Recommendation device

Abstract

This recommendation device comprises: a determination unit which determines, on the basis of a risk level indicating the extent of damage that will be caused to a user by events covered by each of a plurality of insurance products and a coverage level indicating the extent of coverage by each of the plurality of insurance products, a combination of insurance products from among the plurality of insurance products, and an insurance premium to be paid for the insurance products; and an output unit which outputs recommendation information indicating the combination and the insurance premium.

Description

[Technical field]

[0001] The present disclosure relates to a recommendation device. [Background technology]

[0002] There is known a technology for providing a user with a combination of appropriate insurance products. For example, Patent Document 1 describes an information processing device that acquires user behavior information, predicts the user's future risk based on the behavior information, determines a combination of insurance-related products required by the user based on the risk, allocates insurance premiums according to the risk within the range of insurance premiums set by the user, and provides the user with a combination of insurance-related products. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-144775 A Summary of the Invention [Problem to be solved by the invention]

[0004] The amount paid per unit (compensation amount) varies depending on the insurance product. However, the information processing device described in Patent Document 1 allocates insurance premiums without considering the compensation amount, so there is a risk that the amount of damages cannot be fully compensated.

[0005] The present disclosure describes a recommendation device that can optimize insurance product combinations and insurance premiums. [Means for solving the problem]

[0006] A recommendation device according to one aspect of the present disclosure includes a determination unit that determines a combination of insurance products and insurance premiums to be paid for the insurance products from among multiple insurance products based on a risk level indicating the degree of damage caused to a user by an event covered by each of the multiple insurance products and a compensation level indicating the degree of compensation provided by each of the multiple insurance products, and an output unit that outputs recommendation information indicating the combination and insurance premiums.

[0007] In this recommendation device, a combination of insurance products and insurance premiums to be paid for the insurance products are determined from among a plurality of insurance products based on the risk level and compensation level of each of the plurality of insurance products, and recommendation information is output. Since not only the risk level but also the compensation level is taken into consideration, for example, it is possible to determine a combination of insurance products and insurance premiums so as to provide a well-balanced compensation for various risks of the user. As a result, it is possible to optimize the combination of insurance products and insurance premiums. Effect of the Invention

[0008] According to the present disclosure, it is possible to optimize the combination of insurance products and insurance premiums. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a schematic configuration diagram of a recommendation system including a recommendation server according to an embodiment. [Diagram 2] Fig. 2(a) is a diagram showing an example of user basic information stored in the user information DB (database) shown in Fig. 1. Fig. 2(b) is a diagram showing an example of location information stored in the user information DB shown in Fig. 1. Fig. 2(c) is a diagram showing an example of payment information stored in the user information DB shown in Fig. 1. [Diagram 3] FIG. 3 is a diagram showing an example of insurance information stored in the insurance information DB shown in FIG. [Figure 4] FIG. 4 is a block diagram showing a functional configuration of the recommendation server shown in FIG. [Diagram 5] FIG. 5 is a sequence diagram showing a series of processes of the recommendation method performed by the recommendation system shown in FIG. [Figure 6] FIG. 6 is a flowchart showing in detail the determination process shown in FIG. [Figure 7] FIG. 7 is a diagram showing an example of a display screen for recommendation information. [Figure 8] FIG. 8 is a diagram for explaining the overlap between two insurance products. [Figure 9] FIG. 9 is a diagram for explaining the compensation score. [Figure 10] FIG. 10 is a diagram illustrating a hardware configuration of the recommendation server illustrated in FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the description of the drawings, the same elements are given the same reference numerals and duplicated description will be omitted.

[0011] A configuration of a recommendation system including a recommendation device according to an embodiment will be described with reference to Figs. 1 to 3. Fig. 1 is a schematic configuration diagram of a recommendation system including a recommendation device according to an embodiment. Fig. 2(a) is a diagram showing an example of user basic information stored in a user information DB (database) shown in Fig. 1. Fig. 2(b) is a diagram showing an example of location information stored in the user information DB shown in Fig. 1. Fig. 2(c) is a diagram showing an example of payment information stored in the user information DB shown in Fig. 1. Fig. 3 is a diagram showing an example of insurance information stored in the insurance information DB shown in Fig. 1.

[0012] The recommendation system 1 shown in FIG. 1 is a system for recommending combinations of insurance products and insurance premiums (portfolio) to a user.

[0013] The recommendation system 1 includes a plurality of terminal devices 2, a user information DB3, an insurance information DB4, and a recommendation device 10. The plurality of terminal devices 2, the user information DB3, the insurance information DB4, and the recommendation device 10 are configured to be able to communicate with each other via a network NW. The network NW may be configured as either a wired or wireless network. Examples of the network NW include a mobile communication network, the Internet, and a WAN (Wide Area Network). In the following description, the focus is mainly on one terminal device 2, but the same applies to the other terminal devices 2.

[0014] The terminal device 2 is a device used by a user. Examples of the terminal device 2 include a smartphone, a tablet terminal, a notebook PC (Personal Computer), and a desktop PC.

[0015] The terminal device 2 acquires location information (latitude and longitude) of the terminal device 2 using a global positioning system (GPS) or the like. The terminal device 2 may acquire information on the installation location of a master station of a connected wireless network as location information. Examples of the installation location of a master station include a base station of a mobile network and an access point of Wi-Fi. The terminal device 2 may acquire location information of a terminal existing in the vicinity of the terminal device 2 as location information of the terminal device 2. Examples of such terminals include a beacon terminal of Bluetooth (registered trademark). Details of the location information will be described later. The terminal device 2 periodically transmits the location information to the user information DB3.

[0016] The terminal device 2 generates payment information related to a payment made by a user using the terminal device 2. For example, when a user purchases a product using a payment application installed in the terminal device 2, the terminal device 2 generates the payment information. Details of the payment information will be described later. For example, the terminal device 2 transmits the payment information to the user information DB3 every time it generates the payment information.

