JP6868075B2 - Insurance product presentation device and program - Google Patents

Description

The present invention relates to an insurance product presentation device and a program.

There is a computer system that receives an answer to a question to a customer and determines a product or service to be proposed to the customer based on the answer (see, for example, Patent Document 1).
[Prior art literature]
[Patent Document]
[Patent Document 1] Patent No. 6326538

However, the above-mentioned computer system requires the customer to answer the question, and cannot be said to be customer-friendly. In particular, when a product or service is related to insurance, it is required to utilize this information because the information has already been obtained from the insurance policyholder at the time of contract.

In order to solve the above problems, in the first aspect of the present invention, a storage unit for storing information about the policyholder and the insurance policy number before renewal in association with the insurance policy number, and a specific insurance policy number are supported. A contract is made with a reading unit that reads out the information stored with the information and the information stored in association with the insurance policy number before renewal associated with a specific insurance policy number from the storage unit, and information about the policyholder. An insurance product presentation device including a presentation unit that presents an insurance product with respect to the information read by the reading unit is provided by using a machine learning model that learns the relationship with the insurance product.

In the second aspect of the present invention, the computer has a storage function for storing information about the policyholder and the insurance policy number before renewal in the storage unit in association with the insurance policy number, and the insurance policy number in association with a specific insurance policy number. A read function that reads out the stored information and the information stored in association with the insurance policy number before renewal associated with a specific insurance policy number from the storage unit, and insurance contracted with information about the policyholder. A program is provided that realizes a presentation function of presenting insurance products for information read by a reading function using a machine learning model that has learned the relationship with products.

The outline of the above invention does not list all the necessary features of the present invention. Sub-combinations of these feature groups can also be inventions.

The functional block of the insurance product presentation device 10 according to the first embodiment is shown. An example of the contractor information stored in the contractor information DB 130 is shown. An example of the operation flow of the insurance product presentation device 10 is shown. An example of the operation flow of step S10 for integrating the contractor information is shown. An example of contractor information and the like stored in the integrated information storage unit 100 is shown. This is an example of a decision tree that analyzes policyholders of insurance products. An example of the operation flow of step S30 for presenting the insurance product is shown. The functional block of the insurance product presentation device 12 according to the second embodiment is shown. An example of recruitment information which is information about recruitment stored in recruitment information DB 136 is shown. An example of the operation flow of the insurance product presentation device 12 is shown. This is an example of a decision tree that analyzes solicitation information as an explanatory variable for insurance products. An example of a computer 2200 in which a plurality of aspects of the present invention may be embodied in whole or in part is shown.

Hereinafter, the present invention will be described through embodiments of the invention, but the following embodiments do not limit the inventions that fall within the scope of the claims. Also, not all combinations of features described in the embodiments are essential to the means of solving the invention.

FIG. 1 shows a functional block of the insurance product presentation device 10 according to the first embodiment. The insurance product presentation device 10 is, for example, a computer managed by an insurance company. The insurance product presentation device 10 communicates with the terminal 20 used by the recruiter of the agency by a communication network such as the Internet. The solicitor is a sales representative of an agency who solicits insurance.

The insurance product presentation device 10 has a contractor information DB 130 that stores contractor information about the contractor, a complaint information DB 132 that stores complaint information about complaints from the contractor, and an accident related to an accident involving the contractor. It has an accident information DB 134 that stores information. The policyholder information, complaint information and accident information are stored in their respective databases directly by the employees of the insurance company or from the terminal 20 of the recruiter of the agency.

The insurance product presentation device 10 further includes an integrated information storage unit 100, a reading unit 110, an estimation unit 112, and a presentation unit 114. The presentation unit 114 includes a machine learning model 120.

FIG. 2 shows an example of contractor information stored in the contractor information DB 130. The policyholder information in FIG. 2 exemplifies information on policyholders of automobile insurance.

The policyholder information DB 130 stores the policyholder information for each insurance policy number, that is, in association with the insurance policy number. Further, when the insurance contract is renewed due to the expiration of the insurance contract period before that, the insurance policy number before the renewal is stored in association with the current insurance policy number. In the example of FIG. 2, if the insurance contract is not due to renewal, an invalid value "*" is stored in the field of the insurance policy number before renewal.

