Method for providing digital-based virtual compensation experience to increase users' insurance retention rate

Abstract

The present invention relates to a method for providing a digital-based virtual compensation experience to increase a user's insurance retention rate.

Description

Technology Field

[0001] The present invention relates to a method for providing a digital-based virtual compensation experience to increase a user's insurance retention rate. Background Technology

[0002] Policyholders are highly likely to cancel their insurance policies without actual experience with claims, as they do not fully recognize the value of the insurance. To address this, the existing insurance industry employs strategies such as insurance consultations, additional discounts, and personalized product recommendations; however, these methods are costly and have limited effectiveness.

[0003] With the recent advancement of AI, VR / AR, and data analysis technologies, it has become possible to increase insurance retention rates by establishing systems that allow users to experience the insurance claim and payment processes in a virtual environment.

[0004] Accordingly, there is a growing need for research and development on methods to provide digital-based virtual compensation experiences so that policyholders can perceive the value of insurance without directly experiencing the compensation. The problem to be solved

[0005] The problem that the present invention aims to solve is to provide a method for providing a digital-based virtual compensation experience to increase the insurance retention rate of users.

[0006] The problems that the present invention aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below. means of solving the problem

[0007] The method of the present invention for solving the aforementioned problem may be characterized by being provided by a system comprising: an insurance policyholder data analysis module that collects and analyzes insurance policyholder data; a virtual compensation experience provision module that provides a virtual insurance claim and payment experience; and a personalized feedback and recommendation module that analyzes user behavior data and feedback to provide personalized recommendations for increasing the insurance retention rate.

[0008] The above-mentioned insurance policyholder data analysis module may be characterized by designing a customized virtual compensation experience by analyzing the subscriber's age, health status, occupation, insurance subscription history, and insurance maintenance patterns.

[0009] The above-mentioned virtual compensation experience provision module may be characterized by building a virtual environment based on actual case data of the insurance claim and payment process, and including an AI chatbot, VR, AR, or interactive simulation system so that the user can directly experience the virtual insurance claim process.

[0010] The above personalized feedback and recommendation module may be characterized by providing insurance benefit reports and insurance product recommendations to improve insurance retention rates after a virtual compensation experience.

[0011] The above system may be characterized by further including an insurance retention rate analysis module that analyzes changes in insurance retention rates in real time and optimizes insurance retention rate prediction based on machine learning. Effects of the invention

[0012] The method of the present invention has the advantage of constructing a digital-based virtual compensation experience system to increase the retention rate of insurance policyholders, providing personalized insurance claim and compensation simulations, recommending customized insurance products and benefits to improve the insurance retention rate, and enabling early response by identifying subscribers with a high probability of canceling their insurance through data analysis.

[0013] The effects of the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below. Brief explanation of the drawing

[0014] FIG. 1 is a conceptual diagram showing a system comprising a method for providing a digital-based virtual compensation experience to increase the insurance retention rate of a user according to the present invention. FIG. 2 is a flowchart showing the steps of a method for providing a digital-based virtual compensation experience to increase the insurance retention rate of a user according to the present invention. Specific details for implementing the invention

[0015] The advantages and features of the present invention and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the present invention, and the present invention is defined only by the scope of the claims.

[0016] The terms used in this specification are for describing embodiments and are not intended to limit the invention. In this specification, the singular form includes the plural form unless specifically stated otherwise in the text. The terms "comprises" and / or "comprising" used in this specification do not exclude the presence or addition of one or more other components in addition to the components mentioned. Throughout the specification, the same reference numerals refer to the same components, and "and / or" includes each of the mentioned components and all combinations of one or more. Although terms such as "first," "second," etc., are used to describe various components, these components are not limited by these terms. These terms are used merely to distinguish one component from another. Therefore, the first component mentioned below may be the second component within the technical scope of the invention.

[0017] Unless otherwise defined, all terms used herein (including technical and scientific terms) may be used in a meaning commonly understood by those skilled in the art to which the present invention pertains. Additionally, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless explicitly and specifically defined otherwise.

[0019] The method (1000) for providing a digital-based virtual compensation experience to increase the insurance retention rate of the user of the present invention can be performed within a system (100) composed of the following modules.

[0020] The system (100) of the present invention may include an insurance policyholder data analysis module (10), a virtual compensation experience provision module (20), a personalized feedback and recommendation module (30), an insurance retention rate analysis module (40), and a security management module (50).

[0021] The insurance policyholder data analysis module (10) can analyze the user's age, health status, occupation, insurance subscription history and maintenance patterns, and can learn patterns that derive key variables related to the insurance retention rate, thereby providing data to optimize the virtual compensation experience.

