Information processing device and information processing method

Abstract

To enable a user to appropriately determine a use limit amount usable in financial services.SOLUTION: A first determination unit 34 inputs user information of a user to a compulsory cancellation rate model stored in a storage unit 33 and obtains cancellation risk information relating to the cancellation risk of a financial service being forcibly cancelled for the user. The first determination unit 34 inputs the user information of the user to a use predication model stored in the storage unit 33, and obtains use amount information relating to the amount used in the financial service by the user. When cancellation risk information about a certain user indicates being lower than a first level of risk and use amount information indicates being higher than a second level of amount, the first determination unit 34 increases the use limit amount of the user.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] This invention relates to a technology for determining the spending limit available to users in financial services. [Background technology]

[0002] As a technology for determining a credit limit available to a user in financial services, for example, Patent Document 1 discloses a method for determining the risk associated with an increase in the maximum allowable credit amount using a machine learning model. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Special Publication No. 2020-522832 Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention aims to appropriately determine the spending limit available to users in financial services. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems, the present invention provides a method for solving the above-mentioned problems, comprising: an acquisition unit that acquires user information about users who use financial services; a first trained model by machine learning that uses the user information as an explanatory variable and cancellation risk information related to the cancellation risk of the financial service being forcibly canceled by the provider of the financial service as an objective variable; a second trained model by machine learning that uses the user information as an explanatory variable and usage amount information related to the amount used by the user in the financial service as an objective variable; and a first determination unit that determines a usage limit that the user can use in the financial service using the user information acquired by the acquisition unit for each user, and and an information processing device that increases the credit limit of a user when the cancellation risk information obtained by inputting user information about the user into the first trained model means that the cancellation risk information is lower than a first level risk, and the usage amount information obtained by inputting user information about the user into the second trained model means that the usage amount information is higher than a second level amount, and the first level is determined using the rate at which the financial service was forcibly canceled for users for whom it was determined by the first determination unit that the credit limit should be increased, and the rate at which the financial service was not forcibly canceled for users for whom it was not determined by the first determination unit that the credit limit should be increased. Furthermore, the present invention comprises: an acquisition unit that acquires user information relating to users of financial services; a first trained model obtained by machine learning with the user information as an explanatory variable and termination risk information relating to the risk of termination of the financial services by the provider of the financial services as the objective variable; a second trained model obtained by machine learning with the user information as an explanatory variable and usage amount information relating to the amount used by the user in the financial services as the objective variable; and a first determination unit that determines the usage limit that each user can use in the financial services using the user information acquired by the acquisition unit for each user, wherein the first determination unit determines the usage limit that each user can use in the financial services using the user information relating to the user in the first trained model The present invention provides an information processing device in which, when the cancellation risk information obtained by inputting the user information of the user means that the risk is lower than the first level, and the usage amount information obtained by inputting the user information of the user into the second trained model means that the usage limit of the user is increased, and the second level is determined using the percentage of the usage amount used by a user who has been determined by the first decision unit to increase their usage limit, in which case the usage amount used by a user who has been determined by the first decision unit to increase their usage limit, in which case the usage amount used, in which case the usage amount used by a user who has not been determined by the first decision unit to increase their usage limit, in which case the usage limit is increased. The present invention also provides a method for determining a credit limit that a user can use in a financial service, using the user information acquired for each user, and a first trained model created by machine learning in which the user information is an explanatory variable and churn risk information related to the churn risk of the financial service being forcibly terminated is an objective variable, a second trained model created by machine learning in which the user information is an explanatory variable and usage amount information related to the amount used by the user in the financial service is an objective variable, and the user information acquired for each user. In the determining step, the computer increases the credit limit of each user when, for each user, the churn risk information obtained by inputting the user information about the user into the first trained model means that the risk is lower than a first level, and the usage amount information obtained by inputting the user information about the user into the second trained model means that the amount is higher than a second level, and the first level is In the decision-making step The percentage of users for whom it has been decided to increase the credit limit who have had their financial services forcibly terminated, and In the determining step The information processing method is characterized in that the credit limit is determined using the ratio of users for whom the financial service has not been forcibly canceled for which it has not been decided to increase the credit limit. The present invention also provides a method for determining a credit limit that a user can use in a financial service, using the user information acquired for each user, and a first trained model created by machine learning using the user information as an explanatory variable and churn risk information related to the churn risk of the financial service being forcibly terminated as an objective variable, a second trained model created by machine learning using the user information as an explanatory variable and usage amount information related to the amount used by the user in the financial service as an objective variable, and the user information acquired for each user. In the determining step, the computer increases the credit limit of each user when, for each user, the churn risk information obtained by inputting the user information about the user into the first trained model means that the risk is lower than a first level, and the usage amount information obtained by inputting the user information about the user into the second trained model means that the amount is higher than a second level, and the second level is In the decision-making step The percentage of users whose spending limit has been increased, in which the amount spent did not exceed a certain percentage of the spending limit after the decision, and the In the decision-making step The present invention provides an information processing method characterized in that the amount used by a user who has not been designated to increase the aforementioned usage limit is determined using a percentage that exceeds a certain percentage of the said usage limit. [Effects of the Invention]

