Insurance pricing method and device based on data fusion, electronic equipment and medium

Abstract

The embodiment of the invention provides an insurance pricing method and device based on data fusion, electronic equipment and a medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: in response to a pricing instruction of a user, obtaining product basic information corresponding to the pricing instruction through a pricing system; identity information sent by a user is received through the pricing system, and user associated data and a credit platform bound with the pricing system are determined according to the identity information; in response to an authorization instruction of the user, obtaining a credit score corresponding to the user through the credit platform; determining a comprehensive discount parameter according to the credit score and the user associated data; performing risk assessment on the product basic information, the credit score and the user associated data to obtain a risk assessment result; and performing pricing analysis on the risk assessment result and the comprehensive discount parameter through a pricing model to obtain a target insurance price. According to the embodiment of the invention, the risk level of the customer is fully reflected through the multi-dimensional data, and the dynamic adjustment of the insurance pricing of the customer is realized.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to an insurance pricing method, device, electronic device and medium based on data fusion. Background Art

[0002] The rapid development of drone technology has promoted the development of the low-altitude economy. Drones have played an important role in many industries such as logistics, agriculture, and infrastructure inspection. However, since drone flights involve many risk factors, such as pilots' operating habits, weather conditions, and terrain complexity, the existing insurance pricing system often adopts a long-term fixed pricing model that cannot flexibly reflect customers' personalized needs. The rigidity of this pricing model not only limits the personalized development of insurance products, but also ignores the actual impact of customer behavior on risk levels. This makes it impossible for insurance companies to fully utilize customers' comprehensive data to formulate more reasonable insurance pricing, thereby failing to fully reflect customers' true risk levels, resulting in customers being unable to enjoy dynamic premium discounts at different stages of use, reducing customers' insurance purchasing experience and stickiness. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose an insurance pricing method, device, electronic device and medium based on data fusion, which can fully reflect the customer's risk level through multi-dimensional data and realize dynamic adjustment of the customer's insurance pricing.

[0004] To achieve the above objectives, a first aspect of an embodiment of the present application proposes an insurance pricing method based on data fusion, the method comprising: In response to a user's pricing instruction, obtaining, through a pricing system, basic product information corresponding to the pricing instruction; Receiving the identity information sent by the user through the pricing system, and determining user-related data and a credit platform bound to the pricing system according to the identity information, wherein the user-related data is used to characterize the purchase record and claim information of the user; In response to the authorization instruction of the user, obtaining the credit score corresponding to the user through the credit platform; Determining a comprehensive discount parameter based on the credit score and the user association data; Performing risk assessment on the product basic information, the credit score, and the user-related data to obtain a risk assessment result; The risk assessment results and the comprehensive discount parameters are subjected to pricing analysis through a preset pricing model to obtain target insurance pricing.

[0005] In some embodiments, determining a comprehensive discount parameter according to the credit score and the user association data includes: Determine the highest credit score and the lowest credit score of the user within a preset period of time according to the credit score; Normalizing the credit score according to the highest credit score and the lowest credit score to obtain a target credit score; Performing an exponential decay operation on the target credit score based on a preset adjustment parameter to obtain a credit score discount, wherein the adjustment parameter is used to control the decay rate of the discount; Determine the number of claims, insurance type weights, and total number of insurance policies based on the user-related data; Determine a relevance discount based on the number of claims, the insurance type weight, and the total number of insurance policies; A comprehensive discount parameter is determined according to the credit score discount and the relevance discount.

[0006] In some embodiments, determining a comprehensive discount parameter according to the credit score discount and the relevance discount includes: determining a first priority corresponding to the credit score discount, and determining a second priority corresponding to the relevance discount; Setting a first weight parameter corresponding to the credit score discount according to the first priority, and setting a second weight parameter corresponding to the relevance discount according to the second priority; multiplying the credit score discount by the first weight parameter to obtain a credit score, and multiplying the relevance discount by the second weight parameter to obtain a relevance score; A comprehensive discount parameter is determined based on the credit rating score and the relevance score.

[0007] In some embodiments, the performing risk assessment on the product basic information, the credit score, and the user-related data to obtain a risk assessment result includes: Determine a product identifier and product history data according to the product basic information, and perform a correlation analysis on the credit score and the user association data according to the product identifier to obtain an association relationship; The product identification is used as a node, and a plurality of the nodes are connected according to the association relationship to construct a network graph; Performing feature fusion on the network graph through a preset graph neural network to obtain feature representation; Performing feature processing on the product history data to obtain time series features and non-time series features; Performing data prediction on the time series features through a preset long short-term memory network to obtain a hidden state; The hidden state is feature enhanced based on a preset attention mechanism to obtain comprehensive information, and the risk score of the time series feature is calculated based on the comprehensive information and the non-time series feature to obtain a risk assessment result.

[0008] In some embodiments, the feature fusion of the network graph by a preset graph neural network to obtain a feature representation includes: For each node in the network graph, assign a feature vector to the node; Selecting a test node in the network graph, and determining an initial feature vector of the test node and a set of neighboring nodes around the test node; Performing a linear transformation on the neighbor nodes in the neighbor node set through the graph neural network to obtain a first parameter, and performing a linear transformation on the test node to obtain a second parameter; Determine a neighbor feature vector set corresponding to the neighbor node set, determine a first feature representation according to the initial feature vector, and determine a second feature representation according to the neighbor feature vector set; The first parameter, the second parameter, the first feature representation, and the second feature representation are input into the graph neural network, so that the graph neural network performs a hierarchical update on the test node based on the first parameter, the second parameter, the first feature representation, and the second feature representation to obtain a feature representation.

