Delay suppression device and delay suppression method

Abstract

The present disclosure makes it possible to suppress users' delay in payment related to credit transactions while maintaining user satisfaction. A delay suppression device 10 comprises: an acquisition unit 11 that acquires user information about a user who uses a credit transaction; a classification unit 12 that generates type information indicating a user type by classifying the user into one of a plurality of preset types, on the basis of the user information; a generation unit 13 that generates first possibility information indicating the degree of possibility that the user will be late in making a payment related to the credit transaction, on the basis of the user information and the type information; and a notification unit 14 that, on the basis of the type information and the first possibility information, selects a notification method for payment related to the credit transaction, and notifies the user on the basis of the selected notification method.

Description

Delay suppression device and delay suppression method 【0001】 The present invention relates to a delay suppression device and a delay suppression method. 【0002】 Patent Document 1 describes a device that contacts a user who has delayed payment for a credit transaction (for example, payment for the use of a credit card) with a reminder or the like. In the device described in Patent Document 1, when the usage fee of the credit card cannot be withdrawn from the designated account, a display of "Withdrawal was not possible" is presented to the user. And in the device described in Patent Document 1, when the user selects the said display, the flag corresponding to the user is changed from "none" to "yes", and a reminder or the like is preferentially contacted to the member corresponding to the "yes" flag. 【0003】 Japanese Patent Application Laid-Open No. 2006-011566 【0004】 In the device as described above, it is desired not only to execute a payment reminder to a user who has delayed payment for a credit transaction but also to suppress the occurrence of the delay itself. However, if the payment is reminded to the user before the occurrence of the delay in the payment for the credit transaction, there is a risk that the user satisfaction may decrease depending on the reminder method. For example, if a strong payment reminder is given to a user with zero payment delay, the user may feel more discomfort than necessary. In this case, there is a risk that the satisfaction of the user may decrease. Therefore, it is desirable to suppress the user from delaying the payment for the credit transaction while maintaining the user satisfaction. 【0005】 Therefore, an object of the present disclosure is to provide a delay suppression device and a delay suppression method that can suppress a user from delaying a payment for a credit transaction while maintaining the user satisfaction. 【0006】The delinquency prevention device of this disclosure comprises: an acquisition unit that acquires user information relating to users who use credit transactions; a classification unit that generates type information indicating the type of user by classifying the user into one of a plurality of pre-set types based on the user information; a generation unit that generates first probability information indicating the degree to which a user is likely to be delinquent in making payments related to credit transactions based on the user information and the type information; and a notification unit that selects a notification method regarding payments related to credit transactions based on the type information and the first probability information, and notifies the user based on the selected notification method. 【0007】 Alternatively, the delinquency suppression method of this disclosure is a delinquency suppression method executed by a delinquency suppression device, comprising: an acquisition step of acquiring user information relating to a user who uses credit transactions; a classification step of generating type information indicating the type of user by classifying the user into one of a plurality of pre-set types based on the user information; a generation step of generating first probability information indicating the degree of possibility that a user is likely to be delinquent in making payments related to credit transactions based on the user information and the type information; and a notification step of selecting a notification method relating to payments related to credit transactions based on the type information and the first probability information, and notifying the user based on the selected notification method. 【0008】 According to one aspect of this disclosure, it is possible to maintain user satisfaction while preventing users from defaulting on payments related to credit transactions. 【0009】Figure 1 is a block diagram showing the configuration of the delay suppression device of the present disclosure. Figure 2 is a diagram showing an example of the data structure of user information acquired by the acquisition unit of Figure 1. Figure 3(a) is a diagram showing an example of the data structure of user information input to the learning model by the generation unit of Figure 1, Figure 3(b) is a diagram showing an example of the data structure of type information input to the learning model by the generation unit of Figure 1, and Figure 3(c) is a diagram showing an example of the data structure of first possibility information output from the learning model. Figures 4(a), 4(b), and 4(c) are diagrams showing examples of templates used for creating emails by the notification unit of Figure 1, respectively. Figure 5 is a flowchart showing the procedure of delay suppression processing by the delay suppression device. Figure 6 is a flowchart showing the details of the processing in step S3 of Figure 5. Figure 7 is a block diagram showing the configuration of the delay suppression device of the first modified example. Figure 8 is a diagram showing an example of the hardware configuration of the delay suppression device according to one embodiment of the present disclosure. 【0010】 Embodiments of this disclosure will be described with reference to the attached drawings. Where possible, the same parts will be denoted by the same reference numerals, and redundant descriptions will be omitted. 【0011】 Figure 1 is a diagram showing the configuration of the delinquency prevention device according to this embodiment. The delinquency prevention device 10 shown in Figure 1 is a device for preventing users who use credit transactions from defaulting on payments related to those credit transactions. In this embodiment, a credit transaction is a transaction in which a business operator, such as a credit card company, temporarily pays for the goods or services that a user purchases. A credit transaction is, for example, a transaction using a credit card. In a credit transaction, the user pays the business operator the amount that the business operator has advanced. For example, the amount that the business operator has advanced is automatically debited from the user's bank account, etc. The delinquency prevention device is a device for preventing users who use such credit transactions from defaulting on payments that the business operator has advanced. 【0012】 As shown in Figure 1, the delay suppression device 10 is configured to include, as functional components, an acquisition unit 11, a classification unit 12, a generation unit 13, a notification unit 14, and a construction unit 15. 【0013】The acquisition unit 11 acquires user information relating to users who use margin trading. For example, the acquisition unit 11 acquires user information by accepting user information input. For example, the acquisition unit 11 acquires user information when user information is input into the delinquency suppression device 10 by an operator using the delinquency suppression device 10. User information includes information about the user themselves and information about the use of margin trading. Information about the user themselves is, for example, information indicating the user's age. Information about the use of margin trading includes, for example, information indicating the period during which the user used margin trading and information regarding the history of the user's delinquency in payments related to margin trading. 【0014】 Figure 2 shows an example of user information. In the example shown in Figure 2, the user information includes a user ID to identify the user, information indicating the user's age, information indicating the user's gender, information indicating the period during which the user has used credit transactions (information indicating the contract period), information indicating the user's credit score, information regarding the user's history of defaulting on credit transaction payments (information indicating the number of times the user has defaulted on payments), information indicating the expected payment amount for a given month, and information indicating the user's annual income. Note that the user information may also include other information not listed above, for example, information indicating the user's occupation, information indicating the user's address, information indicating the user's contract year and month, and information indicating the user's settlement information. 