[0017] The user information DB3 is a database that stores user information of each user. The user information is information about a user, and includes user basic information, location information, and payment information. The user information may further include other information, such as a usage history (log) of the terminal device 2. The user basic information is basic information about a user. As shown in (a) of FIG. 2, the user basic information includes a user ID (identifier), a terminal ID, gender, and age. The user ID is information that can uniquely identify a user. The terminal ID is information that can uniquely identify a terminal device 2. Here, the terminal ID indicates the terminal device 2 used by the user identified by the user ID. The user basic information may further include other information. The user basic information is, for example, set in advance by the user.

[0018] The location information is information indicating the location of each terminal device. As shown in (b) of Fig. 2, the location information includes a terminal ID, a time (timestamp) when the location information was acquired, latitude, and longitude. When the user information DB3 receives location information from each terminal device 2, the user information DB3 stores the received location information. The user information DB3 stores a plurality of pieces of location information of each terminal device 2 as a history (log) of location information.

[0019] The payment information is information relating to payments made using each terminal device 2. As shown in (c) of Fig. 2, the payment information includes the terminal ID, the time the payment was made, the place where the payment was made, the amount, and the product name. When the user information DB3 receives payment information from each terminal device 2, it stores the received payment information. The user information DB3 stores multiple pieces of payment information for each terminal device 2 as a payment information history.

[0020] The insurance information DB4 is a database that stores the insurance information of each user. The insurance information is information regarding the insurance product to which each user has subscribed. As shown in FIG. 3, the insurance information includes an insurance ID, a user ID, and an insurance premium. The insurance ID is information that can uniquely identify the insurance product. The insurance premium is the amount that a user identified by the user ID pays for the insurance product identified by the insurance ID. The insurance premium is, for example, a monthly insurance premium. The insurance information may include the number of shares purchased instead of the insurance premium, or may include the number of shares purchased together with the insurance premium.

[0021] The recommendation device 10 is a device that recommends to a user an optimal combination of insurance products and insurance premiums from among a plurality of insurance products. An example of the recommendation device 10 is an information processing device such as a server device.

[0022] The functional configuration of the recommendation device 10 will be described with reference to Fig. 4. Fig. 4 is a block diagram showing the functional configuration of the recommendation device shown in Fig. 1. As shown in Fig. 4, the recommendation device 10 functionally includes an acquisition unit 11, a generation unit 12, a calculation unit 13, a risk score storage unit 14, a calculation unit 15, a damage amount storage unit 16, a reception unit 17, a determination unit 18, an output unit 19, and an insurance product information storage unit 20.

[0023] The acquisition unit 11 is a functional unit that acquires user information and insurance information The acquisition unit 11 acquires user information from the user information DB3, and acquires insurance information from the insurance information DB4.

[0024] The generating unit 12 is a functional unit that generates an enrollment prediction model and an insurance premium prediction model. The enrollment prediction model is a machine learning model that uses feature quantities generated from user information as explanatory variables and an insurance product enrollment score as a target variable, and is configured by, for example, a neural network. The enrollment score is a value that indicates the possibility that a user will enroll in an insurance product. The enrollment score is, for example, a numerical value in the range of 0 to 1. For example, the higher the enrollment score of an insurance product, the higher the possibility that a user will enroll in that insurance product. The generating unit 12 generates an enrollment prediction model for each insurance product by performing machine learning for each insurance product.

[0025] The insurance premium prediction model is a machine learning model that uses feature quantities generated from user information as explanatory variables and a predicted insurance premium as a target variable, and is configured, for example, by a neural network. The predicted insurance premium is the insurance premium predicted to be paid by a user for an insurance product, and is obtained, for example, by multiplying the insurance premium per unit by the number of units purchased. The generation unit 12 generates an insurance premium prediction model for each insurance product by performing machine learning for each insurance product. The method of generating feature quantities, the method of generating an enrollment prediction model, and the method of generating an insurance premium prediction model will be described later.

[0026] The calculation unit 13 is a functional unit that calculates a risk score for each of a plurality of insurance products based on user information. The risk score is a value indicating the possibility (occurrence probability) that an event covered by the insurance product will occur to the user. The subscription score is considered to have a correlation with the risk score. Therefore, the calculation unit 13 calculates the risk score based on the subscription score. The calculation unit 13 calculates the subscription score using a subscription prediction model. The calculation unit 13 generates a feature amount from the user information and inputs the generated feature amount into the subscription prediction model to obtain the subscription score from the subscription prediction model. The calculation unit 13 may, for example, use the subscription score as the risk score, or may calculate the risk score by multiplying the subscription score by a predetermined coefficient.

[0027] The risk score storage unit 14 is a functional unit that stores a risk score for each insurance product of each user. The risk score storage unit 14 stores, for example, a data set in which a user ID, an insurance ID, and a risk score are associated with each other.

[0028] The calculation unit 15 is a functional unit that calculates the predicted average loss amount for each of a plurality of insurance products based on user information. The predicted average loss amount is an average amount predicted to be lost due to an event that is the compensation target of the insurance product. Since the predicted insurance premium is predicted as an amount that can compensate for the amount of damage caused by an event that is the compensation target of the insurance product, it is considered to have a correlation with the predicted average loss amount. Therefore, the calculation unit 15 calculates the predicted average loss amount based on the predicted insurance premium. The calculation unit 15 calculates the predicted insurance premium using a premium prediction model. The calculation unit 15 generates a feature amount from the user information, and inputs the generated feature amount into the premium prediction model to obtain the predicted insurance premium from the premium prediction model. The calculation unit 15 calculates the predicted average loss amount, for example, by multiplying the predicted insurance premium by a predetermined coefficient.

[0029] The loss amount storage unit 16 is a functional unit that stores the predicted average loss amount for each insurance product of each user. The loss amount storage unit 16 stores, for example, a data set in which a user ID, an insurance ID, and a predicted average loss amount are associated with each other.

