Insurance product recommendation method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides an insurance product recommendation method and device, electronic equipment and a storage medium, belongs to the technical field of big data processing, and is suitable for the field of finance. The method comprises the steps of obtaining health dynamic data and financial dynamic data of a target user in a preset time period, and obtaining an insurance matching strategy; performing risk assessment on the target user based on the health dynamic data and the financial dynamic data to obtain user comprehensive risk data; based on the comprehensive risk data of the user and an insurance matching strategy, performing insurance matching on the target user to obtain initial insurance product information; based on a preset time period, performing data analysis on the health dynamic data and the financial dynamic data to obtain user dynamic features; adjusting the initial insurance product information based on the user dynamic characteristics to obtain target insurance product information; and based on the target insurance product information, performing insurance recommendation on the target user. According to the embodiment of the invention, the accuracy of insurance product recommendation can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data processing, and is suitable for the financial field, in particular to an insurance product recommendation method and device, an electronic device and a storage medium. BACKGROUND

[0002] Insurance product recommendation is used to recommend appropriate insurance products to users according to their basic information. For example, in the financial field, if a user's health condition is good, the user can be recommended an insurance product with a lower premium through insurance product recommendation, thereby improving the user's insurance purchase experience.

[0003] At present, the method of insurance product recommendation is to recommend insurance products to users according to their basic information at a certain time node, but in this method, the user's basic information will change over time, so recommending insurance products to users according to their basic information at a certain time node alone is prone to errors in insurance product recommendation. Therefore, how to improve the accuracy of insurance product recommendation has become a technical problem to be solved. SUMMARY

[0004] The main purpose of the embodiments of the present application is to provide an insurance product recommendation method and device, an electronic device and a storage medium, which aims to improve the accuracy of insurance product recommendation.

[0005] To achieve the above purpose, the first aspect of the embodiments of the present application provides an insurance product recommendation method, which comprises:

[0006] Obtaining health dynamic data and financial dynamic data of a target user in a preset time period, and obtaining an insurance matching strategy;

[0007] Based on the health dynamic data and the financial dynamic data, risk assessment is performed on the target user to obtain user comprehensive risk data;

[0008] Based on the user comprehensive risk data and the insurance matching strategy, insurance matching is performed on the target user to obtain initial insurance product information;

[0009] Based on the preset time period, data analysis is performed on the health dynamic data and the financial dynamic data to obtain user dynamic characteristics;

[0010] Based on the user dynamic characteristics, the initial insurance product information is adjusted to obtain target insurance product information;

[0011] Based on the target insurance product information, insurance recommendation is performed on the target user.

[0012] In some embodiments, the medical diagnosis annotation sample includes a patient condition annotation sample and a diagnosis result annotation sample, the medical diagnosis model group is evaluated based on the medical diagnosis annotation sample and a preset initial diagnosis strategy network, and model evaluation data is obtained, including:

[0013] The patient condition annotation sample is analyzed based on the medical diagnosis model group and the initial diagnosis strategy network, and insurance product recommendation data is obtained, wherein the insurance product recommendation data includes an insurance product recommendation path and an insurance product recommendation result;

[0014] The insurance product recommendation path is evaluated based on a preset medical diagnosis guideline, and model path evaluation data is obtained;

[0015] The insurance product recommendation result is evaluated based on the diagnosis result annotation sample, and model result evaluation data is obtained;

[0016] The model path evaluation data and the model result evaluation data are merged to obtain the model evaluation data.

[0017] In some embodiments, the medical diagnosis annotation sample includes a patient condition annotation sample and a diagnosis result annotation sample, the medical diagnosis model group is evaluated based on the medical diagnosis annotation sample and a preset initial diagnosis strategy network, and model evaluation data is obtained, including:

[0018] The patient condition annotation sample is analyzed based on the medical diagnosis model group and the initial diagnosis strategy network, and insurance product recommendation data is obtained, wherein the insurance product recommendation data includes an insurance product recommendation path and an insurance product recommendation result;

[0019] The insurance product recommendation path is evaluated based on a preset medical diagnosis guideline, and model path evaluation data is obtained;

[0020] The insurance product recommendation result is evaluated based on the diagnosis result annotation sample, and model result evaluation data is obtained;

[0021] The model path evaluation data and the model result evaluation data are merged to obtain the model evaluation data.

[0022] In some embodiments, the health dynamic data is feature extracted to obtain user health features, including:

[0023] The health dynamic data is timestamped and sorted to obtain health time series data;

[0024] Data positioning is performed on the health time-series data to obtain user health latest data.

[0025] Data mining is performed on the user health latest data to obtain user health characteristics.

[0026] In some embodiments, the health assessment of the target user based on the user health characteristics includes:

[0027] Obtaining health assessment indication information;

[0028] Based on the health assessment indication information, the user health characteristics are evaluated to obtain user health grades;

[0029] The user health grades are quantitatively processed to obtain the user health assessment data.

[0030] In some embodiments, the insurance matching of the target user based on the user comprehensive risk data and the insurance matching strategy includes:

[0031] Based on the user comprehensive risk data and the insurance matching strategy, the target user is recommended for an insurance type to obtain an insurance type;

[0032] Based on the user health assessment data, the user financial assessment data, and the insurance type, an insurance cost is calculated to obtain an insurance cost;

[0033] The insurance type and the insurance cost are combined to obtain the initial insurance product information.