[0009] In some embodiments, the hidden state is enhanced based on a preset attention mechanism to obtain comprehensive information, and the risk score of the time series feature is calculated based on the comprehensive information and the non-time series feature to obtain a risk assessment result, including: Calculating an attention score of the hidden state based on a preset attention mechanism; Determine an attention weight for the current time step according to the attention score; Performing weighted summation on the context of the time series feature based on the attention weight and the hidden state to obtain comprehensive information; Splicing the comprehensive information and the non-time series features to obtain splicing features; Perform risk scoring on the splicing features to obtain a risk assessment result.

[0010] In some embodiments, performing pricing analysis on the risk assessment results and the comprehensive discount parameters using a preset pricing model to obtain target insurance pricing includes: Determine the basic insurance premium for the user according to the user-related data; A pricing analysis is performed based on the basic premium, the risk assessment result and the comprehensive discount parameter to obtain a target insurance pricing.

[0011] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes an insurance pricing device based on data fusion; the device comprises: A data acquisition module, configured to respond to a user's pricing instruction and acquire, through a pricing system, basic product information corresponding to the pricing instruction; A data determination module, configured to receive the identity information sent by the user through the pricing system, and determine user-related data and a credit platform bound to the pricing system according to the identity information, wherein the user-related data is used to characterize the purchase record and claim information of the user; A credit score acquisition module, configured to obtain the credit score corresponding to the user through the credit platform in response to the authorization instruction of the user; A discount calculation module, used to determine a comprehensive discount parameter based on the credit score and the user association data; A risk assessment module, used to perform risk assessment on the product basic information, the credit score and the user-related data to obtain a risk assessment result; The pricing analysis module is used to perform pricing analysis on the risk assessment results and the comprehensive discount parameters through a preset pricing model to obtain target insurance pricing.

[0012] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, an electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the insurance pricing method based on data fusion as described in the first aspect when executing the computer program.

[0013] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the insurance pricing method based on data fusion as described in the first aspect.

[0014] The insurance pricing method, device, electronic device and storage medium based on data fusion proposed in the present application respond to the user's pricing instruction, obtain the basic product information corresponding to the pricing instruction through the pricing system, so as to obtain the basic information of the insurance product, which is convenient for subsequent use as a reference for risk assessment, and then receive the identity information sent by the user through the pricing system, and determine the user-related data and the credit platform bound to the pricing system based on the identity information, so as to obtain the user's purchase record of other products and the current claim record, so as to determine whether the user has made a claim and the specific information of the claim. In response to the user's authorization instruction, the credit score corresponding to the user is obtained through the credit platform, so that the credit situation of the user can be reflected through the credit score, the accuracy of risk management can be improved, and the risk loss can be reduced. Then, the comprehensive discount parameters are determined according to the credit score and the user-related data, so that the discount parameters can be reasonably set according to the user's credit situation, historical claims situation and insurance purchase situation. After that, the basic information of the product, the credit score and the user-related data are risk assessed, and the risk level of the customer is fully reflected by analyzing the multi-dimensional data to obtain the risk assessment results, so as to achieve a comprehensive assessment of the insurance risk. Then, the risk assessment results and the comprehensive discount parameters are priced and analyzed by the preset pricing model, and the premium is flexibly adjusted in combination with the user's insurance situation and actual risk to obtain the target insurance pricing, so as to provide users with more personalized premium discounts. The embodiment of the present application dynamically adjusts the user's insurance premium rate through multi-dimensional data collection, fusion and analysis, and can provide users with more personalized premium discounts by connecting to a third-party credit platform and integrating data such as purchase records of other insurance products. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of an insurance pricing method based on data fusion provided in an embodiment of the present application; Figure 2 yes Figure 1 Flow chart of step S104 in FIG. Figure 3 yes Figure 2 Flow chart of step S206 in FIG. Figure 4 yes Figure 1 Flow chart of step S105 in FIG. Figure 5 yes Figure 4 Flow chart of step S403 in FIG. Figure 6 yes Figure 4 Flow chart of step S406 in FIG. Figure 7 yes Figure 1 Flow chart of step S106 in FIG. Figure 8It is a structural schematic diagram of an insurance pricing device based on data fusion provided in an embodiment of the present application; Fig. 9 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0017] 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 may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0019] First, some nouns involved in this application are analyzed: Natural Language Processing (NLP): NLP uses computers to process, understand, and apply human languages ​​(such as Chinese, English, etc.). NLP is a branch of artificial intelligence and an interdisciplinary subject between computer science and linguistics. It is often referred to as computational linguistics. Natural language processing includes grammatical analysis, semantic analysis, and text understanding. Natural language processing is commonly used in technical fields such as machine translation, handwritten and printed character recognition, speech recognition and text-to-speech conversion, information intent recognition, information extraction and filtering, text classification and clustering, public opinion analysis, and opinion mining. It involves data mining related to language processing, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research, and linguistic research related to language computing.

[0020] The insurance pricing method and device based on data fusion, electronic device and storage medium provided in the embodiments of the present application can fully reflect the risk level of the customer through multi-dimensional data and realize dynamic adjustment of the insurance pricing of the customer.

[0021] The following examples are used to illustrate the invention in detail. First, the insurance pricing method based on data fusion in the examples of the present application is described.

[0022] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0023] The basic technologies of artificial intelligence generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. Artificial intelligence software technologies mainly include computer vision technology, robotics technology, biometrics technology, module management technology of online meeting systems, natural language processing technology, and machine learning / deep learning.