[0030] The receiving unit 17 is a functional unit that receives a recommendation request from the terminal device 2. The recommendation request is a command for requesting recommendation information on insurance products. The recommendation request includes the user ID of the user requesting the recommendation information and the payable amount Cost. max Including. max is the maximum amount that the user can pay for the insurance product. max is, for example, the maximum amount that a user can pay for an insurance product per month.

[0031] The determination unit 18 is a functional unit that determines a portfolio of insurance products to be recommended to the user. The portfolio of insurance products includes a combination of insurance products and the insurance premiums to be paid for each insurance product. Specifically, the determination unit 18 determines a combination of insurance products to be recommended to the user from among multiple (n) insurance products, and the insurance premiums to be paid for each insurance product, based on the risk level and compensation level of each of the multiple insurance products. The risk level is a value indicating the degree of damage caused to the user by an event that is the subject of compensation for the insurance product. For example, a higher risk level indicates a greater degree of damage. The compensation level is a value indicating the degree of compensation provided by the insurance product. For example, a higher compensation level indicates a greater degree of compensation.

[0032] The determination unit 18 determines a portfolio of insurance products so that the sum of the remaining risk levels for a plurality of insurance products is minimized. The remaining risk level is obtained, for example, by subtracting the compensation level from the risk level. The determination unit 18 determines the portfolio of insurance products so that the sum of the remaining risk levels for a plurality of insurance products is minimized. max A portfolio of insurance products is determined within the above range. The method of determining the portfolio of insurance products will be described in detail later.

[0033] The output unit 19 is a functional unit that outputs recommendation information indicating a portfolio of insurance products (a combination of insurance products and insurance premiums). For example, the output unit 19 outputs (transmits) the recommendation information to the terminal device 2. The output unit 19 may output the recommendation information to a memory (not shown) in the recommendation device 10.

[0034] The insurance product information storage unit 20 is a functional unit that stores insurance product information regarding each insurance product. The insurance product information for each insurance product may be, for example, the insurance premium cost per unit, i and the compensation amount per unit C i And the minimum purchase amount LB i and upper limit UB i The insurance product number i is an integer value that is equal to or greater than 1 and equal to or less than the total number n of insurance products that can be recommended.

[0035] Next, the recommendation method performed by the recommendation system 1 (recommendation device 10) will be described with reference to Fig. 5 to Fig. 7. Fig. 5 is a sequence diagram showing a series of processes of the recommendation method performed by the recommendation system shown in Fig. 1. Fig. 6 is a flowchart showing in detail the decision process shown in Fig. 5. Fig. 7 is a diagram showing an example of a display screen for recommendation information.

[0036] 5, first, the acquisition unit 11 of the recommendation device 10 transmits a request to acquire user information to the user information DB 3 (step S1). In step S1, the acquisition unit 11 may transmit an acquisition request to acquire user information of all users, or may transmit an acquisition request to acquire user information of some users. Then, upon receiving the request to acquire user information from the recommendation device 10, the user information DB 3 transmits the requested user information to the recommendation device 10 (step S2).

[0037] Next, the acquisition unit 11 of the recommendation device 10 transmits an acquisition request for insurance information to the insurance information DB4 (step S3). In step S3, the acquisition unit 11 transmits an acquisition request for acquiring insurance information for all insurance products that can be recommended, for example. Then, upon receiving the acquisition request for insurance information from the recommendation device 10, the insurance information DB4 transmits the requested insurance information to the recommendation device 10 (step S4).

[0038] Then, the acquisition unit 11 of the recommendation device 10 receives the user information from the user information DB3, and the insurance subscription information from the insurance subscription information DB4, and outputs the user information and the insurance subscription information to the generation unit 12. Next, when the generation unit 12 receives the user information and the insurance subscription information from the acquisition unit 11, it generates a subscription prediction model (step S5). In step S5, the generation unit 12 generates a subscription prediction model for each insurance product by performing machine learning for each insurance product. The machine learning is performed using, for example, a GBDT (Gradient Boosting Decision Tree) algorithm. For example, in the machine learning, a set of a feature amount generated from the user information of a user who has previously subscribed to an insurance product and a subscription score (=1) of the insurance product is used as correct answer data, and a set of a feature amount generated from the user information of a user who has not subscribed to an insurance product and a subscription score (=0) of the insurance product is used as incorrect answer data. Then, the generation unit 12 outputs the subscription prediction model to the calculation unit 13.

[0039] Here, an example of a method for generating a feature amount will be described. The generating unit 12 sets the gender and age of the user information as the feature amount. The generating unit 12 may estimate the place where the user stayed and the stay time from the time-series location information of the terminal device 2, and set the stay place and the stay time as the feature amount. Furthermore, in order to reduce the influence on the subscription score of a place where the user does not usually visit but happens to stay, the time change of the stay place and the stay time may be used as the feature amount. The generating unit 12 calculates the number of payments, the number of stores where the payment was made, and the total payment amount from the payment information of the terminal device 2 as the feature amount. The amount for each category of the paid product (service) may be used as the feature amount.

[0040] Furthermore, the generation unit 12 generates an insurance premium prediction model (step S6). In step S6, the generation unit 12 generates an insurance premium prediction model for each insurance product by performing machine learning for each insurance product. The machine learning is performed using, for example, the GBDT algorithm. In the machine learning, for example, a pair of feature amounts generated from user information of a user who has previously subscribed to an insurance product and the insurance premium paid by the user for the insurance product is used as correct answer data. The method of generating the feature amounts is as described above. Then, the generation unit 12 outputs the insurance premium prediction model to the calculation unit 15.

[0041] Next, the acquisition unit 11 transmits an acquisition request to acquire user information of all users to the user information DB3 (step S7). Then, when the user information DB3 receives the acquisition request for user information from the recommendation device 10, it transmits the requested user information to the recommendation device 10 (step S8). Then, when the acquisition unit 11 receives the user information from the user information DB3, it outputs the user information to the calculation unit 13 and the calculation unit 15.