The contractor information includes the contract period, contractor name, contractor age, contractor gender, contractor address, vehicle type, vehicle name and license color. This information is essential information at the time of contract. The car name includes information indicating whether the car is a domestic car or a foreign car. In the example of FIG. 2, in the feed of the car name, "(country)" is added after the car name if it is a domestic car, and "(outside)" is added if it is a foreign car.

The policyholder information also includes an insurance type that distinguishes automobile insurance, fire insurance, life insurance, etc., and an insurance product ID that identifies the insurance product. In the example shown in FIG. 2, the insurance type and the insurance product ID are included in the insurance policy number. The first letter of the alphabet of the insurance policy number indicates the insurance type. For example, automobile insurance is "J", fire insurance is "K", life insurance is "S", and so on. Further, the three letters of the alphabet from the second to the fourth of the insurance policy number represent the insurance product ID. Therefore, in the example of FIG. 2, "JBCD0001" and "JBCD0002" are automobile insurance and are the same insurance product, and "JBCX0195" is automobile insurance but are different insurance products.

The contractor information also includes any information that is not required at the time of contract. Examples of arbitrary information in automobile insurance shown in FIG. 2 are the presence or absence of a spouse and the distinction between owning a house and renting. Any information in the automobile insurance may include the color of the car body, the presence or absence of living with the child, the age of the child, the presence or absence of living with the parent, the age of the parent, and the like. Arbitrary information may be obtained by the recruiter from the contractor, or may be obtained by a questionnaire to the contractor accompanying the contract. When the insurance type is fire insurance, information on asset value such as size, area, whether or not it is a corner room, distance from the station, and number of floors may be included.

Although not shown, the complaint information DB 132 stores the content of the complaint filed by the policyholder of the insurance policy number with the agency or the insurance company as complaint information in association with the insurance policy number. If you have never filed a complaint, the field will contain information to that effect, for example, an invalid value "*". The complaint information may include information indicating a category in which the content of the complaint is categorized and information indicating the rank of the degree of the complaint.

Similarly, in the accident information DB 134, the content of the accident involving the policyholder of the insurance policy number is stored as accident information in association with the insurance policy number. In particular, if the contract is automobile insurance, information on accidents related to automobiles is stored, and if the contract is fire insurance, information on accidents related to fire is stored. If there is no accident, information indicating that fact, for example, an invalid value "*" is stored in the field. The matter information may include information indicating a category in which the content of the accident is categorized and information indicating a rank of the degree of the accident.

Further, the insurance product presentation device 10 may store the log of the call from the policyholder to the contact center of the insurance company as the contact center incoming call information in association with the insurance policy number. The insurance product presentation device 10 may be further connected to an external DB in which information associated with the insurance policy number is stored.

FIG. 3 shows an example of the operation flow of the insurance product presentation device 10. The insurance product presentation device 10 integrates the policyholder information such as the policyholder information DB 130 (S10), analyzes each insurance product with a decision tree using the integrated policyholder information, etc., and uses it as a machine learning model (S20). The insurance product is presented to the terminal 20 using the machine learning model (S30).

FIG. 4 shows an example of the operation flow of step S10 for integrating the contractor information and the like. The operation flow is started periodically by direct instruction to the insurance product presentation device 10, quarterly, or the like.

The reading unit 110 reads the insurance policy number and the policyholder information stored in association with the insurance policy number from the policyholder information DB 130 (S101). The reading unit 110 further reads the complaint information stored in the complaint information DB 132 and the accident information stored in the accident information DB 134 in association with the same insurance policy number (same step).

The reading unit 110 determines whether or not the insurance policy number before renewal is stored in association with the insurance policy number read in step S101 (S103). When the insurance policy number before renewal is stored (S103: Yes), the reading unit 110 reads the policyholder information associated with the insurance policy number before renewal from the policyholder information DB (S105). Further, the reading unit 110 reads the complaint information and the accident information associated with the insurance policy number before the renewal from the complaint information DB 132 and the accident information DB 134 (the same step).

In the example shown in FIG. 2, the insurance policy number “JBCD0002” before renewal is stored in association with the insurance policy number “JBCD0571”. Therefore, the reading unit 110 reads the policyholder information, complaint information and accident information of the insurance policy number "JBCD0571" in step S103, and also reads the policyholder information, complaint information and accident of the insurance policy number "JBCD0002" before the renewal in S105. Information is also read. The contact center incoming call information associated with the insurance policy number and external data when an external DB is connected are also read out. The contractor information, complaint information, accident information and other information about the contractor may be collectively referred to as contractor information and the like.

