is "7", and the credit rating of the user U01 regarding the "new credit card contract" is "8" as an individual service. It is shown that the credit rating of the user U01 regarding the "real estate contract" is "7" and the credit rating of the user U01 regarding the "job change offer" is "6".

As shown in FIG. 5, the determination device 100 may determine the overall credit rating of the entire predetermined service with respect to the credit rating of the user, or may determine the individual credit rating of each individual service. For example, the determination device 100 determines that a user with a relatively high annual income has a high credit rating for a "new credit card contract", but if the user's income and expenditure are unbalanced, the credit rating for the "real estate contract" is high. It may be possible to determine the credit rating more suitable for the user by determining the credit rating according to the content of each service, such as determining the credit rating as low.

(About the content storage unit 123)
The content storage unit 123 stores information about the content. Here, FIG. 6 shows an example of the content storage unit 123 according to the first embodiment. FIG. 6 is a diagram showing an example of the content storage unit 123 according to the first embodiment. As shown in FIG. 6, the content storage unit 123 has items such as "content ID", "service", "targeting information", "credit rank", "attribute information", and "delivery form".

The "content ID" indicates identification information for identifying the content. "Service" indicates the content of the service advertised by the content.

"Targeting information" indicates targeting setting information set in the content. The "credit rank" indicates the credit rank set for the content. For example, the determination device 100 performs targeting distribution for determining the content to a user having a credit rating equal to or higher than the credit rating rank of the content. For example, even if the content advertises the same service, the content to be advertised may change according to the set credit rating rank.

"Attribute information" indicates the setting of attribute information related to targeting. In the example of FIG. 6, the attribute information is shown by a concept such as "G01", but in reality, the item of the attribute information includes the age, gender, place of residence, etc. of the user to whom the content is delivered. Specific attribute information of is stored. The determination device 100 identifies a user who matches the attribute information set in the content as a distribution destination, and distributes the content.

“Distribution form” indicates a form in which the content is distributed. For example, the delivery form includes a form in which the user terminal 10 is delivered to an advertisement space displayed together with a web page or an application (for example, a banner advertisement on a web page), or a push notification or an email notification. The form to be delivered and the form to be delivered as a recommendation of a shopping site are included.

That is, in FIG. 6, the content F01 indicated by the content ID “F01” is shown to be content that promotes a “credit card new contract” as a service. Further, the targeting information set in the content F01 indicates that the credit rating rank is "7", the attribute information is "G01", and the distribution form is "advertisement".

(About control unit 130)
The control unit 130 is, for example, a controller, and various programs (as an example of a determination program) stored in a storage device inside the determination device 100 by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. (Equivalent) is realized by executing RAM (Random Access Memory) as a work area. Further, the control unit 130 is a controller, and is realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

As shown in FIG. 3, the control unit 130 includes an acquisition unit 131, a determination unit 132, a determination unit 133, and a distribution unit 134, and realizes or executes an information processing function or operation described below. .. The internal configuration of the control unit 130 is not limited to the configuration shown in FIG. 3, and may be another configuration as long as it is a configuration for performing information processing described later. Further, the connection relationship of each processing unit included in the control unit 130 is not limited to the connection relationship shown in FIG. 3, and may be another connection relationship.

(About acquisition unit 131)
The acquisition unit 131 acquires various types of information. For example, the acquisition unit 131 acquires total asset information, which is information obtained by arbitrarily combining information on assets owned by the user. That is, the acquisition unit 131 acquires total asset information, which is information on the user's total assets. Specifically, the acquisition unit 131 receives registration of information about the financial institution used by the user, information about the store used by the user, and the like from the user terminal 10, and thereby receives individual asset information of the user. And also acquire total asset information, which is information that is integrated with individual asset information.

The acquisition unit 131 acquires user identification information and authentication information (information on login for using individual financial institution services, etc.) for financial institutions, etc., instead of specific asset information, from the user terminal 10. You may do so. In this case, the acquisition unit 131 acquires asset information from each financial institution based on the authentication information or the like acquired from the user terminal 10. The acquisition unit 131 stores the acquired information in the total asset information storage unit 121.

(About judgment unit 132)
The determination unit 132 determines the creditworthiness of the user regarding the predetermined service based on the total asset information acquired by the acquisition unit 131. As shown in FIG. 5, the determination unit 132 may determine the overall credit rating of the user with respect to the entire service, or may determine the credit rating of the user individually for each service.

The determination unit 132 determines the creditworthiness of the financial service as a predetermined service. Specifically, the determination unit 132 determines the creditworthiness of the user regarding financial services such as credit card contract proposals, loan proposals, real estate contract proposals, and financial investment proposals.

For example, the determination unit 132 determines the creditworthiness of a user for a service provided by a credit card company or the like, such as issuing a new credit card, switching a credit card, or changing a credit card limit. Alternatively, the determination unit 132 determines the creditworthiness of the user regarding a service such as a learning loan proposal or a car loan proposal, which is a service provided by a financial institution or the like. Alternatively, the determination unit 132 determines the creditworthiness of the user regarding a service provided by a financial institution or the like, such as a mortgage loan proposal such as a real estate purchase, or a service such as a real estate sale proposal. Alternatively, the determination unit 132 determines the creditworthiness of the user regarding a service provided by a securities company or the like, such as a proposal for financial investment such as purchase of stocks or investment trusts.

In addition, the determination unit 132 calculates the creditworthiness of the user with respect to at least one of the services of the proposal for purchasing behavior or the proposal for job hunting behavior as a predetermined service. For example, the determination unit 132 determines the creditworthiness of a user regarding a service provided by a store that proposes the purchase of golf membership or the purchase of various products. Alternatively, the determination unit 132 determines the creditworthiness of the user regarding the service provided by the recruitment agency or the like to offer a job change. In these cases, the credit rating determined by the determination unit 132 may be rephrased as the suitability of the user for each service. For example, the determination unit 132 may be said to determine aptitude such as whether or not the user should receive a job change offer based on the total asset information.

The determination unit 132 estimates at least one of the user's annual income, public money payment amount, and disposable income amount based on, for example, total asset information, and based on the estimated information, the determination unit 132 estimates the creditworthiness of the above services. Determine the creditworthiness of the user.

