JP2019204327A - Information processing device - Google Patents

Abstract

To provide an information processing device capable of estimating an amount to spend in a settlement service with excellent accuracy.SOLUTION: An information processing device 10 is a device for estimating an amount to spend in a prescribed settlement service by a user, and that comprises: an acquisition section 11 for acquiring service use information for each user including information relating to the use of settlement service; an amount-to-spend estimation model generation section 13 for generating an amount-to-spend estimation model for estimating an amount to spend in the settlement service for each user on the basis of the service use information for each user acquired by the acquisition section 11; and an amount-to-spend estimation section 15 for, based on the service use information of one user, estimating an amount to spend in the settlement service for the one user by applying the amount-to-spend estimation model.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an information processing apparatus.

  A technique for estimating various information related to a user based on historical information such as user behavior and service usage is known. For example, Patent Literature 1 discloses a technique for determining the user's credit information based on the history of the user's position information.

JP 2018-45599 A

  In recent years, payment services using functions of mobile terminals have been provided. In such a settlement service, it is required to accurately estimate the usage amount of the service by the user for proper operation and management.

  Therefore, the present invention has been made in view of the above problems, and an object of the present invention is to provide an information processing apparatus capable of accurately estimating the usage amount of a settlement service.

  In order to solve the above-described problem, an information processing apparatus according to an aspect of the present invention is an information processing apparatus that estimates a usage amount of a predetermined settlement service by a user, and includes information for each user including information on the use of the settlement service. An acquisition unit that acquires service usage information, and a first model generation that generates a usage amount estimation model for estimating a usage amount of a settlement service for each user based on the service usage information for each user acquired by the acquisition unit And a usage amount estimation unit that estimates the usage amount of the settlement service of the one user using a usage amount estimation model based on the service usage information of the one user.

  According to the above aspect, the usage amount estimation model is generated based on the service usage information including information related to the use of the settlement service by the user. Therefore, since a usage amount estimation model that reflects at least information related to the use of the settlement service is used, the usage amount of the user's settlement service can be accurately estimated.

  It is possible to provide an information processing apparatus that can accurately estimate the amount of payment service usage.

It is a figure which shows the functional structure of the information processing apparatus which concerns on this embodiment. It is a hardware block diagram of an information processor. It is a flowchart which shows the production | generation process of an estimation model. It is a figure which shows the example of the feature-value based on service utilization information including the information regarding utilization of a payment service. It is a figure which shows the example of the feature-value based on service utilization information including the information regarding utilization of a communication service. It is a flowchart which shows the processing content of the estimation of a usage amount, and the setting of a usage amount limit. It is a figure which shows the structure of an information processing program.

  An embodiment of an information processing apparatus according to the present invention will be described with reference to the drawings. If possible, the same parts are denoted by the same reference numerals, and redundant description is omitted.

  FIG. 1 is a diagram illustrating a functional configuration of the information processing apparatus 10 according to the present embodiment. The information processing apparatus 10 is an apparatus that estimates a usage amount of a predetermined settlement service by a user. Furthermore, the information processing apparatus 10 may set a usage limit amount for the settlement service based on the estimated usage amount. In addition, the information processing apparatus 10 may further calculate the user's reliability. In this case, the information processing apparatus 10 can set the usage limit amount for the settlement service based on the estimated usage amount and the calculated creditworthiness.

  In the example illustrated in FIG. 1, the information processing apparatus 10 includes an acquisition unit 11, an acquisition data processing unit 12, a usage amount estimation model generation unit 13 (first model generation unit), and a credit calculation model generation unit 14 (second model generation unit). ), A usage amount estimation unit 15, a credit calculation unit 16, and a usage limit setting unit 17. Each functional unit included in the information processing apparatus 10 according to the present embodiment may be configured as one apparatus or may be distributed among a plurality of apparatuses.

  Further, the information processing apparatus 10 includes storage means such as a service usage information storage unit 21, a model storage unit 22, and an estimated usage amount storage unit 23. Each storage unit included in the information processing apparatus 10 according to the present embodiment may be configured in the information processing apparatus 10 or may be configured in another apparatus that can communicate with the information processing apparatus 10.