[0006] According to the present invention, it is possible to appropriately determine the credit limit that a user can use in financial services. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is a diagram showing an example of a configuration of an information processing system 1 according to an embodiment of the present invention. [Figure 2] FIG. 2 is a diagram illustrating a hardware configuration of an information processing device 30 according to the embodiment. [Figure 3] This figure shows an example of the functional configuration of the information processing device 30 according to the same embodiment. [Figure 4] This figure shows an example of data stored by the information processing device 30 according to the same embodiment. [Figure 5] FIG. 10 is a diagram showing an example of a graph illustrating a method for determining a threshold value in the embodiment. [Figure 6] This is a table illustrating the error rate in the same embodiment. [Figure 7] This figure shows an example of a graph illustrating the method for determining the threshold in the same embodiment. [Figure 8] This is a table illustrating the error rate in the same embodiment. [Figure 9] This is a flowchart showing the learning operation by the information processing device 30. [Figure 10] 10 is a flowchart showing a credit limit determination operation performed by the information processing device 30. [Figure 11] This figure shows an example of the functional configuration of the information processing device 30 according to a modified example. [Figure 12] This figure shows an example of rules stored by the modified information processing device 30. [Figure 13] 10 is a table illustrating the boundaries of each risk group in a modified example. [Figure 14] This figure shows an example of how the usage limits are changed according to each risk group in the modified example. [Figure 15] This figure shows an example of a merchant's commission rate in a modified example. DETAILED DESCRIPTION OF THE INVENTION

[0008] [composition] Fig. 1 is a diagram showing an example of an information processing system 1 according to an embodiment of the present invention. Information processing system 1 is a system for electronically making payments in commercial transactions in which a user purchases goods or services at a store in the real world or an online store. As shown in Fig. 1, information processing system 1 includes a user terminal 10 used by the user, a store terminal 20 used by the store, a payment system 40 that includes an information processing device 30 according to the present invention and performs processing related to electronic payments, and a communication network 2 that includes a wireless communication network or a wired communication network that communicatively connects these elements.

[0009] The user terminal 10, the store terminal 20, and the information processing device 30 are all computers capable of communicating with each other. Specifically, the user terminal 10 is a computer that can be carried by a user, such as a smartphone, a wearable device, or a tablet. The store terminal 20 is a communication terminal operated by a store clerk or the like. In this embodiment, the payment system 40 is managed by a telecommunications carrier that provides communication services to the user terminal 10, and the electronic payment service provided by the telecommunications carrier to the user is a financial service known as a combined payment system for communication charges. However, the financial service in the present invention is not limited to this, and may be any service in which a user has a set upper limit on the amount of use (i.e., the credit amount of the user) for the financial service provided by the financial service provider. Note that while FIG. 1 shows one user terminal, one store terminal, and one information processing device, there may be multiple of each.

[0010] FIG. 2 is a diagram showing the hardware configuration of the information processing device 30. The information processing device 30 is physically configured as a computer including a processor 3001, a memory 3002, a storage 3003, a communication device 3004, an input device 3005, an output device 3006, and a bus connecting these devices. Each of these devices operates using power supplied from a battery (not shown). In the following description, the term "device" can be interpreted as a circuit, a device, a unit, or the like. The hardware configuration of the information processing device 30 may be configured to include one or more of the devices shown in FIG. 2, or may be configured without including some of the devices. Furthermore, the information processing device 30 may be configured by communicating with multiple devices each having a different housing.