For example, the determination unit 132 estimates the user's annual income from the total annual income in the total asset information. By referring to the total asset information, the determination unit 132 can obtain information on the user's income for all accounts and securities related to the user, not for a single account, etc., so if only the annual income from salary income Instead, a more accurate estimate of the user's actual annual salary can be made. For example, the determination unit 132 can estimate the annual income including not only the salary but also the user's unearned income (stocks, real estate investment, etc.) by referring to the total asset information.

In addition, the determination unit 132 may estimate the user's public money payment amount. For example, the determination unit 132 estimates the user's public money payment amount by totaling the expenditures of each account constituting the total asset information and indicating the public money. Further, the determination unit 132 may estimate the user's annual income from the estimated public money payment amount. For example, the determination unit 132 can estimate the amount of the user's approximate annual income from the amount of the user's public money payment based on the tax system applied to the user.

In addition, the determination unit 132 may estimate the user's disposable income based on the user's income and expenditure in the total asset information. For example, the determination unit 132 may estimate the user's disposable income by subtracting the estimated public money payment amount from the user's income. As a result, the determination device 100 can obtain not only the annual income of the user but also the amount that can be actually used by the user, so that the creditworthiness of the user can be determined more accurately. In addition, the determination unit 132 may estimate the approximate amount of disposable income of the user by summing up the amount of expenditure excluding the monthly public money payment amount of the user.

The determination unit 132 may accept the setting of a predetermined determination criterion in advance when determining the creditworthiness of the user. For example, the determination unit 132 accepts the setting of the determination criteria regarding the balance of total asset information, the determination criteria regarding the amount obtained by subtracting expenditure from income, the monthly inflow / outflow of funds, the determination criteria regarding unearned income, and the like. Then, the determination unit 132 determines the creditworthiness of the user according to the set determination criteria. Such determination criteria may be set for each service provided by, for example, a financial institution.

Further, the determination unit 132 may use information on the transition of the total asset information when determining the creditworthiness of the user. For example, in the determination unit 132, even if the user has a relatively small balance at the present time, the amount of income is relatively high, the annual rate of increase in income is high, or the amount obtained by subtracting expenditure from income is relatively high. As for the user who does, the creditworthiness may be judged as high as the user who has a high possibility of forming a sufficient asset in the future. Further, the determination unit 132 may generate a predetermined learning model regarding the relationship between the determination of the creditworthiness of the user and the amount shown in the total asset information, and determine the creditworthiness of the user using the model.

Then, the determination unit 132 sets the targeting for the user based on the determined credit rating. That is, the determination unit 132 updates the user information so that the user information can be matched with the targeting information set in advance in the content by adding the credit rating determined to the user to the user information.

(About decision unit 133)
The determination unit 133 determines the content to be distributed to the user based on the credit rating determined by the determination unit 132. Specifically, the determination unit 132 determines the content related to a predetermined financial service as the content to be distributed to the user.

More specifically, the decision-making unit 133 provides the content related to at least one service of the credit card contract proposal, the loan proposal, the real estate contract proposal, or the financial investment proposal as the content related to the predetermined financial service. Determined as the content to be delivered to. For example, as shown in FIG. 5, the determination unit 133 advertises a new credit card contract or urges the user to make a new contract when the credit card of the new credit card contract is determined to be high. Determines the content so that is delivered to the user.

In addition, the determination unit 133 may preferentially determine the content to be distributed to the user according to the rank of the credit rating. For example, if the user's credit rating rank is "7" and the content is related to the same service and the credit rating ranks are "7" and "5", the determination unit 133 has a credit rating rank. Priority is given to the content of "7" that matches the user. This is because the closer the credit rating is, the more likely the content is suitable for the user.

In determining the content, the determination unit 133 may determine the content based not only on the credit rating but also, for example, matching of attribute information. Further, when the content is an advertisement, the determination unit 133 may determine the content based on, for example, the bid amount related to the distribution of the advertisement. In this way, the determination unit 133 may determine the content to be distributed based on the known targeting method or content distribution method.

(About distribution unit 134)
The distribution unit 134 distributes various contents. For example, the distribution unit 134 distributes the content determined by the determination unit 133 to the user terminal 10. In this case, the distribution unit 134 distributes the content determined by the determination unit 133 to the user terminal 10 when a distribution opportunity that matches the distribution form set in advance for the content occurs. For example, the distribution unit 134 has a distribution form corresponding to the advertisement space included in the web page when the user terminal 10 displays the web page and an advertisement distribution opportunity occurs in the advertisement space included in the web page. Deliver content.

The distribution unit 134 may distribute the content via a predetermined distribution server without directly distributing the content from the determination device 100 to the user terminal 10. In this case, the distribution unit 134 controls the predetermined distribution server to execute the distribution of the content to the user terminal 10. Further, the content data distributed by the distribution unit 134 does not necessarily have to be stored in the content storage unit 123, and may be stored in an external storage server. In this case, the distribution unit 134 may specify the content to be distributed to the user terminal 10 based on the content ID, acquire the data of the content from the storage server, and distribute the acquired data to the user terminal 10.

In addition, the distribution unit 134 distributes the content corresponding to various distribution forms such as a banner advertisement of a web page, an e-mail advertisement, a push notification, and a recommendation displayed on the web page.

[4. Processing procedure according to the first embodiment]
Next, the procedure of processing by the determination device 100 according to the first embodiment will be described with reference to FIG. 7. FIG. 7 is a flowchart showing a processing procedure according to the first embodiment.

As shown in FIG. 7, the acquisition unit 131 determines whether or not the total asset information has been acquired from the user terminal 10 (step S101). When the acquisition unit 131 has not acquired the total asset information (step S101; No), the acquisition unit 131 waits until it is acquired.

On the other hand, when the total asset information is acquired (step S101; Yes), the determination unit 132 determines the creditworthiness of the user based on the total asset information (step S102).

After that, the determination unit 132 sets the targeting of the user based on the credit rating (step S103). Then, when a content distribution opportunity occurs, the determination unit 133 determines the content based on the targeting, and the distribution unit 134 distributes the content suitable for the user to the user terminal 10 (step S104).

[5. Effect of the first embodiment]
As described above, the determination device 100 according to the first embodiment has an acquisition unit 131 and a determination unit 132. The acquisition unit 131 acquires total asset information, which is information obtained by arbitrarily combining information on assets owned by the user. The determination unit 132 determines the creditworthiness of the user regarding the predetermined service based on the total asset information acquired by the acquisition unit 131.

