JP7593984B2 - Information processing system, information processing method, and program - Google Patents

Description

This disclosure relates to technology for determining a user's credibility.

A conventional assessment device has been proposed that includes a user information acquisition unit that acquires behavioral information indicating the user's behavior, and a creditworthiness assessment unit that assesses the user's creditworthiness regarding their future ability to repay loans based on the behavioral information (see Patent Document 1).

In addition, an information processing device has been proposed that has a machine learning model that outputs a score according to the level of trust of a user, and a suggestion unit that suggests the use of revolving payments to users whose scores are equal to or above a threshold and who are not using revolving payments (see Patent Document 2).

JP 2021-174039 A JP 2022-157746 A

Previously, technology has been proposed that suggests options for credit payment settlement methods depending on the user's creditworthiness. However, the conventional technology only suggests options as a benefit to highly creditworthy users, and there was room for improvement in terms of encouraging users whose creditworthiness does not meet a certain standard to use a more flexible credit payment service.

In view of the above problems, the present disclosure aims to encourage users whose creditworthiness does not meet a certain standard to use a highly flexible credit payment service.

An example of the present disclosure is an information processing system including: a credit score estimation means for estimating a credit score to be set for a user based on a group of attribute data related to the user; a determination means for determining whether the user's credit score indicated by the credit score is within a predetermined range below a predetermined credit score; and a proposal means for proposing to the user, as a settlement method when the user uses credit payment, a second settlement method different from a first settlement method set as the normal settlement method for the credit payment, when the user's credit score is determined to be within the predetermined range.

The present disclosure can be understood as an information processing device, a system, a method executed by a computer, or a program executed by a computer. The present disclosure can also be understood as such a program recorded on a recording medium readable by a computer or other device, machine, etc. Here, a recording medium readable by a computer, etc. refers to a recording medium that stores information such as data and programs through electrical, magnetic, optical, mechanical, or chemical action and can be read by a computer, etc.

According to this disclosure, it is possible to appeal to users whose creditworthiness does not meet a certain standard to use a highly flexible credit payment service.

1 is a schematic diagram showing a configuration of an information processing system according to an embodiment. 1 is a diagram illustrating an outline of a functional configuration of an information processing device according to an embodiment. 1 is a simplified diagram of a credit score estimation process according to an embodiment. FIG. 1 is a simplified diagram illustrating the concept of a decision tree of a machine learning model employed in an embodiment. FIG. 13 is a diagram showing a predetermined range of credibility in an embodiment. 1 is a flowchart illustrating a flow of a machine learning process according to an embodiment. 10 is a flowchart showing a flow of a score calculation process according to the embodiment. 11 is a flowchart showing the flow of a settlement method suggestion process according to the embodiment. FIG. 13 is a diagram showing an outline of the functional configuration of an information processing device according to a variation.

Below, an embodiment of an information processing device, method, and program according to the present disclosure will be described with reference to the drawings. However, the embodiment described below is merely an example of an embodiment, and the information processing device, method, and program according to the present disclosure are not limited to the specific configurations described below. In implementing the present invention, a specific configuration according to the mode of implementation may be appropriately adopted, and various improvements and modifications may be made. The present invention allows at least a portion of the configurations in the embodiments and variations described below to be adopted in each other as appropriate.

In this embodiment, the technology according to the present disclosure is described as being implemented to propose to users who have not yet signed up for a credit card service that they sign up for the credit card service and that options other than one-time payments (e.g., revolving payments) are available as settlement methods when using the credit card, with the aim of increasing the number of new credit card users. However, the technology according to the present disclosure can be widely used as a technology for proposing settlement methods for credit payments to users, and the application of the present disclosure is not limited to the examples shown in the embodiment. For example, the technology according to the present disclosure may be implemented to propose settlement method options when using a credit card to existing (signed up) credit card service users.