[0011] Each function in the information processing device 30 is realized by loading predetermined software (programs) onto hardware such as the processor 3001 and memory 3002, causing the processor 3001 to perform calculations, control communication via the communication device 3004, and control at least one of reading and writing data in the memory 3002 and storage 3003.

[0012] The processor 3001 controls the entire computer by running, for example, an operating system. The processor 3001 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. Furthermore, for example, a baseband signal processing unit, a call processing unit, etc. may be realized by the processor 3001.

[0013] The processor 3001 reads programs (program codes), software modules, data, etc. from at least one of the storage 3003 and the communication device 3004 into the memory 3002, and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described below. The functional blocks of the information processing device 30 may be implemented by a control program stored in the memory 3002 and running on the processor 3001. Various processes may be executed by one processor 3001, or may be executed simultaneously or sequentially by two or more processors 3001. The processor 3001 may be implemented by one or more chips. The programs may be transmitted to the information processing device 30 via a telecommunications line.

[0014] The memory 3002 is a computer-readable recording medium and may be configured by, for example, at least one of a ROM (Read Only Memory), an EPROM (Erasable Programmable ROM), an EEPROM (Electrically Erasable Programmable ROM), a RAM (Random Access Memory), etc. The memory 3002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 3002 can store an executable program (program code), a software module, etc. for implementing the method according to this embodiment.

[0015] Storage 3003 is a computer-readable recording medium, and may be constituted by at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. Storage 3003 may also be referred to as an auxiliary storage device.

[0016] The communication device 3004 is hardware (transmitting / receiving device) for communicating between computers via the communication network 2, and is also called a network device, network controller, network card, communication module, etc.

[0017] The input device 3005 is an input device (for example, a key, a microphone, a switch, a button, a camera, a sensor, a two-dimensional code reader, etc.) that receives input from the outside. The output device 3006 is an output device (for example, a display, a speaker, an LED lamp, etc.) that outputs to the outside. The input device 3005 and the output device 3006 may be integrated into one device (for example, a touch panel).

[0018] Each device, such as the processor 3001 and memory 3002, is connected by a bus for communicating information. The bus may be configured using a single bus, or different buses may be used for each device.

[0019] The information processing device 30 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 3001 may be implemented using at least one of these pieces of hardware.

[0020] Like the information processing device 30 shown in FIG. 2 , the user terminal 10 and the store terminal 20 each include hardware for implementing a computer, such as a processor, memory, storage, a communication device, an input device, an output device, and a bus connecting these. However, the communication device included in the user terminal 10 is hardware (transmission / reception device) for communicating between computers via the communication network 2 in accordance with a communication standard such as LTE (Long Time Evolution) to achieve wireless communication. This communication device may include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, and the like to implement at least one of frequency division duplex (FDD) and time division duplex (TDD). Furthermore, a transmission / reception antenna, an amplifier, a transmission / reception unit, a transmission path interface, and the like may be implemented by this communication device. The transmission / reception unit may be physically or logically separated into a transmission unit and a reception unit.

[0021] 3 is a block diagram showing the functional configuration of information processing device 30. In information processing device 30, processor 3001 reads programs and the like from storage 3003 into memory 3002 and executes them, thereby realizing the functions of acquisition unit 31, learning unit 32, memory unit 33, and first determination unit 34.

[0022] The acquisition unit 31 reads and acquires user information from a user database (DB) 50 that contains user information on users who use financial services. The user information includes attributes such as the user's age, as well as information such as the content of the contract for the financial service, the usage history of the financial service, and the usage limit. Note that the user attributes used here may include attributes other than age. The user DB 50 may be held by the information processing device 30, the payment system 40, or a system or device other than the payment system 40.

[0023] The learning unit 32 performs machine learning using machine learning algorithms such as logistic regression or random forest, and generates a forced cancellation rate model corresponding to the first trained model in the present invention, and a usage prediction model corresponding to the second trained model in the present invention.

[0024] The forced churn rate model is generated by machine learning using training data with user information as the explanatory variable and churn risk information regarding the churn risk of the financial service being forcibly terminated by the financial service provider (here, a telecommunications carrier) as the objective variable. A case in which the financial service is forcibly terminated by the financial service provider occurs when the financial service is terminated at the will of the financial service provider, not based on the user's intention, due to, for example, the user's fraud or contractual default. Information regarding such forced churn is included in the above-mentioned user information. In the forced churn rate model, explanatory variables include, for example, attributes such as the age of each user, as well as the financial service usage history (e.g., amount used, frequency of use, number of uses, location of use, etc.), and the objective variable is whether or not the financial service has been forcibly terminated for each user. Note that the user attributes used here may include attributes other than age.