As described above, the determination device 100 according to the first embodiment determines the creditworthiness of the user based on the total asset information of the user, not the information declared by the user or the information of a certain bank account. .. As a result, the determination device 100 can include asset information such as securities accounts other than bank accounts and points for each store, which are not normally acquired by businesses such as financial institutions, as determination elements. Based on detailed asset information, the creditworthiness of an individual can be accurately determined.

In addition, the determination unit 132 individually determines the creditworthiness of the user for each predetermined service. That is, since the determination device 100 according to the first embodiment determines the credit rating using different determination elements for each service, the credit rating for the user can be accurately determined.

In addition, the determination unit 132 calculates the creditworthiness of the user regarding the financial service as a predetermined service.

As described above, the determination device 100 according to the first embodiment determines the creditworthiness of the service related to finance among various services provided via the network. As a result, the determination device 100 can accurately determine the creditworthiness of the user.

In addition, the determination unit 132 calculates the creditworthiness of the user for at least one of a credit card contract proposal, a loan proposal, a real estate contract proposal, and a financial investment proposal as a financial service.

In this way, the determination device 100 according to the first embodiment determines the creditworthiness of various services. As a result, the determination device 100 can determine in detail the credit rating suitable for each service.

In addition, the determination unit 132 calculates the creditworthiness of the user with respect to at least one of the services of the proposal for purchasing behavior or the proposal for job hunting behavior as a predetermined service.

As described above, the determination device 100 according to the first embodiment is not limited to financial services, and may determine the creditworthiness of services related to purchasing and services related to job hunting. As a result, the determination device 100 can expand the range of services provided according to the user.

Further, the determination unit 132 estimates at least one of the user's annual income, public money payment amount, and disposable income amount based on the total asset information, and determines the user's creditworthiness based on the estimated information.

As described above, the determination device 100 according to the first embodiment determines the creditworthiness of the user by using the information estimated from the total asset information. As a result, the determination device 100 can determine the credit rating based on information such as the user's annual income, which cannot normally be obtained, so that the accuracy of the determination process can be improved.

Further, the determination device 100 according to the first embodiment further includes a determination unit 133 that determines the content to be distributed to the user based on the credit rating determined by the determination unit 132.

As described above, the determination device 100 according to the first embodiment may perform the content determination process using the determined credit rating. As a result, the determination device 100 can provide content suitable for the user.

In addition, the determination unit 133 determines the content related to a predetermined financial service as the content to be distributed to the user.

In this way, the determination device 100 according to the first embodiment determines to deliver, for example, content that promotes a new financial service to the user according to the creditworthiness. As a result, the determination device 100 can deliver content more suitable for the user.

In addition, the decision-making unit 133 determines the content related to at least one of the credit card contract proposal, the loan proposal, the real estate contract proposal, and the financial investment proposal as the content related to the predetermined financial service.

As described above, the determination device 100 according to the first embodiment can determine the content according to the creditworthiness of the user from among various contents. Therefore, the determination device 100 can improve the accuracy of determining useful contents for the user.

[6. Second Embodiment]
In the first embodiment, an example is shown in which a process of determining the creditworthiness of a user regarding a predetermined financial service is performed based on the total asset information of the user terminal 10. Here, the determination device according to the present application may determine the creditworthiness by performing a predetermined estimation process even for a user who has not acquired the total asset information. This point will be described as a second embodiment. The matters common to the first embodiment will not be described.

[7. An example of determination processing according to the second embodiment]
An example of the determination process according to the second embodiment will be described with reference to FIG. FIG. 8 is a diagram showing an example of the determination process according to the second embodiment. In FIG. 8, a process of generating a model for estimating the creditworthiness of a user who has not acquired total asset information by the determination device 200 according to the second embodiment, which is an apparatus corresponding to the determination device according to the present application. An example will be described.

In the example shown in FIG. 8, the user U01 is a user corresponding to the user U01 shown in the first embodiment, and is a user who provides (registers) total asset information to the determination device 200. Further, it is assumed that the user U02 is a user who does not provide the total asset information to the determination device 200. It is assumed that the user U02 owns the user terminal 11.

The determination device 200 acquires total asset information from the user U01 (step S11). Further, the determination device 200 acquires the information detected from the user terminal 10 (hereinafter, may be referred to as "terminal information") (step S12).

The terminal information is information accumulated in the user terminal 10 when the user U01 uses the user terminal 10, and is, for example, a history in which the user U01 operates the user terminal 10. That is, the terminal information includes the action history of the user U01 on the network.

More specifically, the terminal information includes the history of the website browsed by the user U01, the search query posted by the user U01 to the search service, and the history of the article posted by the user U01 to the SNS (Social Networking Service). , History of purchasing behavior on the shopping site and auction site of user U01 and the like are included. The terminal information may include information corresponding to a part of the total asset information. For example, the terminal information may include a payment history of utility bills settled online, a payment amount, and the like. The determination device 200 stores the acquired terminal information in the terminal information storage unit 124.

Although not shown, the determination device 200 shall appropriately acquire not only the user U01 but also the terminal information of the user who has obtained the total asset information. That is, the determination device 200 shall acquire a sufficient amount of terminal information to generate a model described later. For example, the determination device 200 uses a preset number of samples (for example, terminal information of 2000 users) as a threshold value, and continues to acquire terminal information until at least terminal information exceeding the threshold value is acquired.

Then, the determination device 200 performs machine learning on the total asset information using the user U01 as a positive example (step S13). In other words, the determination device 200 generates a model for total asset information with the user U01 as a positive example. The determination device 200 registers the generated model in the model storage unit 125.

In the model generated by the determination device 200, for example, the input is terminal information and the output is information corresponding to the total asset information (for example, a value showing the total asset information as an index value). That is, the model generated by the determination device 200 is a model showing the relationship between the total asset information and the terminal information, which is how the terminal information of the user U01 shows the relationship with the total asset information of the user U01. It becomes. For example, when terminal information similar to the terminal information of the user U01 is newly input to the model generated by the determination device 200, it is estimated that the user having the terminal information has the total asset information similar to the user U01. The value will be output. The details of the model generation process will be described later.

Subsequently, the determination device 200 acquires the information detected from the user terminal 11 used by the user U02 (step S14). That is, the determination device 200 acquires the terminal information of the user terminal 11.

For example, the determination device 200 acquires the terminal information of the user terminal 11 when a content distribution opportunity occurs for the user U02. Specifically, the determination device 200 causes the user terminal 11 to transmit a request for advertisement distribution to the determination device 200 when the user terminal 11 tries to display a predetermined web page including an advertisement space. Get terminal information.