【0015】 The classification unit 12 generates type information indicating the user's type by classifying the user into one of a plurality of pre-set types based on the user information. Specifically, the classification unit generates type information by classifying the user into one of a plurality of types based on the user information and pre-set criteria. 【0016】The multiple categories represent different types of users. For example, the categories include beginner, good user, late payment history user, and habitual user. The beginner category includes users who have recently started using credit cards. Users classified as beginners are very young or have only recently started using credit cards, and one example is a user who does not fully understand the financial system. The good user category includes users who rarely default on payments. Users classified as good users have never defaulted on a payment, and one example is a meticulous user who manages their household finances systematically and allocates a portion of their salary to a dedicated monthly payment account. The late payment history user category includes users who have defaulted on payments in the past. Users classified as late payment history users have defaulted on payments several times in the past, and one example is a forgetful and careless user who takes some time to realize they are late, but pays immediately once they do. The habitual user category includes users who repeatedly default on payments. Users classified as habitual offenders are those who have repeatedly experienced late payments. Examples include users who tend to procrastinate, do not act until they receive a final warning, and spend money impulsively. The categories include, but are not limited to, beginner, good, late-payment experience, and habitual offender types. For example, the categories may include only beginner, good, and habitual offender types, or only good and late-payment experience types. 【0017】For example, the classification unit 12 classifies a user as a beginner type if the length of time the user has used the credit card is less than a predetermined value (for example, one year) and the user's age falls into the young age group. The young age group is, for example, the age group between 0 and 25 years old. The classification unit 12 classifies a user as a good type if the length of time the user has used the credit card is equal to or greater than the predetermined value and the user has no experience of late payments. The classification unit 12 classifies a user as an extended-term user type if the length of time the user has used the credit card is equal to or greater than the predetermined value and the number of times the user has been late on payments is one or more but less than or equal to a threshold (for example, three times). The classification unit 12 classifies a user as a habitual user type if the length of time the user has used the credit card is less than or equal to the predetermined value and the number of times the user has been late on payments exceeds the threshold. In this way, the classification unit 12 classifies users into one of several types based on at least one of the following: information indicating the user's age, information indicating the period during which the user has used credit transactions (for example, the number of days elapsed from the date the user contracted for a credit card to the present), and information regarding the user's payment history for credit transactions, and generates type information indicating the user's type. The classification unit 12 can classify users into multiple types based on the above criteria when it has a wealth of information about the user and the user's payment history. 【0018】 Furthermore, the classification unit 12 may classify users into one of several categories according to their ability to pay. In this case, the categories may be set according to the user's ability to pay, and may include, for example, a high-ability type and a low-ability type. The "high-ability" category may, for example, correspond to a user with a high annual income, or to a user with a low probability of defaulting on payments. 【0019】The generation unit 13 generates first probability information indicating the degree of likelihood that a user will default on payments related to margin trading, based on user information and type information. The first probability information is, for example, a numerical value indicating the likelihood that a user will default on payments related to margin trading (hereinafter referred to as the "first default score"). For example, the lower limit of the first default score is 0, and the upper limit of the first default score is 1. The closer the first default score is to 0, the lower the likelihood that a user will default on payments related to margin trading. The closer the first default score is to 1, the higher the likelihood that a user will default on payments related to margin trading. Although the lower limit of the first default score is 0 and the upper limit of the first default score is 1, it is not limited to these. The first default score may be a numerical value between 0 and 100. Also, although the first probability information is the first default score, it is not limited to this. The first probability information may be any information indicating the degree of likelihood that a user will default on payments related to margin trading. 【0020】Specifically, the generation unit 13 generates first possibility information by inputting user information and type information into the learning model M1 (first learning model). The learning model M1 is a learning model constructed by the construction unit 15, which will be described later. Figure 3(a) shows an example of user information input into the learning model M1. Figure 3(b) shows an example of type information input into the learning model M1. Figure 3(c) shows an example of first possibility information output from the learning model M2. In the example shown in Figure 3(a), the user information further includes information indicating the number of lines the user has contracted and information indicating how often the user replaces their terminal (for example, information indicating how often they replace it), compared to the user information shown in Figure 2. Thus, the generation unit 13 may add other information about the user to the user information before inputting the user information into the learning model M1. In the example shown in Figure 3(b), the type information includes a user ID for identifying the user and information indicating the customer type to which the user is classified. In the information shown in Figure 3(c), the first possibility information includes a user ID for identifying the user and the user's first delinquency score. The generation unit 13 inputs the user information shown in Figure 3(a) and the type information shown in Figure 3(b) into the learning model M1, but is not limited to this. The generation unit 13 may also input the user information shown in Figure 2 and the type information shown in Figure 3(b) into the learning model M1. 【0021】 The notification unit 14 selects a notification method regarding payments related to credit transactions based on the type information and the first possibility information, and notifies the user based on the selected notification method. Specifically, first, the notification unit 14 selects a notification method according to the combination of the type information and the first possibility information. Then, the notification unit 14 notifies the user based on the selected notification method. 【0022】For example, before a user defaults on a payment, the notification unit 14 selects a notification method, such as the first method or the second method below, according to the user type and the first default score, and notifies the user based on the selected notification method. The notification unit 14 selects a notification method that more strongly reminds the user of payments related to credit transactions, according to the user type and the first default score. If a user defaults on a payment, the notification unit 14 sends the user an email (reminder email) to urge them to make payments related to credit transactions. 【0023】 First, the first method will be explained. In the first method, the notification unit 14 strongly enforces reminders for payments related to margin trading by increasing the types of tools used to remind users of payments related to margin trading, depending on the user type and the first delinquency score. For example, if the user is classified as a habitual borrower and the first delinquency score is less than 0.6, the notification unit 14 selects a notification method that weakly enforces reminders for payments related to margin trading. As an example, the notification unit 14 chooses not to send any notification to the user. The notification unit 14 does not send any notification to the user based on the said notification method. 