[0025] The usage prediction model is generated by machine learning using training data in which user information is used as an explanatory variable and usage amount information related to the amount used by the user in financial services is used as a target variable. In the usage prediction model, the explanatory variables include attributes such as the user's age, as well as the usage history of financial services (e.g., usage frequency, number of times used, location of use, etc.), and the target variable is the amount used by the user, among the information included in the usage history of financial services. Note that the user attributes used here may include attributes other than age.

[0026] The storage unit 33 stores the forced churn rate model and the usage prediction model generated by the learning unit 32.

[0027] The first determination unit 34 inputs the user information of the user into the forced churn rate model stored in the storage unit 33, and obtains churn risk information regarding the churn risk of the financial service being forcibly terminated for that user. Specifically, this churn risk information is obtained as a forced churn score indicating the risk of the financial service being forcibly terminated. This forced churn score takes a value between 0 and 1, for example, with 0 indicating the lowest possibility of forced churn (i.e., forced churn will never occur) and 1 indicating the highest possibility of forced churn (i.e., forced churn will always occur).

[0028] Furthermore, the first determination unit 34 inputs the user information of the user into the usage prediction model stored in the storage unit 33 to obtain usage amount information regarding the amount to be used by the user in financial services. This usage amount information is obtained as a usage prediction score, which is a predicted value of the amount to be used by the user in financial services. This usage prediction score takes a value between 0 and 1, for example, where 0 means that the usage amount is minimum (i.e., no usage at all) and 1 means that the usage amount exceeds a certain percentage (e.g., 80% or more) of the usage limit.

[0029] FIG. 4 shows a table showing the relationship between cancellation risk information (forced cancellation score), usage amount information (predicted usage score), and increases or decreases in credit limit. This table is stored by the first determination unit 34. When the cancellation risk information for a certain user indicates a lower risk level than the first level and the usage amount information indicates a higher amount than the second level, the first determination unit 34 increases the user's credit limit. In the example of FIG. 4, when the cancellation risk information (forced cancellation score) obtained for a certain user is less than 0.2 (first level risk) and the usage amount information (predicted usage score) is 0.3 (second level amount) or greater, i.e., when the user is within the shaded range S, the user's credit limit is increased. When increasing the credit limit for each user in this way, the first determination unit 34 may increase the user's credit limit by one level (e.g., from 30,000 yen to 50,000 yen) out of multiple credit limit levels with different sizes (e.g., three levels: 30,000 yen, 50,000 yen, and 80,000 yen). In Figure 4, if the relationship between cancellation risk information (forced cancellation score) and usage amount information (usage prediction score) falls outside the shaded area S, the usage limit will not be changed.

[0030] The above-mentioned first level is determined using the percentage of users whose financial services were forcibly canceled for whom the first decision unit 34 decided to increase their credit limit, and the percentage of users whose financial services were not forcibly canceled for whom the first decision unit 34 did not decide to increase their credit limit.

[0031] FIG. 5 is a diagram illustrating an example of a graph explaining the first level determination method. In FIG. 5, the horizontal axis represents the forced cancellation score, and the vertical axis represents a value called the error rate. As shown in FIG. 6, even if a user is not forcibly canceled from financial services based on the forced cancellation score (i.e., the forced cancellation score is 0 and the user is called a normal user), there may be cases where the user's financial services are actually forcibly canceled (called a forced canceler). In this case, the credit limit of a user who actually became a forced canceler may be increased because the prediction did not indicate that the user was a forced canceler, which means that the prior prediction was incorrect. Graph c1 in FIG. 5 shows the relationship between the forced cancellation score and the percentage of cases (error rate) in which the credit limit was increased because the prediction did not indicate that the user was a forced canceler. This error rate corresponds to the percentage of users whose financial services were forcibly canceled for whom the first determination unit 34 determined that the credit limit should be increased.