Then, the determination device 200 inputs the terminal information of the user terminal 11 into the model generated in step S13. As a result, the determination device 200 outputs an index value for estimating the total asset information of the user U02, which has not been acquired from the user U02, by using the model. By using the index value, the determination device 200 can estimate the creditworthiness of the user U02 determined from the total asset information of the user U02 and the total asset information as described in the first embodiment. ..

Then, the determination device 200 determines the content according to the credit rating estimated based on the model (step S15). Subsequently, the determination device 200 delivers the content determined according to the credit rating to the user U02 (step S16).

As described above, the determination device 200 according to the second embodiment acquires the total asset information, which is information obtained by arbitrarily combining the information regarding the assets owned by the user U01, and the action history of the user U01 on the network. Then, the determination device 200 generates a model for estimating the total asset information of the user U02 based on the acquired total asset information of the user U01 and the action history on the network. Further, the determination device 200 determines the creditworthiness of the user U02 with respect to a predetermined service by using the generated model.

That is, the determination device 200 according to the second embodiment not only determines the creditworthiness of the user U01 who has acquired the total asset information, but also treats the user U02 who has not acquired the total asset information like the user U01. The credit rating can be determined by using a model generated based on a positive example. As a result, the determination device 200 can accurately determine the creditworthiness of an individual without necessarily acquiring total asset information from all users.

[8. Configuration of the determination device according to the second embodiment]
Next, the configuration of the determination device 200 according to the second embodiment will be described with reference to FIG. FIG. 9 is a diagram showing a configuration example of the determination device 200 according to the second embodiment. As shown in FIG. 9, the determination device 200 further includes a generation unit 135 in addition to the determination device 100 according to the first embodiment. Although the description of each processing unit already described in the first embodiment is omitted in the second embodiment, each processing unit according to the second embodiment describes the processing described in the first embodiment. May be executed as appropriate. Further, in the following, when it is not necessary to distinguish the user terminal 10 or the user terminal 11, it may be described only as the user terminal 10.

(About terminal information storage unit 124)
FIG. 10 shows an example of the terminal information storage unit 124 according to the second embodiment. FIG. 10 is a diagram showing an example of the terminal information storage unit 124 according to the second embodiment. As shown in FIG. 10, the terminal information storage unit 124 has items such as "terminal ID", "user ID", "total asset information ID", "action history", "attribute information", and "public money payment information". ..

The "terminal ID", "user ID", and "total asset information ID" are the same as the items shown in FIG. 4 or FIG. As shown in FIG. 10, the item of the total asset information ID regarding the user U02 who has not acquired the total asset information is blank.

The "action history" is an example of terminal information, and indicates the action history of the user on the network. In the example of FIG. 10, the action history is shown by a concept such as "H01", but in reality, the action history items include the browsing history of the website, the transmission history of the search query to the search server, and the like. All history executed by the user via the network, such as SNS usage history, shopping site purchase history, and auction site transaction history, is stored.

"Attribute information" is an example of terminal information, and indicates terminal attribute information. In the example of FIG. 10, the attribute information is shown by a concept such as "J01", but in reality, the attribute information item includes the terminal manufacturing information (terminal manufacturing date, release start date, etc.). , Terminal model information (terminal manufacturer, brand, etc.) and user attributes such as age, gender, and place of residence of the user who uses the terminal are stored.

"Public money payment information" is an example of terminal information, and indicates information on public money paid via a network. In the example of FIG. 10, the public money payment information is shown by a concept such as "K01", but in reality, the items of the public money payment information include the date when the payment was made and the type of public money (for example,). , Local tax, type of utility bill, etc.) and the amount paid are stored.

That is, FIG. 10 shows an example in which the user U01 using the user terminal 10 registers the total asset information A01 in the determination device 200. Further, the terminal information of the user terminal 10 indicates that the action history is "H01", the attribute information is "J01", and the public money payment information is "K01". Further, the user U02 who uses the user terminal 11 indicates that the total asset information is not registered in the determination device 200. Further, the terminal information of the user terminal 11 indicates that the action history is "H02", the attribute information is "J02", and the public money payment information is "K02".

(About model storage 125)
FIG. 11 shows an example of the model storage unit 125 according to the second embodiment. FIG. 11 is a diagram showing an example of the model storage unit 125 according to the second embodiment. As shown in FIG. 11, the model storage unit 125 has items such as “model ID” and “learning data”.

The "model ID" indicates identification information that identifies the model. “Training data” indicates sample data used to generate the model. In the example of FIG. 11, the learning data is shown by a concept such as "N01", but in reality, the training data items include total asset information of a user (for example, user U01) who is a positive example. Terminal information is stored. The learning data may be updated every time the determination device 200 acquires data as a positive example.

That is, in FIG. 11, it is shown that the model M01 identified by the model ID “M01” is a model generated (learned) based on the training data “N01”.

(About acquisition unit 131)
The acquisition unit 131 according to the second embodiment acquires total asset information, which is information obtained by arbitrarily combining information on assets owned by the user, and terminal information of the user terminal 10. Specifically, the acquisition unit 131 acquires the action history on the network of the user who uses the user terminal 10 as the terminal information.

For example, the acquisition unit 131 acquires a history of the user browsing a predetermined service page as an action history on the user's network. Specifically, the acquisition unit 131 acquires the history of the website browsed by the user. In this case, the acquisition unit 131 acquires the history for each website category (for example, news site, shopping site, stock information site, etc.), and tends to determine what category the user frequently browses. Information indicating that may be acquired. In other words, the acquisition unit 131 acquires information on the user's interests from the browsing history of the website.

In addition, the acquisition unit 131 acquires information transmitted from the user to a predetermined service page as an action history on the user's network. Specifically, the acquisition unit 131 acquires information about the search query sent by the user to the search service. For example, the acquisition unit 131 determines the tendency of the user to frequently send a search query based on the similarity of the dictionary information indicated by the sent search query, the frequency of the sent search query, and the like. You may get the information to show. In other words, the acquisition unit 131 acquires information on the user's interests from the transmission history of the search query.

In addition, the acquisition unit 131 acquires information about the SNS used by the user. Specifically, the acquisition unit 131 determines the content of the latest article posted to the SNS by the user (the content of the article displayed on the so-called timeline), the number of images posted by the user to the SNS, and the relationship on the SNS. Get at least one of the number of users you have.