【0024】 Furthermore, for example, if the user is classified as a habitual offender and the first delinquency score is 0.6 or higher but less than 0.8, the notification unit 14 selects a notification method that will more strongly remind the user about payments related to credit transactions. As an example, the notification unit 14 selects a notification method that sends the user an email (reminder email) to remind them about payments related to credit transactions. The notification unit 14 notifies the user based on the said notification method. 【0025】In addition, if the user is classified as a habitual user and their first delinquency score is 0.8 or higher, the notification unit 14 selects a notification method that more strongly enforces reminders for credit transactions. For example, the notification unit 14 selects a notification method that, in addition to sending the user an email (reminder email) to remind them of credit transaction payments, also displays a message in an application program running on the terminal held by the user indicating that the use of the credit card will be restricted. The notification unit 14 notifies the user based on this notification method. This makes it possible to suppress credit card use by users with high first delinquency scores. 【0026】 Furthermore, in the first method, the notification unit 14 may select a notification method that weakly enforces payment reminders for margin trading if the user is classified as a beginner type and the first delinquency score is less than 0.4. The notification unit 14 may select a notification method that more strongly enforces payment reminders for margin trading if the user is classified as a beginner type and the first delinquency score is 0.4 or more and less than 0.8. The notification unit 14 may select a notification method that even more strongly enforces payment reminders for margin trading if the user is classified as a beginner type and the first delinquency score is 0.8 or more. 【0027】 Furthermore, for example, if the user is classified as a good type and the first delinquency score is less than 0.8, the notification unit 14 may select a notification method that sends a weak reminder about payments related to margin trading. If the user is classified as a good type and the first delinquency score is 0.8 or higher, the notification unit 14 may select a notification method that sends a stronger reminder about payments related to margin trading. 【0028】Next, the second method will be described. In the second method, the notification unit 14 modifies the text of the reminder email to strongly enforce reminders for credit transactions, according to the user type and the first delinquency score. For example, the notification unit 14 selects a template according to the combination of the user type and the first delinquency score, and sends an email created using the selected template to the user. As an example, the notification unit 14 creates an email to remind users of credit transactions using a template corresponding to the beginner type and the first delinquency score, and sends the created email to the user. Figures 4(a), 4(b), and 4(c) show examples of templates for each first delinquency score used for users classified as beginners. Each template contains text that enforces stronger reminders for credit transactions the higher the first delinquency score corresponding to that template. Each template is pre-set by the business operator, and the notification unit 14 stores each template in advance. Furthermore, the creation of emails using templates may be performed using various publicly known technologies or generative AI models. 【0029】 The notification unit 14 does not send any notification to the user if the user is classified as a beginner type and the first delinquency score is less than 0.4, similar to the first method. The notification unit 14 selects the template shown in Figure 4(a) if the user is classified as a beginner type and the first delinquency score is 0.4 or more and less than 0.6. This template includes a statement that the URL of a web page containing payment methods and frequently asked questions (FAQ) regarding credit transactions will be sent, and a statement politely requesting smooth payment. 【0030】The notification unit 14 selects the template shown in Figure 4(b) if the user is classified as a beginner type and the first delinquency score is 0.6 or more and less than 0.8. The template includes a statement to remind the user that the payment deadline for margin trading is approaching, a statement asking the user to check the scheduled payment amount and account balance, and a statement asking the user to contact the customer service center if there are any questions regarding the payment. 【0031】 The notification unit 14 selects the template shown in Figure 4(c) if the user is classified as a beginner type and the first delinquency score is 0.8 or higher. The template includes a statement reminding the user that the payment deadline for margin trading is approaching, a statement asking the user to check the scheduled payment amount and account balance to ensure that the withdrawal from the account will be executed without problems, and a statement that penalties such as late fees will be incurred if the withdrawal is not executed and payment is delayed. 【0032】 The notification unit 14 creates an email to remind the user of payment related to credit transactions by entering the user's name in the [Customer Name] field of the selected template, entering the withdrawal date for the payment related to the credit transaction in the [Date] field of the template, and entering the telephone number of the business operator's contact information in the [Contact Information] field of the template. The notification unit 14 then sends the created email to the user. 【0033】 As shown in the first and second methods, the notification unit 14 stores notification methods in advance according to the combination of type information and first possibility information, and executes the notification method according to the said combination. As a result, the notification unit 14 can execute an appropriate notification method according to the combination of type information and first possibility information. As a result, the notification unit 14 can appropriately and efficiently prevent user delays while maintaining user satisfaction, compared to the case where the same notification method is executed for all users. 【0034】The construction unit 15 constructs a learning model M1 using machine learning, taking user information and type information as input and outputting first possibility information. Specifically, the construction unit 15 acquires training data to be used in the learning model M1. For example, the training data includes information on users who have defaulted on payments related to margin trading. This information includes first possibility information indicating a first default score of "1", and user information and type information relating to the above user. Alternatively, for example, the training data includes information on users who have not defaulted on payments related to margin trading. This information includes first possibility information indicating a first default score of "0", and user information and type information relating to the above user. The construction unit 15 constructs the learning model M1 using the training data. The construction unit 15 stores the constructed learning model M1 in the built-in memory or storage medium within the default suppression device 10. Machine learning includes supervised learning, unsupervised learning, and reinforcement learning, and among these learning methods are deep learning and neural network learning. In addition to being constructed by the construction unit 15, the learning model M1 may also be generated by an external computer or the like and downloaded to the delay suppression device 10. 【0035】 The procedure for delay suppression processing by the delay suppression device 10 configured as described above, that is, the flow of the delay suppression method according to this embodiment, will now be explained. Figures 5 and 6 are flowcharts showing the procedure for delay suppression processing by the delay suppression device 10. 