[0034] In some embodiments, the adjustment of the initial insurance product information based on the user dynamic characteristics includes:

[0035] The user dynamic characteristics are analyzed to obtain user data change information, wherein the user data change information includes user health data change information and user financial data change information;

[0036] Based on the user health data change information, the insurance cost is adjusted to obtain insurance cost adjustment information;

[0037] Based on the user financial data change information, the insurance type is adjusted to obtain insurance adjustment information;

[0038] Based on the insurance cost adjustment information and the insurance adjustment information, the target insurance product information is generated.

[0039] In some embodiments, the data analysis is performed on the health dynamic data and the financial dynamic data based on the preset time period to obtain user dynamic characteristics, including:

[0040] The health dynamic data is extracted based on the preset time period to obtain user health dynamic characteristics;

[0041] The financial dynamic data is extracted based on the preset time period to obtain user financial dynamic characteristics;

[0042] The user dynamic characteristics are generated based on the user health dynamic characteristics and the user financial dynamic characteristics.

[0043] To achieve the above-mentioned purpose, a second aspect of the embodiment of the present application proposes an insurance product recommendation device, the device comprises:

[0044] A data acquisition module is configured to acquire health dynamic data and financial dynamic data of a target user within a preset time period, and acquire an insurance matching strategy;

[0045] A risk assessment module is configured to perform risk assessment on the target user based on the health dynamic data and the financial dynamic data to obtain user comprehensive risk data;

[0046] A product matching module is configured to perform insurance matching on the target user based on the user comprehensive risk data and the insurance matching strategy to obtain initial insurance product information;

[0047] A data analysis module is configured to perform data analysis on the health dynamic data and the financial dynamic data based on the preset time period to obtain user dynamic characteristics;

[0048] A product adjustment module is configured to adjust the initial insurance product information based on the user dynamic characteristics to obtain target insurance product information;

[0049] An insurance recommendation module is configured to perform insurance recommendation on the target user based on the target insurance product information.

[0050] To achieve the above-mentioned purpose, a third aspect of the embodiment of the present application proposes an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the method of the first aspect when executing the computer program.

[0051] To achieve the above-mentioned purpose, a fourth aspect of the embodiment of the present application proposes a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0052] The insurance product recommendation method and device, the electronic device and the storage medium provided by the present application can realize accurate characterization of the risk portrait of the target user by obtaining the health dynamic data and the financial dynamic data of the target user in a preset time period, and performing risk assessment on the target user according to the health dynamic data and the financial dynamic data to obtain user comprehensive risk data. Further, the target user can be matched with an insurance product based on the user comprehensive risk data and a pre-obtained insurance matching strategy, so that a suitable insurance product can be matched according to the risk data of the user, and the situation that the insurance product recommended to the user exceeds the risk bearing range of the user can be avoided, thereby improving the accuracy of insurance recommendation. In addition, the health dynamic data and the financial dynamic data are analyzed according to the preset time period to obtain user dynamic characteristics, which facilitates clear understanding of the subtle changes in the health and financial status of the user in the preset time period, and provides a basis for insurance product recommendation. Then, the initial insurance product information is adjusted according to the user dynamic characteristics to obtain target insurance product information, which improves the matching degree of the target insurance product information and the target user, thereby further improving the accuracy of insurance product recommendation. Finally, the target user is recommended with an insurance product based on the target insurance product information, thereby improving the accuracy of insurance product recommendation. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 is a flowchart of the insurance product recommendation method provided by the present application;

[0054] Figure 2 is a flowchart of step S102 in Figure 1

[0055] Figure 3 is a flowchart of step S201 in Figure 2

[0056] Figure 4 is a flowchart of step S202 in Figure 2

[0057] Figure 5 is a flowchart of step S103 in Figure 1

[0058] Figure 6 is a flowchart of step S104 in Figure 1

[0059] Figure 7 is a flowchart of step S105 in Figure 1

[0060] Figure 8 is a structural schematic diagram of the insurance product recommendation device provided by the present application;

[0061] ​​​​​​Figure 9 Fig. 1 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0062] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0063] It should be noted that although the functional modules are divided in the device schematic diagram, and the logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in a manner different from the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification and claims and the above-described drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0065] First, the meanings of several terms involved in the present application are analyzed:

[0066] Insurance product recommendation system: The insurance product recommendation system is an intelligent decision support tool that analyzes the personal information, health dynamic data, financial situation and other relevant behavior information of customers, uses machine learning, data mining and artificial intelligence technology to identify the risk level, protection needs and payment ability of customers, and then recommends the most suitable insurance products and solutions for customers according to their current situation, so as to achieve effective risk management and reasonable financial planning.

[0067] Insurance premium: Insurance premium refers to the fee paid by the policyholder to the insurance company in order to obtain the insurance protection provided by the insurance company according to the insurance contract, which is the economic basis for the insurance company to bear risks and provide services, and is also the main source of income for the insurance company, used to pay insurance claims, operating costs and investment income, etc.

[0068] Insurance coverage: Insurance coverage, also known as insurance amount, refers to the maximum amount of compensation paid by the insurance company to the insured or the beneficiary in accordance with the contract provisions in the event of an insurance accident. It is an important indicator of insurance protection level, reflecting the risk transfer ability of insurance products, i.e. the maximum amount of compensation that the insurance company can pay in the event of a loss within the scope of insurance liability.

[0069] Artificial intelligence: Artificial intelligence (AI) refers to a technical field that is formed by the cross-fusion of multiple disciplines such as computer science, psychology, philosophy and cognitive science. Artificial intelligence aims to develop systems and machines that can perform tasks that usually require human intelligence. These tasks include, but are not limited to, language understanding, learning, reasoning, perception, pattern recognition, problem solving and planning. Artificial intelligence systems simulate human cognitive functions through algorithms and statistical models, enabling computers to recognize language, images, understand natural language, and even make complex decisions and predictions.

