Method, device and readable storage medium for generating insurance proposal

Abstract

The present application provides a method, device and readable storage medium for generating an insurance proposal, which relates to the insurance field. The solution includes obtaining the customer portrait information of the insured, predicting the target insurance product classification corresponding to the target object based on the customer portrait information and the product classification prediction model; determining the target main insurance product corresponding to the target object under the target insurance product classification based on the customer portrait information and the main insurance product prediction model, determining the target additional product and target product configuration information associated with the target main insurance product based on the customer portrait information, the target main insurance product and the preset association rule information, and generating an insurance proposal including the target main insurance product, the target additional insurance product and the target product configuration information. The present application gradually determines an insurance proposal that better meets the needs of the target object and is more conducive to transaction based on the insured's portrait information. It is not limited to the personal experience of insurance business personnel, has a high degree of intelligence, and is conducive to improving the accuracy of insurance product recommendations.

Description

Technical Field

[0001] The present application relates to the field of insurance technology, and in particular to a method, device, and readable storage medium for generating an insurance proposal. Background Art

[0002] An insurance proposal is a communication plan commonly used by insurance personnel and clients to effectively communicate on insurance purchase and insurance management. It reflects the professional level of insurance services in written form. A reasonable and excellent insurance proposal is conducive to strengthening the client's sense of trust and laying a good foundation for the next project operation.

[0003] Currently, the preparation of insurance proposals mainly adopts the traditional model, which relies entirely on the personal experience of insurance sales personnel. However, due to personal experience limitations, insurance sales personnel sometimes do not know which insurance products are more suitable and more likely to close a deal for customers, and therefore cannot recommend products that customers really need. Insurance sales personnel also sometimes do not know what the reasonable settings for the insured amount, premium, coverage period and payment period of the products recommended to customers are, resulting in inaccurate estimates. This situation is particularly common for new insurance sales personnel.

[0004] Therefore, how to design an intelligent insurance proposal generation solution is an urgent problem to be solved. Summary of the Invention

[0005] In view of this, the present application provides a method, device and readable storage medium for generating an insurance proposal, which is not limited to the personal experience of insurance business personnel, but can obtain an insurance proposal that is more conducive to transaction, has a high degree of intelligence, and is conducive to improving the accuracy of insurance product recommendations.

[0006] To solve the above technical problems, this application provides a method for generating an insurance proposal, comprising:

[0007] Obtain customer profile information of the policyholder;

[0008] Based on the customer profile information and the product classification prediction model, predict the target insurance product classification corresponding to the target object, the target object including the insured and / or the insured's family members;

[0009] Determine, based on the customer profile information and the primary insurance product prediction model, the target primary insurance product corresponding to the target object under the target insurance product category;

[0010] Determine the target supplementary insurance product and target product configuration information associated with the target primary insurance product based on the customer profile information, the target primary insurance product, and preset association rule information;

[0011] An insurance proposal including the target primary insurance product, the target supplementary insurance product and the target product configuration information is generated.

[0012] To solve the above technical problems, the present application also provides a device for generating an insurance proposal, comprising:

[0013] Acquisition module, used to obtain the customer profile information of the policyholder;

[0014] A product classification prediction module, configured to predict a target insurance product classification corresponding to a target object based on the customer profile information and the product classification prediction model, wherein the target object includes the policyholder and / or the policyholder's family members;

[0015] A primary insurance prediction module is used to determine the target primary insurance product corresponding to the target object under the target insurance product category based on the customer profile information and the primary insurance product prediction model;

[0016] A supplementary insurance and configuration information prediction module is used to determine the target supplementary insurance product and target product configuration information associated with the target main insurance product based on the customer profile information, the target main insurance product and preset association rule information;

[0017] The proposal generation module is used to generate an insurance proposal including the target main insurance product, the target supplementary insurance product and the target product configuration information.

[0018] To solve the above technical problems, the present application also provides a readable storage medium on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method for generating an insurance proposal as described above are implemented.

[0019] The present application provides a method, device and readable storage medium for generating an insurance proposal, which includes: obtaining the customer profile information of the insured, predicting the target insurance product classification corresponding to the target object based on the customer profile information and the product classification prediction model, the target object including the insured and / or the insured's family members, determining the target main insurance product corresponding to the target object under the target insurance product classification based on the customer profile information and the main insurance product prediction model, determining the target additional product and target product configuration information associated with the target main insurance product based on the customer profile information, the target main insurance product and the preset association rule information, and finally generating an insurance proposal including the target main insurance product, the target additional insurance product and the target product configuration information. It can be seen that through the above scheme, it is possible to gradually determine the target insurance product classification that is more suitable for the target object based on the insured's profile information. The target main insurance product and the corresponding target additional product and target product configuration information under this classification are not limited to the personal experience of the insurance business personnel, and an insurance proposal that is more conducive to the transaction is obtained. It has a high degree of intelligence and is conducive to improving the accuracy of insurance product recommendations.