【0036】First, as shown in Figure 5, user information is acquired by the acquisition unit 11 of the delinquency prevention device 10 (Step S1: Acquisition step). Next, the classification unit 12 of the delinquency prevention device 10 classifies the user into one of a plurality of pre-set categories based on the user information, thereby generating category information indicating the user's category (Step S2: Classification step). Subsequently, the generation unit 13 of the delinquency prevention device 10 generates first probability information indicating the degree of possibility that the user is likely to default on payments related to credit transactions, based on the user information and category information (Step S3: Generation step). Finally, the notification unit 14 of the delinquency prevention device 10 selects a notification method regarding payments related to credit transactions based on the category information and first probability information, and notifies the user based on the selected notification method (Step S4: Notification step). 【0037】 For example, the classification unit 12 of the delinquency suppression device 10 classifies the user corresponding to user ID "1" shown in Figure 2 as a beginner type (see Figure 3(b)). Subsequently, the generation unit 13 of the delinquency suppression device 10 estimates the first delinquency score of the user corresponding to user ID "1" to be 0.8 (see Figure 3(c)). Subsequently, as shown in the first method, the notification unit 14 of the delinquency suppression device 10 decides to execute a notification means using both an application program running on the user's terminal and email, and an email created using a template corresponding to the beginner type and the first delinquency score "0.8" (for example, the template shown in Figure 4(c)) is sent to the user via both the application program and email, as shown in the second method. 【0038】Furthermore, for example, the classification unit 12 of the delinquency suppression device 10 classifies the user corresponding to user ID "2" shown in Figure 2 into a type of person with a history of delinquency (see Figure 3(b)). Subsequently, the generation unit 13 of the delinquency suppression device 10 estimates that the first delinquency score for the user corresponding to user ID "2" is 0.6 (see Figure 3(c)). Subsequently, the notification unit 14 of the delinquency suppression device 10 decides to execute a notification means using only email, as shown in the first example, and sends an email to the user via email only, created using a template corresponding to the type of person with a history of delinquency and the first delinquency score "0.6", as shown in the second example. 【0039】 Referring to Figure 6, the process of step S3 will be explained in detail. In the process of step S3, the classification unit 12 of the delinquency prevention device 10 determines whether the user's contract period is one year or longer (step S31). If the classification unit 12 of the delinquency prevention device 10 determines that the user's contract period is less than one year (step S31: No), it is determined whether the user is included in the young age group (step S32). If the classification unit 12 of the delinquency prevention device 10 determines that the user is included in the young age group (step S32: Yes), the user is classified as a beginner type (step S33). 【0040】 If the classification unit 12 of the delinquency prevention device 10 determines that the user's contract period is one year or longer (step S31: Yes), or if it determines that the user is not included in the young age group (step S32: No), it is determined whether or not the user has a history of delinquency (step S34). If the classification unit 12 of the delinquency prevention device 10 determines that the user has no history of delinquency (step S34: No), the user is classified as a good customer (step S35). 【0041】 If the classification unit 12 of the delinquency prevention device 10 determines that the user has a history of delinquency (step S35: Yes), it is determined whether the user has been delinquent three times or less (step S36). If the classification unit 12 of the delinquency prevention device 10 determines that the user has been delinquent three times or less (step S36: Yes), the user is classified as a delinquent user type (step S37). 【0042】 When it is determined by the classification unit 12 of the delay suppression device 10 that the number of delays of the user is not less than 3 times (step S36: No), the user is classified as a habitual type (step S38). When the processes of steps S33, S35, S37, and S38 described above are executed, the process of step S3 ends. 【0043】 Next, the operation and effect of the delay suppression device 10 and the delay suppression method of the present disclosure will be described. In the delay suppression device 10 and the delay suppression method of the present disclosure, a notification method regarding payment related to a credit transaction is selected based on the type information and the first possibility information, and the user is notified based on the selected notification method. Thereby, for a user who uses a credit transaction, a notification method corresponding to the type information and the first possibility information of the user is executed. Thereby, an appropriate notification can be made for each user who uses a credit transaction. For example, for a user who needs a reminder for payment related to a credit transaction, the reminder can be executed in a text and method suitable for the user. Also, for example, for a user who does not need a reminder for payment related to a credit transaction, it is possible to avoid the reminder from being made. As described above, according to the delay suppression device 10 of the present disclosure, it is possible to suppress the user from delaying payment related to a credit transaction while maintaining the user's satisfaction. 【0044】 For example, according to the type (type) into which the user is classified, a necessary and appropriate message can be sent to the user. As an example, when executing preventive measures before a delay (for example, sending a reminder message) to a user with a high probability of delay, a reminder message corresponding to the type into which the user is classified is sent to the user. Thereby, a beneficial and appropriate message (beneficial and necessary message) can be sent to the user. As a result, it is possible to suppress a decrease in the user's satisfaction. Note that when executing countermeasures after a delay (for example, sending a reminder message) to a user who has delayed, a reminder message corresponding to the type into which the user is classified may be sent to the user. 【0045】Also, in the delay suppression device 10 of the present disclosure, the classification unit 12 generates type information by classifying a user into one of a plurality of types based on user information and a preset criterion. As a result, the classification unit 12 can appropriately classify the user into one of the plurality of types. Consequently, when a notification method corresponding to the type information and the first possibility information of the user is executed for a user who uses a credit transaction, an appropriate notification can be surely executed. 【0046】 Also, in the delay suppression device 10 of the present disclosure, the user information includes information indicating the age of the user, and the classification unit 12 classifies the user into one of a plurality of types based on the information indicating the age of the user. As a result, the classification unit 12 can more appropriately classify the user into one of the plurality of types. Consequently, when a notification method corresponding to the type information and the first possibility information of the user is executed for a user who uses a credit transaction, an appropriate notification can be more surely executed. 【0047】 Also, in the delay suppression device 10 of the present disclosure, the user information includes information indicating the period during which the user has used a credit transaction, and the classification unit 12 classifies the user into one of a plurality of types based on the information indicating the period. As a result, the classification unit 12 can more appropriately classify the user into one of the plurality of types. Consequently, when a notification method corresponding to the type information and the first possibility information of the user is executed for a user who uses a credit transaction, an appropriate notification can be more surely executed. 【0048】 Also, in the delay suppression device 10 of the present disclosure, the user information includes information regarding a history of the user's delay in payment related to a credit transaction, and the classification unit 12 classifies the user into one of a plurality of types based on the information regarding the history. As a result, the classification unit 12 can more appropriately classify the user into one of the plurality of types. Consequently, when a notification method corresponding to the type information and the first possibility information of the user is executed for a user who uses a credit transaction, an appropriate notification can be more surely executed. 