Next, the estimation unit 112 compares the policyholder information and the like of the insurance policy number with the policyholder information and the like of the insurance policy number before the renewal, and determines whether or not there is a change other than the age and the contract period (S107). ). Here, the age and the contract period are excluded because it is natural that the age and the contract period are different before and after the renewal. The age here includes not only the age of the contractor but also the age of the child or parent as arbitrary information.

When there is a change between the policyholder information of the insurance policy number and the policyholder information of the insurance policy number before the renewal (S107: Yes), the estimation unit 112 estimates the life event of the policyholder. (S109). The estimated life event provides new information about the insurance policy number.

In the example shown in FIG. 2, there is a change between the policyholder address of the insurance policy number "JBCD0571" and the policyholder address of the insurance policy number "JBCD0002" before renewal associated with the insurance number. Therefore, the estimation unit 112 estimates that the contractor had a life event of "moving".

The life event is estimated using a look-up table in which the type of changed information and the life event are associated in advance. As described above, an example of the association is "moving" if "the contractor's address has changed". In addition, if "the number of children has increased", it means "childbirth", and if "the car name has changed", it means "replacement of a car". Alternatively or additionally, life events may be estimated using a learning model that machine-learns the relationship between the type of information that has changed and the life event.

When the determination after step S109 or in any of steps S103 and S107 is No, the estimation unit 112 integrates the read information and the estimated life event and stores them in the integrated information storage unit 100 (S111). As a result, information on a contractor basis has been obtained.

FIG. 5 shows an example of contractor information and the like stored in the integrated information storage unit 100. The integrated information storage unit 100 stores the latest insurance policy number in the policyholder name, the policyholder information of the latest insurance policy number, and the like in association with the policyholder name.

Furthermore, although the latest insurance policy number was not stored, the policyholder information and the like stored corresponding to the insurance policy number before the renewal are also stored. In the example shown in FIG. 5, another information on the owner / rent associated with the latest insurance policy number “JBCD0570” of the policyholder name “Taro Mitsui” was not stored. However, "rental" was stored in association with the insurance policy number "JBCD0001" before the renewal of the insurance policy number "JBCD0570". In addition, there is no change in policyholder address between these insurance policy numbers. Therefore, the integrated information storage unit 100 stores "rental" in association with the contractor's name "Taro Mitsui".

Further, the integrated information storage unit 100 also stores the life event estimated by the estimation unit 112. In the example shown in FIG. 5, "moving" is stored in the life event field in response to the estimation that the contractor name "Sumitomo Hanako" had a life event of "moving" as illustrated in step S109. ing.

As a result, the step S10 for integrating the information is completed. The policyholders stored in the integrated information storage unit 100 are existing policyholders of insurance products corresponding to the respective insurance policy numbers, and are also candidates for contracts for other insurance products. Next, the presentation unit 114 analyzes the policyholders of the insurance product using a decision tree (S20).

FIG. 6 is an example of a decision tree that analyzes policyholders of insurance products. Analysis by decision trees is done by machine learning. The decision tree constructed by the analysis becomes the machine learning model 120 that learns the relationship between the policyholder information and the insurance product.

The objective variable of the decision tree is the policyholder for each insurance product. In the example of FIG. 6, 200 policyholders who have subscribed to the insurance product “automobile insurance JBCD” are the objective variables.

As the explanatory variable, contractor information or the like stored in the integrated information storage unit 100 is used. In the example of FIG. 6, "age" with "35 years old" as a threshold value, "whether a foreign car or a domestic car", "presence or absence of moving", etc. are used as explanatory variables. Of these, for example, "age" is directly stored in the contractor information DB 130, and is the information inherited as it is in the integrated information storage unit 100. On the other hand, "presence or absence of moving" is a so-called synthetic variable that is estimated by the estimation unit 112 as a life event from the "address" information directly stored in the contractor information DB 130 and stored in the integrated information storage unit 100. is there.

As described above, the step S20 of analyzing with the decision tree is completed. Next, the presentation unit 114 presents the insurance product using this decision tree (S30).