[0024] The insurance pricing method based on data fusion provided in the embodiment of the present application relates to the field of financial technology. The insurance pricing method based on data fusion provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server side, and can also be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements an insurance pricing method based on data fusion, etc., but is not limited to the above forms.

[0025] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor 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, etc. 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, etc. that perform specific tasks or implement specific abstract data information types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0026] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with the relevant laws, regulations, and standards of the relevant countries and regions. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0027] The rapid development of drone technology has promoted the development of the low-altitude economy. Drones have played an important role in many industries such as logistics, agriculture, and infrastructure inspection. However, since drone flights involve many risk factors, such as pilots' operating habits, weather conditions, and terrain complexity, the existing insurance pricing system often adopts a long-term fixed pricing model that cannot flexibly reflect customers' personalized needs. The rigidity of this pricing model not only limits the personalized development of insurance products, but also ignores the actual impact of customer behavior on risk levels. This makes it impossible for insurance companies to fully utilize customers' comprehensive data to formulate more reasonable insurance pricing, thereby failing to fully reflect customers' true risk levels, resulting in customers being unable to enjoy dynamic premium discounts at different stages of use, reducing customers' insurance purchasing experience and stickiness.

[0028] In order to solve the above problems, this embodiment provides an insurance pricing method, device, electronic device and storage medium based on data fusion. In response to the user's pricing instruction, the basic product information corresponding to the pricing instruction is obtained through the pricing system, so that the basic information of the insurance product can be obtained, which is convenient for subsequent use as a reference for risk assessment. The pricing system then receives the identity information sent by the user, and determines the user-related data and the credit platform bound to the pricing system based on the identity information. The user's purchase record of other products and the current claim record can be obtained, so that it can be determined whether the user has made a claim and the specific information of the claim. In response to the user's authorization instruction, the credit score corresponding to the user is obtained through the credit platform, so that the credit situation of the user can be reflected through the credit score, the accuracy of risk management can be improved, and the risk loss can be reduced. Then, the comprehensive discount parameters are determined according to the credit score and the user-related data, so that the discount parameters can be reasonably set according to the user's credit situation, historical claims situation and insurance purchase situation. After that, the basic information of the product, the credit score and the user-related data are risk assessed, and the risk level of the customer is fully reflected by analyzing the multi-dimensional data to obtain the risk assessment results, so as to achieve a comprehensive assessment of the insurance risk. Then, the risk assessment results and the comprehensive discount parameters are priced and analyzed by the preset pricing model, and the premium is flexibly adjusted in combination with the user's insurance situation and actual risk to obtain the target insurance pricing, so as to provide users with more personalized premium discounts. The embodiment of the present application dynamically adjusts the user's insurance premium rate through multi-dimensional data collection, fusion and analysis, and can provide users with more personalized premium discounts by connecting to a third-party credit platform and integrating data such as purchase records of other insurance products.

[0029] The following is a detailed description with reference to the accompanying drawings.

[0030] Figure 1 is an optional flow chart of the insurance pricing method based on data fusion provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S106.

[0031] Step S101, in response to a user's pricing instruction, obtaining product basic information corresponding to the pricing instruction through a pricing system.

[0032] In step S101 of some embodiments, the user can send a pricing instruction by triggering a related control on the pricing system. In response to the user's pricing instruction, the pricing system obtains product basic information corresponding to the pricing instruction, wherein the product basic information includes basic information of the product, such as brand, model, market pricing, etc., to facilitate subsequent use as a basic reference for risk assessment.

[0033] It should be noted that the pricing system in the embodiment of the present application is provided with an intelligent question-and-answer module, which includes a plurality of preset questions, wherein the preset questions may be single-choice questions, multiple-choice questions or fill-in-the-blank questions. When the preset questions are single-choice questions or multiple-choice questions, the user can select the corresponding options to answer; when the preset questions are fill-in-the-blank questions, the user can answer in the answer area corresponding to each preset question. For example, the intelligent question-and-answer module is provided with questions such as flight frequency, flight habits and degree of knowledge of drones, and corresponding options are given. At this time, the user can select the corresponding options for different questions to answer. After the user answers, the corresponding pricing instructions are generated to carry out the insurance pricing process.

[0034] Step S102: receiving identity information sent by the user through the pricing system, and determining user association data and a credit platform bound to the pricing system according to the identity information.

[0035] It should be noted that user-related data is used to represent the user's purchase records and claims information.

[0036] In step S102 of some embodiments, after obtaining the basic information of the product, the embodiment of the present application will also receive the identity information sent by the user through the pricing system, and determine the user-related data based on the identity information, so that the user can determine other insurance products purchased or currently held by the user from this insurance company through the user-related data, such as car insurance, pet insurance, health insurance, etc. At the same time, the user's previous historical claims can be determined through the user-related data, which is convenient for subsequent dynamic pricing based on the user's purchase history and claims history, thereby improving the flexibility of insurance pricing. At the same time, the embodiment of the present application will also determine the credit platform bound to the pricing system based on the identity information, such as the Alipay platform, WeChat platform, etc., so as to facilitate the subsequent acquisition of the user's credit score.

[0037] Step S103, in response to the user's authorization instruction, obtaining the user's corresponding credit score through the credit platform.

[0038] In step S103 of some embodiments, in response to the user's authorization instruction, the pricing system sends a query request instruction to the credit platform, and the credit platform queries the credit score corresponding to the user's identity information and returns the corresponding result. The embodiment of the present application obtains the credit score corresponding to the user through the credit platform, thereby obtaining the user's credit status, which is convenient for subsequent risk assessment of premiums.