<System Configuration>
1 is a schematic diagram showing the configuration of an information processing system according to this embodiment. In the information processing system according to this embodiment, an information processing device 1 and one or more service providing systems 5 are communicably connected to each other. A user is a user of a service provided by the service providing system 5, and receives the service by accessing the service providing system 5 from a user terminal.

The information processing device 1 is a computer including a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage device 14 such as an EEPROM (Electrically Erasable and Programmable Read Only Memory) or a HDD (Hard Disk Drive), a communication unit 15 such as a NIC (Network Interface Card), etc. However, the specific hardware configuration of the information processing device 1 can be omitted, replaced, or added as appropriate depending on the embodiment. Furthermore, the information processing device 1 is not limited to a device consisting of a single housing. The information processing device 1 may be realized by multiple devices using so-called cloud or distributed computing technology, etc.

The service providing system 5 is a computer equipped with a CPU, ROM, RAM, a storage device, a communication unit, an input device, an output device, etc. (not shown). Furthermore, these systems and terminals are not limited to devices consisting of a single housing. These systems and terminals may be realized by multiple devices using so-called cloud or distributed computing technology, etc.

The services provided by the service providing system 5 include, for example, online shopping services, online reservation services, banking services, credit card/deferred payment services, electronic money payment services, advertising services, operation center services, map information services, etc. Note that "deferred payment" is not limited to services known as Buy Now Pay Later (BNPL), and may include any purchase of goods/services with deferred payment.

The services provided by the service providing system 5 are not limited to the examples in this embodiment. The service providing system 5 notifies the information processing device 1 of user-related data when providing a service. Here, the user-related data includes service usage history data by the user. The content of the service usage history data varies depending on the content of the service, and may include, for example, history data of the user's location information, payment history data of credit card usage amount/postpaid payment usage amount, electronic money usage history data, transaction history data (including purchase history data of products, etc.), reservation history data, operation history data for the user from the operation center, etc.

In this embodiment, among the various services provided by the service providing system 5, a mode is described in which options for settlement methods (including settlement methods, repayment methods, and refund methods) are proposed to the user for a credit card service. However, the services for which settlement methods are proposed to the user may be any services related to credit payments, and are not limited to credit card services.

When using a credit card service, the settlement methods include 1) one-time payment, 2) one-time bonus payment, 3) installment payment, and 4) revolving payment. In the system according to this embodiment, for example, for a user with a low credit score, various settlement methods such as 2) one-time bonus payment, 3) installment payment, or 4) revolving payment are proposed (recommended) as options instead of the one-time payment settlement method.

The information processing device according to the present disclosure uses a machine learning model that takes user attribute data and the like as input and outputs a credit score (in this embodiment, a score indicating deferred payment risk). The attribute data used as input here may also include data indicating default (non-performance of debt). By using various attribute data as input, the information processing device 1 according to the present disclosure calculates a universal and generalized credit score that is generated by uniformly reflecting the overall attributes of the user. The calculated credit score can be used for credit screening (determination involving approval or rejection of deferred payment) in deferred payment services, etc., but as described above, in this embodiment, the calculated credit score is used to determine whether or not to propose payment method options to the user.

Here, the user attribute data includes fact attribute data and estimated attribute data. The attribute data includes data expressed in a data format such as a score (e.g., a continuous value between 0 and 1) or a label (e.g., a binary value corresponding to presence/absence or yes/no). However, the format of the attribute data is not limited to the examples in this disclosure. The attribute data may also include, for example, online service usage status and electronic value usage status including points. The online service usage status may also include at least one of the number of cancellations, cancellation rate, and number of orders in an online shopping service or online reservation service.