[0022] The virtual compensation experience providing module (20) can provide a virtual compensation experience by simulating the insurance claim and payment process. To this end, the virtual compensation experience providing module (20) may include an AI chatbot and VR / AR-based interactive simulation function. Accordingly, the virtual compensation experience providing module (20) can enhance realism by providing various scenarios that may occur during the insurance claim process.

[0023] The personalized feedback and recommendation module (30) can provide feedback to improve the user's insurance retention rate after the virtual compensation experience. To this end, the personalized feedback and recommendation module (30) can provide a customized insurance benefit report (e.g., expected insurance payout history, coverage scope analysis) and can recommend personalized retention strategies and insurance products to users who are likely to cancel their insurance.

[0024] The insurance retention rate analysis module (40) can perform machine learning-based insurance retention rate prediction and continuous improvement by comparing and analyzing the insurance retention rates of the virtual compensation experience provision group and the non-provision group.

[0025] The security management module (50) is a module for safely storing and protecting personal information and virtual reward experience data, and can protect the data of the system (100) of the present invention by applying encryption and cloud security technology.

[0027] Hereinafter, steps for providing a digital-based virtual compensation experience (1000) to increase the insurance retention rate of a user of the present invention are described.

[0028] The step (S1) in which the insurance policyholder data analysis module (10) collects and analyzes user data may be a step in which, when a user inputs personal information into the insurance policyholder data analysis module (10), the module receives the user’s insurance subscription history, health data, age, and cancellation history by linking with an external insurance company server, and evaluates the user’s insurance retention potential and the need for compensation experience based on this.

[0029] In this case, the insurance policyholder data analysis module (10) can evaluate the user's insurance retention potential and the need for a compensation experience using a machine learning-based algorithm. Furthermore, the insurance policyholder data analysis module (10) can design a personalized virtual compensation experience tailored to the subscriber's insurance type.

[0030] That is, the insurance policyholder data analysis module (10) can collect and analyze various insurance policyholder data to quantify the possibility of maintaining insurance and perform multi-layered analysis to predict the insurance retention rate.

[0031] Specifically, the insurance policyholder data analysis module (10) can perform a data collection and preprocessing step by collecting data on the user's insurance subscription history (currently held insurance, past insurance subscription and cancellation history, maintenance period by insurance company), integrating and analyzing health data (hospital visit history, health checkup results, presence of chronic disease, genetic analysis data), analyzing insurance maintenance patterns based on age and occupation information, and evaluating the user's cancellation patterns and insurance maintenance possibilities based on cancellation history (insurance premium burden, insufficient coverage, subscription to alternative insurance, etc.).

[0032] Additionally, the insurance policyholder data analysis module (10) can calculate the probability of a user maintaining their insurance by utilizing an AI model that has learned data from past insurance policyholders and cancellations. In this case, it can derive an insurance retention probability score (0 to 100) by combining multiple regression analysis, random forest, and deep learning models, and can proceed with a machine learning-based insurance retention probability evaluation step by recommending additional insurance retention strategies by comparing with the retention rate of a customer group with the same conditions.

[0033] Furthermore, the insurance policyholder data analysis module (10) can offer additional maintenance rewards to users predicted to have a 50% or lower chance of maintaining the insurance, provide additional benefits for long-term maintenance to customers with a high chance of maintaining the insurance, and perform continuous improvement of the prediction model using real-time data updates and machine learning feedback loops to proceed with the insurance maintenance prediction step.

[0034] The step (S2) in which the virtual compensation experience providing module (20) receives analysis data from the insurance policyholder data analysis module (10) and provides a virtual compensation experience may be a step in which the virtual compensation experience providing module (20) provides a virtual compensation experience suitable for the user based on the user's insurance retention potential and the need for a compensation experience analyzed by the insurance policyholder data analysis module (10).

[0035] For example, users can experience an insurance claim simulation and proceed with the claim process assuming they have been treated for a specific disease. In this case, an AI chatbot can guide users through the claim review, document submission, and approval processes, and VR / AR can be utilized to allow for an intuitive experience of the actual insurance claim and compensation procedures.

[0036] That is, the virtual compensation experience provision module (20) can provide a personalized virtual insurance claim and payment experience based on the analysis results of the insurance policyholder data analysis module (10).

[0037] Specifically, the virtual compensation experience provision module (20) can automatically generate the most suitable insurance claim scenario based on the insurance retention possibility and health data provided by the insurance policyholder data analysis module (10), and an AI-based chatbot can guide the insurance claim procedure and assist in document creation, and can proceed with a customized insurance claim simulation step by analyzing user input data to predict the most realistic insurance payment result.