  The block diagram shown in FIG. 1 shows functional unit blocks. These functional blocks (components) are realized by any combination of hardware and / or software. Further, the means for realizing each functional block is not particularly limited. That is, each functional block may be realized by one device physically and / or logically coupled, and two or more devices physically and / or logically separated may be directly and / or indirectly. (For example, wired and / or wireless) and may be realized by these plural devices.

  For example, the information processing apparatus 10 according to an embodiment of the present invention may function as a computer. FIG. 2 is a diagram illustrating an example of a hardware configuration of the information processing apparatus 10 according to the present embodiment. The information processing apparatus 10 may be physically configured as a computer apparatus including a processor 1001, a memory 1002, a storage 1003, a communication apparatus 1004, an input apparatus 1005, an output apparatus 1006, a bus 1007, and the like.

  In the following description, the term “apparatus” can be read as a circuit, a device, a unit, or the like. The hardware configuration of the information processing apparatus 10 may be configured to include one or a plurality of each device illustrated in FIG. 2, or may be configured not to include some devices.

  Each function in the information processing apparatus 10 reads predetermined software (program) on hardware such as the processor 1001 and the memory 1002, so that the processor 1001 performs calculation, and communication by the communication apparatus 1004, memory 1002, and storage This is realized by controlling reading and / or writing of data in 1003.

  For example, the processor 1001 controls the entire computer by operating an operating system. The processor 1001 may be configured by a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic device, a register, and the like. Further, the processor 1001 may include a GPU (Graphics Processing Unit). For example, the function units 11 to 17 illustrated in FIG. 1 may be realized by the processor 1001.

  Further, the processor 1001 reads a program (program code), software module, and data from the storage 1003 and / or the communication device 1004 to the memory 1002, and executes various processes according to these. As the program, a program that causes a computer to execute at least a part of the operations described in the above embodiments is used. For example, the function units 11 to 15 and 21 to 24 of the information processing apparatus 10 may be realized by a control program stored in the memory 1002 and operating on the processor 1001. Although the above-described various processes have been described as being executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. Note that the program may be transmitted from a network via a telecommunication line.

  The memory 1002 is a computer-readable recording medium and includes, for example, at least one of ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), and the like. May be. The memory 1002 may be called a register, a cache, a main memory (main storage device), or the like. The memory 1002 can store a program (program code), a software module, and the like that can be executed to execute the shelf allocation information generation method according to the embodiment of the present invention.

  The storage 1003 is a computer-readable recording medium, such as an optical disc such as a CD-ROM (Compact Disc ROM), a hard disc drive, a flexible disc, a magneto-optical disc (eg, a compact disc, a digital versatile disc, a Blu-ray). (Registered trademark) disk, smart card, flash memory (for example, card, stick, key drive), floppy (registered trademark) disk, magnetic strip, and the like. The storage 1003 may be referred to as an auxiliary storage device. The storage medium described above may be, for example, a database, server, or other suitable medium including the memory 1002 and / or the storage 1003.

  The communication device 1004 is hardware (transmission / reception device) for performing communication between computers via a wired and / or wireless network, and is also referred to as a network device, a network controller, a network card, a communication module, or the like.

  The input device 1005 is an input device (for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts an input from the outside. The output device 1006 is an output device (for example, a display, a speaker, an LED lamp, etc.) that performs output to the outside. The input device 1005 and the output device 1006 may have an integrated configuration (for example, a touch panel).

  Each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information. The bus 1007 may be configured with a single bus or may be configured with different buses between apparatuses.

  The information processing apparatus 10 includes hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), and a field programmable gate array (FPGA). A part or all of each functional block may be realized by the hardware. For example, the processor 1001 may be implemented by at least one of these hardware.

  With reference to FIG. 1 again, each functional unit of the information processing apparatus 10 will be described. The acquisition unit 11 acquires service usage information. Specifically, the acquisition unit 11 acquires service usage information stored in advance in the service usage information storage unit 21, for example.

  The service usage information storage unit 21 is a storage unit that stores service usage information. The service usage information is information that can constitute a feature quantity useful for estimating the usage amount of a user's payment service and calculating creditworthiness. For example, information on the use of a predetermined payment service and predetermined communication for each user. Information regarding the use of the service may be included. Specifically, the service usage information may include history information regarding service usage, performance information, contract information, attribute information, setting information, and the like.

  The service usage information may include, for example, payment service usage history, user (user) attribute information, mobile terminal information used for the payment service, and the like as information related to payment service usage.