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0022] Figure 1 A flowchart of a method for generating an insurance proposal provided for this application;

[0023] Figure 2 A schematic diagram of the hierarchical architecture of the process of generating an insurance proposal provided for this application;

[0024] Figure 3 A schematic diagram of the structure of a device for generating an insurance proposal provided in this application;

[0025] Figure 4 is a structural diagram of a computer device in one embodiment of the present application;

[0026] Figure 5 It is another structural diagram of a computer device in one embodiment of the present application. DETAILED DESCRIPTION

[0027] The core of this application is to provide a method, device and readable storage medium for generating an insurance proposal, which is not limited to the personal experience of insurance business personnel, but can obtain an insurance proposal that is more conducive to transaction, has a high degree of intelligence, and is conducive to improving the accuracy of insurance product recommendations.

[0028] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0029] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0030] Please refer to Figure 1 , Figure 1 A flowchart of a method for generating an insurance proposal provided in this application.

[0031] The method for generating the insurance proposal includes:

[0032] S11: Obtain the customer profile information of the policyholder;

[0033] In this embodiment, the customer portrait information may include the insured's basic information, such as age, gender, occupation, annual income, life stage, wealth level and family information, among which the family information may include the age, gender, occupation, annual income, life stage, wealth level and other information of the insured's family members; the customer portrait information may also include the insured's previous transaction behavior information, active behavior information and demand tendency information that represents his or her purchasing intention, etc., which are not specifically limited here.

[0034] Each customer has a unique identity ID, and the corresponding customer portrait information can be stored in the form of tags and uploaded to the database for regular updates. The tags of customer portrait information can include structured tags and unstructured tags. The unstructured tags here refer to customer portrait information data stored in the form of text, voice, video or image. In addition, in actual applications, large model technology can be introduced to process unstructured portrait information data to obtain corresponding structured data tags. For example, by analyzing the customer's voice data or chat record pictures, it can be directly determined whether the customer is interested in insurance to complete the corresponding tag. This processing method can save the loss of manual labeling input tags, which is conducive to improving the prediction accuracy of subsequent product classification prediction models. The large model technology here includes but is not limited to AIGC technology (Artificial Intelligence Generated Content, generative artificial intelligence), etc., use AIGC technology to analyze chat records with customers or relevant descriptive information of customers on their insurance needs, so as to extract customer portrait information and obtain corresponding multiple tags for storage; it is understandable that for different insurance products, corresponding product tags can also be generated, that is, according to product liability and product promotion content, the key tags corresponding to the insurance are mined, such as dividend type, maturity survival benefit and policy loan, etc.; in addition, the preferences and purchasing habits of the population corresponding to the characteristics of different insurance types can be analyzed and stored to achieve effective management of customer information and insurance product information; from the actual application effect, after using the above method to mine more unstructured data value, the accuracy of insurance proposal generation can be improved by 28.33%, which is a significant effect.

[0035] S12: Based on the customer profile information and the product classification prediction model, predict the target insurance product classification corresponding to the target object, the target object including the insured and / or the insured's family members;

[0036] Specifically, considering that the insured's family members may also have insurance needs, the target objects may include the insured and / or the insured's family members, so as not only to recommend more suitable insurance products to the insured, but also to recommend suitable insurance products to their family members, enriching the content of the insurance proposal and helping to increase the probability of transaction.

[0037] S13: Based on the customer profile information and the primary insurance product prediction model, determine the target primary insurance product corresponding to the target object under the target insurance product category;

[0038] Specifically, the target insurance product classification refers to major product categories such as protection and financial management, and the target main insurance product refers to the sub-category of products under the major product categories.

[0039] S14: Based on the customer profile information, the target primary insurance product, and the preset association rule information, determine the target supplementary insurance product and target product configuration information associated with the target primary insurance product;

[0040] S15: Generate an insurance proposal including the target primary insurance product, the target supplementary insurance product and the target product configuration information.

[0041] Specifically, the target product configuration information here may include premium, insured amount, delivery date and insurance period, thereby intelligently generating an insurance proposal that is more suitable for the target object.

[0042] In addition, when the target objects include the insured and the insured's family members, steps S13 and S14 are performed for the insured and his family members respectively, so that the insurance recommendation obtained in step S15 includes insurance recommendations for the insured and his family members at the same time.

[0043] It is understandable that the above models can be evaluated and revised in actual use according to the actual use effect of the generated insurance proposals, so as to continuously optimize the model and generate insurance proposals with higher customer satisfaction.

[0044] Please refer to Figure 2 , Figure 2 A hierarchical architecture diagram of the process of generating an insurance proposal provided for this application. It should be noted that the KYC layer here refers to the layer corresponding to the KYC rules (Know Your Customer), at which the customer profile information of the policyholder is collected; the model layer shows the information flow process when applying each model to execute the above steps to generate an insurance proposal; the application layer shows the insurance recommendation content that the policyholder can actually see when it is finally presented to the policyholder (wherein, AI digital human refers to the use of digital human technology in the following embodiment to generate an exclusive image of an insurance business person, so that the person can serve as the main speaker to explain the plan explanation video corresponding to the insurance proposal); in addition, the operation layer can also be set to continuously optimize and correct the above model in order to generate an insurance proposal that better meets the needs of the policyholder and is conducive to the transaction.