[0039] It can be understood that the credit score in the embodiments of the present application can be determined according to the type of credit platform. For example, when the credit platform is Alipay, the credit score at this time is the Alipay Sesame Credit score; when the credit platform is WeChat, the credit score at this time is the WeChat credit score, etc. The embodiments of the present application do not make specific limitations.

[0040] It is worth noting that the pricing system in the embodiment of the present application will update the credit score in real time to ensure the accuracy of the assessment, wherein the higher the credit score, the greater the premium discount the user will receive.

[0041] Step S104, determining comprehensive discount parameters according to the credit score and user association data.

[0042] In step S104 of some embodiments, a comprehensive discount parameter is determined based on the credit score and user-related data, so that people with higher credit scores can obtain more obvious discounts and obtain credit score discounts. At the same time, it can avoid the sharp changes caused by the logarithmic function at low scores, realize smooth calculation of user's premium discounts, ensure that the discount differences between different credit scores are not too significant, and balance the discounts for customers with low credit scores and high credit scores.

[0043] Step S105, performing risk assessment on the basic product information, credit score and user-related data to obtain a risk assessment result.

[0044] In step S105 of some embodiments, a risk assessment is performed on the basic product information, credit score, and user-related data to obtain a risk assessment result, thereby enabling a comprehensive risk assessment to be achieved by combining multi-dimensional information, improving the accuracy of risk assessment, and achieving a comprehensive analysis of historical data.

[0045] Step S106, performing pricing analysis on the risk assessment results and comprehensive discount parameters through a preset pricing model to obtain target insurance pricing.

[0046] In step S106 of some embodiments, a pricing analysis is performed on the risk assessment results and the comprehensive discount parameters through a preset pricing model, so that a comprehensive risk assessment can be achieved by combining multi-dimensional information, target insurance pricing can be obtained, the accuracy of risk assessment can be improved, and a comprehensive analysis of historical data can be achieved.

[0047] In some embodiments, the present application embodiment will also record data through blockchain technology to ensure the transparency and immutability of each premium calculation and adjustment process. In addition, the data will be asynchronously uploaded to the chain. Specifically, considering the impact of blockchain performance, the data enters the queue in the form of text, files or pictures, and is uploaded to the chain within 5 minutes in the queue to ensure efficiency.

[0048] Specifically, the data of each premium calculation and adjustment process will be signed and uploaded to the blockchain. The data on the chain includes text, files or pictures, etc., to ensure that each link of the premium adjustment is tamper-proof and traceable. Considering the limitations of on-chain operation performance, the system uses an asynchronous on-chain mechanism to queue data for on-chain, ensuring efficient data writing. In addition, the system can use the data stored on the chain, the data of the third-party platform and the internal historical data, and use Bayesian optimization to make real-time pricing adjustments to ensure the personalization and fairness of customer premiums.

[0049] In some embodiments, the embodiments of the present application can also flexibly adjust the premium based on the user's real-time operating behavior, user-related data, and credit score to ensure that the premium is consistent with the user's actual risk level. Users with different mission types, operating environments, and credit scores will receive different premium adjustment strategies. For example, a drone user has performed multiple high-risk missions, but has a high credit score. The system will give an additional 5% premium discount based on his credit record.

[0050] See also Figure 2 In some embodiments, step S104 may also include but is not limited to steps S201 to S206.

[0051] Step S201, determining the highest credit score and the lowest credit score of the user within a preset time period according to the credit score.

[0052] Step S202, normalizing the credit scores according to the highest credit score and the lowest credit score to obtain a target credit score.

[0053] Step S203: performing an exponential decay operation on the target credit score based on preset adjustment parameters to obtain a credit score discount.

[0054] It should be noted that the adjustment parameter is used to control the decay rate of the discount.

[0055] In steps S201 to S203 of some embodiments, in the process of determining the comprehensive discount parameters based on the credit score and user-related data, the embodiments of the present application first determine the highest credit score and the lowest credit score of the user within a preset time period based on the credit score. By determining the lowest and highest credit scores, a standardized comparison benchmark can be provided for the credit status of different users or entities, which is convenient for the subsequent precise adjustment of premium pricing. The credit score is then normalized based on the highest credit score and the lowest credit score to make the value ranges of different features consistent, thereby accelerating the convergence process of optimization algorithms such as gradient descent to obtain a target credit score. Thereafter, an exponential decay operation is performed on the target credit score based on the preset adjustment parameters, so that higher credit scores can obtain more obvious discounts and obtain credit score discounts. At the same time, the drastic changes caused by the logarithmic function at low scores can be avoided, and smooth calculation of premium discounts for users can be achieved, ensuring that the discount differences between different credit scores are not too significant, and balancing the discounts for customers with low credit scores and high credit scores.

[0056] Specifically, in the embodiment of the present application, the exponential decay operation on the target credit score is expressed as follows: ; in, is the normalized credit score, i.e., the target credit score; is the highest credit score; is the minimum credit score; It is the original credit score.

[0057] ; in, is the normalized credit score; is a tuning parameter used to control the discount decay rate, It’s a credit score discount.

[0058] It is understandable that in the embodiment of the present application, customers with higher credit scores enjoy greater discounts, while ensuring that the discounts for customers with lower credit scores are not too low. For example, the discount gap between a customer with a credit score of 700 and a customer with a credit score of 800 is smaller.

[0059] Step S204, determining the number of claims, insurance type weights and the total number of policies based on the user-related data.

[0060] Step S205, determining the correlation discount according to the number of claims, the insurance type weight and the total number of policies.

[0061] Step S206, determining a comprehensive discount parameter according to the credit score discount and the relevance discount.