[0032] On the other hand, even if a user (forced canceller) is judged to be forcibly terminated from financial services based on the forced cancellation score, there may be cases where the user's credit limit was not forcibly terminated (normal user). In this case, the credit limit of a user whose credit limit should not have been reduced may have been reduced because the predicted user was a forced canceller, which means that the prior prediction was incorrect. Graph c2 in Figure 5 shows the relationship between the forced cancellation score and the proportion of cases (error rate) in which a user whose credit limit should not have been reduced had its credit limit reduced because the predicted user was a forced canceller. This error rate corresponds to the proportion of users whose credit limits were not forcibly terminated for financial services for which the first determination unit 34 did not determine that the credit limit should be increased.

[0033] In this embodiment, the forced cancellation score at the intersection of graph c1 and graph c2 is set to the above-mentioned first level, taking into consideration the balance between the rate at which financial services were forcibly canceled for users for whom the first decision unit 34 decided to increase their credit limit, and the rate at which financial services were not forcibly canceled for users for whom the first decision unit 34 did not decide to increase their credit limit.

[0034] Next, the second level described above is determined using the percentage by which the amount used by a user whose credit limit has been determined to be increased by the first determination unit 34 does not exceed an amount equivalent to a certain percentage of the determined credit limit, and the percentage by which the amount used by a user whose credit limit has not been determined to be increased by the first determination unit 34 exceeds an amount equivalent to a certain percentage of the credit limit.

[0035] FIG. 7 is a diagram illustrating an example of a graph illustrating a method for determining the second level. In FIG. 7, the horizontal axis represents the usage prediction score, and the vertical axis represents the error rate. As shown in FIG. 8, even if a user's usage limit is determined to be increased based on the usage prediction score, there may be cases in which the user actually does not use a certain percentage (e.g., 80% or more) of the determined usage limit. In such cases, the increase in the usage limit is unnecessary, meaning that the prior prediction was incorrect. Graph c1 in FIG. 7 shows the relationship between the usage prediction score and the percentage (error rate) of cases in which a certain percentage (80% or more) of the usage limit is not used despite the increase in usage amount. This error rate corresponds to the percentage of cases in which the usage amount used by a user whose usage limit is determined to be increased by the first determination unit 34 does not exceed the amount corresponding to the certain percentage of the determined usage limit.

[0036] On the other hand, even for users whose spending limit has been increased based on their spending prediction score, there may be cases where they actually used a certain percentage (e.g., 80% or more) of that limit. In such cases, an increase in the spending limit was necessary, meaning the prior prediction was incorrect. Graph c2 in Figure 7 shows the relationship between the spending prediction score and the percentage (error rate) of cases where a certain percentage (80% or more) of the spending limit was used despite not having the limit increased. This error rate corresponds to the percentage of cases where the amount spent by users who were not determined by the first decision unit 34 to have their spending limit increased exceeded a certain percentage of that limit.

[0037] In this embodiment, the usage prediction score at the intersection of graph c3 and graph c4 is set to the second level described above, taking into consideration the balance between the percentage of users whose usage amount was not increased by the first determination unit 34 and exceeded a certain percentage of that usage limit, and the percentage of users whose usage amount was not increased by the first determination unit 34 and exceeded a certain percentage of that usage limit.

[0038] In FIG. 4, the credit limit is increased when the relationship between the churn risk information (forced churn score) and the usage amount information (predicted usage score) corresponds to the shaded range S. However, for example, the first determination unit 34 may decrease, or reduce, the credit limit of a user when the churn risk information indicates a higher level of risk than the third level and the usage amount information indicates a lower level than the fourth level. For example, in FIG. 4, when the churn risk information (forced churn score) obtained for a certain user is 0.5 (third level risk) or higher and the usage amount information (predicted usage score) is less than 0.1 (fourth level amount), that is, when the credit limit is in the shaded range V, the credit limit of the user may be decreased. When decreasing the credit limit of each user in this way, the first determination unit 34 may decrease the credit limit of the user by one level out of multiple levels of credit limits with different sizes.

[0039] In addition, instead of setting or reducing the total amount of the credit limit in certain stages as described above, the first determination unit 34 may, for example, for each user, reduce the credit limit for that user the greater the risk indicated by the cancellation risk information, and increase the credit limit for that user the greater the amount indicated by the usage amount information.

[0040] [Operation] Next, the operation of the information processing device 30 will be described with reference to Figures 9 and 10. In the following description, for example, when the information processing device 30 is described as the subject of processing, this specifically means that the processor 3001 performs calculations by loading predetermined software (programs) onto hardware such as the processor 3001 and memory 3002, and controls communication via the communication device 3004 and reading and / or writing of data from and to the memory 3002 and storage 3003, thereby executing the processing.

