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, and belongs to the technical field of financial science and technology and intelligent recommendation. The method comprises the steps of performing risk degree identification on target insurance request information of a user, performing risk premium calculation on candidate insurance products according to an identified target user risk level, screening out a target insurance product from the candidate insurance products according to the calculated target risk premium and the target user risk level, and recommending the target insurance product to the user. According to the embodiment of the invention, risk premium calculation is carried out on the candidate insurance products according to the identified user risk levels, the insurance products suitable for the risk conditions of the users can be recommended according to the users of different risk levels, and personalized insurance product recommendation can be provided for the users; and furthermore, the target insurance product is screened from the candidate insurance products according to the target risk premium and the target user risk level, and is recommended to the user, so that the insurance product recommendation efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent recommendation technology, applicable to the field of financial technology, and in particular to an insurance product recommendation method and device, electronic device and storage medium. Background Art

[0002] At present, the process of recommending insurance products mainly relies on manual identity and asset verification of the insured user information, and then recommending corresponding insurance products to the insured user based on the verification results. For example, if Zhang San needs to purchase a corporate property insurance, he needs to manually verify the legitimacy of his identity. After the verification is passed, the company information associated with Zhang San is input into the enterprise system for authenticity verification, and the corresponding corporate property insurance is recommended to Zhang San only after the verification is passed again. However, this method requires manual input of user-related information multiple times, and the user needs to wait for the manual verification results to pass before entering the insurance product inquiry and insurance stage, resulting in low efficiency in recommending insurance products. Therefore, how to improve the efficiency of recommending insurance products has become an urgent problem to be solved. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose an insurance product recommendation method and device, an electronic device and a storage medium, aiming to improve the efficiency of insurance product recommendations.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes an insurance product recommendation method, the method comprising:

[0005] In response to an insurance request from a target user, obtaining a user ID image of the target user;

[0006] Performing document recognition on the user document image to obtain user document information;

[0007] Acquire target insurance request information of the target user based on the user certificate information;

[0008] Identify the risk level of the target insurance request information to obtain the target user risk level;

[0009] Acquire candidate insurance products according to the target user risk level, and calculate risk premiums for the candidate insurance products according to the target user risk level to obtain target risk premiums;

[0010] A target insurance product is screened out from the candidate insurance products according to the target risk premium and the risk level of the target user, and the target insurance product is recommended to the target user.

[0011] In some embodiments, the step of identifying the risk level of the target insurance request information to obtain the target user risk level includes:

[0012] Identifying the disaster risk level of the target insurance request information to obtain a disaster risk level;

[0013] Identify the industry risk level of the target insurance request information to obtain the industry risk level;

[0014] The target user risk level is determined according to the disaster risk level and the industry risk level.

[0015] In some embodiments, the step of identifying the disaster risk level of the target insurance request information to obtain the disaster risk level includes:

[0016] Obtaining historical disaster data of the geographical location of the target insurance request information;

[0017] Acquire the geographical location coordinates of the historical disaster data, and construct a disaster layer of the historical disaster data according to the geographical location coordinates;

[0018] Acquire the historical disaster risk level of the historical disaster data, and construct a digital disaster risk map according to the historical disaster risk level and the disaster layer;

[0019] Acquire target disaster data of the geographical location within a preset time period;

[0020] The disaster risk level of the target disaster data is identified according to the disaster digital risk map to obtain the disaster risk level.

[0021] In some embodiments, calculating the risk premium of the candidate insurance product according to the risk level of the target user to obtain a target risk premium includes:

[0022] Obtaining historical claims data and historical product premiums of the candidate insurance products;

[0023] Determine the historical claim data of the candidate insurance product based on the historical claim data and the historical product premiums;

[0024] The basic product premium of the candidate insurance product is obtained, and the risk premium of the candidate insurance product is calculated according to the basic product premium, the historical claims data and the target user risk level to obtain the target risk premium.