[0062] In steps S204 to S206 of some embodiments, the embodiments of the present application will also determine the number of claims, insurance type weights and the total number of policies based on user-related data, thereby achieving comprehensive collection of the user's historical claims data and purchased insurance data, and then determine the relevance discount based on the number of claims, insurance type weights and the total number of policies, so that users who have purchased more products or have fewer claims have higher corresponding premiums, thereby achieving flexible adjustment of premium pricing. Afterwards, comprehensive discount parameters are determined based on credit score discounts and relevance discounts, and dynamic adjustment of discount parameters is achieved through comprehensive combination of multiple data. The discount parameters can be determined based on the user's purchased products, historical claims and credit score, thereby improving the accuracy and flexibility of insurance pricing.

[0063] Specifically, the relevance discount in the embodiment of the present application is calculated as follows: ; in, is the relevance discount; is the insurance type weight; is the number of claims; It is the total number of policies.

[0064] It can be understood that in the embodiment of the present application, the insurance types for which the user customers have no claims record will receive the maximum weighted discount, and the insurance types with a higher number of claims will have their corresponding discount ratios reduced.

[0065] It should be noted that the preset duration in the embodiment of the present application can be set according to the needs of the user, for example, one year, two years or half a month, etc., and the embodiment of the present application does not make specific restrictions.

[0066] See also Figure 3 In some embodiments, step S206 may also include but is not limited to steps S301 to S304.

[0067] Step S301, determining a first priority corresponding to a credit score discount, and determining a second priority corresponding to a relevance discount.

[0068] Step S302: setting a first weight parameter corresponding to the credit score discount according to the first priority, and setting a second weight parameter corresponding to the relevance discount according to the second priority.

[0069] Step S303, multiplying the credit score discount by the first weight parameter to obtain a credit score, and multiplying the relevance discount by the second weight parameter to obtain a relevance score.

[0070] Step S304, determining a comprehensive discount parameter according to the credit score and the relevance score.

[0071] In steps S301 to S304 of some embodiments, in the process of determining the comprehensive discount parameters based on the credit score discount and the relevance discount, the embodiments of the present application first determine the first priority corresponding to the credit score discount, and determine the second priority corresponding to the relevance discount, thereby determining the priority relationship between the credit score discount and the relevance discount, and then set the first weight parameter corresponding to the credit score discount according to the first priority, and set the second weight parameter corresponding to the relevance discount according to the second priority, so that different weight parameters can be set for different priorities, and a larger weight parameter can be set for a discount with a higher priority, and a smaller weight parameter can be set for a discount with a lower priority. Afterwards, the credit score discount is multiplied by the first weight parameter to obtain a credit score value, and the relevance discount is multiplied by the second weight parameter to obtain a relevance score. Then, the comprehensive discount parameters are determined based on the credit score value and the relevance score, so that the comprehensive discount parameters can be reasonably determined, which is convenient for providing personalized services and products to users in the future.

[0072] Specifically, the operation of determining the comprehensive discount parameters in the embodiment of the present application is as follows: ; in, discounts for credit scores; Discount for relevance; is the first weight parameter; is the second weight parameter.

[0073] It can be understood that the sum of the weights of the first weight parameter and the second weight parameter in the embodiment of the present application is 1, and the first priority and the second priority can be set according to the needs of the user. For example, the first priority is set higher than the second priority. At this time, the first weight parameter is greater than the second weight parameter. The embodiment of the present application does not make specific restrictions.

[0074] See also Figure 4 In some embodiments, step S105 may also include but is not limited to steps S401 to S406.

[0075] Step S401, determining the product identification and product history data according to the product basic information, and performing correlation analysis on the credit score and user correlation data according to the product identification to obtain the correlation relationship.

[0076] Step S402, taking the product identification as a node, and connecting multiple nodes according to the association relationship to construct a network graph.

[0077] Step S403, performing feature fusion on the network graph through a preset graph neural network to obtain feature representation.

[0078] Step S404: perform feature processing on the product history data to obtain time series features and non-time series features.

[0079] Step S405, performing data prediction on the time series features through a preset long short-term memory network to obtain a hidden state.

[0080] Step S406, based on the preset attention mechanism, the hidden state is feature enhanced to obtain comprehensive information, and the risk score of the time series feature is calculated based on the comprehensive information and the non-time series feature to obtain a risk assessment result.

[0081] In steps S401 to S406 of some embodiments, in the process of risk assessment of product basic information, credit score and user-related data, the embodiment of the present application first determines the product identification and product historical data based on the product basic information, and performs correlation analysis on the credit score and user-related data based on the product identification, analyzes the correlation between the credit score and user-related data and the product identification, obtains the correlation relationship, and then uses the product identification as a node, and connects multiple nodes according to the correlation relationship to construct a network graph, wherein each node represents a different product, and the edge between the nodes represents the correlation between different nodes, and the edge between two nodes is used as a common feature of the data source, so that the corresponding relationship between products can be clearly described, and the relationship between product data can be intuitively displayed through the graph. Afterwards, the network graph is subjected to feature fusion through a preset graph neural network, which can improve the model convergence speed while ensuring the balance of feature fusion, and obtain feature representation. Then, feature processing is performed on the product historical data, and the product historical data is converted into time series features. Normalization and standardization are used to ensure that the feature values ​​are within a reasonable range, and time series features and non-time series features are obtained. After that, data prediction is performed on the time series features through the preset long short-term memory network, so that the long short-term memory network can capture the risk pattern of the product and pay attention to the key risk points in the risk pattern to obtain the hidden state. Finally, the hidden state is feature enhanced based on the preset attention mechanism, so as to obtain comprehensive information of important moments in the time series, and calculate the risk score of the time series features based on the comprehensive information and non-time series features to obtain the risk assessment results, so as to combine multi-dimensional information to achieve comprehensive risk assessment, improve the accuracy of risk assessment, and achieve comprehensive analysis of historical data.