【0049】Furthermore, in the delay suppression device 10 of this disclosure, the generation unit 13 generates first possibility information by inputting user information and type information into the learning model M1. As a result, the generation unit 13 can generate first possibility information with high accuracy. Consequently, appropriate notifications can be delivered to the user with greater accuracy. 【0050】 Furthermore, in the delinquency prevention device 10 of this disclosure, the notification unit 14 selects a notification method according to the combination of type information and first possibility information, and notifies the user based on the selected notification method. This makes it possible to more reliably prevent users from defaulting on payments related to credit transactions while maintaining user satisfaction. 【0051】 In the above embodiment, the classification unit 12 generates type information by classifying users into one of a plurality of types based on user information and pre-set criteria, but is not limited to this. For example, as in the delay suppression device 10 of the first and second modified examples below, the classification unit 12 can generate type information indicating the user's type by classifying users into one of a plurality of pre-set types based on user information. 【0052】 Figure 7 shows a first modified example of the delinquency suppression device 10. In the delinquency suppression device 10 according to the first modified example, the classification unit 12 inputs user information into a learning model M2 to generate second probability information indicating the degree of likelihood that a user is likely to be late on payments related to credit transactions, and may classify the user into one of several categories based on the second probability information. Specifically, the classification unit 12 may use the learning model M2 to calculate a second delinquency score for each type of information included in the user information as second probability information, and may classify the user into one of several categories based on the sum of the calculated second delinquency scores. 【0053】For example, the classification unit 12 first calculates the second delinquency score for each type of information contained in the user information using a learning model M2 that takes user information as input and outputs a second delinquency score for each type of information contained in the user information. The types of information contained in the user information include, for example, age, gender, address, and annual income. The types of information contained in the user information may further include contract period, credit score, number of past delinquencies, planned payment amount for a given month, annual income, number of line contracts, and terminal replacement frequency (see Figure 3(a)). 【0054】 The classification unit 12 calculates the sum of the second delinquency scores for each type of information included in the user information as second probability information. Based on the calculated second probability information, the classification unit 12 classifies the user into a category corresponding to the sum of the second delinquency scores indicated by the second probability information. For example, the lower limit of the second delinquency score is 0, and the upper limit of the second delinquency score is 1. The closer the second delinquency score is to 0, the lower the likelihood that the user will default on payments related to margin trading. The closer the second delinquency score is to 1, the higher the likelihood that the user will default on payments related to margin trading. Also, for example, the second probability information is a numerical value that indicates the sum of the second delinquency scores. In this case, the larger the numerical value indicated by the second probability information, the higher the likelihood that the user will default on payments related to margin trading. The classification unit 12 also stores information in advance that shows the correspondence between the numerical value indicated by the second probability information and each of the multiple categories. For example, in the information showing the correspondence, if the value indicated by the second possibility is 0 or greater and less than the first threshold, it is associated with the excellent type; if the value indicated by the second possibility is 1 or greater and less than the second threshold, it is associated with the habitual user type; and if the value indicated by the second possibility is 2 or greater and less than the third threshold, it is associated with the beginner type. The second threshold is greater than the first threshold, and the third threshold is greater than the second threshold. The classification unit 12 refers to this correspondence and classifies the user into one of the multiple types based on the information indicated by the second possibility information. 【0055】The construction unit 15 uses machine learning to construct a learning model M2 (second learning model) that takes user information as input and outputs second possibility information. Specifically, the construction unit 15 acquires training data to be used in the learning model M2. For example, the training data includes information on users who have defaulted on payments related to margin trading. This information includes user information relating to the user and information indicating a second default score of "1" corresponding to each type of information included in the user information. Also, for example, the training data includes information on users who have not defaulted on payments related to margin trading. This information includes user information relating to the user and information indicating a second default score of "0" corresponding to each type of information included in the user information. The construction unit 15 constructs the learning model M2 using the training data. The construction unit 15 stores the constructed learning model M2 in the built-in memory or storage medium of the default suppression device 10. 【0056】 In the first modified example of the delinquency suppression device 10, the classification unit 12 inputs user information into the learning model M2 to generate second probability information indicating the degree of likelihood that a user is likely to be delinquent in making payments related to credit transactions, and classifies the user into one of several categories based on the second probability information. This makes it possible to appropriately classify the user into one of several categories. 【0057】 In the second modified example of the delay suppression device 10, the multiple types may be multiple types of users that have been set in advance, and the classification unit 12 may determine the type of user that is most similar to the user among the multiple types of users based on multiple type information and user information relating to the multiple types of users, and classify the user to that type of user. 【0058】Multiple user types (personas) are examples of users who use credit transactions. Each of the multiple user types corresponds to a type such as beginner type, habitual user type, and good user type in the above embodiment. Multiple user types are pre-set by the user, etc., and may be generated, for example, using a generation AI model based on past history, set by the developer of the delinquency prevention device 10, or generated by any other method not mentioned above. Type information is information about the type of user, and is similar to the user information in the above embodiment. Type information is pre-set by the user, etc., and may be generated, for example, using a generation AI model based on past history, set by the developer of the delinquency prevention device 10, or generated by any other method not mentioned above. Below are three examples of type information for type users. First, an example of type information for a type of user corresponding to the beginner type is: "Name: AAA Age: 24 Gender: Male Occupation: Company employee Address: DDD Usage period: 7 months (A young company employee who has been using a credit card for 7 months)." Next, an example of a user type corresponding to a habitual offender is: "Name: BBB Age: 32 Gender: Male Occupation: Company employee Address: EEE Usage period: 11 years and 2 months (A company employee who repeatedly defaults on payments once or twice a year)." Finally, an example of a user type corresponding to a good user is: "Name: CCC Age: 32 Gender: Female Occupation: Temporary employee Address: FFF Usage period: 11 years and 2 months (A temporary employee with zero defaults)." 【0059】 The classification unit 12 associates users with the type user that best matches the user information, based on the similarity between the user information and the type information. For example, the classification unit 12 extracts the type user that best matches the user information by performing a similarity search based on the user information on multiple type users using various known methods such as vector search. 