[0041] [Learning behavior] First, the learning operation by the information processing device 30 will be explained. In Figure 9, the acquisition unit 31 acquires user information for multiple users from the user DB 50 (step S11).

[0042] Next, the learning unit 32 generates training data to be used in a predetermined machine learning algorithm using the user information acquired by the acquisition unit 31 (step S12). Then, the learning unit 32 generates a forced churn rate model and a usage prediction model by performing machine learning using the training data and the machine learning algorithm (step S13).

[0043] The storage unit 33 stores the forced churn rate model and the usage prediction model generated by the learning unit 32 (step S14).

[0044] [Determination of spending limit] First, the operation of the information processing device 30 to determine the usage limit will be explained. The acquisition unit 31 acquires user information from the user DB 50 regarding the user for whom the usage limit is to be determined (step S21).

[0045] Next, the first decision unit 34 inputs the user information acquired by the acquisition unit 31 that corresponds to the explanatory information for each model to the forced cancellation rate model and the usage prediction model stored in the memory unit 33 (step S22).

[0046] Then, the first decision unit 34 determines the maximum amount of money that the user can use in financial services based on the information obtained from each model (step S23).

[0047] The credit limit determined by the first determination unit 34 is stored in the storage unit 33 as user information relating to the user (step S24).

[0048] According to the embodiment described above, by using the forced cancellation rate model and the usage prediction model and user information about the user, it is possible to appropriately determine the usage limit that the user can use in financial services.

[0049] [Variations] The present invention is not limited to the above-described embodiment. The above-described embodiment may be modified as follows. Furthermore, two or more of the following modifications may be combined and implemented. [Variation 1] In the above embodiment, the user's credit limit was determined using a forced churn rate model (first trained model) and a usage prediction model (second trained model). Alternatively, the information processing device 30 may be modified to include a second determination unit 35 and a setting unit 36, as shown in FIG. 11 . The second determination unit 35 determines the credit limit for each user using a rule that associates information about the user with the credit limit within a range of low credit limits (e.g., 80,000 yen or less, referred to as the first range) rather than using the forced churn rate model (first trained model) and the usage prediction model (second trained model). Then, the first determination unit 24 determines the user's credit limit within a range of high credit limits (e.g., over 80,000 yen, referred to as the second range) using the forced churn rate model (first trained model) and the usage prediction model (second trained model). In other words, for users with small credit limits, the credit limit is determined according to pre-determined rules using the user's attributes such as age and past payment history, while for users with large credit limits, the credit limit is determined using a machine learning algorithm.

[0050] When determining the credit limit within the first range using rules, specifically, as shown in Fig. 12, rules are used that associate each credit limit (e.g., 30,000 yen, 50,000 yen, 80,000 yen) with a combination of information about the user, such as the user's age, contract period for financial services, whether or not there have been any late payments in the past certain period, and the amount of credit used in the past certain period. These rules are stored in the second determination unit 35.

[0051] [Variation 2] In the above modified example 1, the setting unit 36 ​​may set the usage amount that forms the boundary between the first range and the second range based on the aforementioned forced cancellation rate score. Specifically, the setting unit 36 ​​compares the forced cancellation rate score obtained for each user with the actual result of the financial service being canceled, and sets the boundary between the first range and the second range of the usage limit by setting a usage limit such that the degree of agreement between the two exceeds a certain threshold.

[0052] [Variation 3] In the above modified example, users may be divided into multiple groups based on their forced cancellation score, and a different usage limit may be set for each group. Specifically, as shown in Figure 13, the setting unit 36 ​​divides users into, for example, a low-risk score group with a relatively low forced cancellation score, a high-risk score group with a relatively high forced cancellation score, and a medium-risk score group whose forced cancellation score falls between the low-risk and high-risk score groups, according to their forced cancellation rate score. Then, as illustrated in the table in Figure 14, the first decision unit 34 does not change the usage limit for users belonging to the high-risk group, increases the usage limit for users belonging to the low-risk group, and determines a usage limit for users belonging to the medium-risk group according to their usage prediction score.