Factual attribute data is data indicating factual attributes that can be confirmed as factual for a user based on user-provided data obtained by providing the data from the user himself or on history data collected about the user. Examples of user-provided data include registration data including the user's name, email address, telephone number, address, place of employment, place of study, etc., registered by the user himself, and data obtained as a result of the user's answering a questionnaire or the like. Examples of history data include the above-mentioned usage history data of the electronic commerce service provided by the service providing system 5. It is preferable that the factual attribute data is data obtained by converting the above-mentioned user-provided data or history data into a data format suitable for marketing and/or analysis purposes. For example, factual attribute data that can be obtained based on usage history data includes genres/categories and brands of products/services frequently used by the user, as well as commercial locations, recreational spots, and tourist spots frequently visited by the user.

The estimated attribute data is data indicating an inferred attribute obtained by inferring based on user-provided data, history data, fact attribute data, etc. In this embodiment, the estimated attribute data includes the user's personality, etc., estimated or predicted using machine learning technology. It is preferable that the estimated attribute data is data about attributes that affect the user's actions (behavior) when used for targeting.

In this embodiment, a weight is set for each attribute data. The weight indicates the degree of correlation between the attribute data and the credit score when the attribute data is used to calculate the credit score, and each time the appropriateness of the credit score is evaluated by the machine learning unit 24 described below, the parameters of the model are adjusted so that the credit score becomes a more appropriate value. The weight corresponding to each attribute data corresponds to, for example, the weight corresponding to each node (each regression tree) in a model for calculating the credit score, such as a decision model described below, and is determined appropriately in the process of calculating the credit score. Note that the credit score is determined, for example, based on the weight of each node.

Here, the attribute data group may include demographic attributes, behavioral attributes, or psychographic attributes. Demographic attributes are, for example, the user's gender, family structure, age, etc., and behavioral attributes may be based on service usage history data, such as whether or not a cash advance has been used, whether or not a revolving payment has been used, deposit and withdrawal history for a specific account, commercial transaction history for some product/service including gambling or lottery (which may include online transaction history in an online marketplace, etc.), user movement history using location information or place information, etc., and psychographic attributes are, for example, preferences for gambling or lottery. However, the available user attributes are not limited to the examples in this embodiment. For example, "time required for operation (calls, etc.)" and "credit card usage amount/deferred payment usage amount" from an operation center service, etc. may also be used as attributes. Demographic attributes and behavioral attributes may be treated as factual attributes. Psychographic attributes may be treated as estimated attributes. In addition, attributes similar to demographic attributes may be estimated attributes estimated based on factual attributes based on user-provided data or historical data. Similarly, attributes similar to behavioral attributes may be estimated based on factual attributes based on user-provided data or historical data. Psychographic attributes may be factual attributes based on user-provided data, including, for example, the results of user-input intentions.

FIG. 2 is a diagram showing an outline of the functional configuration of the information processing device 1 according to this embodiment. The information processing device 1 functions as an information processing device including a fact attribute determination unit 21, an estimated attribute determination unit 22, a credit score estimation unit 23, a machine learning unit 24, a judgment unit 25, and a proposal unit 26, by a program recorded in the storage device 14 being read into the RAM 13 and executed by the CPU 11, which controls each piece of hardware included in the information processing device 1. Note that in this embodiment and other embodiments described below, each function included in the information processing device 1 is executed by the CPU 11, which is a general-purpose processor, but some or all of these functions may be executed by one or more dedicated processors.

The fact attribute determination unit 21 determines fact attribute data that can be confirmed as fact for the user based on user-provided data provided by the user and/or the user's history data. In this embodiment, the fact attribute determination unit 21 determines fact attribute data related to the user using a method such as aggregating user-provided data and/or history data, determining the relevant attribute by referring to other data such as a map, or using the user-provided data and/or history data as is. Note that, although this embodiment employs a method of determining fact attribute data related to a user based on user-provided data and/or the user's history data, fact attribute data related to a user may be obtained by other methods.