For example, the acquisition unit 131 acquires the tendency of words included in the text data of the latest article posted to the SNS by the user. As a result, the acquisition unit 131 acquires information about the event that the user is interested in. Alternatively, the acquisition unit 131 acquires the number of images posted on the SNS and the number of users who are related on the SNS, that is, the number of users who are connected on the SNS (for example, the number of friends and the number of followers). Then, the information that is an element for guessing what kind of assets the user has and what kind of social relationship is being built on the network is acquired.

In addition, the acquisition unit 131 acquires information on public money paid by the user via the network. Specifically, the acquisition unit 131 acquires the amount of taxes paid by the user via the network and the amount of utility costs. For example, the determination device 200 is an element for estimating the creditworthiness of the creditworthiness determination process for a user who does not have the total asset information from the relationship between the public money information and the total asset information, that is, for model generation. You can get the identity of. The acquisition unit 131 stores the acquired information in the terminal information storage unit 124 as appropriate.

(About generator 135)
The generation unit 135 is based on the total asset information of the first user (user U01 in the example of FIG. 8) acquired by the acquisition unit 131 and the action history on the network, and the second user (example of FIG. 8). Now, a model for estimating information about the assets of user U02) is generated. For example, the generation unit 135 generates a model for estimating the total asset information of the second user.

That is, the generation unit 135 generates a model in which the terminal information of the second user is input and the index value indicating the total asset information of the second user is output. The information output by the model is not limited to the index value indicating the total asset information. For example, the generation unit 135 inputs the terminal information of the second user and generates a model that outputs the credit rating of the second user. You may. Further, as the calculation formula of the model, various known calculation formulas may be used.

For example, the generation unit 135 uses a predetermined index value indicating total asset information as an objective variable in machine learning, and each information constituting the terminal information as an explanatory variable in machine learning. Then, the generation unit 135 generates a model showing the relationship between the total asset information and the terminal information by using the objective variable and the explanatory variable.

The generation unit 135 generates a model using various explanatory variables. For example, the generation unit 135 uses a model for estimating the creditworthiness of the second user based on the relationship between the information about the search query sent by the first user and the information about the search query sent by the second user. Generate.

That is, the generation unit 135 captures the tendency of the search query transmitted by the first user based on the category of the query transmitted by the first user several times or more within a predetermined period. The search query can be an element that indicates an event that the first user is interested in. The second user, who tends to resemble the first user with respect to the search query, is also a user who is interested in the same phenomenon as the first user, and the first user also has income and expenses. It is expected to show the same tendency as. From this, the generation unit 135 obtains the total asset information about the second user based on the relationship between the information about the search query sent by the first user and the information about the search query sent by the second user. A model for estimating, in other words, a model for estimating the creditworthiness of a second user can be generated. The same can be said for the browsing history of websites and information on SNS, which will be described later.

In addition, the generation unit 135 generates a model for estimating the creditworthiness of the second user based on the relationship between the information about the SNS used by the first user and the information about the SNS used by the second user. ..

Specifically, the generation unit 135 has the content, the number of images, and the number of related users of the latest article in the SNS of the first user, and the content, the number of images, and the relationship of the latest article in the SNS of the second user. A model for estimating the creditworthiness of a second user is generated based on the relationship with the number of users who have. In addition, the generation unit 135 estimates the creditworthiness of the second user based on the relationship between the information on the payment of public money based on the total asset information of the first user and the information on the public money paid by the second user. You may generate a model to do. The generation unit 135 may generate an optimized model by appropriately combining the terminal used by the first user and the terminal information that can be acquired from the terminal used by the second user.

An example of the model generated by the generation unit 135 will be described below. The learning method for the model generated by the generation unit 135 is not limited to the following examples, and various known machine learning methods may be adopted.

For example, the generation unit 135 generates an expression showing the relationship between the index value (or the creditworthiness of the first user) indicating the total asset information of the first user and the terminal information. Further, the generation unit 135 calculates what kind of weight the individual feature information extracted from the terminal information of the user terminal 10 has with respect to the total asset information of the first user. As a result, the generation unit 135 can obtain information such as how much each characteristic information contributes to the total asset information. For example, the generation unit 135 creates the following equation (1).

y (A01) = ω1 ・ x1 ＋ ω2 ・ x2 ＋ ω3 ・ x3 ・ ・ ・ ＋ ωN ・ xN ・ ・ ・ (1) (N is an arbitrary number)

The above equation (1) is, for example, an equation created corresponding to the total asset information A01. In the above formula (1), "y (A01)" indicates an index value indicating total asset information.

Further, in the above equation (1), "x" corresponds to each explanatory variable of the terminal information of the user (user U01 in the example of FIG. 8) having the total asset information A01. For example, the explanatory variables "x1, x2, x3, ..., XN" in the above formula (1) correspond to the browsing history of the website, the transmission history of the search query, the information related to SNS, the purchasing history, and the like. To do. That is, the right side of the above equation (1) corresponds to the feature information extracted from the terminal information of the user terminal 10.

Further, in the above equation (1), "ω" is a coefficient of "x" and indicates a predetermined weight value. Specifically, "ω1" is a weight value of "x1", "ω2" is a weight value of "x2", and "ω3" is a weight value of "x3". As described above, the above equation (1) includes variables (for example, "ω1 · x1") including the explanatory variable "x" corresponding to the feature information extracted from the terminal information and the predetermined weight value "ω". Created by combining.

For example, in the above formula (1), "x1" corresponds to "website browsing history", "x2" corresponds to "search query transmission history", and "x3" corresponds to "SNS information". It is assumed that it is an explanatory variable corresponding to. In this case, the above equation (1) corresponding to the user U01 can be expressed as the following equation (2).

y (A01, U01) (= 1) = ω1 ・ (Website browsing history) ＋ ω2 ・ (Search query transmission history) ＋ ω3 ・ (Information about SNS) ＋ ωN ・ xN ・ ・ ・ (2)

Using an equation like the above equation (2), the generation unit 135 creates not only the user U01 but also a relational expression between a sufficient amount of terminal information and total asset information for model generation, and performs machine learning. By doing so, a model showing the relationship between the terminal information and the total asset information is generated. For example, if the weight value for the relationship between the search query transmission history and the total asset information becomes large after the above learning, the search query transmission is compared with the website browsing history and SNS information. Users with similar histories are judged to be users with similar total asset information.