[0042] Next, when the calculation unit 13 receives the user information from the acquisition unit 11, it calculates the risk score of each user for each of the multiple insurance products (step S9). In step S9, the calculation unit 13 first calculates the enrollment score using the enrollment prediction model. Specifically, the calculation unit 13 generates a feature from the user information of each user in the same manner as the generation method of the feature by the generation unit 12. Then, the calculation unit 13 inputs the feature into the enrollment prediction model of each insurance product for each user, and obtains the enrollment score output from each enrollment prediction model. Then, the calculation unit 13 calculates the risk score, for example, by multiplying the enrollment score by a predetermined coefficient. Then, the calculation unit 13 outputs a data set in which the user ID, the insurance ID, and the risk score are associated with each other to the risk score storage unit 14, and stores the data in the risk score storage unit 14.

[0043] Next, when the calculation unit 15 receives the user information from the acquisition unit 11, it calculates the predicted average loss amount of each user for each of the multiple insurance products (step S10). In step S10, the calculation unit 15 first calculates the predicted insurance premium using the insurance premium prediction model. Specifically, the calculation unit 15 generates features from the user information of each user in the same manner as the generation of features by the generation unit 12. Then, the calculation unit 15 inputs the features into the insurance premium prediction model of each insurance product for each user, and obtains the predicted insurance premium output from each insurance premium prediction model. Then, the calculation unit 15 calculates the predicted average loss amount by multiplying the predicted insurance premium by a predetermined coefficient. Then, the calculation unit 15 outputs a data set in which the user ID, the insurance ID, and the predicted average loss amount are associated with each other to the loss amount storage unit 16, and stores the data in the loss amount storage unit 16.

[0044] Next, the terminal device 2 transmits a recommendation request to the recommendation device 10 (step S11). Then, when the receiving unit 17 of the recommendation device 10 receives the recommendation request transmitted from the terminal device 2, the receiving unit 17 reads the user ID and the payable amount Cost included in the recommendation request. max is output to the determination unit 18.

[0045] Next, the determination unit 18 receives the user ID and the payable amount Cost from the reception unit 17. max 6, in the determination process of step S12, the determination unit 18 first determines a risk score r i Specifically, the determination unit 18 acquires the insurance ID and the risk score r associated with the user ID received from the receiving unit 17 (step S21). i The set of is obtained from the risk score storage unit 14.

[0046] Then, the determination unit 18 calculates the predicted average loss Loss for each insurance product of the user identified by the user ID. iSpecifically, the determination unit 18 acquires the insurance ID and the predicted average loss amount Loss associated with the user ID received from the receiving unit 17 (step S22). i The set of n insurance products is obtained from the loss amount storage unit 16. Then, the decision unit 18 obtains insurance product information on n insurance products that can be recommended to the user (step S23). Specifically, the decision unit 18 obtains insurance product information on n insurance products from the insurance product information storage unit 20.

[0047] Next, the determination unit 18 determines a portfolio of insurance products to be recommended to the user (step S24). In step S24, the determination unit 18 uses the insurance ID and the risk score r acquired from the risk score storage unit 14 to determine the portfolio of insurance products to be recommended to the user (step S24). i The set of the insurance ID and the predicted average loss amount Loss obtained from the loss amount storage unit 16 i Using the set of the insurance products and the insurance product information acquired from the insurance product information storage unit 20, a combination of insurance products to be recommended to the user from among the n insurance products and the insurance premium to be paid for each insurance product are determined based on the risk level and the compensation level for each insurance product. In this embodiment, the risk level is the predicted amount of damage caused by an event that is the subject of compensation for the insurance product, and the compensation level is the amount of compensation paid according to the insurance premium for the insurance product.

[0048] Specifically, the determination unit 18 determines the combination of insurance products and the insurance premiums so that the total of the remaining loss amount obtained by subtracting the compensation amount from the estimated loss amount for n insurance products is minimized, as shown in formula (1). The estimated loss amount is calculated by multiplying the risk score r i and the predicted average loss amount i The compensation amount is calculated by multiplying the compensation amount per unit C i and the number of shares purchased x i It is obtained by multiplying the number of shares purchased x i is an integer value equal to or greater than 0. The compensation amount (=C i x i ) is the expected loss amount (=r i ×Loss i), it indicates overcompensation, in which case the remaining damages are considered to be zero.

number

[0049] Furthermore, the determination unit 18 minimizes the equation (1) so as to satisfy the constraint conditions shown in the equations (2) to (4).

number

number

number

[0050] Equation (2) specifies the upper limit of the number of insurance products that can be included in the portfolio, and the selection flags u of the 1st to nth insurance products are i The constraint is that the sum of is less than or equal to the upper limit K. Selection flag u i indicates whether the i-th insurance product is selected as an insurance product to be included in the portfolio. If the i-th insurance product is selected as an insurance product to be included in the portfolio, the selection flag u i is set to 1. If the i-th insurance product has not been selected as an insurance product to be included in the portfolio, the selection flag u i is set to 0. Therefore, the determination unit 18 determines the number of insurance products so that the number is within the upper limit number K.

[0051] Formula (3) specifies the upper limit of the total amount of insurance premiums, and the sum of the premiums for the 1st to nth insurance products is the payable amount Cost max The constraints are as follows: The premiums for each insurance product are the premium cost per unit. i and the number of shares purchased x i Therefore, the determination unit 18 multiplies the payable amount Cost set by the user by maxWithin this range, determine the portfolio of insurance products.

[0052] Equation (4) specifies the minimum and maximum number of shares to be purchased for each insurance product. i is the lower limit LB i to upper limit UB i In addition, for insurance products for which no shares are purchased, the number of shares purchased is x i becomes 0, so the lower limit LB i to upper limit UB i Therefore, the determination unit 18 may not fall within the range of the lower limit LB i and selection flag u i The upper limit UB is calculated by multiplying i and selection flag u i Within the range of the multiplication result, the number of shares purchased x i Determine.