FIG. 7 shows an example of the operation flow of step S30 for presenting the insurance product. The operation flow of the step S30 is started by the instruction of the recruiter via the terminal 20.

The presentation unit 114 reads out the contractor information and the like integrated for each contractor from the integrated information storage unit 100 (S131). The presentation unit 114 applies the policyholder information and the like to the decision tree for a specific insurance product (S133). In this case, the specific insurance product excludes the insurance product already contracted by the policyholder. This is because it is not necessary to judge the possibility of contracting insurance products that have already been contracted.

That is, the policyholder information of the policyholder X who has a contract with the insurance product A is applied to the decision tree of the insurance product B. As a result, it is possible to estimate the possibility that the policyholder X also contracts with the insurance product B.

For example, in the examples of FIGS. 5 and 6, if the decision tree of the insurance product “JBCD” is applied to the policyholder “Tokyo Jiro”, “age is 35 years old or older”, “domestic car”, and “no moving”. From the information, it can be seen that the profile of policyholder information and the like matches only 4% of the policyholders of the insurance product “JBCD”. Therefore, it is estimated that it is unlikely that the policyholder "Tokyo Jiro" will contract for the insurance product "JBCD".

The presentation unit 114 applies the policyholder to the decision tree of all insurance products except the insurance product with which he / she has a contract (S135). As a result, the presentation unit 114 outputs the insurance product having many policyholders with the same profile to the terminal 20 as the proposed insurance product (S137). In this case, among the plurality of insured products to which the insurance products have been applied, a predetermined number of insurance products having a high percentage of policyholders with matching profiles may be proposed, or the policyholders with matching profiles may be proposed. You may propose an insurance product in which% is a certain value or more, for example, 50% or more.

All of the above steps S131 to S137 are executed for the contractors stored in the integrated information storage unit 100 (S139). As a result, the operation flow of step S30 ends.

As described above, according to the present embodiment, since the decision tree is analyzed using the integrated information, it is possible to select an appropriate explanatory variable from more explanatory variable candidates and perform machine learning. Furthermore, since the integrated information is used to fit the decision tree, it is possible to present an insurance product with a higher contract possibility.

FIG. 8 shows a functional block of the insurance product presentation device 12 according to the second embodiment. In the insurance product presentation device 12, the same reference number is assigned to the same configuration as the insurance product presentation device 10, and the description thereof will be omitted.

The insurance product presentation device 12 further includes a recruitment information DB 136 and a machine learning model 122 in the presentation unit 116, in addition to the configuration of the insurance product presentation device 10. The presentation unit 116 of the insurance product presentation device 12 presents the solicitation information to the terminal 20 by using the machine learning model 122 that has learned the relationship between the insurance product and the solicitation information.

FIG. 9 shows an example of recruitment information which is information regarding recruitment stored in the recruitment information DB 136. The recruitment information DB 136 is associated with the recruiter ID, the recruiter name, the recruiter age, the recruiter gender, the agency ID, the skill level, the insurance policy number contracted by the recruiter, and the recruitment activity that contributed to the contract. Is stored. This information is stored in the recruitment information DB 136 directly by the employee of the insurance company or from the terminal 20 of the recruiter of the agency.

FIG. 10 shows an example of the operation flow of the insurance product presentation device 12. The operation flow of the insurance product presentation device 12 includes, in addition to the operation flow of the insurance product presentation device 10, a step in which the presentation unit 116 reads out the solicitation information (S22) and a step of analyzing with a decision tree using the solicitation information. (S24), the step (S40) of proposing the recruitment using the decision tree of the recruitment information is provided.

FIG. 11 is an example of a decision tree in which solicitation information is analyzed as an explanatory variable for insurance products. The analysis by the decision tree is performed by machine learning in steps S22 and S24. The decision tree constructed by the analysis becomes the machine learning model 122 that learned the relationship between the recruitment information and the insurance product.

The objective variable of the decision tree is the total number of contracts for each insurance product. In the example of FIG. 11, the objective variable is 500 contracts for the insurance product “automobile insurance JBCD”. Here, the total number of cases is calculated by the insurance policy number that the solicitor has concluded, which is stored in the solicitation information DB 136. Therefore, unlike FIG. 6, even if the same policyholder has a total of multiple contracts for the insurance product due to renewal or the like, each may be counted as a separate number.