The generation unit 135 may generate a model by using various known methods such as normalizing total asset information and expressing it as an index value so that it can be substituted into an equation for model generation. Further, the generation unit 135 may update the model at any time when the acquisition unit 131 acquires new terminal information after the model is generated. Further, the generation unit 135 may generate a model that estimates not only the total asset information but also the individual asset information of the second user that constitutes the total asset information as the information regarding the assets of the second user. Further, the generation unit 135 generates a model for estimating (determining) not only the asset information but also the creditworthiness of the second user as the information regarding the assets of the second user according to the same method as described above. May be good.

(About judgment unit 132)
The determination unit 132 according to the second embodiment determines the creditworthiness of the second user with respect to a predetermined service by using the model generated by the generation unit 135. That is, the determination unit 132 can determine the creditworthiness of the second user who has not acquired the total asset information in the same manner as the first user who has acquired the total asset information.

[9. Processing procedure according to the second embodiment]
Next, the procedure of processing by the determination device 200 according to the second embodiment will be described with reference to FIG. FIG. 12 is a flowchart (1) showing a processing procedure according to the second embodiment.

As shown in FIG. 12, the acquisition unit 131 determines whether or not the terminal information has been acquired from the user terminal 10 (step S201). If the terminal information has not been acquired (step S201; No), the acquisition unit 131 waits until it is acquired.

On the other hand, when the terminal information is acquired (step S201; Yes), the acquisition unit 131 stores the acquired terminal information in the terminal information storage unit 124 and stores it (step S202).

The generation unit 135 determines whether or not a sufficient amount of data has been accumulated for model generation (step S203). When a sufficient amount of data is not accumulated (step S203; No), the acquisition unit 131 continues the process of acquiring the terminal information.

On the other hand, when a sufficient amount of data is accumulated (step S203; Yes), the generation unit 135 generates a model based on the total asset information and the terminal information (step S204). Then, the generation unit 135 registers the generated model in the model storage unit 125 (step S205).

Next, with reference to FIG. 13, a procedure in which the determination device 200 according to the second embodiment performs the determination process using the model will be described. FIG. 13 is a flowchart (2) showing a processing procedure according to the second embodiment.

As shown in FIG. 13, the acquisition unit 131 determines whether or not new terminal information has been acquired from the user terminal 10 (step S301). If the acquisition unit 131 has not acquired the terminal information (step S301; No), the acquisition unit 131 waits until it is acquired.

On the other hand, when the terminal information is acquired (step S301; Yes), the determination unit 132 inputs the terminal information to the model generated by the generation unit 135 (step S302). Then, the determination unit 132 determines the creditworthiness of the new user based on the output value (step S303).

Subsequently, the determination unit 133 determines the content based on the determined credit rating (step S304). Then, the distribution unit 134 distributes the determined content to the user terminal 10 (step S305).

[10. Effect of the second embodiment]
As described above, the determination device 200 according to the second embodiment (corresponding to the determination device according to the present application) has an acquisition unit 131 and a generation unit 135. The acquisition unit 131 acquires total asset information, which is information obtained by arbitrarily combining information about assets owned by the first user, and an action history on the network of the first user. The generation unit 135 generates a model that estimates information about the assets of the second user based on the total asset information of the first user acquired by the acquisition unit 131 and the behavior history on the network.

As described above, the determination device 200 according to the second embodiment generates a model based on the relationship between the behavior history of the first user and the total asset information. As a result, the determination device 200 can estimate the information about the assets of the second user as long as the action history is acquired, even if the total asset information is not acquired. Therefore, the determination device 200 can accurately determine the creditworthiness of an individual.

In addition, the generation unit 135 generates a model for estimating the total asset information of the second user by inputting the action history of the second user on the network.

As described above, the determination device 200 according to the second embodiment generates a model for estimating total asset information as information regarding assets. As a result, the determination device 200 can estimate the total asset information even for the second user whose asset information is not clear.

Further, the determination device 200 according to the second embodiment further includes a determination unit 133 for determining the creditworthiness of the second user regarding a predetermined service by using the model generated by the generation unit 135.

That is, the determination device 200 according to the second embodiment uses a model generated based on a positive example such as the first user even for the second user who has not acquired the total asset information. , The credit rating can be judged. As a result, the determination device 200 can accurately determine the creditworthiness of an individual without necessarily acquiring total asset information from all users.

In addition, the acquisition unit 131 acquires the browsing history of a predetermined service page as the action history on the network.

As described above, the determination device 200 according to the second embodiment uses the model based on the history of the user browsing a predetermined service page (for example, a web page, a page provided by a news application or an information providing application). It may be generated. In general, the browsing history of a service page on a network represents the interests of the user, and the interests of the user tend to have a certain similarity depending on the assets of the user. Therefore, the determination device 200 can generate a model capable of accurately extracting users having similar total asset information by acquiring the browsing history of the service page as an element.

In addition, the acquisition unit 131 acquires information transmitted from the first user or the second user to a predetermined service page as an action history on the network.

As described above, the determination device 200 according to the second embodiment may acquire not only the browsing history of the predetermined service page but also the information transmitted by the user to the service page. The information that the user sends to the service page can also be information that indicates the user's interests. By acquiring such information, the determination device 200 can generate a model capable of accurately extracting users having similar total asset information.

In addition, the acquisition unit 131 acquires information regarding a search query sent by the first user or the second user to the search service. The generation unit 135 generates a model for estimating the creditworthiness of the second user based on the relationship between the information about the search query sent by the first user and the information about the search query sent by the second user. ..

As described above, the determination device 200 according to the second embodiment acquires the information regarding the search query transmitted by the user. As described above, the search query sent by the user can be said to more specifically express the interest of the user. Then, the determination device 200 can generate a model that extracts users having similar total asset information with higher accuracy by using the search query as an element of model generation.

In addition, the acquisition unit 131 acquires information about the SNS used by the first user or the second user. The generation unit 135 generates a model for estimating the creditworthiness of the second user based on the relationship between the information about the SNS used by the first user and the information about the SNS used by the second user.

In this way, the determination device 200 according to the second embodiment may acquire information about the SNS used by the user. Since the results of the user's interests and actions in daily life are uploaded to the SNS as a record, the information held in the SNS can be information indicating the characteristics of the user. It is assumed that users who have similar lives and behaviors are likely to have similar incomes and expenses. Therefore, the determination device 200 can generate a model that extracts users having similar total asset information with higher accuracy by using the information about the SNS as the feature of the model.