  Further, the service usage information may include, for example, information on mobile terminals owned by the user, communication service usage history, communication service contract information, and the like as information on communication service usage.

  Information on the use of these communication services can be easily collected in a carrier that provides communication services by mobile terminals. In addition, information related to the use of a payment service can be easily collected by a payment service provider. Furthermore, when a carrier of a communication service provides a payment service using a mobile terminal, the carrier can easily collect both information related to use of the communication service and information related to use of the payment service.

  The acquisition data processing unit 12 processes the service usage information acquired by the acquisition unit 11 into a data format as a feature amount for model generation or a data format as a feature amount for application to a generated model. (Convert. For example, the acquisition data processing unit 12 may perform a predetermined normalization process or the like as the service use information processing process.

  The usage amount estimation model generation unit 13 generates a usage amount estimation model for estimating the usage amount of the settlement service for each user based on the service usage information for each user acquired by the acquisition unit 11. A model generation process will be described with reference to FIGS. FIG. 3 is a flowchart showing model generation processing. FIG. 4 is a diagram illustrating an example of a feature amount based on service usage information including information related to usage of a settlement service.

  In the model generation process, as shown in FIG. 3, first, in step S1, the acquisition unit 11 acquires service usage information of a plurality of users. Next, in step S2, the acquisition data processing unit 12 processes the service usage information acquired by the acquisition unit 11 into a data format as a feature amount for model generation.

  As shown in FIG. 4, the feature amount for generating the usage amount estimation model is the usage amount Y1 which is an explanatory variable and an objective variable such as contractor age x11, settlement amount x12, and settlement count x13 for each of a plurality of users. including. In the example shown in FIG. 4, information related to the use of the settlement service is mainly used as the feature amount, but the information used as the feature amount is not limited to these information.

  In subsequent step S3, the usage amount estimation model generation unit 13 generates a usage amount estimation model based on the feature amount illustrated in FIG. An example of generating a usage amount estimation model is shown below.

The generation of the usage amount estimation model as an example corresponds to obtaining the coefficient of the following equation.
Usage Y1 = a10 + a11.x11 + a12.x12 + a13.x13 + ... + a1n.x1n
(X11 to x1n: variable, a11 to a1n: coefficient, a10: constant term (correction value))
... (1)

  Specifically, the usage amount estimation model generation unit 13 applies a feature amount set (explanatory variables x11 to x1n, objective variable Y1) related to a plurality of users as shown in FIG. A usage amount estimation model is generated by calculating coefficients a11 to a1n using a least square method or the like.

  The generation of the usage amount estimation model is not limited to the method based on the calculation of the coefficient of the above formula (1). For example, a model including a neural network that uses the explanatory variable and the objective variable shown in FIG. 4 as input values (features) and output values, respectively, may be generated by machine learning (deep learning), and used as a usage amount estimation model.

  In step S <b> 4, the usage amount estimation model generation unit 13 stores the generated model in the model storage unit 22.

  As described above, the model storage unit 22 is a storage unit that stores the generated model.

  The credit calculation model generation unit 14 generates a credit calculation model for calculating the user's credit based on the service usage information for each user acquired by the acquisition unit 11. The credit level calculation model generation process is represented by the flowchart shown in FIG. 3, similarly to the usage amount estimation model generation process. FIG. 5 is a diagram illustrating an example of a feature amount based on service usage information including information related to the use of a user's communication service as information useful for calculation of creditworthiness.

  In the generation process of the credit calculation model, as shown in FIG. 3, first, in step S1, the acquisition unit 11 acquires service usage information of a plurality of users. Next, in step S2, the acquisition data processing unit 12 processes the service usage information acquired by the acquisition unit 11 into a data format as a feature amount for model generation.

  As shown in FIG. 5, the feature amount for generating the credit calculation model includes explanatory variables such as the number of arrears x21 and the contract period x22 and the trustworthiness Y2 that is an objective variable for each of a plurality of users. In the example shown in FIG. 5, information related to the use of the communication service is mainly used as the feature amount, but the information used as the feature amount is not limited to such information.

  For example, the service usage information used as the feature amount for generating the model may include position information acquired as the user uses the communication service.

  In subsequent step S3, the credit calculation model generation unit 14 generates a credit calculation model based on the feature quantity illustrated in FIG. An example of generating a credit calculation model is shown below.