As the explanatory variable, the recruitment information stored in the recruitment information DB 136 is used. Further, as the explanatory variable, a synthetic variable based on the recruitment information of the recruitment information DB 136 and the contractor information of the integrated information storage unit 100 may be used. In this case, the synthetic variable is generated from the policyholder information and the solicitation information associated with the same insurance policy number. The generation procedure of the synthetic variable may be stored in the presentation unit 116 in advance.

In the example of FIG. 11, the explanatory variable "face-to-face, internet, or telephone" of "recruitment activity" is "recruitment activity that contributed to the contract" stored in the recruitment information DB 136. On the other hand, the explanatory variables "whether the solicitor is older or younger than the policyholder" are the "age of the solicitor" of the solicitation information DB 136 and the "contractor age" of the integrated information storage unit 100 in the same insurance policy number. It is a composite variable generated by comparison. Even if "closeness of address" generated from "contractor address and" agency address "of recruitment information DB 136 (not shown in FIG. 11)" of the integrated information storage unit 100 is used as another synthetic variable. Good.

In step S40, the presentation unit 116 makes a solicitation proposal using the decision tree, which is a machine learning model 122 that has learned the relationship between the solicitation information and the insurance product. In this case, the presentation unit 116 uses a decision tree that analyzes the relationship between the insurance product presented in step S30 and the solicitation information. For example, when the insurance product C is presented to the policyholder Y in step S30, the solicitation proposal to the policyholder Y is made using the decision tree of the insurance product C.

In this case, it is possible to propose a predetermined number of recruitment information having a high percentage of the number of existing contracts by analysis in the decision tree, or recruitment information in which the percentage of the number of existing contracts is a certain value or more, for example, 50% or more. May be proposed. In the example of FIG. 11, if the recruitment information with the highest percentage of the number of existing contracts is proposed, the recruiter whose "recruiter age" is "older" than the "contractor age" is "face-to-face" as a recruitment activity. At the same time, we propose to "guidance at the time of vehicle inspection".

As described above, according to the present embodiment, in addition to the effect in the first embodiment, the recruitment is proposed using the decision tree analyzed by the recruitment information, so that the possibility of the contract can be further enhanced. In addition, the information possessed by the insurance company can be further utilized.

In any of the embodiments, instead of applying to the decision trees of all insurance products for each policyholder in step S30, the policyholders who apply to the profile with a high percentage of existing policyholders in each decision tree. May be searched and the insurance product corresponding to the decision tree may be presented to the policyholder.

Further, in any of the embodiments, the insurance type of the policyholder analyzed by the decision tree and the insurance type of the policyholder who applies the decision tree may be the same or different. In the above examples, all are automobile insurance, and the insurance types are the same. Among insurance products of the same type, among the insurance products extracted in step S30, those with thicker compensation than the current insurance products may be selected and presented. This makes it possible to propose renewal to insurance products with thicker compensation.

As an example of different insurance types, by constructing a decision tree with automobile insurance policyholders and applying the policyholder information of fire insurance and life insurance policyholders to the decision tree, it becomes a fire insurance and life insurance policyholder. You may offer car insurance. As a result, insurance products can be efficiently sold repeatedly.

Further, in the second embodiment, instead of constructing a decision tree for solicitation information for individual insurance products, a decision tree may be constructed for each insurance type. This makes it possible to propose appropriate solicitation for each insurance type.

In any of the embodiments, the decision trees have been described as machine learning models 120 and 122, but other machine learning models using supervised learning may be used. Other machine learning models include support vector machines, neural networks, and deep learning.

In any of the embodiments, the example of an individual as a contractor has been described, but this may be applied when the contractor is a corporation. When the contractor is a corporation, the contractor information may include information on the contractor's business partners and affiliated companies. Information on business partners and affiliated companies may be provided by, for example, a corporate research company such as Teikoku Databank Co., Ltd. These business partner information and affiliated company information may be used as explanatory variables of a decision tree, or may be targeted for insurance products to be presented as candidates for insurance contracts.