In addition, the acquisition unit 131 acquires at least one of the contents of the latest article posted to the SNS by the first user or the second user, the number of images posted on the SNS, and the number of users having a relationship on the SNS. To do. The generation unit 135 includes the content of the latest article in the SNS of the first user, the number of images, and the number of users having a relationship, and the content of the latest article in the SNS of the second user, the number of images, and the number of users having a relationship. Generate a model that estimates the creditworthiness of a second user based on the relationship between.

As described above, the determination device 200 according to the second embodiment may acquire more specific information on the SNS. The determination device 200 can generate a model having higher accuracy by using specific information as the feature of the model.

In addition, the acquisition unit 131 acquires information on public money paid by the first user or the second user via the network. The generation unit 135 is a model that estimates the creditworthiness of the second user based on the relationship between the information on the payment of public money based on the total asset information of the first user and the information on the public money paid by the second user. To generate.

As described above, the determination device 200 according to the second embodiment may generate a model using such information when the information on public money payment can be obtained. For example, it is assumed that users with similar payments of taxes and utilities tend to have similar total asset information. Therefore, the determination device 200 can generate a model having higher accuracy by using such a specific numerical value as the feature of the model.

[11. Modification example]
The determination device 100 (similar to the determination device 200) described above may be implemented in various different forms other than the above embodiment. Therefore, a modified example of the embodiment will be described below.

[11-1. Use of creditworthiness]
In the above embodiment, the determination device 100 shows an example in which the content is targeted and the content is distributed based on the creditworthiness of the user. However, the credit rating determined by the determination device 100 may be used for other purposes.

For example, the determination device 100 may provide the credit rating determined to the user to a bank, a securities company, a credit card company, or the like. Then, a bank, a securities company, a credit card company, or the like may carry out business based on the creditworthiness. For example, a credit card company may use the creditworthiness of a user for credit determination at the time of card issuance.

[11-2. Use of correct answer data]
In the above embodiment, the determination device 100 shows an example of estimating the user's annual income, public money payment amount, and disposable income based on the total asset information. In this case, the determination device 100 may generate a model using the correct answer data as long as the correct answer data of the annual income, the public money payment amount, and the disposable income can be obtained for a predetermined number of users.

In this case, the determination device 100 inputs the total asset information based on the correct answer data, and generates a model that outputs the user's annual income, public money payment amount, and disposable income corresponding to the total asset information. By using such a model (learning process), the determination device 100 can accurately estimate the user's annual income, public money payment amount, and disposable income.

[11-3. Estimating total asset information]
The determination device 200 may use various information in generating a model for estimating total asset information. For example, the determination device 200 may use not only the search query sent from the user but also attribute information such as the user's gender, age, and place of residence.

[12. Hardware configuration]
The determination device 100 (the same applies to the determination device 200) and the user terminal 10 according to the above-described embodiment are realized by, for example, a computer 1000 having a configuration as shown in FIG. Hereinafter, the determination device 100 will be described as an example. FIG. 14 is a hardware configuration diagram showing an example of a computer 1000 that realizes the function of the determination device 100. The computer 1000 has a CPU 1100, a RAM 1200, a ROM 1300, an HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400, and controls each part. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started, a program that depends on the hardware of the computer 1000, and the like.

The HDD 1400 stores a program executed by the CPU 1100, data used by such a program, and the like. The communication interface 1500 receives data from another device via the communication network 500 (corresponding to the network N shown in FIG. 2) and sends the data to the CPU 1100, and the data generated by the CPU 1100 is transmitted to the other device via the communication network 500. Send to the device.

The CPU 1100 controls an output device such as a display or a printer, and an input device such as a keyboard or a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. Further, the CPU 1100 outputs the data generated via the input / output interface 1600 to the output device.

The media interface 1700 reads a program or data stored in the recording medium 1800 and provides the program or data to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable Disk), a magneto-optical recording medium such as MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. And so on.

For example, when the computer 1000 functions as the determination device 100 according to the first embodiment, the CPU 1100 of the computer 1000 realizes the function of the control unit 130 by executing the program loaded on the RAM 1200. Further, the data in the storage unit 120 is stored in the HDD 1400. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them, but as another example, these programs may be acquired from another device via the communication network 500.

[13. Others]
Further, among the processes described in the above-described embodiment, all or a part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed can be performed. All or part of it can be done automatically by a known method. In addition, the processing procedure, specific name, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

Further, each component of each of the illustrated devices is a functional concept, and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or part of the device is functionally or physically distributed / physically in arbitrary units according to various loads and usage conditions. It can be integrated and configured. For example, the determination unit 132 shown in FIG. 3 and the determination unit 133 may be integrated. Further, for example, the information stored in the storage unit 120 may be stored in an externally provided storage device via the network N.

Further, for example, in the above embodiment, an example is shown in which the determination device 100 performs an acquisition process for acquiring the total asset information of the user terminal 10 and a determination process for determining the creditworthiness. However, the determination device 100 described above may be separated into an acquisition device that performs acquisition processing and a determination device that performs determination processing. In this case, the acquisition device has at least the acquisition unit 131. The determination device has at least a determination unit 132. In this case, the processing by the determination device 100 according to the first embodiment is realized by the determination system 1 including the acquisition device and each device such as the determination device.

In addition, the above-described embodiments and modifications can be appropriately combined as long as the processing contents do not contradict each other.

Although some of the embodiments of the present application have been described in detail with reference to the drawings, these are examples, and various modifications are made based on the knowledge of those skilled in the art, including the embodiments described in the disclosure column of the invention. It is possible to carry out the present invention in other improved forms.

Further, the above-mentioned "section, module, unit" can be read as "means" or "circuit". For example, the acquisition unit can be read as an acquisition means or an acquisition circuit.

