Life insurance customer service method and device, electronic equipment and storage medium

Abstract

The invention provides a life insurance customer service method and device, electronic equipment and a storage medium, and is applied to the fields of finance and medical treatment. The life insurance customer service method provided by the invention comprises the following steps: acquiring health data of a current customer; according to the health data and a risk assessment model, a health risk score is obtained, the health risk of the current customer is determined according to the health risk score, and the risk assessment model is obtained through training according to a machine learning model and a health data sample; extracting a plurality of entities from the health data, constructing a relationship between a patient and a disease and a relationship between the patient and a medicine according to the plurality of entities to obtain a knowledge graph, and recommending medical services and providing health suggestions for the current customer according to the knowledge graph. According to the life insurance customer service method, the service quality can be improved, medical services are recommended and health suggestions are provided for customers, and the continuity of the services can be enhanced.

Description

Technical Field

[0001] This invention relates to the field of insurance technology, and in particular to a life insurance customer service method, device, electronic device, and storage medium. Background Technology

[0002] Insurance business can be involved in both the financial and healthcare sectors, and insurance companies are the core entities in this business. Against the backdrop of an aging society, the level of service provided to life insurance customers is crucial for life insurance companies to enhance customer value and competitiveness. Existing service models typically rely on manual operations and fixed rules, lacking intelligent health management tools and dynamic service optimization capabilities. This makes it difficult to respond in real-time to changes in customer health, thus impacting service quality. Furthermore, the fragmentation of services in existing models makes it difficult for life insurance companies to effectively enhance customer loyalty and brand value in the health and elderly care sector, affecting their long-term development and market competitiveness. Summary of the Invention

[0003] The purpose of this invention is to provide a life insurance customer service method, device, electronic device, and storage medium to solve the technical problems of low service quality and fragmented services in the prior art.

[0004] The technical solution of the present invention is as follows: a life insurance customer service method is provided, comprising:

[0005] Obtain the current customer's health data;

[0006] Based on the health data and risk assessment model, a health risk score is obtained, and the health risk of the current customer is determined by the health risk score. The risk assessment model is trained based on a machine learning model and health data samples.

[0007] Multiple entities are extracted from the health data, and relationships between patients and diseases, and between patients and drugs are constructed based on these entities to obtain a knowledge graph. Medical services and health advice are then recommended to the current customer based on the knowledge graph.

[0008] Furthermore, the health data includes physiological data, insurance information, health questionnaire data, and medical record data.

[0009] Furthermore, the life insurance customer service method also includes:

[0010] Obtain the similarity between the current customer and other customers in the customer group, identify the customer in the customer group with the highest similarity to the current customer as a similar customer, and recommend the service strategy based on the historical preferences of the similar customer to the current customer.

[0011] Furthermore, obtaining the similarity between the current customer and other customers in the customer group includes:

[0012] Obtain the first rating of the current customer for the preset service strategy and the second rating of other customers in the customer group for the preset service. Based on the first rating and the second rating, obtain the similarity between the current customer and other customers in the customer group.

[0013] Furthermore, the life insurance customer service method also includes:

[0014] Obtain the health characteristics, behavioral characteristics, and background characteristics of all customers in the customer group. Based on the health characteristics, behavioral characteristics, and background characteristics, determine all customers in the customer group and segment them to generate user profiles.

[0015] Furthermore, the life insurance customer service method also includes:

[0016] A predetermined number of customers within the customer group are randomly divided into an experimental group and a control group. The experimental group receives a new service strategy, while the control group receives the original service strategy. Based on service ratings, customer satisfaction, and service usage frequency, it is determined whether the predetermined number of customers should adopt the new service strategy.

[0017] Furthermore, the life insurance customer service method also includes: acquiring physiological data including heart rate, blood pressure, body temperature, and blood oxygen saturation; if any indicator in the physiological data exceeds the corresponding preset threshold, an early warning is triggered.

[0018] Another technical solution of the present invention is as follows: a life insurance customer service device is provided, including a data acquisition module, a risk determination module, and a recommendation module;

[0019] The data acquisition module is used to acquire the current customer's health data;

[0020] The risk determination module is used to obtain a health risk score based on the health data and the risk assessment model, and to determine the health risk of the current customer based on the health risk score. The risk assessment model is trained based on a machine learning model and health data samples.

[0021] The recommendation module is used to extract multiple entities from the health data, construct relationships between patients and diseases, and between patients and drugs based on the multiple entities to obtain a knowledge graph, and recommend medical services and provide health advice to the current customer based on the knowledge graph.