[0045] In summary, this application provides a method for generating an insurance proposal. This solution can gradually determine the target insurance product classification that is more suitable for the target object based on the insured's portrait information. The target main insurance product under this classification and the corresponding target additional product and target product configuration information are not limited to the personal experience of the insurance business personnel, and an insurance proposal that is more conducive to the transaction is obtained. It has a high degree of intelligence and is conducive to improving the accuracy of insurance product recommendations.

[0046] Based on the above embodiment:

[0047] In some embodiments, the product classification prediction model includes at least one of a historical customer multi-classification prediction sub-model, a new customer prediction sub-model, a business experience scoring sub-model, and a customer attention review molecular model; based on the customer profile information and the product classification prediction model, predicting the target insurance product classification corresponding to the target object includes:

[0048] Determine the type of the target object, and predict the target insurance product classification corresponding to the target object according to the sub-model corresponding to the type of the target object.

[0049] In this embodiment, considering that the target object may be a new customer or an old customer, may have concerns or may not have concerns, the type of the target object is determined based on the customer portrait information, and then prediction is performed according to the sub-model corresponding to the type, which helps to improve the accuracy of the prediction results.

[0050] It should also be noted that the historical customer multi-classification prediction sub-model is designed based on the customer's historical trace data; the business experience scoring sub-model is designed based on the fact that high-performing insurance sales personnel primarily consider the customer's life stage and wealth level when recommending insurance product plans to customers; the customer attention review molecular model is designed based on the customer's demand tendency information for purchasing insurance products, that is, the focus can be mapped to different product categories; the new customer prediction sub-model is obtained by statistically analyzing the product categories that customers may be interested in based on experience or research.

[0051] In some embodiments, predicting the target insurance product classification corresponding to the target object according to the sub-model corresponding to the type of the target object includes:

[0052] If the target object is a historical customer and the target object has focus points, then based on the life stage information and wealth level information in the customer portrait information, as well as the business experience scoring sub-model, the insurance product classification corresponding to the target object's grid customer group demand tendency and the score of each insurance product classification are predicted. Also, based on the customer portrait information and the historical customer multi-classification prediction sub-model, the insurance product classification corresponding to the target object's similar customer demand tendency and the score of each insurance product classification are predicted. Also, based on the focus point information in the customer portrait information and the customer focus review molecular model, the insurance product classification corresponding to the target object's focus point and the score of each insurance product classification are predicted.

[0053] If the target object is a historical customer and the target object has no focus, then based on the life stage information and wealth level information in the customer profile information, as well as the business experience scoring sub-model, the insurance product classification corresponding to the target object's grid customer group demand tendency and the score of each insurance product classification are predicted. Based on the customer profile information and the historical customer multi-classification prediction sub-model, the insurance product classification corresponding to the target object's similar customer demand tendency and the score of each insurance product classification are predicted;

[0054] If the target object is a new customer and the target object has focus points, then based on the life stage information and wealth level information in the customer portrait information, as well as the business experience scoring sub-model, the insurance product classification corresponding to the target object's grid customer group demand tendency and the score of each insurance product classification are predicted, and based on the life stage information in the customer portrait information and the new customer prediction sub-model, the insurance product classification corresponding to the target object's similar customer demand tendency and the score of each insurance product classification are predicted, and based on the focus information in the customer portrait information and the customer focus review molecular model, the insurance product classification corresponding to the target object's focus points and the score of each insurance product classification are predicted;

[0055] If the target object is a new customer and the target object has no focus, then based on the life stage information and wealth level information in the customer profile information, as well as the business experience scoring sub-model, the insurance product categories corresponding to the target object's grid customer group demand tendencies and the scores of each insurance product category are predicted. Also, based on the life stage information in the customer profile information and the new customer prediction sub-model, the insurance product categories corresponding to the target object's similar customer demand tendencies and the scores of each insurance product category are predicted.

[0056] For each insurance product classification, calculating a total score of the insurance product classification; wherein the method of calculating the total score of the insurance product classification includes: multiplying each score of the insurance product classification by the weight coefficient of the corresponding sub-model to obtain multiple products, and calculating the sum of the multiple products to obtain the total score of the insurance product classification;

[0057] The top n insurance product categories ranked from high to low in terms of total scores are classified as the target insurance product categories corresponding to the target objects, where n is a positive integer.

[0058] In this embodiment, the target object having a focus here means that the target object has a corresponding insurance product demand tendency. Different focus points can be mapped to different product categories. For example, if the insured is interested in wealth insurance products, then the product category with the highest priority obtained by further relying on the customer focus review molecular model is financial insurance products, which reflects the influence of focus points on customer choices.