【0060】In the second modified version of the delinquency suppression device 10, the multiple types may be multiple pre-set user types, and the classification unit 12 may determine the user type that is most similar to the user among the multiple user types based on multiple type information and user information relating to the multiple user types, and classify the user into that user type. This makes it possible to appropriately classify users into one of multiple more finely classified types. As a result, more appropriate notifications can be made to each user who uses credit transactions. 【0061】 Other variations will be described. In the above embodiments and their variations, the construction unit 15 constructs one learning model M1 by machine learning, but is not limited thereto. For example, the construction unit 15 may construct multiple learning models M1 for each combination of the user's income and type information. As an example, the construction unit 15 may construct a learning model M1 for a user who has an income of 4 million yen or more but less than 5 million yen and is of the beginner type. The construction unit 15 may also construct multiple learning models M1 for each combination of the user's income, other information included in the user information, and type information. The construction unit 15 may construct a learning model M1 for a user who has an income of 4 million yen or more but less than 5 million yen, is single, and is of the beginner type. In such a case, the generation unit 13 may select a learning model M1 corresponding to the user information and type information from the multiple learning models M1, and generate first possibility information using the selected learning model M1. 【0062】 Similarly, in the second modified example described above, the construction unit 15 may construct multiple learning models M1 corresponding to each of the multiple user types. In this case, the generation unit 13 may select a learning model M1 corresponding to the user type into which the user has been classified from the multiple learning models M1, and generate first possibility information using the selected learning model M1. This makes it possible to generate first possibility information with greater accuracy using a learning model M1 tailored to each individual user. 【0063】In the above embodiment, the first delinquency score was a numerical value indicating the likelihood that a user is likely to default on payments related to credit transactions, but it is not limited to this. For example, the first delinquency score may be a numerical value calculated by multiplying the above numerical value by the amount used by the user. In this case, the generation unit 13 can calculate the first delinquency score as a numerical value that takes into account the amount that the user is likely to default on. 【0064】 In the first modified example described above, the notification unit 14 selected a notification method regarding payments related to credit transactions based on type information and first possibility information. However, the notification method may also be selected based on either the first possibility information or the second possibility information, and the type information. This makes it possible to estimate the likelihood of a user defaulting on payments related to credit transactions with greater accuracy from multiple perspectives. Specifically, if there is a user who is considered likely to default on payments based on only one of the first possibility information or the second possibility information, the notification means intended for users who are considered likely to default on payments can be executed more reliably. 【0065】 For example, the notification unit 14 may select a notification method based on the information indicating a higher risk level from among the first possibility information and the second possibility information, or it may select a notification method based on the information indicating a lower risk level. In this case, the level of risk is estimated by various known methods. Furthermore, the notification unit 14 may select the information used when selecting a notification method from among the first possibility information and the second possibility information, for example, by using a generative AI model. 【0066】In the above embodiments and their variations, the notification unit 14 may estimate the risk that the user may feel dissatisfied and stop using their credit card (the risk of canceling their credit card) if it sends an email to the user to remind them of payments related to credit transactions. For example, the notification unit 14 may generate third possibility information using a learning model that takes user information and type information as input and outputs third possibility information indicating the degree of possibility that the user will stop using their credit card within one year after the email is sent. In this case, the construction unit 15 may generate the learning model using training data that includes user information, type information, and information indicating whether or not the user stopped using their credit card within one year after the email is sent. The notification unit 14 may also select a notification method that does not send a reminder email if the degree of possibility indicated by the third possibility information satisfies predetermined conditions. Furthermore, the notification unit 14 may generate third possibility information based on user information and type information using a generation AI model. [About this disclosure] The delinquency suppression device 10 of this disclosure has the following configuration. 【0067】 [1] A delinquency suppression device comprising: an acquisition unit that acquires user information relating to a user who uses margin trading; a classification unit that generates type information indicating the type of the user by classifying the user into one of a plurality of pre-set types based on the user information; a generation unit that generates first probability information indicating the degree of possibility that the user is likely to be in arrears on payments relating to margin trading based on the user information and the type information; and a notification unit that selects a notification method relating to payments relating to margin trading based on the type information and the first probability information, and notifies the user based on the selected notification method. 【0068】 [2] The delay suppression device according to [1], wherein the classification unit generates the type information by classifying the user into one of the plurality of types based on the user information and pre-set criteria. 【0069】[3] The delay suppression device according to [2], wherein the user information includes information indicating the user's age, and the classification unit classifies the user into one of the plurality of categories based on the information indicating the user's age. 【0070】 [4] The delinquency suppression device according to [2] or [3], wherein the user information includes information indicating the period during which the user used the credit transaction, and the classification unit classifies the user into one of the plurality of types based on the information indicating the period. 【0071】 [5] The delinquency suppression device according to any one of [2] to [4], wherein the user information includes information regarding the history of the user's default on payments related to the credit transaction, and the classification unit classifies the user into one of the plurality of categories based on the information regarding the history. 【0072】 [6] The delay suppression device according to any one of [1] to [5], wherein the generation unit generates the first possibility information by inputting the user information and the type information into the first learning model. 【0073】 [7] The delinquency suppression device according to [1], wherein the classification unit inputs the user information into a second learning model to generate second probability information indicating the degree to which the user is likely to be delinquent in making payments related to the credit transaction, and classifies the user into one of the plurality of categories based on the second probability information. 【0074】 [8] The delay suppression device according to [1], wherein the plurality of types are a plurality of pre-set user types, and the classification unit determines the user type that is most similar to the user among the plurality of user types based on the plurality of type information relating to the plurality of user types and the user information, and classifies the user to that user type. 【0075】 [9] The delay suppression device according to any one of [1] to [8], wherein the notification unit selects a notification method according to the combination of the type information and the first possibility information, and notifies the user based on the selected notification method. 【0076】