[0022] Another technical solution of the present invention is as follows: an electronic device is provided, including a memory and a processor. The memory stores a computer program that can be executed by the processor. When the processor executes the computer program, it implements the life insurance customer service method as described in any of the above technical solutions.

[0023] Another technical solution of the present invention is as follows: a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements the life insurance customer service method as described in any of the above technical solutions.

[0024] The beneficial effects of this invention are as follows: It acquires the current customer's health data; based on the health data and a risk assessment model, it obtains a health risk score, and uses this score to determine the current customer's health risk. The risk assessment model is trained based on a machine learning model and health data samples. It extracts multiple entities from the health data, constructs relationships between patients and diseases, and between patients and medications based on these entities to obtain a knowledge graph, and recommends medical services and provides health advice to the current customer based on the knowledge graph. Through the above technical solution, it can respond to changes in the customer's health in real time, thereby improving service quality. By continuously acquiring health data and recommending medical services and providing health advice to customers, it can enhance the continuity of services. Attached Figure Description

[0025] Figure 1 A flowchart illustrating the life insurance customer service method provided in an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of the structure of the life insurance customer service device provided in an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0029] In the description of this application, the terms "first," "second," etc., are used only for distinguishing purposes and should not be construed as indicating or implying relative importance or order. In this specification, the terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0030] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0031] Figure 1 This is a flowchart illustrating a life insurance customer service method according to an embodiment of the present invention. It should be noted that if substantially the same result is achieved, the life insurance customer service method of the present invention does not necessarily follow the same approach. Figure 1 The illustrated process sequence is limited. For example... Figure 1 As shown, this life insurance customer service method mainly includes the following steps:

[0032] S101, Obtain the current customer's health data;

[0033] In one optional implementation, the health data includes physiological data, insurance information, health questionnaire data, and medical record data.

[0034] In some embodiments, physiological data (such as heart rate, blood pressure, sleep quality, etc.) of customers are collected in real time from smart wearable devices (such as smartwatches and health monitors). Customer insurance information and health questionnaire data are collected through a customer management system. Medical records and health assessment reports can be obtained through cooperation with medical institutions. Data integration tools (such as Apache Kafka and ETL tools) are used to integrate multi-source data into a unified customer health profile. Irrelevant characters and stop words can be removed from health data to extract meaningful words and phrases; for example, punctuation marks, numbers, and special characters are removed, retaining health-related vocabulary. The collected health data can be cleaned, denoised, and normalized to ensure data quality; for example, missing values ​​and outliers are handled, and data formats are standardized. Useful features, such as heart rate, blood pressure, sleep quality, age, gender, medical history, medication use, and examination results, are extracted from the health data. These features can then be used as input for machine learning models.

[0035] In some embodiments, physiological data may include heart rate, blood pressure, sleep quality, weight, BMI, etc.; insurance information may include age, gender, occupation, past medical history, family medical history, etc.; health questionnaire data may include dietary habits, exercise frequency, smoking and drinking habits, etc.; medical records may include disease diagnosis, medication, examination results, surgical records, etc.

[0036] S102, Based on the health data and risk assessment model, obtain a health risk score, and determine the current customer's health risk using the health risk score. The risk assessment model is trained based on a machine learning model and health data samples.

[0037] In some embodiments, text can be converted into numerical form to facilitate processing by machine learning models. For example, word vectors can be generated using models such as Word2Vec and GloVe to capture the semantic information of words. Model performance can be evaluated using methods such as cross-validation and A / B testing, and model parameters can be adjusted to optimize prediction accuracy. For example, the number of trees in a random forest, the number of layers in a neural network, and the number of neurons can be adjusted. The trained model is then deployed to a real-world system for real-time analysis of customer health data, providing personalized health management recommendations. The current health risk of a customer can be determined based on a health risk score, i.e., low risk, medium risk, or high risk, with higher scores indicating greater risk.

[0038] In some embodiments, the risk assessment model can calculate the customer's health risk using a weighted summation formula.

[0039] The weighted summation formula is: Where RS is the total score, ω i Let x be the weight of the i-th health indicator in the health data. i Let be the value corresponding to the i-th health indicator in the health data.

[0040] S103, extract multiple entities from the health data, construct relationships between patients and diseases, and between patients and drugs based on the multiple entities to obtain a knowledge graph, and recommend medical services and provide health advice to the current customer based on the knowledge graph.