[0059] Specifically, the customer portrait information includes various label information of the target object, including customer basic information labels (such as age, gender, education and marital status, etc.), customer purchase intention labels (such as number of appointments, life stage, etc.), and customer purchasing ability labels (such as income, whether or not a VIP member customer, annual premium payment); more specifically, the data middle platform of the group where the insurance business personnel is located can also store the information of each insurance business personnel in the form of labels, including but not limited to the basic information labels of the insurance business personnel (such as age, gender, education and marital status, etc.), performance information labels (such as premiums, the number of products sold by agents in a year) and customer contact information labels (such as the number of face-to-face interviews, where insurance proposals are made, and whether there is telephone communication with customers), etc., so that the data middle platform can uniformly manage customer information and insurance business personnel information; in this embodiment, the customer portrait information is input as an input item into the historical customer multi-classification prediction sub-model, and the insurance product classification corresponding to the similar customer demand tendencies of the target object and the score of each insurance product classification can be obtained. It is understandable that the input data can be preprocessed before entering the sub-model, including but not limited to cleaning data, processing missing values ​​and outliers, converting data according to standard formats, etc.; in addition, the historical customer multi-classification prediction sub-model can be specifically established based on multinomial logistic regression and multi-classification prediction models.

[0060] Customer profile information includes life stage and wealth level information. Using these as inputs, we can predict the insurance product categories corresponding to the preferences of the target customer segments and the scores for each insurance product category. It's understood that the experience scoring sub-model can be a rule-based grid-like model designed based on the experience of high-performing insurance agents, allowing us to extract information from the customer profile. More specifically, please refer to Table 1, which shows the insurance product categories preferred by different customer segments at different life stages and wealth levels, as well as the appeal of each insurance product category to these customers. Life stages are categorized as single (unmarried), married (young, married, childless), with children (young, with children under 10 years old), mature (middle-aged, with children over 10 years old), and retired (over 55 years old). Wealth level is categorized as the general public (annual income less than 100,000 yuan), the quasi-wealthy (annual income 100,000-500,000 yuan), the wealthy (annual income 500,000-1,000,000 yuan), and the high net worth (annual income over 1,000,000 yuan).

[0061] Table 1

[0062]

[0063]

[0064] The new customer prediction sub-model here can be obtained by ranking the interest levels of insurance products that customers may be interested in under the five life stages. Based on the life stage information in the customer portrait information and combined with this sub-model, the insurance product classification corresponding to the target object's similar customer demand tendencies and the scores of each insurance product classification can be obtained.

[0065] Furthermore, the weight coefficients corresponding to the sub-models used in calculating the total score of the insurance product classification can be dynamically adjusted by insurance business personnel based on business results in actual applications to improve the accuracy of insurance recommendations; for example, for new customers with focus points, the predicted total score of the insurance product classification can be calculated as: the score of the insurance product classification corresponding to the focus point × 80% + the score of the insurance product classification corresponding to the demand tendency of the palace customer group × 20%; for old users with focus points, the predicted total score of the insurance product classification can be calculated as: the score of the insurance product classification corresponding to the focus point × 80% + the score of the insurance product classification corresponding to the demand tendency of the palace customer group × 20% × 60% + the score of the insurance product classification corresponding to the demand tendency of similar customers × 20 × 40%, among which two weight coefficients need to be multiplied because there are two focus points corresponding to this insurance product classification, namely life stage and wealth level, but it should be noted that the sum of the weight coefficients of each sub-model should be 100%.

[0066] It can be seen that through the above method, the experience of high-performing insurance sales personnel can be combined with big data models to accurately and reliably recommend the most likely insurance plans for different target objects and whether the target objects have any concerns, so as to understand the customers better than the customers themselves, which is conducive to ultimately improving the compatibility of insurance proposals with customers.

[0067] In some embodiments, based on the customer profile information and the primary insurance product prediction model, determining the target primary insurance product corresponding to the target object under the target insurance product category includes:

[0068] Based on customer profile information and the main insurance product prediction model, multiple main insurance products under different target insurance product categories are predicted;

[0069] Determine the target primary insurance product corresponding to the target object among multiple primary insurance products.

[0070] In this embodiment, considering that the target insurance product classification is a major product category, there are a considerable number of main insurances under it. Therefore, when determining the main insurance under the major product category, the customer portrait information is input as an input item into a pre-set main insurance product prediction model, so that multiple main insurance products under the target insurance product classification can be screened, and then the target main insurance product corresponding to the target object can be determined from the multiple main insurance products; in addition, the main insurance product prediction model here can be a pre-established Cube rule model prediction table, which defines one or more main insurances that customers tend to choose under different Cube tags. The Cube tags here refer to dimensional tags corresponding to customer portrait information such as age, gender, life stage, wealth level, income, etc. Finally, relying on the model to execute the above steps, the target object can select the main insurance product with the highest sales as the target main insurance product after comprehensively considering the target object's life stage, gender, occupation, income and other customer portrait information, without recommending main insurance products that exceed their income level to the target object.

[0071] In some embodiments, based on the customer profile information, the target primary insurance product, and the preset association rule information, determining the target supplementary insurance product and target product configuration information associated with the target primary insurance product includes:

[0072] In the preset association rule information, find the top m supplementary insurance products ranked from high to low in terms of correlation with the customer profile information and the target primary insurance product, and use them as the target supplementary insurance products associated with the target primary insurance product, where m is a positive integer;

[0073] In the preset association rule information, the target product configuration information corresponding to the customer portrait information and the target main insurance product is determined. The target product configuration information includes at least one of the following: premium, insured amount, delivery date, and insurance period.