1 Judgment system 10 User terminal 45 Financial institution server 65 Store server 100 Judgment device 110 Communication unit 120 Storage unit 121 Total asset information storage unit 122 User information storage unit 123 Content storage unit 124 Terminal information storage unit 125 Model storage unit 130 Control unit 131 Acquisition unit 132 Judgment unit 133 Decision unit 134 Distribution unit 135 Generation unit 200 Judgment device

Claims (14)

Based on the identification information of the first and second user, by the first user to acquire each of the information about the asset from the service server that holds each of the information about the assets owned by the first user owns Total asset information that is any combination of at least bank account, service point, securities account, credit card contracted by the first user, and insurance related to the first user. And the acquisition unit that acquires the behavior history of the first user and the second user on the network and stores the acquired total asset information and the behavior history in the storage unit.
A generation unit that refers to the total asset information and the action history stored in the storage unit and generates a model for estimating the total asset information of the second user based on the total asset information and the action history of the first user. When,
The creditworthiness of the second user regarding the behavior of trying to use a predetermined service is determined based on whether the amount of income or expenditure in a predetermined period in the total asset information estimated by the model exceeds a predetermined threshold value. Judgment unit and
Judgment device characterized by being equipped with. The determination unit
The creditworthiness of the second user is individually determined for each of the predetermined services.
The determination device according to claim 1, wherein the determination device is characterized by the above. The determination unit
As the predetermined service, the credit rating of the second user regarding the financial service is calculated.
The determination device according to claim 2, wherein the determination device is characterized by the above. The determination unit
As the financial service, the creditworthiness of the second user for at least one service of a credit card contract proposal, a loan proposal, a real estate contract proposal, or a financial investment proposal is calculated.
The determination device according to claim 3, wherein the determination device is characterized by the above. The determination unit
Examples predetermined service, calculates the creditworthiness of the second user about the services proposed to the purchasing behavior,
The determination device according to any one of claims 2 to 4, wherein the determination device is characterized. The determination unit
At least one of the annual income, public money payment amount, and disposable income amount of the second user is estimated based on the total asset information, and the creditworthiness of the second user is determined based on the estimated information.
The determination device according to any one of claims 1 to 5, wherein the determination device is characterized. A determination unit that determines the content to be delivered to the second user based on the credit rating determined by the determination unit.
The determination device according to any one of claims 1 to 6, further comprising. The decision unit
As the content to be delivered to the second user, the content related to a predetermined financial service is determined.
The determination device according to claim 7, wherein the determination device is characterized by the above. The decision unit
As the content related to the predetermined financial service, the content related to at least one of a credit card contract proposal, a loan proposal, a real estate contract proposal, and a financial investment proposal is determined.
The determination device according to claim 8, wherein the determination device is characterized by the above. It is a judgment method executed by a computer.
Based on the identification information of the first and second user, by the first user to acquire each of the information about the asset from the service server that holds each of the information about the assets owned by the first user owns Total asset information that is any combination of at least bank account, service point, securities account, credit card contracted by the first user, and insurance related to the first user. And the acquisition process of acquiring the behavior history of the first user and the second user on the network and storing the acquired total asset information and the behavior history in the storage unit.
A generation step of generating a model for estimating the total asset information of the second user based on the total asset information and the action history of the first user by referring to the total asset information and the action history stored in the storage unit. When,
The creditworthiness of the second user regarding the behavior of trying to use a predetermined service is determined based on whether the amount of income or expenditure in a predetermined period in the total asset information estimated by the model exceeds a predetermined threshold value. Judgment process and
A determination method characterized by including. Based on the identification information of the first and second user, by the first user to acquire each of the information about the asset from the service server that holds each of the information about the assets owned by the first user owns Total asset information that is any combination of at least bank account, service point, securities account, credit card contracted by the first user, and insurance related to the first user. And the acquisition procedure of acquiring the behavior history of the first user and the second user on the network and storing the acquired total asset information and the behavior history in the storage unit.
A generation procedure for generating a model for estimating the total asset information of the second user based on the total asset information and the action history of the first user by referring to the total asset information and the action history stored in the storage unit. When,
Based on whether the amount of income or expenditure for a predetermined period in the total asset information estimated by the model exceeds a predetermined threshold value, the creditworthiness of the second user regarding the behavior of trying to use the predetermined service is determined. Judgment procedure and
A judgment program characterized by having a computer execute. Based on the identification information of the first and second user, by the first user to acquire each of the information about the asset from the service server that holds each of the information about the assets owned by the first user owns Total asset information that is any combination of at least bank account, service point, securities account, credit card contracted by the first user, and insurance related to the first user. And the acquisition unit that acquires the behavior history of the first user and the second user on the network and stores the acquired total asset information and the behavior history in the storage unit.
A generation unit that refers to the total asset information and the action history stored in the storage unit and generates a model for estimating the total asset information of the second user based on the total asset information and the action history of the first user. When,
Estimate at least one of the annual income, public money payment amount, and disposable income amount of the second user based on the total asset information estimated by the model, and use the predetermined service based on the estimated information. A judgment unit that determines the creditworthiness of the second user regarding the behavior to be attempted ,
Judgment device characterized by being equipped with. It is a judgment method executed by a computer.
Based on the identification information of the first and second user, by the first user to acquire each of the information about the asset from the service server that holds each of the information about the assets owned by the first user owns Total asset information that is any combination of information about at least bank accounts, service points, securities accounts, credit cards contracted by the first user, and insurance that the first user subscribes to. And the acquisition process of acquiring the behavior history of the first user and the second user on the network and storing the acquired total asset information and the behavior history in the storage unit.
A generation step of generating a model for estimating the total asset information of the second user based on the total asset information and the action history of the first user by referring to the total asset information and the action history stored in the storage unit. When,
At least one of the annual income, public money payment amount, and disposable income amount of the second user is estimated based on the total asset information estimated by the model, and the predetermined service is used based on the estimated information. A determination process for determining the creditworthiness of the second user regarding the behavior to be attempted , and
A determination method characterized by including. Based on the identification information of the first and second user, by the first user to acquire each of the information about the asset from the service server that holds each of the information about the assets owned by the first user owns Total asset information that is any combination of at least bank account, service point, securities account, credit card contracted by the first user, and insurance related to the first user. And the acquisition procedure of acquiring the behavior history of the first user and the second user on the network and storing the acquired total asset information and the behavior history in the storage unit.
A generation procedure for generating a model for estimating the total asset information of the second user based on the total asset information and the action history of the first user by referring to the total asset information and the action history stored in the storage unit. When,
Estimate at least one of the annual income, public money payment amount, and disposable income amount of the second user based on the total asset information estimated by the model, and use the predetermined service based on the estimated information. Judgment procedure for determining the creditworthiness of the second user regarding the behavior to be attempted ,
A judgment program characterized by having a computer execute.

Images