[0053] [Variation 4] In the above modified example, the setting unit 36 ​​may set the churn rate risk corresponding to the boundary between the high-risk group and the medium-risk group based on the average commission rate paid from the store where the user uses the financial service to the provider of that financial service. In this case, the setting unit 36 ​​stores the commission rate paid from each store (merchant) to the provider of financial service, as shown in Figure 15. For example, if the average commission rate paid to the provider of financial service is 4% of the amount used, the setting unit 36 ​​sets the forced churn rate score corresponding to the boundary between the high-risk group and the medium-risk group to 0.04. Alternatively, the setting unit 36 ​​may set the risk corresponding to the boundary between the medium-risk group and the low-risk group based on the minimum commission rate paid from the store where the user uses the financial service to the provider of that financial service. For example, if the minimum commission rate paid to the provider of financial service is 1% of the amount used, the forced churn rate score corresponding to the boundary between the medium-risk group and the low-risk group is set to 0.01.

[0054] [Other variations] 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 or wireless connections). A functional block may also be realized by combining the above one device or the above multiple devices with software.

[0055] Functions include, but are not limited to, judgment, determination, assessment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0056] For example, an information processing device according to an embodiment of the present disclosure may function as a computer that performs the processing of the present disclosure.

[0057] Each aspect / embodiment described in the present disclosure may be applied to at least one of systems using LTE (Long Term Evolution), LTE-Advanced (LTE-A), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), FRA (Future Radio Access), NR (New Radio), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark), IEEE 802.20, UWB (Ultra-Wideband), Bluetooth (registered trademark), or other appropriate systems, and next-generation systems extended based on these. Furthermore, a combination of multiple systems (e.g., a combination of at least one of LTE and LTE-A with 5G, etc.) may also be applied.

[0058] The present invention may also be an information processing method comprising the steps of: acquiring user information about users who use financial services; and determining a credit limit that each user can use in the financial services using a first trained model generated by machine learning using the user information as an explanatory variable and churn risk information related to the churn risk of forcibly terminating the financial services as an objective variable; a second trained model generated by machine learning using the user information as an explanatory variable and usage amount information related to the amount used by the user in the financial services as an objective variable; and the user information acquired for each user. However, the order of the processing procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed as long as there is no contradiction. For example, the method described in this disclosure presents various step elements in an exemplary order and is not limited to the specific order presented.

[0059] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be sent to another device.

[0060] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0061] Although the present disclosure has been described in detail above, it is 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 spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0062] Software, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise, shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc. Additionally, software, instructions, information, etc. may be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then such wired and / or wireless technologies are included within the definition of a transmission medium.

[0063] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof. In addition, terms explained in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

[0064] Furthermore, the information, parameters, etc., described in this disclosure may be expressed using absolute values, relative values ​​from a predetermined value, or corresponding other information.

[0065] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0066] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0067] In the above-described configuration of each device, the term "part" may be replaced with "means," "circuit," "device," etc.

[0068] When used in this disclosure, the terms "include," "including," and variations thereof are intended to be inclusive, similar to the term "comprising." Furthermore, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0069] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0070] In the present 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 "coupled" may also be interpreted in the same way as "different." [Explanation of symbols]

[0071] 1: Information processing system, 2: Communication network, 10: User terminal, 20: Store terminal, 30: Information processing device, 31: Acquisition unit, 32: Learning unit, 33: Memory unit, 34: First determination unit, 35: Second determination unit, 36: Setting unit, 3001: Processor, 3002: Memory, 3003: Storage, 3004: Communication device, 3005: Input device, 3006: Output device, 40: Payment system, 50: User database.

Claims

1. an acquisition unit that acquires user information relating to users who use financial services; a first determination unit that determines a credit limit that a user can use in the financial service, using a first trained model by machine learning that uses the user information as an explanatory variable and cancellation risk information related to the cancellation risk of the financial service being forcibly canceled by the financial service provider as an objective variable, a second trained model by machine learning that uses the user information as an explanatory variable and usage amount information related to the amount used by the user in the financial service as an objective variable, and the user information acquired for each user by the acquisition unit; and Equipped with The first determination unit increases the usage limit of each user when, for each user, the cancellation risk information obtained by inputting user information about the user into the first trained model means that the risk is lower than a first level, and the usage amount information obtained by inputting user information about the user into the second trained model means that the amount is higher than a second level; The first level is determined using a ratio of the number of users for whom the first decision unit has determined that the credit limit should be increased and a ratio of users for whom the first decision unit has not determined that the credit limit should be increased and a ratio of users for whom the financial service should not be forcibly canceled.