The generation of the credit calculation model as an example corresponds to obtaining a coefficient of the following formula.
Trust Y2 = a20 + a21 * x21 + a22 * x22 + a23 * x23 + ... + a2n * x2n
(X21 to x2n: variable, a21 to a2n: coefficient, a20: constant term (correction value))
... (2)

  Specifically, the credit calculation model generation unit 14 applies a feature amount set (explanatory variables x21 to x2n, objective variable Y2) related to a plurality of users as shown in FIG. By calculating the coefficients a21 to a2n using a square method or the like, a credit calculation model model is generated.

  The generation of the credit calculation model is not limited to the method based on the calculation of the coefficient of the above formula (2). For example, a model including a neural network that uses the explanatory variable and the objective variable shown in FIG. 5 as input values (features) and output values, respectively, may be generated by machine learning (deep learning) to be used as a reliability calculation model.

  In step S <b> 4, the credit calculation model generation unit 14 stores the generated credit calculation model in the model storage unit 22.

  Referring to FIG. 1 again, the usage amount estimation unit 15 estimates the usage amount of the settlement service of the one user using the usage amount estimation model based on the service usage information of the one user.

  Specifically, in order to estimate the usage amount of one user, the acquisition unit 11 acquires service usage information related to the one user and corresponding to the explanatory variable input to the usage amount estimation model. To do. Next, the acquired data processing unit 12 processes the acquired service usage information into a data format as a feature amount to be applied to the usage amount estimation model. And the usage amount estimation part 15 inputs the processed service usage information regarding one user into the usage amount estimation model memorize | stored in the model memory | storage part, and obtains the usage amount which is the output. The usage amount estimation unit 15 stores the obtained usage amount in the estimated usage amount storage unit 23.

  As described above, the estimated usage amount storage unit 23 is a storage unit that stores the usage amount of each user estimated by the usage amount estimation unit 15.

  The credit level calculation unit 16 calculates the credit level of the one user using the credit level calculation model based on the service usage information of the one user. Specifically, in order to calculate the credit rating of one user, the acquisition unit 11 acquires service usage information relating to the one user and corresponding to the explanatory variable input to the credit rating calculation model. Next, the acquired data processing unit 12 processes the acquired service usage information into a data format as a feature amount to be applied to the credit calculation model. Then, the credit calculation unit 16 inputs the processed service usage information related to one user to the credit calculation model stored in the model storage unit, and obtains the credit that is the output. Note that in the generation of the credit calculation model and the calculation of the credit, the credit may be calculated as a forced churn probability related to one user in a predetermined communication service and / or settlement service.

  Based on the usage amount estimated by the usage amount estimation unit 15, the usage limit setting unit 17 sets a usage limit amount for one user's payment service. Specifically, the usage limit setting unit 17 may set, as the usage limit, a value obtained by multiplying the usage amount of one user stored in the estimated usage amount storage unit 23 by a predetermined coefficient.

  Further, the usage limit setting unit 17 further uses the credit level (forced cancellation probability of the settlement service) calculated by the credit level calculation unit 16 to determine the credit level value, the usage level value, the usage limit level, and the usage limit amount for one user. You may set based on the usage limit table which linked | related.

  The usage limit setting unit 17 outputs information on the set usage limit in a predetermined manner. That is, the usage limit setting unit 17 outputs the set usage limit to a predetermined system and sets it as a set value, stores it in a predetermined storage means, or displays it on a predetermined display device.

  Next, with reference to FIG. 6, an information processing method according to the usage limit setting processing in the information processing apparatus 10 will be described. FIG. 6 is a flowchart showing the processing contents of the information processing method of this embodiment.

  In step S <b> 11, the acquisition unit 11 acquires service usage information of a user for which a usage limit is set. In step S12, the acquired data processing unit 12 processes the acquired service usage information into a data format as a feature amount to be applied to the usage amount estimation model and / or the credit calculation model.

  In step S13, the usage amount estimation unit 15 estimates the usage amount of the user's settlement service using the usage amount estimation model based on the processed service usage information. Further, the creditworthiness calculation unit 16 may calculate the creditworthiness of the user using a creditworthiness calculation model based on the processed service use information.