[0053] Next, the determination unit 18 generates recommendation information indicating a portfolio of insurance products (step S25). For example, the determination unit 18 generates recommendation information including the names and premiums of insurance products included in the portfolio. The recommendation information includes the payable amount Cost max The recommendation information may further include the estimated loss amount and the compensation amount for each insurance product, and the total amount of insurance premiums (total payment amount). The recommendation information may further include the estimated loss amount and the compensation amount for each insurance product. Then, the determination unit 18 outputs the recommendation information to the output unit 19.

[0054] Next, the output unit 19 transmits the recommendation information to the terminal device 2 (step S13). Then, when the terminal device 2 receives the recommendation information transmitted from the recommendation device 10, it displays the recommendation information on the display. For example, as shown in FIG. 7, the names and premiums of the insurance products included in the portfolio are displayed together with a graph showing the risk (estimated loss amount) and compensation (compensation amount) of each insurance product. Furthermore, the total amount of insurance premiums (total payment amount) is displayed based on the payable amount Cost set by the user. max is displayed along with.

[0055] According to the display screen example in Fig. 7, the extent to which the recommended insurance product covers the user's potential risk is visually shown. This increases the user's sense of satisfaction. Since the user can recognize the insufficient coverage, it becomes clear what kind of insurance product the user should subscribe to when customizing the insurance product.

[0056] This completes a series of processes of the recommendation method. Steps S1 to S10 are performed in advance before receiving a recommendation request from the terminal device 2 (offline processing). Steps S3 and S4 may be performed before steps S1 and S2, or may be performed in parallel with steps S1 and S2. Step S6 may be performed before step S5, or may be performed in parallel with step S5. Step S10 may be performed before step S9, or may be performed in parallel with step S9. Steps S21 to S23 may be performed in any order, or may be performed in parallel with each other.

[0057] Steps S7 to S10 may be performed after step S11. In this case, in step S7, the acquisition unit 11 transmits an acquisition request to the user information DB3 to acquire user information of a user identified by a user ID included in the recommendation request, and in step S8, the user information DB3 transmits the user information of the requested user to the recommendation device 10. Furthermore, in step S9, the calculation unit 13 calculates a risk score of a user identified by a user ID included in the recommendation request, and outputs the risk score to the determination unit 18. In step S10, the calculation unit 15 calculates a predicted average damage amount of a user identified by a user ID included in the recommendation request, and outputs the predicted average damage amount to the determination unit 18.

[0058] In the recommendation device 10 described above, a combination of insurance products and insurance premiums to be paid for the insurance products are determined from among n insurance products based on the estimated loss amount and compensation amount of each of the n insurance products, and recommendation information is output. Since not only the estimated loss amount but also the compensation amount is taken into consideration, for example, it is possible to determine the combination of insurance products and the insurance premiums so that compensation is provided in a balanced manner for various risks of the user. As a result, it is possible to optimize the combination of insurance products and the insurance premiums.

[0059] Specifically, the determination unit 18 determines the combination of insurance products and the insurance premiums so that the total remaining loss amount obtained by subtracting the compensation amount from the estimated loss amount for n insurance products is minimized. Since it can be said that the smaller the total remaining loss amount is, the more adequate the preparation for all risks is, the combination of insurance products and the insurance premiums that minimize the total remaining loss amount can be said to be optimal for the user. Therefore, with the above configuration, it is possible to optimize the combination of insurance products and the insurance premiums.

[0060] The determination unit 18 calculates the expected damage amount based on a risk score indicating the occurrence probability of an event covered by the insurance product. According to the Courtney theory, risk is calculated by multiplying the occurrence probability of the risk by the impact degree. The impact degree can be regarded as the average amount of damage. Therefore, the expected damage amount is calculated by multiplying the risk score r i and the predicted average loss amount i It can be obtained by multiplying

[0061] For example, after a combination of insurance products and premiums are presented to a user without considering the total payment amount, the user may select the amount he or she is willing to pay. max It is conceivable to adjust the insurance premium so that the insurance premium is as follows. However, since the compensation content of the insurance product varies depending on the insurance premium, there is a risk that optimal compensation may not be obtained. In the recommendation device 10, the determination unit 18 adjusts the insurance premium so that the insurance premium is as follows: maxAccording to this configuration, the combination of insurance products and the insurance premiums are determined and recommended to the user after taking into consideration the upper limit of the total payment amount. Therefore, the combination of insurance products and the insurance premiums can be further optimized. max Since the above is true, it is possible to increase the likelihood that the user will accept the recommended content.

[0062] The calculation unit 13 calculates an enrollment score indicating the possibility of enrolling in the insurance product for each of the n insurance products based on the user information, and calculates a risk score for each of the n insurance products based on the enrollment score. It is considered that users who have commonalities in gender, age, behavior, etc. are equally likely to enroll in insurance products. It is considered that the more likely a user is to enroll in an insurance product, the more likely an event covered by the insurance product is to occur to the user. In other words, there is a correlation between the enrollment score and the risk score. Therefore, the risk score can be obtained based on the enrollment score. From the above, it is possible to accurately calculate the risk score of each insurance product by using the user information.

[0063] In order to directly calculate the risk score from the user information, it is necessary to use the user information of the user who actually experienced the event covered by the insurance product. However, since the event does not necessarily occur frequently, a sufficient amount of user information may not be obtained, and the calculation accuracy of the risk score may decrease. On the other hand, since it is considered that many more users than the number of users who experienced the event have subscribed to the insurance product, the accuracy of calculating the subscription score from the user information is higher than the accuracy of calculating the risk score from the user information. Therefore, by using the subscription score, it is possible to improve the calculation accuracy of the risk score.

[0064] Although the embodiments of the present disclosure have been described above, the present disclosure is not limited to the above embodiments.