The estimated attribute determination unit 22 determines estimated attribute data estimated for the target user based on user-related data including at least one or more fact attribute data determined for the target user by the fact attribute determination unit 21. In this embodiment, the estimated attribute determination unit 22 determines an estimated attribute based on an output value obtained by inputting user-related data including one or more fact attribute data related to the target user into an attribute estimation model, which is a machine learning model. In this embodiment, the output value from the attribute estimation model is a value indicating the probability that the target user has a predetermined estimated attribute, and the estimated attribute determination unit 22 determines that the target user has the estimated attribute when the output value obtained from the attribute estimation model is within a predetermined range. When it is determined that the target user has a predetermined estimated attribute, the estimated attribute determination unit 22 sets the label of the attribute data estimated for the target user to a value indicating the presence or absence of an attribute or the type of the attribute. The estimated attribute data may be indicated by a score instead of a label. In this case, the estimated attribute determination unit 22 sets a value indicating the degree (probability) that the estimated attribute can be applied to the score of the attribute data estimated for the target user. The degree may be the output value of the attribute estimation model.

The credit score estimation unit 23 estimates the credit score of the target user (in this embodiment, the credit score calculated based on the deferred payment risk) based on an attribute data group including the fact attributes and estimated attributes related to the target user. Here, the deferred payment risk is a risk that the settlement of the deferred payment will not be properly performed by the target user when the target user uses the deferred payment, expressed by some index (in this embodiment, the credit score that changes according to the magnitude of the deferred payment risk). At this time, the credit score estimation unit 23 may perform some processing (normalization, ranking, labeling, etc.) on the determined fact attributes and/or estimated attributes to make them part of the attribute data group, or may use other types of scores (for example, so-called credit scores, etc.) or labels calculated using the determined fact attributes and/or estimated attributes as all or part of the attribute data group. Here, other machine learning models may be involved in the calculation of other types of scores or labels.

Figure 3 is a simplified diagram of the credit score estimation process according to this embodiment. In this embodiment, the credit score estimation unit 23 estimates (calculates) the credit score of a user by inputting a group of attribute data of the user into a credit score estimation model. Here, the output value of the credit score estimation model is, for example, a credit score that is normalized/standardized with 0 as the minimum value and 1 as the maximum value.

The machine learning unit 24 generates and/or updates a credit score estimation model used for credit score estimation by the credit score estimation unit 23. The credit score estimation model is a machine learning model that outputs a credit score indicating the degree of risk that the target user will not correctly settle the deferred payment if the target user uses deferred payment when one or more attribute data (group of attribute data) related to the target user are input. By using such a machine learning model, in this embodiment, it is possible to obtain an output as a credit score that takes into account factors specialized for deferred payment, rather than a simple credit score. In addition, in this embodiment, an example is shown and explained in which a credit score in which the larger the credit score value, the higher the creditworthiness (lower the risk) and the smaller the credit score value, the lower the creditworthiness (higher the risk), but the relationship between the large and small score values and the high and low risk may be reversed.

In generating and/or updating the credit score estimation model, the machine learning unit 24 creates teacher data for each user, in which the attribute data group of the user is defined as an input value and the credit score of users who have the attribute data group in common is defined as an output value. Then, the machine learning unit 24 generates and/or updates the credit score estimation model based on the teacher data. As described above, the attribute data group input to the credit score estimation model includes fact attribute data determined by the fact attribute determination unit 21 and estimated attribute data estimated by the estimated attribute determination unit 22 based on user-related data including the fact attribute data, and is combined with the credit score of the corresponding user and input to the machine learning unit 24 as teacher data. In this embodiment, the credit score set in the teacher data is a credit score determined based on the payment history data of the deferred payment of the user corresponding to the combination of user attributes as the input value. Here, the payment history data of the deferred payment includes data indicating the presence or absence of default (non-performance of debt) in the deferred payment and the default amount. In this case, the credit score may be a rule-based credit score or may be a manually set (annotated) credit score. It may also be a credit score that was previously output by a credit score estimation model and then revised by an administrator, etc.