[0082] Specifically, the specific process of performing data prediction on time series features through a preset long short-term memory network in the embodiment of the present application is as follows: = ; = ; ; ; in, is the weight matrix of the forget gate, which is used to control the forgetting ratio of the state at the previous moment. is the weight matrix of the input gate, which determines the extent to which new input data enters the cell state. It is the fusion matrix of input data and previous state, which is used to update the cell state. Represents the forget gate, which controls the state information of the previous time step. It is the input gate that controls the inflow of new information. It is the control signal of the output gate, which is used to determine the weight of the output at the current moment. For cell states, long-term dependencies in the time series are recorded. is the hidden state output at the current moment. The sigmoid function is used to compress the value between 0 and 1. The input for the current time step. is the bias term of the forget gate. is the bias term of the input gate. is the bias term of the cell state. is the input for the current time step.

[0083] See also Figure 5 In some embodiments, step S403 may also include but is not limited to steps S501 to S505.

[0084] Step S501, for each node in the network graph, assign a feature vector to the node.

[0085] Step S502, selecting a test node in the network graph, and determining an initial feature vector of the test node and a set of neighbor nodes around the test node.

[0086] Step S503, linearly transform the neighbor nodes in the neighbor node set through the graph neural network to obtain the first parameter, and linearly transform the test node to obtain the second parameter.

[0087] Step S504, determining a neighbor feature vector set corresponding to the neighbor node set, and determining a first feature representation according to the initial feature vector, and determining a second feature representation according to the neighbor feature vector set.

[0088] Step S505: input the first parameter, the second parameter, the first feature representation and the second feature representation into the graph neural network, so that the graph neural network hierarchically updates the test node based on the first parameter, the second parameter, the first feature representation and the second feature representation to obtain a feature representation.

[0089] In steps S501 to S505 of some embodiments, in the process of feature fusion of the network graph through a preset graph neural network, for each node in the network graph, a feature vector is assigned to the node, and the feature vector is used as an initial feature vector, wherein these feature vectors may be attributes of the node, for example, the brand and model of the drone, etc., and then a test node is randomly selected in the network graph, and the initial feature vector of the test node and the set of neighbor nodes around the test node are determined, so as to realize message transmission between the test node and the neighbor nodes, and then the neighbor nodes in the neighbor node set are linearly transformed through the graph neural network to obtain the first parameter, and the test node is initialized and initialized. The test node is linearly transformed to obtain the second parameter, and then the set of neighbor feature vectors corresponding to the set of neighbor nodes is determined, and the first feature representation is determined according to the initial feature vector, and the second feature representation is determined according to the set of neighbor feature vectors. Finally, the first parameter, the second parameter, the first feature representation and the second feature representation are input into the graph neural network, so that the graph neural network performs hierarchical update on the test node based on the first parameter, the second parameter, the first feature representation and the second feature representation, which can improve the convergence speed of the model while ensuring the balance of feature fusion, and obtain the feature representation of the aggregated neighbor node information and the self-information, so that features can be extracted in multidimensional data to improve the accuracy of risk assessment.

[0090] Specifically, the embodiment of the present application performs a hierarchical update on the test node as follows: ; in, Indicates Test nodes in the layer The feature representation of Represents a test node The set of neighbor nodes of Represents the normalization coefficient, which is used to prevent the node eigenvalue from exploding or disappearing, ensure the balance of feature fusion, and improve the model convergence speed; represents the first feature representation; represents the second feature representation; represents the weight matrix; represents a nonlinear activation function; Representation and test nodes Adjacent neighbor nodes.

[0091] See also Figure 6 In some embodiments, step S406 may also include but is not limited to steps S601 to S605.

[0092] Step S601, calculating the attention score of the hidden state based on the preset attention mechanism.

[0093] Step S602, determining the attention weight of the current time step according to the attention score.

[0094] Step S603, weighted summing of the context of the time series features is performed based on the attention weight and the hidden state to obtain comprehensive information.

[0095] Step S604, concatenating the comprehensive information and the non-time series features to obtain concatenated features.

[0096] Step S605, performing risk scoring on the splicing features to obtain a risk assessment result.

[0097] In steps S601 to S605 of some embodiments, in the process of performing feature enhancement on the hidden state based on a preset attention mechanism to obtain comprehensive information, and calculating the risk score of the time series feature based on the comprehensive information and the non-time series features to obtain the risk assessment result, the embodiment of the present application first calculates the attention score of the hidden state based on the preset attention mechanism, so that the importance of the current moment can be measured by the attention score, and then determines the attention weight of the current time step based on the attention score, so that the relative importance of the current time step can be measured. Thereafter, the context of the time series feature is weighted and summed based on the attention weight and the hidden state, so as to obtain the comprehensive information of the important moments in the time series, and then the comprehensive information and the non-time series features are spliced ​​to obtain the spliced ​​features. Thereafter, the spliced ​​features are risk scored to obtain the risk assessment result, so that a comprehensive risk assessment can be achieved by combining multi-dimensional information, the accuracy of the risk assessment can be improved, and a comprehensive analysis of historical data can be achieved.