[0070] Insurance product recommendation is used to recommend appropriate insurance products to users according to their basic information. For example, in the financial field, if the user's health condition is good, the insurance product recommendation can be used to recommend an insurance product with lower premium to the user, thereby improving the user's insurance purchase experience.

[0071] At present, the method of insurance product recommendation is to recommend insurance products to users according to their basic information at a certain time node. However, in this insurance product recommendation method, the user's basic information will change over time. Therefore, recommending insurance products to users based solely on their basic information at a certain time node is prone to insurance product recommendation errors. Therefore, how to improve the accuracy of insurance product recommendation has become a technical problem to be solved.

[0072] Therefore, the embodiments of the present application provide an insurance product recommendation method and device, an electronic device and a storage medium, which aims to improve the accuracy of insurance product recommendation.

[0073] The insurance product recommendation method and device, the electronic device and the storage medium provided by the embodiments of the present application are specifically explained by the following embodiments. First, the insurance product recommendation method in the embodiments of the present application is described.

[0074] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive environment, acquire knowledge and use knowledge to obtain optimal results.

[0075] The basic technology of artificial intelligence generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. The software technology of artificial intelligence mainly includes computer vision technology, robot technology, biometric technology, speech processing technology, natural language processing technology and machine learning / deep learning, etc.

[0076] The insurance product recommendation method provided by the embodiments of the present application relates to the technical field of big data processing and is suitable for the financial field. The insurance product recommendation method provided by the embodiments of the present application can be applied to a terminal, can be applied to a server end, and can also be software running in the terminal or the server end. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, or the like; the server end can be configured as a stand-alone physical server, can be configured as a server cluster or a distributed system formed by multiple physical servers, or can be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, and big data and artificial intelligence platforms; and the software can be an application that implements the insurance product recommendation method, but is not limited to the above forms.

[0077] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected by a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0078] It should be noted that in each specific embodiment of the present application, when relevant processing needs to be performed according to user information, user behavior data, user historical data, and user location information and other data related to the identity or characteristics of the user, the user's permission or consent will be obtained first, and the collection, use, and processing of the data will comply with relevant laws, regulations, and standards. In addition, when the embodiments of the present application need to obtain sensitive personal information of the user, the separate permission or separate consent of the user will be obtained through a pop-up window or by jumping to a confirmation page, and after obtaining the separate permission or separate consent of the user, the necessary user-related data for enabling the embodiments of the present application to normally operate will be obtained.

[0079] Figure 1 is an optional flowchart of the insurance product recommendation method provided by the embodiments of the present application, which can be used in an insurance product recommendation system, Figure 1The method in the method can include but not limited to including steps S101 to S106.

[0080] Step S101, obtaining the health dynamic data and financial dynamic data of the target user in a preset time period, obtaining an insurance matching strategy;

[0081] Step S102, based on the health dynamic data and financial dynamic data, risk assessment is carried out on the target user, and user comprehensive risk data is obtained;

[0082] Step S103, based on the user comprehensive risk data and the insurance matching strategy, the target user is matched with the insurance, and the initial insurance product information is obtained;

[0083] Step S104, based on the preset time period, the health dynamic data and the financial dynamic data are analyzed, and the user dynamic characteristics are obtained;

[0084] Step S105, based on the user dynamic characteristics, the initial insurance product information is adjusted, and the target insurance product information is obtained;

[0085] Step S106, based on the target insurance product information, the target user is recommended for insurance.

[0086] The steps S101 to S106 shown in the embodiment of the application, by obtaining the health dynamic data and financial dynamic data of the target user in a preset time period, and according to the health dynamic data and financial dynamic data, risk assessment is carried out on the target user, and user comprehensive risk data is obtained, which can realize the accurate description of the risk portrait of the target user. Further, based on the user comprehensive risk data and the pre-obtained insurance matching strategy, the target user is matched with the insurance, which can realize the matching of the appropriate insurance product according to the risk data of the user, and can avoid the situation that the recommended insurance product of the user exceeds the risk bearing range of the user, thereby improving the accuracy of the insurance recommendation. Secondly, according to the preset time period, the health dynamic data and the financial dynamic data are analyzed, and the user dynamic characteristics are obtained, which is convenient for determining the subtle changes of the user's health and financial status in the preset time period, and provides a basis for insurance product recommendation. According to the user dynamic characteristics, the initial insurance product information is adjusted, and the target insurance product information is obtained, which improves the matching degree of the target insurance product information and the target user, thereby further improving the accuracy of the insurance product recommendation. Finally, based on the target insurance product information, the target user is recommended for insurance, which improves the accuracy of the insurance product recommendation.

[0087] In step S101 of some embodiments, the target user refers to an object requiring insurance product recommendation. The preset time period refers to a pre-set data observation period. For example, in a medical insurance purchase scenario, if the user's health data in the last 12 months needs to be viewed, the preset time period is 12 months. The health dynamic data refers to multi-dimensional data reflecting the physiological state of the user within the preset time period, such as CT / MRI images of the target user, health trend curve data such as blood glucose fluctuation range generated when the target user wears a blood glucose meter and other health monitoring devices, and hospital examination reports of the target user. The financial dynamic data refers to data that changes within the preset time period and reflects the user's payment ability and debt level, such as the target user's customer bank flow within the preset time period, credit reports, and the like.

[0088] It also needs to be known that the insurance matching strategy refers to a set of rules for associating user risk data with insurance products.