[0041] In some embodiments, key health information, such as disease names, medication details, and examination results, can be extracted from the text of health data. For example, Named Entity Recognition (NER) technology can be used to identify diseases, medications, examination items, names of people, places, and disease names, such as hypertension and diabetes. Relationships between multiple different entities can be identified, such as the relationship between patients and diseases, or patients and medications, to construct a knowledge graph. For example, relationships such as "Patient A has hypertension" and "Patient A is taking medication B" can be identified. Knowledge graphs can be used for applications such as health risk assessment and health management suggestion generation. For example, medical services and health advice can be recommended based on a patient's disease and medication. Health advice on diet, exercise, and medication can be provided based on the client's health status, and suitable medical services (including elderly care resources) can be recommended, such as rehabilitation nursing, health monitoring, and emergency rescue.

[0042] In an optional implementation, the life insurance customer service method further includes:

[0043] Obtain the health characteristics, behavioral characteristics, and background characteristics of all customers in the customer group. Based on the health characteristics, behavioral characteristics, and background characteristics, determine all customers in the customer group and segment them to generate user profiles.

[0044] In some embodiments, physiological data (such as heart rate and blood pressure) and health questionnaire data (such as dietary habits and exercise frequency) can be extracted as health features; user rating history, click behavior, and collection records can be analyzed to extract user preference features and obtain behavioral features; background features include basic information such as user age, gender, occupation, and past medical history. Machine learning algorithms (such as cluster analysis) can be used to segment users and generate user profiles, for example, dividing users into groups such as "hypertensive patients," "diabetic patients," and "healthy youth." The same service can be recommended to customers in the same segment based on user profiles.

[0045] In an optional implementation, the life insurance customer service method further includes:

[0046] Obtain the similarity between the current customer and other customers in the customer group, identify the customer in the customer group with the highest similarity to the current customer as a similar customer, and recommend the service strategy based on the historical preferences of the similar customer to the current customer.

[0047] In an optional implementation, obtaining the similarity between the current customer and other customers in the customer group includes:

[0048] Obtain the first rating of the current customer for the preset service strategy and the second rating of other customers in the customer group for the preset service. Based on the first rating and the second rating, obtain the similarity between the current customer and other customers in the customer group.

[0049] In some embodiments, the similarity between the current customer and other customers in the customer group can be obtained using a collaborative filtering algorithm. The formula for the collaborative filtering algorithm is as follows: Where, r u,i For user u, rate service (strategy) i. r is the average rating of user u for all services. υ,i For user υ, rate service i. Let υ be the average rating of all services, and n be the total number of services. Based on user similarity, find users similar to the current user and recommend services that these similar users like but that the target user has not yet experienced. Service strategies can include at least one of the following: service type (health monitoring, rehabilitation care, etc.) and service provider (hospital, nursing home, etc.). The recommendation strategy can also be dynamically adjusted and optimized based on service evaluations (user ratings, reviews, etc.). It should be noted that blockchain technology can be used to store user health data and service recommendation records.

[0050] In an optional implementation, the life insurance customer service method further includes: acquiring physiological data including heart rate, blood pressure, body temperature, and blood oxygen saturation; if any indicator in the physiological data exceeds a corresponding preset threshold, an early warning is triggered.

[0051] In some embodiments, the collected physiological data can be transmitted to the cloud platform in real time via a 5G network, ensuring high-speed and low-latency data transmission. Simple analysis algorithms run on smart wearable devices or edge devices close to the data source to preliminarily process and analyze the physiological data, quickly identifying abnormalities such as excessively high or low heart rate, or abnormal blood pressure. By performing preliminary analysis on edge devices, the time for data transmission to the cloud is reduced, improving real-time response speed.

[0052] In some embodiments, based on real-time monitored physiological data, the system determines whether a customer's health condition is at risk and issues an alert. Specifically, physiological data includes heart rate, blood pressure, body temperature, and blood oxygen saturation. User information includes age, gender, medical history, and medication use. Useful features are extracted from the physiological data, such as the average and standard deviation of heart rate, and the fluctuation range of blood pressure. Normal ranges are set for each physiological indicator, and an alert is triggered when an indicator exceeds its normal range; for example, the normal range for heart rate is 60-100 beats / minute, and the normal range for blood pressure is 90 / 60 mmHg-120 / 80 mmHg. Combining abnormalities in multiple physiological indicators, the customer's health risk level is comprehensively assessed. For example, if a heart rate that is too high (>100 beats / minute), blood pressure that is too high (>120 / 80 mmHg), and body temperature that is too high (>37°C) occur simultaneously, it is considered a high-risk condition. An alert can also be triggered when the current customer's health risk is determined based on a health risk score.