[10] A method for preventing late payments that is executed by a late payment prevention device, comprising: an acquisition step of acquiring user information relating to a user who uses credit transactions; a classification step of classifying the user into one of a plurality of pre-set types based on the user information to generate type information indicating the type of the user; a generation step of generating first possibility information indicating the degree of possibility that the user is likely to be late on payments relating to credit transactions based on the user information and the type information; and a notification step of selecting a notification method relating to payments relating to credit transactions based on the type information and the first possibility information, and notifying the user based on the selected notification method. 【0077】 [Definitions of Terms, etc.] The block diagrams used in the description of the above embodiments show functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or it may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wired, wireless, etc.). A functional block may be realized by combining the above one device or the above multiple devices with software. 【0078】 Functions include, but are not limited to, judgment, decision, determination, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, assumption, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating (mapping), and assigning. For example, a functional block (configuration part) that enables transmission is called a transmitting unit or transmitter. In all cases, as mentioned above, the method of implementation is not particularly limited. 【0079】 For example, the delay suppression device 10 in one embodiment of the present disclosure may function as a computer that processes the delay suppression method of the present disclosure. Figure 8 is a diagram showing an example of the hardware configuration of the delay suppression device 10 according to one embodiment of the present disclosure. The above-described delay suppression device 10 may be physically configured as a computer device including a processor 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc. 【0080】 In the following explanation, the term "device" can be replaced with "circuit," "device," "unit," etc. The hardware configuration of the delay suppression device 10 may include one or more of the devices shown in the figure, or it may be configured to omit some of the devices. 【0081】 Each function in the delay suppression device 10 is realized by loading predetermined software (programs) onto hardware such as the processor 1001 and memory 1002, which causes the processor 1001 to perform calculations, control communication by the communication device 1004, and control at least one of data reading and writing in the memory 1002 and storage 1003. 【0082】 The processor 1001 controls the entire computer, for example, by running an operating system. The processor 1001 may be composed of a central processing unit (CPU) that includes interfaces with peripheral devices, control devices, arithmetic units, registers, etc. For example, the notification unit 14 described above may be implemented by the processor 1001. 【0083】Furthermore, the processor 1001 reads programs (program code), software modules, data, etc., from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes accordingly. The program used is one that causes the computer to execute at least a part of the operations described in the above embodiment. For example, the notification unit 14, etc., may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and other functional blocks may be implemented similarly. The above-described various processes have been explained as being executed by one processor 1001, but they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may also be transmitted from a network via a telecommunications line. 【0084】 The memory 1002 is a computer-readable recording medium and may consist of at least one of the following: ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. The memory 1002 may also be called a register, cache, main memory, etc. The memory 1002 can store executable programs (program code), software modules, etc., for implementing the virtual space provision method according to one embodiment of the present disclosure. 【0085】The storage 1003 is a computer-readable recording medium and may consist of at least one of the following: an optical disc such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disc, a digital multipurpose disc, a Blu-ray® disc), a smart card, flash memory (e.g., a card, a stick, a key drive), a floppy® disk, a magnetic strip, etc. The storage 1003 may also be called an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, server, or other suitable medium including at least one of memory 1002 and storage 1003. 【0086】 The communication device 1004 is hardware (transmitting / receiving device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, network controller, network card, communication module, etc. The communication device 1004 may be configured to include high-frequency switches, duplexers, filters, frequency synthesizers, etc., in order to implement at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the acquisition unit 11 described above may be implemented by the communication device 1004. The communication device 1004 may be implemented with physically or logically separated transmitting and receiving units. 【0087】 The input device 1005 is an input device that accepts input from an external source (e.g., a keyboard, mouse, microphone, switch, button, sensor, etc.). The output device 1006 is an output device that outputs to an external source (e.g., a display, speaker, LED lamp, etc.). The input device 1005 and the output device 1006 may be configured as an integrated unit (e.g., a touch panel). 【0088】Furthermore, each device, such as the processor 1001 and memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or different buses may be configured for each device. 【0089】 Furthermore, the delay suppression device 10 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array), and some or all of each functional block may be realized by such hardware. For example, the processor 1001 may be implemented using at least one of these hardware components. 【0090】 Information notification is not limited to the embodiments described herein and may be carried out by other means. For example, information notification may be carried out by physical layer signaling (e.g., DCI (Downlink Control Information), UCI (Uplink Control Information)), upper layer signaling (e.g., RRC (Radio Resource Control) signaling, MAC (Medium Access Control) signaling, broadcast information (MIB (Master Information Block), SIB (System Information Block))), other signals, or combinations thereof. RRC signaling may also be called RRC messages, and may be, for example, RRC Connection Setup messages, RRC Connection Reconfiguration messages, etc. 【0091】The processing procedures, sequences, flowcharts, etc., of each aspect / embodiment described in this disclosure may be reordered, provided they do not contradict each other. For example, the methods described in this disclosure present various step elements using exemplary order and are not limited to the specific order presented. 【0092】 Input and output information may be stored in a specific location (e.g., memory) or managed using a management table. Input and output information may be overwritten, updated, or appended to. Output information may be deleted. Input information may be transmitted to other devices. 【0093】 The determination may be made by a value represented by one bit (0 or 1), by a boolean value (true or false), or by a numerical comparison (for example, a comparison with a predetermined value). 【0094】 Each aspect / embodiment described in this disclosure may be used individually, in combination, or switched between as needed during implementation. Furthermore, notification of specific information (e.g., notification that "X is") is not limited to explicit notification, but may also be implicit (e.g., by not providing such notification). 【0095】 Although the present disclosure has been described in detail above, it will be clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the intent and scope of the present disclosure as defined by the claims. Accordingly, the descriptions in the present disclosure are illustrative and not intended to be restrictive in any way. 【0096】Software should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, and so on, whether they are called software, firmware, middleware, microcode, hardware description languages, or by any other name. 【0097】 Furthermore, software, instructions, information, etc., may be transmitted and received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of wired technologies (such as coaxial cable, fiber optic cable, twisted pair, or digital subscriber line (DSL)) and wireless technologies (such as infrared or microwave), then at least one of these wired and wireless technologies is included in the definition of a transmission medium. 【0098】 The information, signals, etc. described in this disclosure may be represented using any of the various different techniques. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof. 【0099】 In addition, terms used in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of the channel and symbol may be a signal (signaling). Also, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, cell, frequency carrier, etc. 【0100】Furthermore, the information, parameters, etc., described in this disclosure may be expressed using absolute values, relative values ​​from a given value, or other corresponding information. For example, wireless resources may be indicated by an index. 【0101】 The names used for the parameters described above are not restrictive in any way. Furthermore, the formulas and other expressions using these parameters may differ from those expressly disclosed in this disclosure. Various channels (e.g., PUCCH, PDCCH, etc.) and information elements can be identified by any suitable name, and therefore, the various names assigned to these various channels and information elements are not restrictive in any way. 【0102】 In this disclosure, terms such as "Mobile Station (MS)," "user terminal," "User Equipment (UE)," and "terminal" may be used interchangeably. 【0103】 A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other appropriate term. 【0104】As used in this disclosure, the terms “determining” and “determining” may encompass a wide variety of actions. “Determining” may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, or inquiring (e.g., searching in a table, database, or other data structure), or ascertaining. “Determining” may also include, for example, receiving (e.g., receiving information), transmitting (e.g., sending information), inputting, outputting, or accessing (e.g., accessing data in memory). Furthermore, "judgment" and "decision" can include considering something as having been "judged" or "decided" after resolving, selecting, choosing, establishing, comparing, etc. In other words, "judgment" and "decision" can include considering something as having been "judged" or "decided" after some action. Also, "judgment (decision)" can be reinterpreted as "assuming," "expecting," or "considering." 【0105】The terms “connected,” “coupled,” or any variation thereof, mean any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are “connected” or “coupled” with each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, “connection” may be reinterpreted as “access.” As used in this disclosure, two elements may be considered to be “connected” or “coupled” with each other using at least one of one or more wires, cables, and printed electrical connections, and, in some non-limiting and non-exclusive examples, electromagnetic energy having wavelengths in the radio frequency domain, microwave domain, and optical (both visible and invisible) domain. 【0106】 In this disclosure, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based at least on." 【0107】 Any reference to elements using designations such as “first,” “second,” etc., as used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Accordingly, references to first and second elements do not imply that only two elements may be employed, or that the first element must precede the second element in any way. 【0108】 Where the terms “include,” “including,” and their variations are used in this disclosure, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, the term “or” as used in this disclosure is not intended to be exclusive OR. 【0109】In this disclosure, if articles are added by translation, such as a, an, and the in English, this disclosure may include the fact that the noun following these articles is plural. 【0110】 In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combine" may be interpreted similarly to "different." 【0111】 10... Delay suppression device, 11... Acquisition unit, 12... Classification unit, 13... Generation unit, 14... Notification unit, 1001... Processor, 1002... Memory, 1003... Storage, 1004... Communication device, 1005... Input device, 1006... Output device, M1... Learning model (first learning model), M2... Learning model (second learning model).