[0074] In this embodiment, after the target main insurance product is confirmed, the above method can be used to further obtain the hot-selling target supplementary insurance products under the target main insurance product, recommend them to the insured for selection, and clarify the target product configuration information, making the entire insurance proposal clear and concise.

[0075] In some embodiments, the method further comprises:

[0076] Based on the insured's basic information, obtain the supplementary insurance products and product configuration information corresponding to each dimension feature in the basic information from the stored data corresponding to the main insurance product;

[0077] Combine each dimension feature to obtain multiple dimension feature combinations, and calculate the correlation between different supplementary insurance products and the main insurance product under each dimension feature combination, and calculate the median of the product configuration information under each dimension feature combination, and use the median as the product configuration information corresponding to the dimension combination;

[0078] The supplementary insurance products corresponding to each dimensional feature combination and their correlation, as well as product configuration information, are stored as preset association rule information.

[0079] In this embodiment, specifically, the dimensional features here may include gender dimension, age dimension, income dimension, occupation dimension, and life stage dimension, etc., and then through the above settings, the product configuration information corresponding to each main insurance under different dimensional feature combinations and the correlation between the main insurance and the supplementary insurance product can be obtained, and stored as preset association rule information, so that the target supplementary insurance product and target product configuration information corresponding to the target main insurance can be determined according to the customer portrait information of the target object and the preset association rule information.

[0080] In some embodiments, the method further comprises:

[0081] Obtaining first recommendation data input by an insurance business person, and replacing at least one of the target primary insurance product, the supplementary insurance product, and the target product configuration information with the first recommendation data; or,

[0082] Determine the second recommended data based on the business guarantee plan, and use the second recommended data to replace at least one of the target main insurance product, supplementary insurance product and target product configuration information.

[0083] In this embodiment, considering that there are some cases where the insurance business personnel may have more information about the target object and a deeper and more comprehensive understanding, or during the communication with the target object, they learn that the target object has other ideas about the selection of insurance products, the first recommendation data input by the insurance business personnel can be received to replace at least one of the target main insurance product, supplementary insurance product and target product configuration information to better meet the actual insurance needs of the target object, which is conducive to improving the accuracy of the insurance proposal; more specifically, the insurance business personnel can operate on the human-computer interaction interface to input the first recommendation data.

[0084] Furthermore, a business backstop plan can be pre-configured to correct or modify currently generated insurance proposals that are clearly unreasonable, in order to better meet the insurance needs of the target customers. Specifically, this business backstop plan includes, but is not limited to, setting it based on the company's current insurance promotion direction. This helps recommend the company's key insurance products to customers, thereby increasing sales of these key products while meeting customer needs.

[0085] In some embodiments, the method further comprises:

[0086] Generate insurance proposals into explanation text based on insurance salesperson's experience and / or insurance training information;

[0087] Based on the plan explanation text, digital human technology is used to generate a plan explanation video with the exclusive image of insurance sales personnel.

[0088] In this embodiment, it is further considered that in actual applications, insurance sales personnel may not know how to explain insurance proposals to facilitate transactions, and may not fully and clearly explain the insurance proposals. The language used may also be unprofessional and not attractive enough to customers. In addition, in actual applications, insurance sales personnel may sell insurance products through recorded videos or video accounts, but they may not always be able to maintain a full state, and video shooting is long and complicated. There may also be situations where the explanation is not good or the shooting is not good and needs to be restarted. Therefore, in this application, the experience and language information and / or insurance training information of high-performing insurance sales personnel are obtained in advance, and the insurance proposal is generated based on this information.

[0089] More specifically, GPT technology (Generative Pre-Trained Transformer) can be used to generate one-stop explanation scripts for products and services based on different policyholder customer portraits, including demand analysis, family protection details and configuration suggestions, insurance product introduction, service reception, service video, and insurance proposal plan interpretation. Through experience script information and / or insurance training information, AI (Artificial Intelligence) based on GPT technology can be used to The training can involve multiple aspects, such as topic entry (including current affairs, children's education, event invitations and customer cases), concept introduction (such as health, medical and elderly care resources, family responsibilities and protection functions), sales skills and the touching points corresponding to the segmented customer matrix to form explanation points and labels, and build the following knowledge base, including customer library (segment portraits, behavioral preferences and risk reviews), insurance policy library (including insurance tendencies and insurance type preferences), product library (including product liability and service rights). In actual application, the current insurance proposal is processed according to the key point matching to generate the corresponding plan explanation text. The plan explanation text can include an overall introduction to the plan that reflects the characteristics of the insurance proposal, applicable demand scenarios that reflect the needs of the target object, product protection content that reflects the responsibility, value-added service content that reflects the service, and exemplary customer case analysis.

[0090] Furthermore, digital human technology can be used to clone the voice and image of an insurance agent to generate a plan explanation video with the agent's unique image, elaborating on the plan explanation text. This video can then be directly shared to the policyholder's electronic device via social media accounts. It is understood that the plan explanation video can also include content such as a cover, back cover, introduction to the insurance agent, introduction to the company's strengths, demand stimulation, and a presentation of the insurance proposal plan, including insurance product liability, features, benefits, and liability statements. Furthermore, the plan explanation video can be combined with a PowerPoint presentation to create a more diverse and attractive presentation format, without any specific restrictions.