[0098] Specifically, the calculation process of weighted summation of the context of time series features based on attention weights and hidden states is as follows: ; ; ; in, is the weight matrix used to calculate the attention score. is the bias term of the attention mechanism. is the time step An attention score that measures the importance of the current moment. Time step The attention weight indicates the relative importance of the moment. The context vector is a weighted summation that represents the comprehensive information of important moments in the time series.

[0099] Specifically, the calculation process of risk scoring of splicing features in the embodiment of the present application is as follows: ; in, ] represents the time series context vector With non-time series features splicing features. Represents the weight matrix, used to connect comprehensive features To risk score . Represents the partial rank vector, used to compare Adjust the linear output of the model together. Represents an activation function, such as the sigmoid function.

[0100] See also Figure 7 In some embodiments, step S106 may also include but is not limited to steps S701 to S702.

[0101] Step S701, determining the basic insurance premium of the user according to the user association data.

[0102] Step S702, performing pricing analysis based on the basic premium, risk assessment results and comprehensive discount parameters to obtain target insurance pricing.

[0103] In steps S701 to S702 of some embodiments, in the process of performing pricing analysis on the risk assessment results and the comprehensive discount parameters through a preset pricing model, the embodiments of the present application first determine the basic premium of the user's insurance based on the user-related data, and then perform pricing analysis based on the basic premium, the risk assessment results and the comprehensive discount parameters to obtain the target insurance pricing. It can dynamically adjust the user's insurance premium rate in combination with multi-dimensional data to achieve more personalized pricing.

[0104] Specifically, the embodiment of the present application uses Bayesian optimization technology to dynamically adjust the customer's insurance premium rate. The specific process is as follows: ; in, time Personalized premiums, i.e. target insurance pricing. The basic premium. is the risk adjustment factor. is the comprehensive discount parameter.

[0105] ; in, is the mean function of the prediction distribution. is the covariance function, which is used to measure the similarity between data points.

[0106] See also Figure 8 The embodiment of the present application also provides an insurance pricing device based on data fusion, the device comprising: The data acquisition module 801 is used to respond to the user's pricing instruction and obtain the basic product information corresponding to the pricing instruction through the pricing system; The data determination module 802 is used to receive the identity information sent by the user through the pricing system, and determine the user-related data and the credit platform bound to the pricing system according to the identity information, wherein the user-related data is used to represent the user's purchase record and claim information; The credit score acquisition module 803 is used to obtain the credit score corresponding to the user through the credit platform in response to the user's authorization instruction; A discount calculation module 804, for determining comprehensive discount parameters based on the credit score and user association data; The risk assessment module 805 is used to perform risk assessment on product basic information, credit score and user-related data to obtain risk assessment results; The pricing analysis module 806 is used to perform pricing analysis on the risk assessment results and comprehensive discount parameters through a preset pricing model to obtain target insurance pricing.

[0107] The specific implementation of the insurance pricing device based on data fusion is basically the same as the specific implementation of the insurance pricing method based on data fusion mentioned above, and will not be repeated here.

[0108] The embodiment of the present application also provides an electronic device, the electronic device comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program is executed by the processor to realize the above-mentioned insurance pricing method based on data fusion. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0109] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes: The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application; The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the insurance pricing method based on data fusion in the embodiment of this application; Input / output interface 903, used to implement information input and output; Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.); A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904); The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0110] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned insurance pricing method based on data fusion.

[0111] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0112] The insurance pricing method, device, electronic device and storage medium based on data fusion provided in the embodiments of the present application respond to the user's pricing instruction, obtain the basic product information corresponding to the pricing instruction through the pricing system, so as to obtain the basic information of the insurance product, which is convenient for subsequent use as a reference for risk assessment, and then receive the identity information sent by the user through the pricing system, and determine the user-related data and the credit platform bound to the pricing system based on the identity information, so as to obtain the user's purchase record of other products and the current claim record, so as to determine whether the user has made a claim and the specific information of the claim. In response to the user's authorization instruction, the credit score corresponding to the user is obtained through the credit platform, so that the credit situation of the user can be reflected through the credit score, the accuracy of risk management can be improved, and the risk loss can be reduced. Then, the comprehensive discount parameters are determined according to the credit score and the user-related data, so that the discount parameters can be reasonably set according to the user's credit situation, historical claims situation and insurance purchase situation. After that, the basic information of the product, the credit score and the user-related data are risk assessed, and the risk level of the customer is fully reflected by analyzing the multi-dimensional data to obtain the risk assessment results, so as to achieve a comprehensive assessment of the insurance risk. Then, the risk assessment results and the comprehensive discount parameters are priced and analyzed by the preset pricing model, and the premium is flexibly adjusted in combination with the user's insurance situation and actual risk to obtain the target insurance pricing, so as to provide users with more personalized premium discounts. The embodiment of the present application dynamically adjusts the user's insurance premium rate through multi-dimensional data collection, fusion and analysis, and can provide users with more personalized premium discounts by connecting to a third-party credit platform and integrating data such as purchase records of other insurance products.

[0113] The embodiments described in the embodiments of the present application are intended 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 in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0114] It can be understood by those skilled in the art that Figure 1-9 The technical solutions shown in the figure do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figure, or a combination of certain steps, or different steps.

[0115] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0116] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0117] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0118] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: 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 indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. 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, c can be single or multiple.