Various embodiments of the present invention may be described with reference to flowcharts and block diagrams, wherein the block is (1) a stage of the process in which the operation is performed or (2) a device responsible for performing the operation. May represent a section of. Specific stages and sections are implemented by dedicated circuits, programmable circuits supplied with computer-readable instructions stored on a computer-readable medium, and / or processors supplied with computer-readable instructions stored on a computer-readable medium. You can. Dedicated circuits may include digital and / or analog hardware circuits, and may include integrated circuits (ICs) and / or discrete circuits. Programmable circuits are memory elements such as logical AND, logical OR, logical XOR, logical NAND, logical NOR, and other logical operations, flip-flops, registers, field programmable gate arrays (FPGA), programmable logic arrays (PLA), etc. May include reconfigurable hardware circuits, including, etc.

The computer readable medium may include any tangible device capable of storing instructions executed by the appropriate device, so that the computer readable medium having the instructions stored therein is specified in a flowchart or block diagram. It will be equipped with a product that contains instructions that can be executed to create means for performing the operation. Examples of computer-readable media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, and the like. More specific examples of computer-readable media include floppy (registered trademark) disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), Electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disk read-only memory (CD-ROM), digital versatile disk (DVD), Blu-ray (RTM) disk, memory stick, integrated A circuit card or the like may be included.

Computer-readable instructions are assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or object-oriented programming such as Smalltalk, JAVA®, C ++, etc. Contains either source code or object code written in any combination of one or more programming languages, including languages and traditional procedural programming languages such as the "C" programming language or similar programming languages. Good.

Computer-readable instructions are applied to a general-purpose computer, a special purpose computer, or the processor or programmable circuit of another programmable data processing device, either locally or in a wide area network (WAN) such as the local area network (LAN), the Internet, etc. ) May be executed to create a means for performing the operation specified in the flowchart or block diagram. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers and the like.

FIG. 12 shows an example of a computer 2200 in which a plurality of aspects of the present invention may be embodied in whole or in part. The program installed on the computer 2200 can cause the computer 2200 to function as an operation associated with the device according to an embodiment of the present invention or as one or more sections of the device, or the operation or the one or more. Sections can be run and / or the computer 2200 can be run a process according to an embodiment of the invention or a stage of such process. Such a program may be run by the CPU 2212 to cause the computer 2200 to perform certain operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.

The computer 2200 according to this embodiment includes a CPU 2212, a RAM 2214, a graphic controller 2216, and a display device 2218, which are interconnected by a host controller 2210. The computer 2200 also includes input / output units such as a communication interface 2222, a hard disk drive 2224, a DVD-ROM drive 2226, and an IC card drive, which are connected to the host controller 2210 via the input / output controller 2220. There is. The computer also includes legacy input / output units such as the ROM 2230 and keyboard 2242, which are connected to the input / output controller 2220 via an input / output chip 2240.

The CPU 2212 operates according to the programs stored in the ROM 2230 and the RAM 2214, thereby controlling each unit. The graphic controller 2216 acquires the image data generated by the CPU 2212 in a frame buffer or the like provided in the RAM 2214 or itself, and the image data is displayed on the display device 2218.

The communication interface 2222 communicates with other electronic devices via the network. The hard disk drive 2224 stores programs and data used by the CPU 2212 in the computer 2200. The DVD-ROM drive 2226 reads the program or data from the DVD-ROM 2201 and provides the program or data to the hard disk drive 2224 via the RAM 2214. The IC card drive reads programs and data from the IC card and / or writes programs and data to the IC card.

The ROM 2230 contains a boot program or the like executed by the computer 2200 at the time of activation, and / or a program depending on the hardware of the computer 2200. The input / output chip 2240 may also connect various input / output units to the input / output controller 2220 via a parallel port, serial port, keyboard port, mouse port, and the like.

The program is provided by a computer-readable medium such as a DVD-ROM 2201 or an IC card. The program is read from a computer-readable medium, installed on a hard disk drive 2224, RAM 2214, or ROM 2230, which is also an example of a computer-readable medium, and executed by the CPU 2212. The information processing described in these programs is read by the computer 2200 and provides a link between the program and the various types of hardware resources described above. The device or method may be configured by implementing manipulation or processing of information in accordance with the use of computer 2200.