[0091] It can be seen that the above method, on the one hand, allows insurance sales personnel to no longer have to worry about the explanation language. Combined with the experience of high-performing sales personnel, more appropriate explanation language is recommended to them, which is conducive to improving the success rate of signing contracts; on the other hand, the determination of the plan explanation video is conducive to subsequent insurance sales personnel forwarding it to customers and / or posting the insurance proposal explanation video on social media such as video accounts, demonstrating professionalism and promoting insurance sales. In addition, digital people replace insurance sales personnel to explain insurance products, which saves time and effort for insurance sales personnel.

[0092] Please refer to Figure 3 , Figure 3 A schematic structural diagram of a device for generating an insurance proposal provided by the present invention.

[0093] The device for generating the insurance proposal includes:

[0094] Acquisition module 21, used to obtain the customer profile information of the policyholder;

[0095] A product classification prediction module 22 is configured to predict the target insurance product classification corresponding to a target object based on the customer profile information and the product classification prediction model. The target object includes the policyholder and / or the policyholder's family members.

[0096] The primary insurance prediction module 23 is used to determine the target primary insurance product corresponding to the target object under the target insurance product category based on the customer profile information and the primary insurance product prediction model;

[0097] The supplementary insurance and configuration information prediction module 24 is used to determine the target supplementary insurance product and target product configuration information associated with the target main insurance product based on the customer profile information, the target main insurance product and the preset association rule information;

[0098] The proposal generation module 25 is used to generate an insurance proposal including a target primary insurance product, a target supplementary insurance product and target product configuration information.

[0099] For an introduction to the device for generating an insurance proposal provided in this application, please refer to the embodiment of the method for generating an insurance proposal described above, which will not be repeated here.

[0100] In some embodiments, the product classification prediction model includes at least one of a historical customer multi-classification prediction sub-model, a new customer prediction sub-model, a business experience scoring sub-model, and a customer attention review molecular model; the product classification prediction module 22 includes:

[0101] The category determination module is used to determine the type of the target object and predict the target insurance product classification corresponding to the target object according to the sub-model corresponding to the type of the target object.

[0102] In some embodiments, the category determination module includes a first processing module, a second processing module, a third processing module, a fourth processing module, a score statistics module, and a first ranking module; wherein, if the target object is a historical customer and the target object has a focus, the first processing module is triggered; if the target object is a historical customer and the target object has no focus, the second processing module is triggered; if the target object is a new customer and the target object has a focus, the third processing module is triggered; if the target object is a new customer and the target object has no focus, the fourth processing module is triggered;

[0103] The first processing module is used to predict the insurance product categories corresponding to the target object's grid customer group demand tendencies and the scores of each insurance product category based on the life stage information and wealth level information in the customer portrait information and the business experience scoring sub-model, and to predict the insurance product categories corresponding to the target object's similar customer demand tendencies and the scores of each insurance product category based on the customer portrait information and the historical customer multi-classification prediction sub-model, and to predict the insurance product categories corresponding to the target object's focus points and the scores of each insurance product category based on the focus point information in the customer portrait information and the customer focus comment molecular model;

[0104] The second processing module is used to predict the insurance product categories corresponding to the target object's grid customer group demand tendencies and the scores of each insurance product category based on the life stage information and wealth level information in the customer profile information and the business experience scoring sub-model, and to predict the insurance product categories corresponding to the target object's similar customer demand tendencies and the scores of each insurance product category based on the customer profile information and the historical customer multi-classification prediction sub-model;

[0105] The third processing module is used to predict the insurance product categories corresponding to the target object's grid customer group demand tendencies and the scores of each insurance product category based on the life stage information and wealth level information in the customer portrait information and the business experience scoring sub-model, and to predict the insurance product categories corresponding to the target object's similar customer demand tendencies and the scores of each insurance product category based on the life stage information in the customer portrait information and the new customer prediction sub-model, and to predict the insurance product categories corresponding to the target object's focus points and the scores of each insurance product category based on the focus point information in the customer portrait information and the customer focus comment molecular model;

[0106] The fourth processing module is used to predict the insurance product categories corresponding to the target object's grid customer group demand tendencies and the scores of each insurance product category based on the life stage information and wealth level information in the customer profile information and the business experience scoring sub-model, and to predict the insurance product categories corresponding to the target object's similar customer demand tendencies and the scores of each insurance product category based on the life stage information in the customer profile information and the new customer prediction sub-model;

[0107] A score statistics module is used to calculate the total score of each insurance product category. The method of calculating the total score of the insurance product category includes: multiplying each score of the insurance product category by the weight coefficient of the corresponding sub-model to obtain multiple products, and calculating the sum of the multiple products to obtain the total score of the insurance product category.

[0108] The first sorting and screening module is used to classify the top n insurance products ranked from high to low in terms of total scores as target insurance product categories corresponding to the target objects, where n is a positive integer.