[0065] The recommendation device 10 may be configured by one device that is physically or logically coupled, or may be configured by multiple devices that are physically or logically separated from each other. For example, the recommendation device 10 may be realized by multiple computers distributed on a network, such as in cloud computing. As described above, the configuration of the recommendation device 10 may include any configuration that can realize the functions of the recommendation device 10.

[0066] The generating unit 12 may generate a risk prediction model instead of the subscription prediction model. The risk prediction model is a machine learning model in which the feature amount generated from the user information is an explanatory variable and the risk score of the insurance product is an objective variable, and is configured, for example, by a neural network. The generating unit 12 may generate an average loss prediction model instead of the insurance premium prediction model. The average loss prediction model is a machine learning model in which the feature amount generated from the user information is an explanatory variable and the predicted average loss amount caused to the user due to an event covered by the insurance product is an objective variable, and is configured, for example, by a neural network.

[0067] The recommendation device 10 may not include the calculation unit 13 and the risk score storage unit 14. In this case, the determination unit 18 may acquire a set of an insurance ID and a risk score associated with a user ID included in the recommendation request from an external risk score storage unit. The recommendation device 10 may not include the calculation unit 15 and the damage amount storage unit 16. In this case, the determination unit 18 may acquire a set of an insurance ID and a predicted average damage amount associated with a user ID included in the recommendation request from an external damage amount storage unit. The recommendation device 10 may not include the insurance product information storage unit 20. In this case, the determination unit 18 may acquire insurance product information from an external insurance product information storage unit.

[0068] The recommendation device 10 may not include the generation unit 12. In this case, the calculation unit 13 calculates the subscription score using a subscription prediction model generated in advance, and calculates the risk score based on the subscription score. The calculation unit 13 may calculate the subscription score based on user information on a rule basis or the like without using the subscription prediction model, and calculate the risk score based on the subscription score. The calculation unit 13 may calculate the risk score using a risk prediction model generated in advance. The calculation unit 13 may calculate the risk score based on user information on a rule basis or the like.

[0069] Similarly, the calculation unit 15 calculates the predicted insurance premium using a pre-generated insurance premium prediction model, and calculates the predicted average loss amount based on the predicted insurance premium. The calculation unit 15 may calculate the predicted insurance premium based on a rule base or the like based on user information without using an insurance premium prediction model, and calculate the predicted average loss amount based on the predicted insurance premium. The calculation unit 15 may calculate the predicted average loss amount using a pre-generated average loss prediction model. The calculation unit 15 may calculate the predicted average loss amount based on a rule base or the like based on user information.

[0070] The recommendation device 10 may not include the acquisition unit 11, the generation unit 12, the calculation unit 13, the risk score storage unit 14, the calculation unit 15, the damage amount storage unit 16, and the insurance product information storage unit 20. In this case, the determination unit 18 may acquire a pair of an insurance ID and a risk score associated with a user ID included in a recommendation request from an external risk score storage unit, acquire a pair of an insurance ID and a predicted average damage amount associated with the same user ID from an external damage amount storage unit, and acquire insurance product information from an external insurance product information storage unit.

[0071] The compensation target of each insurance product varies depending on the insurance product, but there may be a case where the compensation target of some insurance products overlaps partially. Therefore, the determination unit 18 determines the correlation coefficient ρ ij The combination of insurance products and the premiums may be determined based on the correlation coefficient ρ ijis a value that indicates the degree of correlation between two insurance products (the i-th insurance product and the j-th insurance product) among n insurance products. ij is a number in the range of 0 to 1. ij The larger the correlation coefficient ρ, the stronger the correlation between the i-th insurance product and the j-th insurance product. ij is calculated and set in advance for every two insurance products among the n insurance products. ij is included in the insurance product information, for example, and is obtained from the insurance product information storage unit 20.

[0072] Specifically, the determination unit 18 minimizes equation (1) so as to further satisfy the constraint condition shown in equation (5). Equation (5) indicates the constraint condition that the sum of the overlaps for all pairs of two insurance products selectable from n insurance products is less than a specified value Sa. The overlap is a value indicating the degree of overlap of the coverage of the two insurance products. The greater the overlap, the greater the degree of overlap of the coverage of the two insurance products. The determination unit 18 determines the compensation amount and correlation coefficient ρ for all pairs of two insurance products selectable from n insurance products, as shown on the left side of equation (5). ij The overlapping degree is calculated based on the correlation coefficient, and the sum of the overlapping degrees is calculated. As shown in Fig. 8, the overlapping degree increases as the correlation coefficient increases. Then, the determination unit 18 determines a portfolio of insurance products (combinations and insurance premiums) such that the sum of the overlapping degrees of all pairs is smaller than a specified value Sa.

number

[0073] When several insurance products that are highly correlated with each other are selected as a combination of insurance products to be included in a portfolio, the range of events that can be compensated for by the insurance products may be narrowed. In other words, risk concentration may occur. On the other hand, when several insurance products that are weakly correlated with each other are selected as a combination of insurance products to be included in a portfolio, the range of events that can be compensated for by the insurance products can be widened, and risk can be dispersed. Therefore, it can be said that the smaller the total overlap for all pairs of two insurance products that can be selected from n insurance products, the wider the range of coverage. Therefore, by determining the combination of insurance products and the insurance premiums so that the total overlap is smaller than the specified value Sa, it is possible to further optimize the combination of insurance products and the insurance premiums.

[0074] The risk level is not limited to the expected loss amount. The compensation level is not limited to the compensation amount. For example, the risk level can be expressed as a risk score r i In this case, the compensation score C i (x i ) is used. Compensation score C i (x i ) is a value that indicates the possibility (probability) that the amount of compensation paid according to the insurance premium will fully compensate for the predicted amount of damage caused by the event covered by the insurance product. i (x i ) is preset for each insurance product. i (x i ) is included in the insurance product information, for example, and is obtained from the insurance product information storage unit 20.