[0053] In an optional implementation, the life insurance customer service method further includes:

[0054] A predetermined number of customers in the customer group are randomly divided into an experimental group and a control group. The new service strategy is applied to the customers in the experimental group, while the original service strategy is applied to the customers in the control group. Based on service ratings, customer satisfaction, and service usage frequency, it is determined whether the predetermined number of customers should adopt the new service strategy.

[0055] In some embodiments, statistical methods (such as t-tests) can be used to compare the performance metrics of the two groups to determine whether the new service strategy is superior to the original service strategy; based on the comparison results, the better-performing strategy is selected as the new service strategy. Alternatively, reinforcement learning algorithms (such as Q-learning) can be used to dynamically adjust the service strategy and service resource allocation.

[0056] In some embodiments, intelligent dispatch systems can be used to quickly mobilize medical and elderly care service resources, ensuring emergency services are provided in the shortest possible time. These systems optimize the allocation of medical resources based on the customer's geographical location, health condition, and urgency, such as dispatching the nearest ambulance or contacting nearby medical institutions. Collaboration with emergency medical systems and elderly care service systems ensures comprehensive and efficient emergency response.

[0057] The life insurance customer service method provided in this invention obtains the current customer's health data; obtains a health risk score based on the health data and a risk assessment model; determines the current customer's health risk based on the health risk score; the risk assessment model is trained based on a machine learning model and health data samples; extracts multiple entities from the health data; constructs relationships between patients and diseases, and between patients and medications based on the multiple entities to obtain a knowledge graph; and recommends medical services and provides health advice to the current customer based on the knowledge graph. This method can respond to changes in the customer's health in real time, thereby improving service quality. By continuously acquiring health data and recommending medical services and providing health advice to customers, the continuity of service can be enhanced.

[0058] The life insurance customer service method provided in this invention enhances customer reliance and loyalty by offering personalized health management and service recommendations, improves the utilization efficiency of medical and elderly care service resources through dynamic service recommendations and resource allocation, enhances service response speed and efficiency through intelligent health management tools and real-time monitoring technology, and improves the brand value and market competitiveness of life insurance companies in the health and elderly care scenario by building a customer-centric full life cycle service system.

[0059] The life insurance customer service method provided in this invention can be constructed based on artificial intelligence. It acquires and processes relevant data using AI technology to achieve unattended, AI-powered life insurance customer service. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0060] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0061] Figure 2 This is a schematic diagram of the structure of a life insurance customer service device according to an embodiment of the present invention, as shown below. Figure 2 As shown, the life insurance customer service device 20 includes a data acquisition module 21, a risk determination module 22, and a recommendation module 23;

[0062] The data acquisition module 21 is used to acquire the current customer's health data;

[0063] The risk determination module 22 is used to obtain a health risk score based on the health data and the risk assessment model, and to determine the health risk of the current customer based on the health risk score. The risk assessment model is trained based on a machine learning model and health data samples.

[0064] The recommendation module 23 is used to extract multiple entities from the health data, construct relationships between patients and diseases, and between patients and drugs based on the multiple entities to obtain a knowledge graph, and recommend medical services and provide health advice to the current customer based on the knowledge graph.

[0065] In one optional implementation, the health data includes physiological data, insurance information, health questionnaire data, and medical record data.

[0066] In an optional implementation, the life insurance customer service device 20 further includes a strategy recommendation module, which is used to obtain the similarity between the current customer and other customers in the customer group, and to recommend the service strategies of the similar customer with the highest similarity to the current customer to the current customer.

[0067] In an optional implementation, the strategy recommendation module obtains the similarity between the current customer and other customers in the customer group, including:

[0068] Obtain the first rating of the current customer for the preset service strategy and the second rating of other customers in the customer group for the preset service. Based on the first rating and the second rating, obtain the similarity between the current customer and other customers in the customer group.

[0069] In an optional implementation, the life insurance customer service device 20 further includes a user profile generation module, which is used to acquire the health characteristics, behavioral characteristics and background characteristics of all customers in the customer group, and to determine the segmentation of all customers in the customer group based on the health characteristics, behavioral characteristics and background characteristics to generate user profiles.