[0038] In addition, the virtual compensation experience provision module (20) can simulate the process of claiming, reviewing, approving, and receiving insurance money in a VR environment, and can guide the process of preparing and submitting documents required for claiming insurance money using AR technology, and can proceed with the VR / AR-based insurance payment experience stage by providing a customized virtual experience according to the type of accident selected by the user (traffic accident, disease treatment, etc.).

[0039] Furthermore, the virtual compensation experience provision module (20) can provide different insurance screening results depending on the user's selection, provide detailed explanations regarding the possibility of insurance payment and screening criteria, automatically suggest the reason for rejection and necessary additional documents if insurance payment is rejected, and perform a process of providing additional compensation and benefits to improve the insurance retention rate after insurance payment is completed, thereby conducting an interactive insurance payment decision process.

[0040] The step (S3) in which the personalized feedback and recommendation module (30) provides feedback and customized insurance retention strategies may be a step of providing an insurance benefit analysis report after the user's virtual compensation experience ends (e.g., estimated insurance payout amount, claimable coverage range). In this case, the personalized feedback and recommendation module (30) may use an artificial intelligence engine to recommend customized retention strategies and products to users who are likely to cancel their insurance.

[0041] The step (S4) in which the insurance retention rate analysis module (40) analyzes and optimizes insurance retention rate data may be a step of monitoring changes in the insurance retention rates of the user and other users in real time to compare and analyze the retention rates of the virtual compensation experience provided group and the non-provided group, and may be a step of continuously optimizing insurance retention rate improvement methods through machine learning.

[0043] The method (1000) of the present invention described above may be implemented as a program (or application) to be executed in combination with a server, which is hardware, and stored on a medium.

[0044] The aforementioned program may include code encoded in a computer language such as C, C++, JAVA, or machine language, which can be read by the computer's processor (CPU) through the computer's device interface, in order for the computer to read the program and execute the methods implemented in the program. Such code may include functional code related to functions that define the necessary functions for executing the methods, and may include control code related to execution procedures necessary for the computer's processor to execute the functions according to a predetermined procedure. Additionally, such code may further include memory reference code regarding where (address) additional information or media necessary for the computer's processor to execute the functions should be referenced in the computer's internal or external memory. In addition, if the processor of the computer needs to communicate with any other computer or server located remotely in order to execute the above functions, the code may further include communication-related code regarding how to communicate with any other computer or server located remotely using the communication module of the computer, and what information or media to transmit or receive during communication.

[0045] The above-mentioned storage medium refers to a medium that stores data semi-permanently and is readable by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specifically, examples of the above-mentioned storage medium include, but are not limited to, ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage device. That is, the above-mentioned program may be stored on various recording media on various servers that the computer can access, or on various recording media on the user's computer. Additionally, the above-mentioned medium may be distributed across networked computer systems, and computer-readable code may be stored in a distributed manner.

[0046] The steps of the method or algorithm described in connection with embodiments of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. The software module may reside in RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), Flash Memory, a hard disk, a removable disk, a CD-ROM, or any form of computer-readable recording medium well known in the art to which the present invention belongs.

[0047] Although embodiments of the present invention have been described above with reference to the attached drawings, those skilled in the art will understand that the present invention may be implemented in other specific forms without altering its technical concept or essential features. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive.

Claims

Claim 1 A method for providing a digital-based virtual compensation experience to increase insurance retention rates, characterized by being provided by a system comprising: an insurance policyholder data analysis module that collects and analyzes insurance policyholder data; a virtual compensation experience provision module that provides a virtual insurance claim and payment experience; and a personalized feedback and recommendation module that analyzes user behavior data and feedback to provide personalized recommendations to increase insurance retention rates. Claim 2 A method according to claim 1, wherein the insurance policyholder data analysis module analyzes the subscriber's age, health status, occupation, insurance subscription history, and insurance maintenance pattern to design a customized virtual compensation experience. Claim 3 A method according to claim 1, wherein the virtual compensation experience providing module constructs a virtual environment based on actual case data of the insurance claim and payment process, and includes an AI chatbot, VR, AR, or interactive simulation system so that the user can directly experience the virtual insurance claim process. Claim 4 A method according to claim 1, wherein the personalized feedback and recommendation module provides insurance benefit reports and insurance product recommendations to improve insurance retention rates after a virtual compensation experience. Claim 5 A method according to claim 1, wherein the system further comprises an insurance retention rate analysis module that analyzes changes in insurance retention rates in real time and optimizes insurance retention rate prediction based on machine learning.

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