[0075] In the example shown in FIG. i (x i ) is expressed as the area of ​​the probability density function. As the insurance premium increases, the compensation amount also increases, so the possibility of full compensation for the loss increases. Therefore, as shown in Figure 9, the compensation score C i (x i) increases as the premium increases. On the other hand, the possibility of incurring losses greater than the amount of compensation decreases as the amount of compensation increases. Therefore, as shown in Figure 9, the compensation score C i (x i ) increase per unit premium (number of shares purchased) decreases as the premium increases.

[0076] In this case, the determination unit 18 uses the formula (6) instead of the formula (1) and minimizes the formula (6) so as to satisfy the constraint conditions shown in the formulas (2) to (4). That is, the determination unit 18 minimizes the risk score r i Compensation score C i (x i The combination of insurance products and premiums are determined so that the total of the residual risk score obtained by subtracting the compensation score C i (x i ) is the risk score r i If it is greater than , it indicates overcompensation, in which case the residual risk score is considered to be 0.

number

[0077] Furthermore, the determination unit 18 determines the correlation coefficient ρ ij The combination of insurance products and the insurance premiums may be determined further based on the above. Specifically, the determination unit 18 minimizes the formula (1) so as to further satisfy the constraint condition shown in the formula (7) instead of the formula (5). The formula (7) indicates the constraint condition that the sum of the overlap degrees for all pairs of two insurance products selectable from the n insurance products is less than a specified value Sb. The determination unit 18 determines the compensation score and the correlation coefficient ρ for all pairs of two insurance products selectable from the n insurance products as shown on the left side of the formula (7). ij The overlap degree is calculated based on the above, and the sum of the overlap degrees is calculated. Then, the determination unit 18 determines a portfolio of insurance products (combinations and premiums) such that the sum of the overlap degrees of all combinations is smaller than a specified value Sb.

number

[0078] In this variant, the risk scores r i and compensation score C i (x i ), a combination of insurance products and the insurance premiums to be paid for the insurance products are determined from among n insurance products, and recommendation information is output. i Not only that, but also the compensation score C i (x i ) is taken into consideration, so that, for example, a combination of insurance products and insurance premiums can be determined so as to provide a well-balanced compensation for various risks of the user. As a result, it is possible to optimize the combination of insurance products and insurance premiums.

[0079] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. The method of realizing each functional block is not particularly limited. That is, each functional block may be realized by using one device that is physically or logically combined, or may be realized by using two or more devices that are physically or logically separated and directly or indirectly connected (for example, by wire, wirelessly, etc.). The functional block may be realized by combining the one device or the multiple devices with software.

[0080] Functions include, but are not limited to, judgment, determination, judgement, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs the function of transmission is called a transmitting unit or a transmitter. In either case, as described above, there is no particular limitation on the method of realization.

[0081] For example, the recommendation device 10 according to an embodiment of the present disclosure may function as a computer that performs the processing of the present disclosure. Fig. 10 is a diagram showing an example of a hardware configuration of the recommendation device 10 according to an embodiment of the present disclosure. The recommendation device 10 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, and a bus 1007.

[0082] In the following description, the term "apparatus" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the recommendation apparatus 10 may be configured to include one or more of the apparatuses shown in the figure, or may be configured to exclude some of the apparatuses.

[0083] Each function in the recommendation device 10 is realized by loading a specific software (program) onto hardware such as a processor 1001 and a memory 1002, so that the processor 1001 performs calculations, controls communications via a communication device 1004, and controls at least one of reading and writing of data in the memory 1002 and the storage 1003.

[0084] The processor 1001, for example, operates an operating system to control the entire computer. The processor 1001 may be configured by a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, and a register. For example, each function of the recommendation device 10 described above may be realized by the processor 1001.

[0085] The processor 1001 reads out a program (program code), a software module, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes according to the read out programs. As the program, a program that causes a computer to execute at least a part of the operations described in the above-mentioned embodiment is used. For example, each function of the recommendation device 10 may be realized by a control program stored in the memory 1002 and operated in the processor 1001. Although the above-mentioned 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. The program may be transmitted from a network via a telecommunication line.

[0086] The memory 1002 is a computer-readable recording medium, and may be composed of at least one of, for example, a Read Only Memory (ROM), an Erasable Programmable ROM (EPROM), an Electrically Erasable Programmable ROM (EEPROM), and a Random Access Memory (RAM). 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, or the like that is executable to implement a recommendation method according to an embodiment of the present disclosure.

[0087] Storage 1003 is a computer-readable recording medium, and may be composed of at least one of 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, and a magnetic strip. Storage 1003 may be called an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other suitable medium including at least one of memory 1002 and storage 1003.

[0088] The communication device 1004 is hardware (transmission / reception device) for performing communication between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, or a communication module. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, and a frequency synthesizer to realize, for example, at least one of Frequency Division Duplex (FDD) and Time Division Duplex (TDD). For example, the above-mentioned acquisition unit 11, reception unit 17, and output unit 19 may be realized by the communication device 1004.

[0089] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that performs output to the outside. The input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0090] 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 using a single bus, or may be configured using different buses between each device.

[0091] The recommendation device 10 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), and a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0092] Notification of information is not limited to the aspects / embodiments described in this disclosure and may be performed using other methods.

[0093] In the processing procedures, sequences, and flowcharts of each aspect / embodiment described in this disclosure, the order of processing may be changed unless there is a contradiction. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0094] The information may be output from a higher layer to a lower layer, or may be output from a lower layer to a higher layer. The information may be input / output via a plurality of network nodes.

[0095] The input and output information may be stored in a specific location (e.g., memory) or may be managed using a management table. The input and output information may be overwritten, updated, or added to. The output information may be deleted. The input information may be transmitted to another device.