A machine learning model generation/update framework that can be adopted as a credit score estimation model or the like when implementing the technology disclosed herein is based on an ensemble learning algorithm, for example. For example, a machine learning framework (e.g., LightGBM) based on a gradient boosting decision tree (GBDT) may be adopted for the framework. In other words, the framework may be a machine learning framework based on a decision tree model that transfers the error between the correct answer and the predicted value between the previous and next weak learners (weak classifiers). The predicted value here refers to the predicted value of the credit score, for example. In addition to LightGBM, the framework may adopt boosting methods such as XGBoost and CatBoost. According to a framework using a decision tree, a machine learning model with relatively high performance can be generated/updated with less effort in parameter adjustment compared to a framework using a neural network. However, the machine learning model generation/update framework that can be adopted when implementing the technology according to the present disclosure is not limited to the example in this embodiment. For example, instead of a gradient boosting decision tree, another learning device such as a random forest may be adopted as a learning device, or a learning device that is not called a weak learning device such as a neural network may be adopted. In particular, when a learning device that is not called a weak learning device such as a neural network is adopted, ensemble learning may not be adopted.

Figure 4 is a simplified diagram showing the concept of a decision tree of a machine learning model employed as a credit score estimation model or the like in this embodiment. When a gradient boosting machine learning framework based on a decision tree algorithm is employed, the branching conditions of each node of the decision tree are optimized. Specifically, in the gradient boosting machine learning framework based on a decision tree algorithm, a credit score is calculated for each user group having attributes indicated by two child nodes branched from one parent node, and the branching conditions of the parent node are optimized so that the difference between these credit scores is large (for example, so that the difference is maximized or is equal to or greater than a predetermined threshold), that is, so that the two child nodes branch neatly. For example, if the attribute indicated as the branching condition of the node is age, the age set as the branching threshold may be changed, or the branching condition may be changed to an attribute other than age. In this way, the branching conditions of all nodes of the decision tree are recursively optimized, thereby improving the estimation accuracy of the credit score based on the attribute data group.

The determination unit 25 determines whether the user's creditworthiness indicated by the credit score is within a predetermined range below a predetermined creditworthiness. In this embodiment, the determination unit 25 determines whether the user's creditworthiness is within a predetermined range by comparing the user's credit score with a predetermined threshold. The credit score used in this embodiment is a score in which the higher the value, the higher the creditworthiness. Therefore, when the user's credit score is less than the predetermined threshold, the determination unit 25 determines that the user's creditworthiness indicated by the credit score is within a predetermined range below the predetermined creditworthiness.

The first threshold and the second threshold are preferably determined according to the settlement method to be proposed. For example, if the settlement method to be proposed is revolving payment, the first threshold is preferably set to a value that can exclude users with high credit scores who are presumed to have no motivation to use revolving payment instead of one-time payment. Also, the second threshold is preferably set to a value that can exclude users who do not have a credit score high enough to use credit payment.

Figure 5 is a diagram showing the predetermined range of creditworthiness in this embodiment. That is, the above "predetermined range" may have a lower limit. In this case, the determination unit 25 determines whether the user's creditworthiness indicated by the credit score is within a predetermined range that is less than the first creditworthiness and is equal to or greater than the second creditworthiness. More specifically, the determination unit 25 determines whether the credit score is less than a first threshold corresponding to the first creditworthiness and equal to or greater than a second threshold corresponding to the second creditworthiness. By setting an upper limit and a lower limit for the "predetermined range," it is possible to extract users who correspond to the second settlement method to be proposed from the users to whom the proposal is to be made.