[0089] The embodiments of the present application can obtain the CT / MRI images, blood glucose fluctuation range, hospital examination reports and other health data of the target user within the data observation period by setting the data observation period. In addition, the customer bank flow, credit reports and other data of the target user within the data observation period can also be collected according to the authorization information of the target user. Further, by integrating the CT / MRI images, blood glucose fluctuation range, hospital examination reports and other health data of the target user within the data observation period, health dynamic data can be formed. By integrating the customer bank flow, credit reports and other data of the target user within the data observation period, financial dynamic data can be formed.

[0090] The embodiments of the present application can associate different risk data with different insurance products according to the experience of experts in the insurance field, that is, the insurance matching strategy can be obtained. The insurance matching strategy can also be integrated into a constrained utility function according to the insurance regulatory red line, insurance profit requirements and user affordability. Further, the health data and financial data can be compressed into a unified strategy variable. In addition, the unified strategy variable can be adjusted using Bayesian optimization or reinforcement learning to search for the optimal solution of the constrained utility function. It needs to be known that the optimal solution can represent the optimal association information between risk data and insurance products, thereby generating the insurance matching strategy.

[0091] In step S102 of some embodiments, the user comprehensive risk data refers to a quantitative indicator for measuring the overall risk of the user from the health and financial dimensions.

[0092] The embodiments of the present application can extract user health features and user financial features from health dynamic data and financial dynamic data respectively, and generate health assessment data and financial assessment data according to the user health features and the user financial features respectively. Further, the user comprehensive risk data can be obtained by merging the health assessment data and the financial assessment data.

[0093] In detail, please refer to Figure 2 In some embodiments, step S102 can include, but is not limited to, steps S201 to S203:

[0094] Step S201: performing feature extraction on the health dynamic data to obtain user health features, and performing feature extraction on the financial dynamic data to obtain user financial features;

[0095] Step S202: performing health assessment on the target user based on the user health features to obtain user health assessment data, and performing financial assessment on the target user based on the user financial features to obtain user financial assessment data;

[0096] Step S203: performing data merging on the user health assessment data and the user financial assessment data to obtain user comprehensive risk data.

[0097] In step S201 of some embodiments, the user health features refer to quantifiable indexes in the health dynamic data. For example, in the medical insurance purchase scenario, if the health dynamic data is real-time data of dynamic electrocardiogram, the user health features can be "average resting heart rate of 75 times per minute for 7 consecutive days", and if the health dynamic data is real-time data of a blood glucose meter, the user health features can be "glycosylated hemoglobin 6.2%". The user financial features refer to key variables in the financial dynamic data. For example, in the financial insurance purchase scenario, if the financial dynamic data is bank flow, the user financial features can be "disposable income ratio 70%, debt-income ratio 30%".

[0098] The embodiments of the present application can take the latest data in the health dynamic data as the latest health data of the target user, and further perform data mining on the latest health data to obtain the user health features. Meanwhile, the latest data in the financial dynamic data can be taken as the latest financial data of the target user, and further data mining is performed on the latest financial data to obtain the user financial features.

[0099] In detail, please refer to Figure 3 In some embodiments, the feature extraction on the health dynamic data in step S201 to obtain the user health features can include, but is not limited to, steps S301 to S303:

[0100] Step S301, time stamp sorting of health dynamic data, to obtain health time series data;

[0101] Step S302, data positioning of health time series data, to obtain user health latest data;

[0102] Step S303, data mining of user health latest data, to obtain user health characteristics.

[0103] In step S301 of some embodiments, health time series data refers to a sequence of continuous health data arranged in chronological order, for example, a time series of blood glucose fluctuations of the applicant recorded by minute and sorted by time.

[0104] The embodiments of the present application can determine the data generation time of each health dynamic data by querying the time stamp of the health dynamic data, and further arrange the health dynamic data in ascending or descending order according to the chronological order of the data generation time, that is, the health time series data can be obtained, for example, by reordering the blood glucose data of the target user within 30 days from early to late according to the blood glucose data recording time, the health time series data formed by the blood glucose data can be obtained.

[0105] In step S302 of some embodiments, user health latest data refers to health time series data with the latest time stamp.

[0106] The embodiments of the present application can determine the latest data generation time, i.e., the target data generation time, by comparing the data generation time of each data in the health time series data, and further can take the health time series data corresponding to the target data generation time as the user health latest data.

[0107] In step S303 of some embodiments, the embodiments of the present application can select a suitable model for feature extraction according to the data type of the user health latest data, so as to obtain the user health characteristics, for example, if the user health latest data is image data such as CT / MRI image, a convolutional neural network can be selected for feature extraction of the health dynamic data, and if the user health latest data is text data such as hospital physical examination report, a recurrent neural network can be selected for feature extraction of the health dynamic data.

[0108] The steps S301 to S303 shown in the embodiments of the present application obtain health time series data by time stamping and sorting the health dynamic data, so that the health time series data has a clear time context, laying a foundation for positioning the latest data. In addition, the user health latest data representing the latest health status of the target user can be obtained by positioning the health time series data, thereby improving the timeliness of risk assessment. Finally, the user health features are obtained by data mining the user health latest data, ensuring that the user health features are the health features of the target user at the latest time node, thereby improving the accuracy of risk assessment.

[0109] In step S202 of some embodiments, the user health assessment data refers to the risk quantification result of the disease of the target user. The user financial assessment data refers to the risk quantification result of the solvency or premium bearing capacity of the target user.