[0096] The determination may be based on a value represented by a single bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0097] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched according to execution. Notification of predetermined information (e.g., notification of "X") is not limited to explicit notification, and may be performed implicitly (e.g., by not notifying the predetermined information).

[0098] 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 in the present disclosure. The present disclosure can be implemented as 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 for illustrative purposes only and does not have any limiting meaning on the present disclosure.

[0099] Software 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., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0100] Software, instructions, information, and the like may be transmitted and received over a transmission medium. For example, if the 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 these wired and / or wireless technologies are included within the definition of transmission media.

[0101] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0102] 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.

[0103] As used in this disclosure, the terms "system" and "network" are used interchangeably.

[0104] The information, parameters, etc. described in this disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using corresponding other information.

[0105] The names used for the above parameters are not limiting in any way, and the formulas etc. using these parameters may differ from the formulas etc. explicitly disclosed in this disclosure.

[0106] The terms "determining" and "determining" as used in this disclosure may encompass a wide variety of actions. "Determining" and "determining" may each include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, search, inquiry (e.g., looking up in a table, database, or another data structure), and ascertaining. "Determining" and "determining" may each include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, and accessing (e.g., accessing data in a memory). "Determining" and "determining" may each include resolving, selecting, choosing, establishing, and comparing. That is, "judgment" may include any action that can be regarded as a "judgment." "Decision" may include any action that can be regarded as a "decision." "Judgment" may be read as "assuming," "expecting," "considering," or the like.

[0107] The terms "connected" and "coupled" or any variation thereof refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "accessed." When "connected" or "coupled" is used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using at least one of one or more wires, cables, and printed electrical connections, and may be considered to be "connected" or "coupled" to each other using electromagnetic energy having wavelengths in the radio frequency range, microwave range, and light (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0108] 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."

[0109] Any reference to an element using designations such as "first" and "second" used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, a reference to a first and a second element does not imply either that only two elements may be employed or that the first element must precede the second element in some way.

[0110] The "parts" in the configuration of each of the above devices may be replaced with "circuits" or "devices", etc.

[0111] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Further, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0112] 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.

[0113] 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]

[0114] 1... recommendation system, 2... terminal device, 3... user information DB, 4... insurance subscription information DB, 10... recommendation device, 11... acquisition unit, 12... generation unit, 13... calculation unit, 14... risk score memory unit, 15... calculation unit, 16... loss amount memory unit, 17... receiving unit, 18... determination unit, 19... output unit, 20... insurance product information memory unit, 1001... processor, 1002... memory, 1003... storage, 1004... communication device, 1005... input device, 1006... output device, 1007... bus.

Claims

1. a determination unit that determines a combination of insurance products and an insurance premium to be paid for the insurance products from among the plurality of insurance products, based on a risk level indicating the degree of damage caused to a user due to an event covered by each of the plurality of insurance products and a compensation level indicating the degree of compensation provided by each of the plurality of insurance products; an output unit that outputs recommendation information indicating the combination and the insurance premium; Equipped with The recommendation device, wherein the determination unit determines the combination and the insurance premium further based on a correlation coefficient indicating a degree of correlation between two insurance products among the plurality of insurance products.

2. The recommendation device according to claim 1 , wherein the determination unit determines the combination and the insurance premium within a range of an available amount to be paid set by the user.

3. The recommendation device according to claim 1 or claim 2, wherein the determination unit determines the combination and the insurance premium so that the sum of the remaining risk levels obtained by subtracting the compensation level from the risk levels for the multiple insurance products is minimized.

4. The risk level is the expected amount of damage caused by an event covered by the insurance product, The recommendation device according to any one of claims 1 to 3, wherein the compensation level is an amount of compensation paid according to the insurance premium.

5. The recommendation device according to claim 4 , wherein the determination unit calculates the estimated loss amount based on an occurrence probability of an event that is a compensation target of an insurance product.

6. The risk level is the probability of occurrence of an event that is the subject of compensation for the insurance product, The recommendation device according to any one of claims 1 to 3, wherein the compensation degree is a probability that the amount of compensation paid according to the insurance premium can fully compensate for the predicted amount of damage caused by the event.

7. The recommendation device of claim 5 or claim 6, further comprising a calculation unit that calculates an enrollment score indicating the possibility of enrolling in an insurance product for each of the multiple insurance products based on user information regarding the user, and calculates the occurrence probability for each of the multiple insurance products based on the enrollment score.

8. The recommendation device described in any one of claims 1 to 7, wherein the determination unit calculates an overlap indicating the degree of overlap of the coverage provided by the two insurance products for every pair of two insurance products selectable from the plurality of insurance products based on the compensation degree and the correlation coefficient, and determines the combination and the insurance premium so that the sum of the overlap for all the pairs is smaller than a specified value.

9. a determination unit that determines a combination of insurance products and an insurance premium to be paid for the insurance products from among the plurality of insurance products, based on a risk level indicating the degree of damage caused to a user due to an event covered by each of the plurality of insurance products and a compensation level indicating the degree of compensation provided by each of the plurality of insurance products; an output unit that outputs recommendation information indicating the combination and the insurance premium; Equipped with The risk level is the probability of occurrence of an event that is the subject of compensation for the insurance product, The compensation degree is a probability that the amount of compensation paid according to the insurance premium can fully compensate for the predicted amount of damage caused by the event, the recommendation device.

10. The recommendation device according to claim 9 , wherein the determination unit determines the combination and the insurance premium within a range of an available amount to be paid set by the user.

11. The recommendation device according to claim 9 or claim 10, wherein the determination unit determines the combination and the insurance premium so that the sum of the remaining risk levels obtained by subtracting the compensation level from the risk levels for the multiple insurance products is minimized.

12. The recommendation device according to any one of claims 9 to 11, further comprising a calculation unit that calculates an enrollment score indicating the possibility of enrolling in an insurance product for each of the multiple insurance products based on user information regarding the user, and calculates the occurrence probability for each of the multiple insurance products based on the enrollment score.

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