For example, when communication is executed between the computer 2200 and an external device, the CPU 2212 executes a communication program loaded in the RAM 2214, and performs communication processing on the communication interface 2222 based on the processing described in the communication program. You may order. Under the control of the CPU 2212, the communication interface 2222 reads and reads transmission data stored in a transmission buffer processing area provided in a recording medium such as a RAM 2214, a hard disk drive 2224, a DVD-ROM 2201, or an IC card. The data is transmitted to the network, or the received data received from the network is written to the reception buffer processing area or the like provided on the recording medium.

Further, the CPU 2212 causes the RAM 2214 to read all or necessary parts of a file or database stored in an external recording medium such as a hard disk drive 2224, a DVD-ROM drive 2226 (DVD-ROM2201), or an IC card. Various types of processing may be performed on the data on the RAM 2214. The CPU 2212 then writes back the processed data to an external recording medium.

Various types of information, such as various types of programs, data, tables, and databases, may be stored on recording media and processed. The CPU 2212 describes various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, and information retrieval described in various parts of the present disclosure with respect to the data read from the RAM 2214. Various types of processing may be performed, including / replacement, etc., and the results are written back to RAM 2214. Further, the CPU 2212 may search for information in a file, a database, or the like in the recording medium. For example, when a plurality of entries each having an attribute value of the first attribute associated with the attribute value of the second attribute are stored in the recording medium, the CPU 2212 specifies the attribute value of the first attribute. Search for an entry that matches the condition from the plurality of entries, read the attribute value of the second attribute stored in the entry, and associate it with the first attribute that satisfies the predetermined condition. The attribute value of the second attribute obtained may be acquired.

The program or software module described above may be stored on or near a computer 2200 on a computer-readable medium. Also, a recording medium such as a hard disk or RAM provided within a dedicated communication network or a server system connected to the Internet can be used as a computer-readable medium, thereby providing the program to the computer 2200 over the network. To do.

Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various changes or improvements can be made to the above embodiments. It is clear from the description of the claims that such modified or improved forms may also be included in the technical scope of the present invention.

The order of execution of operations, procedures, steps, steps, etc. in the devices, systems, programs, and methods shown in the claims, specification, and drawings is particularly "before" and "prior to". It should be noted that it can be realized in any order unless the output of the previous process is used in the subsequent process. Even if the scope of claims, the specification, and the operation flow in the drawings are explained using "first", "next", etc. for convenience, it means that it is essential to carry out in this order. It's not a thing.

10, 12 Insurance product presentation device, 20 terminals, 100 integrated information storage unit, 110 reading unit, 112 estimation unit, 114, 116 presentation unit, 120, 122 machine learning model, 130 contractor information DB, 132 complaint information DB, 134 Accident information DB, 136 Recruitment information DB

Claims (3)

A storage unit that stores information about the policyholder and the insurance policy number before renewal in association with the insurance policy number,
The information stored in association with the specific insurance policy number and the information stored in association with the insurance policy number before renewal associated with the specific insurance policy number are stored from the storage unit. The reading unit to read and
The information read by the reading unit and stored in association with the specific insurance policy number and the information stored in association with the insurance policy number before renewal associated with the specific insurance policy number. The life event of the policyholder was estimated by comparing with the information, the information associated with the specific insurance policy number, the information associated with the insurance policy number before the renewal, and the estimation. An estimation unit that integrates life events as information about the contractor,
An insurance product presentation device including a presentation unit that presents insurance products to information integrated by the estimation unit using a machine learning model that learns the relationship between information about policyholders and contracted insurance products. The insurance product presentation device according to claim 1, wherein the presentation unit further presents information on solicitation by using a machine learning model that learns the relationship between insurance products and information on solicitation. On the computer
A storage function that stores information about the policyholder and the insurance policy number before renewal in the storage unit in association with the insurance policy number,
The information stored in association with the specific insurance policy number and the information stored in association with the insurance policy number before renewal associated with the specific insurance policy number are stored from the storage unit. Read read function and
The information read by the reading function and stored in association with the specific insurance policy number is stored in association with the insurance policy number before renewal associated with the specific insurance policy number. The life event of the policyholder is estimated by comparing with the above information, the information associated with the specific insurance policy number, the information associated with the insurance policy number before the renewal, and the above. An estimation function that integrates estimated life events as information about the contractor, and
Using a machine learning model that learns the relationship between information about policyholders and contracted insurance products, the reading function realizes a presentation function that presents insurance products to the information integrated by the estimation function. program.

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