  In step S14, the usage limit setting unit 17 sets the usage limit for the settlement service of the user based on the estimated usage amount. Further, the usage limit setting unit 17 may set the usage limit by further using the calculated reliability.

  Next, an information processing program for causing a computer to function as the information processing apparatus 10 of this embodiment will be described. FIG. 7 is a diagram showing the configuration of the information processing program P1.

  The information processing program P1 includes a main module m10 that comprehensively controls information processing in the information processing apparatus 10, an acquisition module m11, an acquisition data processing module m12, a usage amount estimation model generation module m13, a credit calculation model generation module m14, a usage amount An estimation module m15, a credit calculation module m16, and a usage limit setting module m17 are provided. Then, by each module m11 to m17, the acquisition unit 11, the acquisition data processing unit 12, the usage amount estimation model generation unit 13, the credit level calculation model generation unit 14, the usage amount estimation unit 15, the credit level calculation unit 16, and the information processing device 10 Each function for the usage limit setting unit 17 is realized. The information processing program P1 may be transmitted through a transmission medium such as a communication line, or may be stored in the recording medium M1 as shown in FIG.

  In the information processing apparatus 10 of the present embodiment described above, a usage amount estimation model is generated based on service usage information including information related to the use of a settlement service by a user. Therefore, since a usage amount estimation model that reflects at least information related to the use of the settlement service is used, the usage amount of the user's settlement service can be accurately estimated.

  In addition, the information processing apparatus according to another aspect further includes a usage limit setting unit that sets a usage limit amount for one user's payment service based on the usage amount estimated by the usage amount estimation unit. Also good.

  According to the said form, the usage limit amount of the payment service of one user is set based on the usage amount estimated using the usage amount estimation model. Therefore, an appropriate usage limit can be set based on the usage amount with high accuracy.

  Moreover, in the information processing apparatus according to another aspect, based on the service usage information, the second model generation unit that generates a credit level calculation model for calculating the credit level of the user, and based on the service usage information of one user A credit level calculation unit that calculates the credit level of the one user using a credit level calculation model, and the usage limit setting unit may set the usage limit based on the usage amount and the credit rating. .

  According to the said form, the creditworthiness of one user is calculated using the creditworthiness calculation model produced | generated based on service utilization information. Since this credit quality reflects the risk in settlement, it is possible to set an appropriate usage limit considering the risk.

  In the information processing apparatus according to another aspect, the usage limit setting unit may set the usage limit based on a preset association between the credit value and the usage amount and the usage limit. Good.

  According to the above aspect, it is possible to set an appropriate usage limit based on the credit rating and the usage amount.

  In the information processing apparatus according to another aspect, the service use information may include information related to use of a predetermined communication service by the user.

  The information related to the use of the communication service reflects the user's credit rating and the prospect of using the payment service by the user. According to the said form, since the information regarding utilization of a communication service is contained in service utilization information, the precision of calculation of creditworthiness and estimation of a usage amount improves.

  In the information processing apparatus according to another aspect, the service usage information may include position information acquired with the use of the user's communication service.

  According to the said form, a user's reliability and the expectation of utilization of the payment service by a user are reflected in a user's positional information. According to the said form, since a user's positional information is contained in service utilization information, the precision of calculation of a reliability and estimation of a usage amount improves.

  Although the present embodiment has been described in detail above, it will be apparent to those skilled in the art that the present embodiment is not limited to the embodiment described in this specification. The present embodiment can be implemented as a modification and change without departing from the spirit and scope of the present invention defined by the description of the scope of claims. Therefore, the description of the present specification is for illustrative purposes and does not have any limiting meaning to the present embodiment.

  As long as there is no contradiction, the order of the processing procedures, sequences, flowcharts, and the like of each aspect / embodiment described in this specification may be changed. For example, the methods described herein present the elements of the various steps in an exemplary order and are not limited to the specific order presented.

  Information or the like can be output from the upper layer (or lower layer) to the lower layer (or upper layer). Input / output may be performed via a plurality of network nodes.

  Input / output information and the like may be stored in a specific location (for example, a memory) or may be managed by a management table. Input / output information and the like can be overwritten, updated, or additionally written. The output information or the like may be deleted. The input information or the like may be transmitted to another device.

  The determination may be performed by a value represented by 1 bit (0 or 1), may be performed by a true / false value (Boolean: true or false), or may be performed by comparing numerical values (for example, a predetermined value) Comparison with the value).