Claims

1. A delinquency suppression device comprising: an acquisition unit that acquires user information relating to a user who uses margin trading; a classification unit that generates classification information indicating the user's classification by classifying the user into one of a plurality of pre-set classifications based on the user information; a generation unit that generates first probability information indicating the degree of possibility that the user is likely to be delinquent in making payments related to margin trading based on the user information and the classification information; and a notification unit that selects a notification method for payments related to margin trading based on the classification information and the first probability information, and notifies the user based on the selected notification method.

2. The delay suppression device according to claim 1, wherein the classification unit generates the type information by classifying the user into one of the plurality of types based on the user information and pre-set criteria.

3. The delay suppression device according to claim 2, wherein the user information includes information indicating the user's age, and the classification unit classifies the user into one of the plurality of categories based on the information indicating the user's age.

4. The delinquency suppression device according to claim 2, wherein the user information includes information indicating the period during which the user used the credit transaction, and the classification unit classifies the user into one of the plurality of categories based on the information indicating the period.

5. The delinquency suppression device according to claim 2, wherein the user information includes information regarding the user's history of defaulting on payments related to credit transactions, and the classification unit classifies the user into one of the plurality of categories based on the information regarding the history.

6. The delay suppression device according to claim 1, wherein the generation unit generates the first possibility information by inputting the user information and the type information into the first learning model.

7. The delinquency suppression device according to claim 1, wherein the classification unit inputs the user information into a second learning model to generate second probability information indicating the degree to which the user is likely to default on payments related to the credit transaction, and classifies the user into one of the plurality of categories based on the second probability information.

8. The delay suppression device according to claim 1, wherein the plurality of types are a plurality of pre-set user types, and the classification unit determines the user type that is most similar to the user among the plurality of user types based on the plurality of type information relating to the plurality of user types and the user information, and classifies the user to that user type.

9. The delay suppression device according to claim 1, wherein the notification unit selects a notification method according to the combination of the type information and the first possibility information, and notifies the user based on the selected notification method.

10. A method for preventing late payments that is executed by a late payment prevention device, comprising: an acquisition step of acquiring user information relating to a user who uses credit transactions; a classification step of generating type information indicating the type of user by classifying the user into one of a plurality of pre-set types based on the user information; a generation step of generating first probability information indicating the degree of possibility that the user is likely to be late on payments relating to credit transactions based on the user information and the type information; and a notification step of selecting a notification method relating to payments relating to credit transactions based on the type information and the first probability information, and notifying the user based on the selected notification method.

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