[0109] In some embodiments, the primary risk prediction module 23 includes:

[0110] The primary insurance prediction submodule is used to predict multiple primary insurance products under different target insurance product categories based on customer profile information and the primary insurance product prediction model;

[0111] The screening module is used to determine the target primary insurance product corresponding to the target object among multiple primary insurance products.

[0112] In some embodiments, the supplementary insurance and configuration information prediction module 24 includes:

[0113] The second sorting and screening module is used to search for the top m supplementary insurance products ranked from high to low in terms of correlation with the customer profile information and the target primary insurance product in the preset association rule information, and use them as the target supplementary insurance products associated with the target primary insurance product, where m is a positive integer;

[0114] The product configuration information determination module is used to determine the target product configuration information corresponding to the customer portrait information and the target main insurance product in the preset association rule information. The target product configuration information includes at least one of the following: premium, insured amount, delivery date, and insurance period.

[0115] In some embodiments, the insurance proposal generating device further includes:

[0116] The basic information acquisition module is used to obtain the supplementary insurance products and product configuration information corresponding to each dimension feature in the basic information from the stored data corresponding to the main insurance product based on the basic information of the insured;

[0117] The combination evaluation module is used to combine each dimension feature to obtain multiple dimension feature combinations, and calculate the correlation between different supplementary insurance products and the main insurance product under each dimension feature combination, and calculate the median of the product configuration information under each dimension feature combination, and use the median as the product configuration information corresponding to the dimension combination;

[0118] The storage module is used to store the supplementary insurance products corresponding to each dimensional feature combination and their correlation, as well as product configuration information, as preset association rule information.

[0119] In some embodiments, the insurance proposal generating device further includes:

[0120] A first replacement module is configured to obtain first recommendation data input by an insurance business person and replace at least one of the target primary insurance product, the supplementary insurance product, and the target product configuration information using the first recommendation data; or

[0121] The second replacement module is used to determine the second recommended data according to the business guarantee plan, and use the second recommended data to replace at least one of the target main insurance product, supplementary insurance product and target product configuration information.

[0122] In some embodiments, the insurance proposal generating device further includes:

[0123] A text generation module is used to generate a text explaining the insurance proposal based on the insurance salesperson's experience and / or insurance training information;

[0124] The video generation module is used to generate a plan explanation video with the exclusive image of insurance business personnel based on the plan explanation text using digital human technology.

[0125] The present application also provides a readable storage medium having a program or instruction stored thereon, which, when executed by a processor, implements the steps of the method for generating an insurance proposal as described above.

[0126] For an introduction to the readable storage medium provided in this application, please refer to the embodiment of the above-mentioned method for generating an insurance proposal, which will not be repeated here.

[0127] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and computer program in the non-volatile storage medium to run. The network interface of the computer device is used to communicate with an external client via a network connection. When executed by the processor, the computer program implements the functions or steps of a method for generating an insurance proposal.

[0128] In one embodiment, a computer device is provided. The computer device may be a client, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and computer program stored in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When executed by the processor, the computer program implements the functions or steps of a method for generating an insurance proposal.

[0129] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0130] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

Claims

1. A method for generating an insurance proposal, characterized in that: include: Obtain customer profile information of the policyholder; Determining the type of the target object, and based on the customer profile information and a sub-model corresponding to the type of the target object, determining the insurance product category corresponding to the target object and the score of each insurance product category, the sub-model including a business experience scoring sub-model and at least one of a historical customer multi-classification prediction sub-model, a new customer prediction sub-model, and a customer attention review molecular model, wherein the target object includes the policyholder and / or the policyholder's family members; For each insurance product classification, calculating a total score for the insurance product classification; wherein the method for calculating the total score for the insurance product classification includes: multiplying each score of the insurance product classification by a weight coefficient of a corresponding sub-model to obtain multiple products, and calculating the sum of the multiple products to obtain the total score for the insurance product classification; The top n insurance product categories ranked from highest to lowest in terms of total scores are used as target insurance product categories corresponding to the target object, where n is a positive integer; Based on the customer profile information and the primary insurance product prediction model, predict a plurality of primary insurance products under different target insurance product categories, and determine the target primary insurance product corresponding to the target object among the plurality of primary insurance products; Determine the target supplementary insurance product and target product configuration information associated with the target primary insurance product based on the customer profile information, the target primary insurance product, and preset association rule information; An insurance proposal including the target primary insurance product, the target supplementary insurance product and the target product configuration information is generated.