When it is determined that the user's credit rating is within a predetermined range, the suggestion unit 26 proposes to the user a second settlement method different from the first settlement method as a settlement method when the user uses credit payment. Here, the first settlement method is a settlement method set as the normal settlement method for the credit payment. In this embodiment, the first settlement method and the second settlement method have different settlement timings. For example, assume that the credit payment is a credit card payment, and the normal settlement method (first settlement method) is set to one lump sum payment the following month. In this case, as the second settlement method, a settlement method with a different settlement timing, specifically, a one-time bonus payment, an installment payment, or a revolving payment, can be adopted. In addition, all of these second settlement methods described above have a delayed settlement timing compared to the one-time payment the following month. In this way, a settlement method with a delayed settlement timing compared to the normal settlement method can be proposed to a user whose credit rating does not meet a predetermined credit rating, thereby improving user convenience.

In this embodiment, a specific method of credit payment is described using a credit card as an example, but the specific method of credit payment is not limited to credit card payment. The technology disclosed herein may be adopted for other credit payment methods.

The specific method of suggestion to the user by the suggestion unit 26 is not limited to a specific suggestion method, but may be a method that can be suggested instantly at the time of payment, such as a suggestion by an in-app message on the payment screen or a suggestion by a general-purpose message service (social network system, short message service, etc.). Also, a suggestion format may be adopted in which an in-app message is displayed when the user confirms the payment amount on a credit card app, or an object that highlights other payment methods is displayed near an object that indicates the payment amount.

The suggestion unit 26 may also suggest to the user, in addition to proposing the second settlement method, that he or she sign up for a service that provides credit payment (in this embodiment, a credit card service). In this way, it is possible to effectively encourage new sign-up for a service to users who have not previously been offered a service for credit payment because no credit-related data has been accumulated.

When proposing the second settlement method to the user, the suggestion unit 26 may inform the user that electronic value such as points or an electronic coupon corresponding to a specified commercial transaction will be granted to the user on condition that the settlement is made by credit payment using the second settlement method. At this time, the electronic value and/or electronic coupon is granted to the user by a credit card service or a service platform that manages a user ID and electronic value common to the credit card service. The suggestion unit 26 may determine the electronic value and/or electronic coupon based on the payment amount, payment details, or the second settlement method.

<Processing flow>
Next, a flow of processing executed by the information processing device according to the present embodiment will be described. Note that the specific contents and processing order of the processing described below are an example for implementing the present disclosure. The specific contents and processing order may be appropriately selected according to the embodiment of the present disclosure.

Figure 6 is a flowchart showing the flow of the machine learning process according to this embodiment. The process shown in this flowchart is executed periodically or at a timing specified by the administrator.

In this embodiment, in the machine learning process, a credit score estimation model is generated and/or updated. The machine learning unit 24 creates training data including a combination of a group of attribute data for each user accumulated in the past and a credit score determined in advance for the corresponding user (step S101). Then, the machine learning unit 24 inputs the created training data into the credit score estimation model, and generates and/or updates the credit score estimation model used for credit score estimation by the credit score estimation unit 23 (step S102). Thereafter, the process shown in this flowchart ends.

Figure 7 is a flowchart showing the flow of the score calculation process according to this embodiment. The process shown in this flowchart is executed for each target user periodically or at a specified timing.

In steps S201 and S202, fact attribute data and estimated attribute data are determined. The fact attribute determination unit 21 determines fact attribute data related to the target user based on the user-provided data and/or history data of the target user (step S201). Then, the estimated attribute determination unit 22 determines estimated attribute data related to the target user based on at least the fact attribute data determined in step S201 (step S202). After that, the process proceeds to step S203.

In steps S203 and S204, a credit score is determined and output. The credit score estimation unit 23 determines a group of attribute data including the actual attribute data determined in step S201 and the estimated attribute data determined in step S202 (step S203). Then, the credit score estimation unit 23 inputs the group of attribute data determined in step S203 into a credit score estimation model, and acquires the output value as a credit score that changes according to the risk that the target user will not correctly settle the deferred payment if the target user uses deferred payment (step S204). However, the method of estimating the credit score is not limited to the example in this embodiment. For example, the credit score may include a value calculated by inputting the group of attribute data into a predetermined function or statistical model that is not a machine learning model. After that, the processing shown in this flowchart ends.