[0110] The embodiments of the present application can evaluate the features in the user health features through the pre-set health assessment indication information, thereby obtaining the health level of each user health feature. Further, the health level is quantified into a score value, and the user health assessment data is obtained.

[0111] It should also be noted that the embodiments of the present application can evaluate the features in the user financial features through the pre-set financial assessment indication information, thereby obtaining the health level of each user financial feature. Further, the financial level is quantified into a score value, and the user financial assessment data is obtained.

[0112] In detail, please refer to Figure 4 In some embodiments, the health assessment of the target user based on the user health features in step S202 to obtain the user health assessment data can include but is not limited to steps S401 to S403:

[0113] Step S401: obtaining health assessment indication information;

[0114] Step S402: evaluating the features of the user health features based on the health assessment indication information to obtain the user health level;

[0115] Step S403: quantifying the user health level to obtain the user health assessment data.

[0116] In step S401 of some embodiments, the health assessment indication information refers to rules, weights or threshold sets used to guide the rating of the user health features. For example, in the medical insurance field, the health assessment indication information can be a scoring rule of "BMI>30 and blood pressure>140 / 90, then the health level is sub-standard body".

[0117] The embodiment of the application can learn the general rules of the health characteristics scoring of the field experts by using the pre-constructed deep learning model, and then generate the user health assessment data according to the general rules.

[0118] In step S402 of some embodiments, the user health level refers to the discrete risk level corresponding to the user health characteristics, wherein the user health level can include "standard body" and "substandard body".

[0119] The embodiment of the application can obtain the user health level of the user health characteristics by querying the health level of the user health characteristics in the health assessment indication information.

[0120] In step S403 of some embodiments, the user health level can be mapped to the numerical space by using a quantization function, thereby obtaining the user health assessment data, wherein the quantization function can be a linear function or a nonlinear function.

[0121] The steps S401 to S403 shown in the embodiment of the application lay the foundation for the user health assessment by obtaining the health assessment indication information. Secondly, based on the health assessment indication information, the user health characteristics are evaluated to obtain the user health level, and the user health level is quantized to obtain the user health assessment data. The health risk is changed from the fuzzy user health level description to the calculable numerical description, i.e., the user health assessment data, thereby improving the accuracy of the user health assessment.

[0122] In step S203 of some embodiments, the user health assessment data and the user financial assessment data can be merged by using a double-channel attention fusion network to obtain the user comprehensive risk data. The user health assessment data of the target user can be subtracted from the user financial assessment data of the target user to realize the data merging of the user health assessment data and the user financial assessment data, thereby obtaining the user comprehensive risk data.

[0123] The steps S201 to S203 shown in the embodiment of the application can extract the user health characteristics from the health dynamic data and extract the user financial characteristics from the financial dynamic data, which can clearly determine the key information in the health dynamic data and the financial dynamic data, thereby improving the accuracy of the risk assessment. Secondly, the health of the target user is assessed based on the user health characteristics to obtain the risk assessment data of the target user in the health dimension, and the financial of the target user is assessed based on the user financial characteristics to obtain the risk assessment data of the target user in the financial dimension. Finally, the user health assessment data and the user financial assessment data are merged to obtain the user comprehensive risk data, thereby improving the comprehensiveness of the user comprehensive risk data and improving the accuracy of the user risk assessment.

[0124] In step S103 of some embodiments, the initial insurance product information refers to an insurance product scheme applicable to the user comprehensive risk data, and it is known that the initial insurance product information includes initial insurance type, initial insurance premium, initial insurance amount, and the like.

[0125] The embodiments of the present application can determine the insurance type recommended to the target user by querying the insurance type associated with the insurance matching strategy in the user comprehensive risk data, further calculate the insurance premium of the insurance type according to the user health assessment data and the user financial assessment data of the target user, and finally, information of the above-mentioned insurance type and insurance premium is merged to form complete initial insurance product information.

[0126] In detail, please refer to Figure 5 In some embodiments, step S103 can include but is not limited to steps S501 to S503:

[0127] Step S501, based on the user comprehensive risk data and the insurance matching strategy, recommending an insurance type for the target user to obtain an insurance type;

[0128] Step S502, based on the user health assessment data, the user financial assessment data, and the insurance type, calculating an insurance premium to obtain an insurance premium;

[0129] Step S503, merging information of the insurance type and the insurance premium to obtain initial insurance product information.

[0130] In step S501 of some embodiments, the insurance type refers to the type of the insurance product, for example, major disease insurance, life insurance, and the like.

[0131] The embodiments of the present application can query the optimal insurance type that can be matched by the user comprehensive risk data according to the user comprehensive risk data of the target user, i.e., the above-mentioned insurance matching strategy, to obtain the insurance type.

[0132] In step S502 of some embodiments, the insurance premium refers to the premium and the amount of the insurance.

[0133] The embodiments of the present application learn the premium function relationship between the premium of each insurance type and the health assessment data and the financial assessment data of the user according to the historical premium data and the historical user data of each insurance type, further substitute the user health assessment data and the user financial assessment data of the target user into the above-mentioned function relationship to obtain the premium information that the target user should pay for the above-mentioned insurance type, and further determine the amount of the insurance amount of the above-mentioned insurance type for the target user according to the limitation of the insurance amount,

[0134] In step S503 of some embodiments, the above-mentioned insurance type and insurance cost are combined into the pre-constructed empty set, and initial insurance product information of the target user can be obtained, for example, if the insurance type is major illness insurance, the insurance cost is 5000 yuan per year, and the insurance amount is 5 million, the initial insurance product information can be "major illness insurance, 5000 yuan per year, and insurance amount of 5 million".