[0070] In an optional implementation, the life insurance customer service device 20 further includes a strategy adjustment module, which is used to randomly divide a preset number of customers in the customer group into an experimental group and a control group, apply a new service strategy to the customers in the experimental group, and allow the customers in the control group to use the original service strategy, and determine whether to adopt the new service strategy for the preset number of customers based on service rating, customer satisfaction and service usage frequency.

[0071] In an optional implementation, the life insurance customer service device 20 further includes an early warning module, which is used to acquire physiological data including heart rate, blood pressure, body temperature and blood oxygen saturation. If any indicator in the physiological data exceeds the corresponding preset threshold, an early warning is triggered.

[0072] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 3 As shown, the electronic device 30 includes a processor 31 and a memory 32 communicatively connected to the processor 31.

[0073] The memory 32 stores program instructions for implementing the life insurance customer service method of any of the above embodiments.

[0074] The processor 31 is used to execute program instructions stored in the memory 32 to provide life insurance customer service.

[0075] The processor 31 can also be referred to as a CPU (Central Processing Unit). The processor 31 may be an integrated circuit chip with signal processing capabilities. The processor 31 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0076] This invention provides a storage medium that stores program instructions capable of implementing all the methods described above. The storage medium can be non-volatile or volatile. These program instructions can be stored in the storage medium as a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0077] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0078] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

[0079] The above description is merely an embodiment of the present invention. It should be noted that those skilled in the art can make improvements without departing from the inventive concept of the present invention, but these improvements all fall within the protection scope of the present invention.

Claims

1. A method for serving life insurance customers, characterized in that, include: Obtain the current customer's health data; Based on the health data and risk assessment model, a health risk score is obtained, and the health risk of the current customer is determined by the health risk score. The risk assessment model is trained based on a machine learning model and health data samples. Multiple entities are extracted from the health data, and relationships between patients and diseases, and between patients and drugs are constructed based on these entities to obtain a knowledge graph. Medical services and health advice are then recommended to the current customer based on the knowledge graph.

2. The life insurance customer service method according to claim 1, characterized in that, The health data includes physiological data, insurance information, health questionnaire data, and medical record data.

3. The life insurance customer service method according to claim 1, characterized in that, The life insurance customer service methods also include: Obtain the similarity between the current customer and other customers in the customer group, identify the customer in the customer group with the highest similarity to the current customer as a similar customer, and recommend the service strategy based on the historical preferences of the similar customer to the current customer.

4. The life insurance customer service method according to claim 1, characterized in that, The process of obtaining the similarity between the current customer and other customers in the customer group includes: Obtain the first rating of the current customer for the preset service strategy and the second rating of other customers in the customer group for the preset service. Based on the first rating and the second rating, obtain the similarity between the current customer and other customers in the customer group.

5. The life insurance customer service method according to claim 4, characterized in that, The life insurance customer service methods also include: Obtain the health characteristics, behavioral characteristics, and background characteristics of all customers in the customer group. Based on the health characteristics, behavioral characteristics, and background characteristics, determine all customers in the customer group and segment them to generate user profiles.

6. The life insurance customer service method according to claim 4, characterized in that, The life insurance customer service methods also include: A predetermined number of customers in the customer group are randomly divided into an experimental group and a control group. The new service strategy is applied to the customers in the experimental group, while the original service strategy is applied to the customers in the control group. Based on service ratings, customer satisfaction, and service usage frequency, it is determined whether the predetermined number of customers should adopt the new service strategy.

7. The life insurance customer service method according to claim 1, characterized in that, The life insurance customer service method further includes: acquiring physiological data including heart rate, blood pressure, body temperature, and blood oxygen saturation; if any indicator in the physiological data exceeds the corresponding preset threshold, an early warning is triggered.

8. A life insurance customer service device, characterized in that, It includes a data acquisition module, a risk assessment module, and a recommendation module; The data acquisition module is used to acquire the current customer's health data; The risk determination module is used to obtain a health risk score based on the health data and the risk assessment model, and to determine the health risk of the current customer based on the health risk score. The risk assessment model is trained based on a machine learning model and health data samples. The recommendation module is used to extract multiple entities from the health data, construct relationships between patients and diseases, and between patients and drugs based on the multiple entities to obtain a knowledge graph, and recommend medical services and provide health advice to the current customer based on the knowledge graph.

9. An electronic device, comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, characterized in that, When the processor executes the computer program, it implements the life insurance customer service method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the life insurance customer service method as described in any one of claims 1 to 7.

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