Figure 8 is a flowchart showing the flow of the payment method suggestion process according to this embodiment. The process shown in this flowchart is executed for each target user when a user's instruction to settle the purchase price of a product or service is accepted. However, the timing of execution of the payment method suggestion process does not have to be limited to the time of settlement. For example, the payment method suggestion process may be executed when an advertisement is displayed to a target, an advertising message is sent, etc.

In steps S301 and S302, it is determined whether the credit score of the target user is within a predetermined range that does not meet a predetermined credit rating. The determination unit 25 obtains the credit score of the target user estimated in the score calculation process (step S301), and determines whether the credit score is less than a first threshold and greater than or equal to a second threshold set in advance (step S302). If it is determined that the credit score of the target user is greater than or equal to the first threshold or less than the second threshold (NO in step S302), the process shown in this flowchart ends. On the other hand, if it is determined that the credit score of the target user is less than the first threshold and greater than or equal to the second threshold (YES in step S302), the process proceeds to step S303.

In step S303, a second settlement method is proposed to the target user. When it is determined in step S302 that the credit score of the target user is less than the first threshold and equal to or greater than the second threshold, the proposal unit 26 proposes to the target user, as a settlement method when the target user uses credit payment, a second settlement method (e.g., revolving payment) different from the first settlement method (e.g., one-time payment) set as the normal settlement method for the credit payment. After that, the process shown in this flowchart ends.
＜Effects＞
According to this embodiment, it is possible to encourage users whose credit rating does not meet a predetermined standard to use a highly flexible credit payment service.

<Variations>
In the embodiment described above, a credit judgment based on the risk of deferred payment is performed without narrowing down the target user group, and a settlement method option is proposed to a user whose credit is determined to be within a predetermined range. However, for a user group with sufficient accumulated credit data (credit history, etc.), a settlement method option based on the risk of deferred payment is not proposed, and for a user group with insufficient accumulated credit data (credit history, etc.), a settlement method option based on the risk of deferred payment (for example, revolving payment) may be proposed. Here, a user group with insufficient accumulated credit data is, for example, a user group such as a young person, who has a high need for revolving payment due to relatively low financial power and credit. Hereinafter, a description of the information processing device 1b according to this variation that is common to the first variation described above will be omitted, and a description will be given of the differences from the first variation described above.

Figure 9 is a diagram showing an outline of the functional configuration of information processing device 1b according to this variation. The hardware configuration of information processing device 1b is similar to that of information processing device 1 described with reference to Figure 1, and therefore description thereof is omitted. Information processing device 1b functions as an information processing device including a fact attribute determination unit 21, an estimated attribute determination unit 22, a credit score estimation unit 23, a machine learning unit 24, a determination unit 25, a proposal unit 26, and a credit data determination unit 27, by a program recorded in storage device 14 being read into RAM 13 and executed by CPU 11, which controls each piece of hardware included in information processing device 1. That is, information processing device 1b according to this variation further includes a credit data determination unit 27.

The credit data determination unit 27 determines whether or not a user has accumulated a quantity of credit data (e.g., credit history) appropriate for determining whether or not credit payment or a settlement method can be used. Specifically, the credit data determination unit 27 determines whether or not a predetermined criterion set in advance is met based on the type and/or amount of credit history accumulated for the user. Here, the predetermined criterion is, for example, the presence or absence of a specific type of credit history that is required, or a threshold value for the amount of credit history.

In this variation, the credit score estimation unit estimates a credit score that changes according to the deferred payment risk for a user who is determined to have at least an insufficient amount of accumulated credit data. The proposal unit 26 also proposes a second settlement method to a user who is determined to have at least an insufficient amount of accumulated credit data. The process flow for calculating the credit score and proposing the settlement method is roughly similar to the embodiment described above, so a description thereof will be omitted.