1. An information processing device comprising:

2. An acquisition unit that acquires user information regarding users who use financial services; a first determination unit that determines a credit limit that a user can use in the financial service, using a first trained model by machine learning that uses the user information as an explanatory variable and cancellation risk information related to the cancellation risk of the financial service being forcibly canceled by the financial service provider as an objective variable, a second trained model by machine learning that uses the user information as an explanatory variable and usage amount information related to the amount used by the user in the financial service as an objective variable, and the user information acquired for each user by the acquisition unit; and Equipped with The first determination unit increases the usage limit of each user when, for each user, the cancellation risk information obtained by inputting user information about the user into the first trained model means that the risk is lower than a first level, and the usage amount information obtained by inputting user information about the user into the second trained model means that the amount is higher than a second level; The second level is determined using the rate at which the amount of money used by a user for whom it has been determined by the first determination unit that the credit limit should be increased does not exceed an amount equivalent to a certain percentage of the determined credit limit, and the rate at which the amount of money used by a user for whom it has not been determined by the first determination unit that the credit limit should be increased exceeds an amount equivalent to a certain percentage of the credit limit.

1. An information processing device comprising:

3. The first decision unit reduces the usage limit of each user when the cancellation risk information obtained by the first trained model means a higher risk than a third level and the usage amount information obtained by the second trained model means a lower amount than a fourth level.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

4. When increasing the credit limit for each of the users, the first determination unit increases the credit limit for the user by one level among a plurality of levels of credit limits having different sizes.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

5. When reducing the credit limit for each of the users, the first determination unit reduces the credit limit for the user by one level among a plurality of levels of credit limits having different sizes.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

6. The first determination unit reduces the credit limit of each user as the risk indicated by the cancellation risk information increases, and increases the credit limit of the user as the amount indicated by the credit limit information increases.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

7. a second determination unit that determines a credit limit for each user using a rule that associates information about the user with the credit limit within a first range of a credit limit that the user can use in financial services; The first determination unit determines the credit limit for each of the users within a second range in which the credit limit is greater than the first range, using the first trained model, the second trained model, and the user information.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

8. A step in which a computer acquires user information regarding a user who uses a financial service; a step in which a computer determines a credit limit that a user can use in the financial service by using a first trained model by machine learning in which the user information is an explanatory variable and cancellation risk information related to the cancellation risk of the financial service being forcibly canceled is an objective variable, a second trained model by machine learning in which the user information is an explanatory variable and usage amount information related to the amount used by the user in the financial service is an objective variable, and the user information acquired for each user; Equipped with In the determining step, when the computer determines that, for each of the users, the cancellation risk information obtained by inputting user information about the user into the first trained model means that the risk is lower than a first level, and the usage amount information obtained by inputting user information about the user into the second trained model means that the amount is higher than a second level, the computer increases the usage limit of the user; The first level is determined using a ratio of the number of users for whom the financial service was forcibly terminated in the determining step for which the credit limit was to be increased, and a ratio of users for whom the financial service was not forcibly terminated in the determining step for which the credit limit was not to be increased.

1. An information processing method comprising:

9. A step in which a computer acquires user information regarding a user who uses a financial service; a step in which a computer determines a credit limit that a user can use in the financial service by using a first trained model by machine learning in which the user information is an explanatory variable and cancellation risk information related to the cancellation risk of the financial service being forcibly canceled is an objective variable, a second trained model by machine learning in which the user information is an explanatory variable and usage amount information related to the amount used by the user in the financial service is an objective variable, and the user information acquired for each user; Equipped with In the determining step, when the computer determines that, for each of the users, the cancellation risk information obtained by inputting user information about the user into the first trained model means that the risk is lower than a first level, and the usage amount information obtained by inputting user information about the user into the second trained model means that the amount is higher than a second level, the computer increases the usage limit of the user; The second level is determined using the percentage of the amount of money used by a user for whom it has been determined in the determining step that the credit limit should be increased that does not exceed an amount equivalent to a certain percentage of the determined credit limit, and the percentage of the amount of money used by a user for whom it has not been determined in the determining step that the credit limit should be increased that exceeds an amount equivalent to a certain percentage of the credit limit.

1. An information processing method comprising:

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