[0119] In the several embodiments provided in 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 units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0120] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0122] If the integrated unit is implemented 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 solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0123] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. An insurance pricing method based on data fusion, characterized in that: The method comprises: In response to a user's pricing instruction, obtaining, through a pricing system, basic product information corresponding to the pricing instruction; Receiving the identity information sent by the user through the pricing system, and determining user-related data and a credit platform bound to the pricing system according to the identity information, wherein the user-related data is used to characterize the purchase record and claim information of the user; In response to the authorization instruction of the user, obtaining the credit score corresponding to the user through the credit platform; Determining a comprehensive discount parameter based on the credit score and the user association data; Performing risk assessment on the product basic information, the credit score, and the user-related data to obtain a risk assessment result; The risk assessment results and the comprehensive discount parameters are subjected to pricing analysis through a preset pricing model to obtain target insurance pricing.

2. The insurance pricing method based on data fusion according to claim 1, characterized in that: The determining of the comprehensive discount parameter according to the credit score and the user association data comprises: Determine the highest credit score and the lowest credit score of the user within a preset period of time according to the credit score; Normalizing the credit score according to the highest credit score and the lowest credit score to obtain a target credit score; Performing an exponential decay operation on the target credit score based on a preset adjustment parameter to obtain a credit score discount, wherein the adjustment parameter is used to control the decay rate of the discount; Determine the number of claims, insurance type weights, and total number of insurance policies based on the user-related data; Determine a relevance discount based on the number of claims, the insurance type weight, and the total number of insurance policies; A comprehensive discount parameter is determined according to the credit score discount and the relevance discount.

3. The insurance pricing method based on data fusion according to claim 2 is characterized in that: The determining of a comprehensive discount parameter according to the credit score discount and the relevance discount comprises: determining a first priority corresponding to the credit score discount, and determining a second priority corresponding to the relevance discount; Setting a first weight parameter corresponding to the credit score discount according to the first priority, and setting a second weight parameter corresponding to the relevance discount according to the second priority; multiplying the credit score discount by the first weight parameter to obtain a credit score, and multiplying the relevance discount by the second weight parameter to obtain a relevance score; A comprehensive discount parameter is determined based on the credit rating score and the relevance score.

4. The insurance pricing method based on data fusion according to claim 1, characterized in that: The risk assessment of the product basic information, the credit score and the user-related data to obtain a risk assessment result includes: Determine a product identifier and product history data according to the product basic information, and perform a correlation analysis on the credit score and the user association data according to the product identifier to obtain an association relationship; The product identification is used as a node, and a plurality of the nodes are connected according to the association relationship to construct a network graph; Performing feature fusion on the network graph through a preset graph neural network to obtain feature representation; Performing feature processing on the product history data to obtain time series features and non-time series features; Performing data prediction on the time series features through a preset long short-term memory network to obtain a hidden state; The hidden state is feature enhanced based on a preset attention mechanism to obtain comprehensive information, and the risk score of the time series feature is calculated based on the comprehensive information and the non-time series feature to obtain a risk assessment result.

5. The insurance pricing method based on data fusion according to claim 4 is characterized in that: The method of performing feature fusion on the network graph through a preset graph neural network to obtain feature representation includes: For each node in the network graph, assign a feature vector to the node; Selecting a test node in the network graph, and determining an initial feature vector of the test node and a set of neighboring nodes around the test node; Performing a linear transformation on the neighbor nodes in the neighbor node set through the graph neural network to obtain a first parameter, and performing a linear transformation on the test node to obtain a second parameter; Determine a neighbor feature vector set corresponding to the neighbor node set, determine a first feature representation according to the initial feature vector, and determine a second feature representation according to the neighbor feature vector set; The first parameter, the second parameter, the first feature representation, and the second feature representation are input into the graph neural network, so that the graph neural network performs a hierarchical update on the test node based on the first parameter, the second parameter, the first feature representation, and the second feature representation to obtain a feature representation.

6. The insurance pricing method based on data fusion according to claim 4, characterized in that: The hidden state is enhanced based on the preset attention mechanism to obtain comprehensive information, and the risk score of the time series feature is calculated according to the comprehensive information and the non-time series feature to obtain a risk assessment result, including: Calculating an attention score of the hidden state based on a preset attention mechanism; Determine an attention weight for the current time step according to the attention score; Performing weighted summation on the context of the time series feature based on the attention weight and the hidden state to obtain comprehensive information; Splicing the comprehensive information and the non-time series features to obtain splicing features; Perform risk scoring on the splicing features to obtain a risk assessment result.

7. The insurance pricing method based on data fusion according to claim 1, characterized in that: The method of performing pricing analysis on the risk assessment results and the comprehensive discount parameters through a preset pricing model to obtain target insurance pricing includes: Determine the basic insurance premium for the user according to the user-related data; A pricing analysis is performed based on the basic premium, the risk assessment result and the comprehensive discount parameter to obtain a target insurance pricing.

8. An insurance pricing device based on data fusion, characterized in that: The device comprises: A data acquisition module, configured to respond to a user's pricing instruction and acquire, through a pricing system, basic product information corresponding to the pricing instruction; A data determination module, configured to receive the identity information sent by the user through the pricing system, and determine user-related data and a credit platform bound to the pricing system according to the identity information, wherein the user-related data is used to characterize the purchase record and claim information of the user; A credit score acquisition module, configured to obtain the credit score corresponding to the user through the credit platform in response to the authorization instruction of the user; A discount calculation module, used to determine a comprehensive discount parameter based on the credit score and the user association data; A risk assessment module, used to perform risk assessment on the product basic information, the credit score and the user-related data to obtain a risk assessment result; The pricing analysis module is used to perform pricing analysis on the risk assessment results and the comprehensive discount parameters through a preset pricing model to obtain target insurance pricing.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the insurance pricing method based on data fusion as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the insurance pricing method based on data fusion according to any one of claims 1 to 7 is implemented.

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