[0135] The steps S501 to S503 shown in the embodiments of the present application can recommend an insurance type for a target user based on user comprehensive risk data and an insurance matching strategy, obtain an insurance type, improve the accuracy of insurance recommendation, further calculate an insurance cost based on user health assessment data, user financial assessment data and the insurance type, obtain the insurance cost, realize personalized pricing, make the insurance cost more suitable for the bearing capacity of the target user, and finally combine the insurance type and the insurance cost to obtain initial insurance product information, thereby laying a foundation for insurance product recommendation.

[0136] In step S104 of some embodiments, the user dynamic feature refers to a set of quantifiable features that can reflect the latest differences in the state of the user over time.

[0137] The embodiments of the present application can extract dynamic features from health dynamic data and financial dynamic data according to a preset time period, obtain user health dynamic features and user financial dynamic features, and further splice the user health dynamic features and the user financial dynamic features to obtain user dynamic features of the target user.

[0138] In detail, please refer to Figure 6 In some embodiments, step S104 can include but is not limited to steps S601 to S603:

[0139] In step S601, dynamic features are extracted from health dynamic data based on a preset time period, and user health dynamic features are obtained.

[0140] In step S602, dynamic features are extracted from financial dynamic data based on a preset time period, and user financial dynamic features are obtained.

[0141] In step S603, user dynamic features are generated based on the user health dynamic features and the user financial dynamic features.

[0142] In steps S601 and S602 of some embodiments, the user health dynamic feature refers to a set of features capable of describing the change of the user's health condition over time, for example, the target user's blood pressure gradually increased in the past three months, etc. The user financial dynamic feature refers to a set of features capable of describing the change of the user's financial condition over time, for example, the target user's assets grew steadily and the proportion of liabilities gradually decreased in the past year, etc.

[0143] The embodiments of the present application can obtain the user health dynamic feature by identifying the key information capable of reflecting the health condition of the target user from the health dynamic data, and then converting the key information into interpretable index features. For example, for the heart rate trend curve, a plurality of heart rate data can be identified, and the average heart rate of the plurality of heart rate data can be calculated, and the maximum heart rate and the minimum heart rate can be queried, so as to convert the heart rate data into interpretable index features, thereby obtaining the user health dynamic feature of the heart rate trend curve.

[0144] Meanwhile, the embodiments of the present application can obtain the user financial dynamic feature by extracting the financial static features in the financial dynamic data, such as asset data, bank flow data, etc. Further, by converting the financial static features into interpretable index features, the user financial dynamic feature can be obtained. For example, based on the asset data of the target user, the asset-liability ratio, the income stability index, or the financial pressure index of the target user can be calculated, and the user financial dynamic feature of the asset data can be obtained.

[0145] In step S603 of some embodiments, the user health dynamic feature and the user financial dynamic feature can be fused by splicing the user health dynamic feature and the user financial dynamic feature, thereby generating the user dynamic feature.

[0146] The steps S601 to S603 shown in the embodiments of the present application can accurately capture the changes of the user's health and financial condition through dynamic feature extraction, ensure the real-time and accuracy of the evaluation, and then generate the user health and financial evaluation data based on these dynamic features, provide a scientific basis for insurance pricing, finally, combine these evaluation data to obtain the user comprehensive risk data, realize the personalized insurance product recommendation and cost calculation, not only improve the pertinence and satisfaction of insurance services, but also optimize the risk management and capital efficiency of insurance companies.

[0147] In step S105 of some embodiments, the target insurance product information refers to the insurance product scheme finally determined to be recommended to the user based on the user dynamic feature. It needs to be known that the target insurance product information includes target insurance type, target insurance premium, target insurance amount, etc.

[0148] The embodiment of the application can determine the change information of the target user in the health dimension and the financial dimension by analyzing the user dynamic characteristics, adjust the insurance fee according to the change information of the health dimension, adjust the insurance type according to the change information of the financial dimension, and merge the adjusted insurance fee and the insurance type, to obtain the target insurance product information.

[0149] In detail, please refer to Figure 7 In some embodiments, step S105 can include but is not limited to steps S701-S704:

[0150] Step S701, performing feature analysis on the user dynamic characteristics to obtain user data change information, wherein the user data change information includes user health data change information and user financial data change information;

[0151] Step S702, adjusting the insurance fee based on the user health data change information to obtain insurance fee adjustment information;

[0152] Step S703, adjusting the insurance type based on the user financial data change information to obtain insurance adjustment information;

[0153] Step S704, generating target insurance product information based on the insurance fee adjustment information and the insurance adjustment information.

[0154] In step S701 of some embodiments, the user data change information refers to specific information of user health data and financial data changing over time, for example, in the insurance product marketing scenario, the user data change information can include information that the target user's blood pressure has been rising continuously or the income has increased significantly in the past six months. The user health data change information refers to specific information of user health-related data changing over time, for example, in the insurance product marketing scenario, the user health data change information can include information that the target user's blood sugar level has risen significantly in the past three months. The user financial data change information refers to specific information of user financial-related data changing over time, for example, in the insurance product marketing scenario, the user financial data change information can be information that the target user's total assets have increased by 20% in the past year.

[0155] The embodiment of the application can describe the static feature change by splicing the user health dynamic characteristics and the user financial dynamic characteristics in the user dynamic characteristics, to obtain the user health data change information and the user financial data change information, or the user data change information containing the user health data change information and the user financial data change information.

[0156] In step S702 of some embodiments, the insurance premium adjustment information refers to specific information for adjusting the insurance premium according to the user health data change, for example, in the insurance product marketing scenario, if the user health data change information shows that the user's health condition deteriorates, the insurance premium adjustment information can contain information of premium increase.