2. The method according to claim 1, characterized in that The determining, based on the customer profile information and the sub-model corresponding to the type of the target object, the insurance product category corresponding to the target object and the score of each insurance product category includes: If the target object is a historical customer and the target object has focus points, then based on the life stage information and wealth level information in the customer portrait information, and the business experience scoring sub-model, the insurance product classification corresponding to the target object's grid customer group demand tendency and the score of each insurance product classification are predicted, and based on the customer portrait information and the historical customer multi-classification prediction sub-model, the insurance product classification corresponding to the target object's similar customer demand tendency and the score of each insurance product classification are predicted, and based on the focus point information in the customer portrait information and the customer focus review molecular model, the insurance product classification corresponding to the target object's focus point and the score of each insurance product classification are predicted; If the target object is a historical customer and the target object has no focus, then based on the life stage information and wealth level information in the customer portrait information and the business experience scoring sub-model, the insurance product classification corresponding to the target object's grid customer group demand tendency and the score of each insurance product classification are predicted, and based on the customer portrait information and the historical customer multi-classification prediction sub-model, the insurance product classification corresponding to the target object's similar customer demand tendency and the score of each insurance product classification are predicted; If the target object is a new customer and the target object has focus points, then based on the life stage information and wealth level information in the customer portrait information and the business experience scoring sub-model, the insurance product classification corresponding to the target object's grid customer group demand tendency and the score of each insurance product classification are predicted, and based on the life stage information in the customer portrait information and the new customer prediction sub-model, the insurance product classification corresponding to the target object's similar customer demand tendency and the score of each insurance product classification are predicted, and based on the focus point information in the customer portrait information and the customer focus review molecular model, the insurance product classification corresponding to the target object's focus point and the score of each insurance product classification are predicted; If the target object is a new customer and the target object has no focus, then based on the life stage information and wealth level information in the customer portrait information and the business experience scoring sub-model, the insurance product classification corresponding to the target object's grid customer demand tendency and the score of each insurance product classification are predicted, and based on the life stage information in the customer portrait information and the new customer prediction sub-model, the insurance product classification corresponding to the target object's similar customer demand tendency and the score of each insurance product classification are predicted.

3. The method according to claim 1, characterized in that The determining, based on the customer profile information, the target primary insurance product, and preset association rule information, the target supplementary insurance product and target product configuration information associated with the target primary insurance product includes: In the preset association rule information, search for the top m supplementary insurance products ranked from high to low in terms of association with the customer profile information and the target primary insurance product, and use them as the target supplementary insurance products associated with the target primary insurance product, where m is a positive integer; In the preset association rule information, the target product configuration information corresponding to the customer portrait information and the target main insurance product is determined, and the target product configuration information includes at least one of the following: premium, insured amount, delivery date, and insurance period.

4. The method according to claim 3, characterized in that The method further comprises: Based on the basic information of the insured, obtain the supplementary insurance products and product configuration information corresponding to each dimensional feature in the basic information from the stored data corresponding to the main insurance product; Combine each of the dimensional features to obtain multiple dimensional feature combinations, calculate the correlation between different supplementary insurance products and the main insurance product under each dimensional feature combination, and calculate the median of the product configuration information under each dimensional feature combination, and use the median as the product configuration information corresponding to the dimensional combination; The supplementary insurance products corresponding to each of the dimensional feature combinations and their correlation, as well as product configuration information, are stored as the preset association rule information.

5. The method according to claim 1, wherein The method further comprises: Obtaining first recommendation data input by an insurance business person, and using the first recommendation data to replace at least one of the target primary insurance product, the supplementary insurance product, and the target product configuration information; or, Determine second recommended data based on the business guarantee plan, and use the second recommended data to replace at least one of the target main insurance product, the supplementary insurance product and the target product configuration information.

6. The method according to claim 1, characterized in that The method further comprises: Generating a plan explanation text for the insurance proposal based on the insurance salesperson's experience and / or insurance training information; Based on the solution explanation text, digital human technology is used to generate a solution explanation video with the exclusive image of insurance business personnel.

7. A device for generating an insurance proposal, characterized in that: include: Acquisition module, used to obtain the customer profile information of the policyholder; A product classification prediction module is configured to: determine the type of a target object, and based on the customer profile information and a sub-model corresponding to the type of the target object, determine the insurance product classification corresponding to the target object and the score of each insurance product classification, wherein the sub-model includes a business experience scoring sub-model and at least one of a historical customer multi-classification prediction sub-model, a new customer prediction sub-model, and a customer attention review molecular model; the target object includes the policyholder and / or the policyholder's family members; For each insurance product classification, calculating a total score for the insurance product classification; wherein the method for calculating the total score for the insurance product classification includes: multiplying each score of the insurance product classification by a weight coefficient of a corresponding sub-model to obtain multiple products, and calculating the sum of the multiple products to obtain the total score for the insurance product classification; The top n insurance product categories ranked from highest to lowest in terms of total scores are used as target insurance product categories corresponding to the target object, where n is a positive integer; A primary insurance prediction module is used to predict multiple primary insurance products under different target insurance product categories based on the customer profile information and the primary insurance product prediction model, and determine the target primary insurance product corresponding to the target object among the multiple primary insurance products; A supplementary insurance and configuration information prediction module is used to determine the target supplementary insurance product and target product configuration information associated with the target main insurance product based on the customer profile information, the target main insurance product and preset association rule information; The proposal generation module is used to generate an insurance proposal including the target main insurance product, the target supplementary insurance product and the target product configuration information.

8. A readable storage medium having a program or instruction stored thereon, characterized in that: When the program or instruction is executed by a processor, the steps of the method for generating an insurance proposal according to any one of claims 1 to 6 are implemented.

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