1. Information processing device

Claims (13)

A credit score estimation means for estimating a credit score to be set for a user based on a group of attribute data related to the user;
a determination means for determining whether the creditworthiness of the user indicated by the credit score is within a predetermined range not exceeding a predetermined creditworthiness;
a suggestion means for suggesting to the user, when it is determined that the creditworthiness of the user is within the predetermined range, a second settlement method different from a first settlement method set as a normal settlement method for the credit payment, as a settlement method when the user uses the credit payment;
An information processing system comprising: When it is determined that the creditworthiness of the user is within the predetermined range, the suggestion means proposes to the user, as a settlement method when the user uses the credit payment, the second settlement method in which a settlement timing different from the settlement timing set for the first settlement method.
The information processing system according to claim 1 . When it is determined that the creditworthiness of the user is within the predetermined range, the suggestion means proposes to the user, as a settlement method when the user uses the credit payment, the second settlement method in which a settlement timing delayed from the settlement timing set for the first settlement method is set.
The information processing system according to claim 2 . The determination means determines whether the creditworthiness of the user indicated by the credit score is within a predetermined range that is less than a first creditworthiness and is equal to or greater than a second creditworthiness.
The information processing system according to claim 1 . The suggestion means further suggests to the user that he or she subscribe to a service that provides the credit payment.
The information processing system according to claim 1 . the suggestion means proposes the second payment method to the user when an instruction from the user to settle the purchase price of a product or service is accepted;
The information processing system according to claim 1 . The credit score estimation means estimates a credit score to be set for the user using a credit score estimation model generated and/or updated using training data in which the attribute data group is used as an input value and the credit score determined based on the payment history of deferred payment related to users who have the same attribute data group as an output value.
The information processing system according to claim 1 . The output value of the credit score estimation model is a score that changes depending on a deferred payment risk that the user will not correctly settle the deferred payment when the user uses the deferred payment.
The information processing system according to claim 7. The method further includes a credit data determination means for determining whether or not a sufficient amount of credit data is accumulated for the user to determine whether or not the credit payment can be used or whether or not the settlement method can be used;
The credit score estimation means estimates a credit score that changes according to the deferred payment risk for at least a user determined to have an insufficient amount of accumulated credit data,
the suggestion means proposes the second settlement method to at least a user for whom it is determined that the accumulated amount of the credit data is insufficient;
The information processing system according to claim 8. The credit score estimation means estimates the credit score using the credit score estimation model generated and/or updated using a machine learning framework based on gradient boosting decision trees.
The information processing system according to claim 7. a fact attribute determining means for determining a fact attribute that can be confirmed as a fact about the user based on user-provided data provided by the user himself or history data of the user;
and determining an estimated attribute of the user based on at least the factual attribute of the user,
The credit score estimation means estimates a credit score of the target user based on a group of attribute data including the actual attributes and estimated attributes related to the target user.
The information processing system according to claim 1 . The computer
a credit score estimating step of estimating a credit score to be set for the user based on a group of attribute data related to the user;
a determination step of determining whether the user's creditworthiness indicated by the credit score is within a predetermined range below a predetermined creditworthiness;
a proposal step of proposing to the user, when it is determined that the credit rating of the user is within the predetermined range, a second settlement method different from the first settlement method set as a normal settlement method for the credit payment, as a settlement method when the user uses the credit payment;
An information processing method for performing the above. Computer,
A credit score estimation means for estimating a credit score to be set for a user based on a group of attribute data related to the user;
a determination means for determining whether the creditworthiness of the user indicated by the credit score is within a predetermined range not exceeding a predetermined creditworthiness;
a suggestion means for suggesting to the user, when it is determined that the creditworthiness of the user is within the predetermined range, a second settlement method different from a first settlement method set as a normal settlement method for the credit payment, as a settlement method when the user uses the credit payment;
A program that functions as a

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