[0157] The embodiments of the present application can adjust the insurance premium when the user health data change information indicates that the health status of the target user changes, specifically, if the user health data change information shows that the user's health condition deteriorates, the premium is increased, and if the user health data change information shows that the user's health condition improves, the premium is reduced.

[0158] In step S703 of some embodiments, the insurance type adjustment information refers to specific information for adjusting the insurance type according to the user financial data change, for example, in the insurance product marketing scenario, if the user financial data change information shows that the user's payment ability improves, the insurance type adjustment information can contain information of suggesting the target user to upgrade to a more comprehensive insurance type.

[0159] The embodiments of the present application can adjust the type of insurance when the user financial data change information indicates that the financial status of the target user changes, specifically, if the user financial data change information shows that the user's payment ability improves, the initial insurance type in the initial insurance product information is replaced to a more comprehensive insurance type, and if the user health data change information shows that the user's payment ability decreases, the initial insurance type in the initial insurance product information is replaced to an insurance type with more detailed concerns.

[0160] In step S704 of some embodiments, the adjusted insurance premium adjustment information and insurance adjustment information are spliced into a pre-set blank set, and the target insurance product information is generated.

[0161] In step S106 of some embodiments, after obtaining the target insurance product information, the target user can be recommended according to the insurance type, the insurance premium, and the insurance amount in the target insurance product information.

[0162] The present application can realize accurate description of the risk portrait of the target user by obtaining the health dynamic data and the financial dynamic data of the target user in a preset time period, and performing risk assessment on the target user according to the health dynamic data and the financial dynamic data to obtain user comprehensive risk data. Further, based on the user comprehensive risk data and the pre-obtained insurance matching strategy, the target user is matched with insurance, which can realize matching of appropriate insurance products according to the risk data of the user, and can avoid the situation that the recommended insurance products to the user exceed the risk bearing range of the user, thereby improving the accuracy of insurance recommendation. In addition, the health dynamic data and the financial dynamic data are analyzed according to the preset time period to obtain user dynamic characteristics, which facilitates to determine the subtle changes of the health and financial status of the user in the preset time period, and provides a basis for insurance product recommendation. Then, the initial insurance product information is adjusted according to the user dynamic characteristics to obtain target insurance product information, which improves the matching degree of the target insurance product information and the target user, thereby further improving the accuracy of insurance product recommendation. Finally, the target user is recommended with insurance based on the target insurance product information, which improves the accuracy of insurance product recommendation.

[0163] Please refer to Figure 8 The present application also provides an insurance product recommendation device, which can realize the above-mentioned insurance product recommendation method. The device comprises:

[0164] The data acquisition module 801 is configured to obtain the health dynamic data and the financial dynamic data of the target user in a preset time period, and obtain an insurance matching strategy.

[0165] The risk assessment module 802 is configured to perform risk assessment on the target user based on the health dynamic data and the financial dynamic data to obtain user comprehensive risk data.

[0166] The product matching module 803 is configured to perform insurance matching on the target user based on the user comprehensive risk data and the insurance matching strategy to obtain initial insurance product information.

[0167] The data analysis module 804 is configured to analyze the health dynamic data and the financial dynamic data based on the preset time period to obtain user dynamic characteristics.

[0168] The product adjustment module 805 is configured to adjust the initial insurance product information based on the user dynamic characteristics to obtain target insurance product information.

[0169] The insurance recommendation module 806 is configured to recommend insurance to the target user based on the target insurance product information.

[0170] The specific embodiments of the insurance product recommendation device are basically the same as the specific embodiments of the above-mentioned insurance product recommendation method, and will not be repeated here.

[0171] The embodiment of the present application further provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the insurance product recommendation method. The electronic device can be any intelligent terminal, such as a tablet computer or a vehicle-mounted computer.

[0172] Please refer to Figure 9 , Figure 9 The hardware structure of the electronic device of another embodiment is shown, which comprises:

[0173] The processor 901 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is used to execute related programs to realize the technical solutions provided by the embodiments of the present application.

[0174] The memory 902 can be implemented in the form of a ROM (ReadOnly Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory). The memory 902 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 902 and are called and executed by the processor 901 to implement the insurance product recommendation method of the embodiments of the present application.

[0175] The input / output interface 903 is used to realize information input and output.

[0176] The communication interface 904 is used to realize the communication interaction between the device and other devices, and can realize communication through a wired manner (such as a USB, a network cable, etc.) or a wireless manner (such as a mobile network, WIFI, Bluetooth, etc.).

[0177] The bus 905 is used to transmit information between various components (such as the processor 901, the memory 902, the input / output interface 903, and the communication interface 904) of the device.

[0178] The processor 901, the memory 902, the input / output interface 903, and the communication interface 904 are connected to each other through the bus 905 to realize communication connection between them in the device.

[0179] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the insurance product recommendation method.

[0180] The memory, as a non-transitory computer readable storage medium, can be used to store non-transitory software programs and non-transitory computer executable programs. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0181] The insurance product recommendation method, the insurance product recommendation device, the electronic equipment and the storage medium provided by the embodiment of the present application are used to obtain health dynamic data and financial dynamic data of a target user in a preset time period, obtain an insurance matching strategy, perform risk assessment on the target user based on the health dynamic data and the financial dynamic data, obtain user comprehensive risk data, perform insurance matching on the target user based on the user comprehensive risk data and the insurance matching strategy, obtain initial insurance product information, perform data analysis on the health dynamic data and the financial dynamic data based on the preset time period, obtain user dynamic characteristics, adjust the initial insurance product information based on the user dynamic characteristics, obtain target insurance product information, and perform insurance recommendation on the target user based on the target insurance product information.