  Each aspect / embodiment described in this specification may be used independently, may be used in combination, or may be switched according to execution. In addition, notification of predetermined information (for example, notification of being “X”) is not limited to explicitly performed, but is performed implicitly (for example, notification of the predetermined information is not performed). Also good.

  Software, whether it is called software, firmware, middleware, microcode, hardware description language, or other names, instructions, instruction sets, code, code segments, program codes, programs, subprograms, software modules , Applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, etc. should be interpreted broadly.

  Also, software, instructions, etc. may be transmitted / received via a transmission medium. For example, software may use websites, servers, or other devices using wired technology such as coaxial cable, fiber optic cable, twisted pair and digital subscriber line (DSL) and / or wireless technology such as infrared, wireless and microwave. When transmitted from a remote source, these wired and / or wireless technologies are included within the definition of transmission media.

  Information, signals, etc. described herein may be represented using any of a variety of different technologies. For example, data, commands, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description are voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, light fields or photons, or any of these May be represented by a combination of

  Note that the terms described in this specification and / or terms necessary for understanding this specification may be replaced with terms having the same or similar meaning.

  As used herein, the terms “system” and “network” are used interchangeably.

  In addition, information, parameters, and the like described in this specification may be represented by absolute values, may be represented by relative values from a predetermined value, or may be represented by other corresponding information. .

  As used herein, the phrase “based on” does not mean “based only on,” unless expressly specified otherwise. In other words, the phrase “based on” means both “based only on” and “based at least on.”

  Where the designation "first", "second", etc. is used herein, any reference to that element does not generally limit the amount or order of those elements. These designations can be used herein as a convenient way to distinguish between two or more elements. Thus, a reference to the first and second elements does not mean that only two elements can be employed there, or that in some way the first element must precede the second element.

  These terms are similar to the term “comprising” as long as “include”, “including” and variations thereof are used herein or in the claims. It is intended to be comprehensive. Further, the term “or” as used herein or in the claims is not intended to be an exclusive OR.

  In this specification, a plurality of devices are also included unless there is only one device that is clearly present in context or technically.

  Throughout this disclosure, the plural is included unless the context clearly indicates one.

  DESCRIPTION OF SYMBOLS 10 ... Information processing apparatus, 11 ... Acquisition part, 12 ... Acquisition data processing part, 13 ... Usage amount estimation model generation part, 14 ... Reliability calculation model generation part, 15 ... Usage amount estimation part, 16 ... Credit calculation part, 17 ... Usage limit setting unit, 21 ... Service usage information storage unit, 22 ... Model storage unit, 23 ... Estimated usage amount storage unit, M1 ... Recording medium, m10 ... Main module, m11 ... Acquisition module, m12 ... Acquisition data processing module, m13: Usage amount estimation model generation module, m14: Credit calculation model generation module, m15 ... Usage amount estimation module, m16 ... Credit calculation module, m17 ... Usage limit setting module, P1 ... Information processing program.

Claims (6)

An information processing apparatus for estimating a usage amount of a predetermined settlement service by a user,
An acquisition unit for acquiring service usage information for each user including information on the use of the payment service;
A first model generation unit that generates a usage amount estimation model for estimating a usage amount of the settlement service for each user based on the service usage information for each user acquired by the acquisition unit;
A usage amount estimation unit that estimates the usage amount of the settlement service of the one user using the usage amount estimation model based on the service usage information of the one user;
An information processing apparatus comprising: A usage limit setting unit that sets a usage limit of the settlement service of the one user based on the usage amount estimated by the usage amount estimation unit;
The information processing apparatus according to claim 1. A second model generation unit for generating a credit calculation model for calculating the credit of the user based on the service usage information;
A credit degree calculation unit that calculates the credit degree of the one user using the credit degree calculation model based on the service usage information of the one user;
The usage limit setting unit sets the usage limit based on the usage amount and the creditworthiness.
The information processing apparatus according to claim 2. The usage limit setting unit sets the usage limit based on a preset association between the credit value and the usage amount and the usage limit.
The information processing apparatus according to claim 3. The service use information includes information related to use of a predetermined communication service by a user.
The information processing apparatus according to any one of claims 1 to 4. The service usage information includes location information acquired along with the use of the communication service of a user.
The information processing apparatus according to claim 5.

Images