[0182] The embodiments described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0183] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than the figures shown, or combine certain steps, or different steps.

[0184] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separated, that is, can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0185] Those skilled in the art can understand that all or some of the steps in the method disclosed above, the function modules / units in the system and the device can be implemented as software, firmware, hardware or appropriate combination thereof.

[0186] The terms "first", "second", "third", "fourth" and the like in the description of the application and in the claims hereof, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of the terms so termed herein is to be interpreted to only cover the embodiments of the application described herein and not a prior art. Moreover, the use of the terms "first", "second", "third", "fourth", and / or the like, to describe a variety of elements / parameters / operators does not imply that the combination of the elements / parameters / operators are limited by the nomenclature thus designated. It is also to be understood that the use of relational terms, if any, such as "first", "second", "third", "fourth", and the like, are used solely to distinguish one from another means for clarity, not by way of limitation. The permanent reference signs in the drawings indicate no certain sequential or chronological order between the embodiments of the application.

[0187] It should be understood that, in the present application, "at least one" means one or more, and "multiple" means two or more. "And / or" is used to describe the relationship between the associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects. "At least one of the following" or similar expressions means any combination of these items, including single or multiple combinations. For example, at least one of a, b or c can mean a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be singular or plural.

[0188] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above-mentioned units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be omitted or not executed. The coupling or direct coupling or communication connection between the shown or discussed objects can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0189] The units described as separate components above can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0190] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0191] If the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application, essentially or the part that contributes to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program storage media.

[0192] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and the scope of the rights of the embodiments of the present application is not limited thereto. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the rights of the embodiments of the present application.

Claims

1. An insurance product recommendation method characterized by comprising: The method comprises: acquiring health dynamic data and financial dynamic data of a target user in a preset time period, acquiring an insurance matching strategy; based on the health dynamic data and the financial dynamic data, performing risk assessment on the target user to obtain user comprehensive risk data; based on the user comprehensive risk data and the insurance matching strategy, performing insurance matching on the target user to obtain initial insurance product information; based on the preset time period, performing data analysis on the health dynamic data and the financial dynamic data to obtain user dynamic characteristics; based on the user dynamic characteristics, adjusting the initial insurance product information to obtain target insurance product information; based on the target insurance product information, performing insurance recommendation on the target user.

2. The method of claim 1, wherein, The method comprises: performing feature extraction on the health dynamic data to obtain user health characteristics, and performing feature extraction on the financial dynamic data to obtain user financial characteristics; based on the user health characteristics, performing health assessment on the target user to obtain user health assessment data, and based on the user financial characteristics, performing financial assessment on the target user to obtain user financial assessment data; performing data merging on the user health assessment data and the user financial assessment data to obtain the user comprehensive risk data.

3. The method of claim 2, wherein, The method comprises: performing timestamp sorting on the health dynamic data to obtain health time series data; performing data positioning on the health time series data to obtain user health latest data; performing data mining on the user health latest data to obtain the user health characteristics.

4. The method of claim 2, wherein, The method comprises: acquiring health assessment indication information; based on the health assessment indication information, performing feature evaluation on the user health characteristics to obtain user health grade; performing quantitative processing on the user health grade to obtain the user health assessment data.

5. The method of claim 2, wherein, The method comprises: based on the user comprehensive risk data and the insurance matching strategy, performing risk type recommendation on the target user to obtain insurance risk type; based on the user health assessment data, the user financial assessment data and the insurance risk type, performing insurance cost calculation to obtain insurance cost; performing information merging on the insurance risk type and the insurance cost to obtain the initial insurance product information.

6. The method of claim 5, wherein, The method comprises: performing feature analysis on the user dynamic characteristics to obtain user data change information, wherein the user data change information comprises user health data change information and user financial data change information; adjust the insurance premium based on the user health data change information to obtain insurance premium adjustment information; adjust the insurance type based on the user financial data change information to obtain insurance adjustment information; generate the target insurance product information based on the insurance premium adjustment information and the insurance adjustment information.

7. The method according to any one of claims 1 to 5, characterized in that, The data analysis based on the preset time period on the health dynamic data and the financial dynamic data obtains user dynamic characteristics, including: dynamic characteristic extraction based on the preset time period on the health dynamic data obtains user health dynamic characteristics; dynamic characteristic extraction based on the preset time period on the financial dynamic data obtains user financial dynamic characteristics; generate the user dynamic characteristics based on the user health dynamic characteristics and the user financial dynamic characteristics.

8. An insurance product recommendation device characterized by comprising: The device comprises: a data acquisition module configured to acquire health dynamic data and financial dynamic data of a target user within a preset time period and acquire an insurance matching strategy; a risk assessment module configured to assess the risk of the target user based on the health dynamic data and the financial dynamic data to obtain user comprehensive risk data; a product matching module configured to match the target user with an insurance product based on the user comprehensive risk data and the insurance matching strategy to obtain initial insurance product information; a data analysis module configured to analyze the health dynamic data and the financial dynamic data based on the preset time period to obtain user dynamic characteristics; a product adjustment module configured to adjust the initial insurance product information based on the user dynamic characteristics to obtain target insurance product information; an insurance recommendation module configured to recommend the target user with an insurance product based on the target insurance product information.

9. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the insurance product recommendation method of any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1-9. The computer program is executed by the processor to implement the insurance product recommendation method of any one of claims 1 to 7.

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