[0025] In some embodiments, performing certificate recognition on the user certificate image to obtain user certificate information includes:

[0026] Using a preset certificate recognition model to perform text area detection on the user certificate image to obtain the certificate text area;

[0027] Performing character segmentation on the text area to obtain segmented text characters;

[0028] Perform certificate recognition on the segmented text characters to obtain the user certificate information.

[0029] In some embodiments, the step of selecting a target insurance product from the candidate insurance products according to the target risk premium and the target user risk level includes:

[0030] Scoring the candidate insurance products according to a preset scoring function, the target risk premium and the target user risk level to obtain a candidate product score;

[0031] The candidate insurance product with the highest score is selected from the candidate product scores as the target insurance product.

[0032] In some embodiments, after performing certificate recognition on the user certificate image to obtain user certificate information, the method further includes:

[0033] Performing face recognition on the target user based on the user ID information to obtain the target user's face;

[0034] Performing authenticity verification on the user ID information according to the target user's face to obtain legitimate user ID information;

[0035] The legal user certificate information is verified for validity to obtain valid user certificate information, and the valid user certificate information is used as the user certificate information.

[0036] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides an insurance product recommendation device, the device comprising:

[0037] The certificate image acquisition module is used to obtain the user certificate image of the target user in response to the insurance request of the target user;

[0038] The certificate recognition module is used to perform certificate recognition on the user certificate image to obtain user certificate information;

[0039] An insurance information acquisition module, used to acquire the target insurance request information of the target user based on the user certificate information;

[0040] A risk level identification module is used to identify the risk level of the target insurance request information and obtain the risk level of the target user;

[0041] A premium calculation module, used to obtain candidate insurance products according to the risk level of the target user, and calculate the risk premium of the candidate insurance products according to the risk level of the target user to obtain a target risk premium;

[0042] The insurance product recommendation module is used to screen out a target insurance product from the candidate insurance products according to the target risk premium and the risk level of the target user, and recommend the target insurance product to the target user.

[0043] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0044] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0045] The insurance product recommendation method and device, electronic device and storage medium proposed in the present application obtain a user ID image of the target user in response to the insurance request of the target user; perform ID recognition on the user ID image to obtain user ID information; obtain target insurance request information of the target user based on the user ID information; perform risk level recognition on the target insurance request information to obtain the risk level of the target user; obtain candidate insurance products based on the risk level of the target user, and calculate the risk premium for the candidate insurance products based on the risk level of the target user to obtain the target risk premium; screen out the target insurance product from the candidate insurance products based on the target risk premium and the risk level of the target user, and recommend the target insurance product to the target user. The present application first performs ID recognition on a user ID image to obtain user ID information, and obtains target insurance request information of a target user based on the user ID information, and can automatically obtain user ID information and insurance information associated with the ID without manual intervention, thereby facilitating improving the efficiency of subsequent insurance product recommendations; secondly, the risk level of the target insurance request information is identified to obtain the risk level of the target user; candidate insurance products are obtained based on the risk level of the target user, and risk premiums are calculated for the candidate insurance products based on the risk level of the target user to obtain the target risk premium, which can recommend insurance products suitable for the risk status of users with different risk levels, improve user satisfaction, and help provide users with personalized insurance product recommendations and reasonable premium pricing; finally, the target insurance product is screened out from the candidate insurance products based on the target risk premium and the risk level of the target user, and the target insurance product is recommended to the target user, which can achieve accurate recommendation of insurance products and improve the efficiency of insurance product recommendation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of the insurance product recommendation method provided in an embodiment of the present application;

[0047] Figure 2 yes Figure 1 Flow chart of step S102 in FIG.

[0048] Figure 3 is another flow chart of the insurance product recommendation method provided in an embodiment of the present application;

[0049] Figure 4 yes Figure 1 Flow chart of step S104 in FIG.

[0050] Figure 5 yes Figure 4 Flow chart of step S401 in FIG.

[0051] Figure 6 yes Figure 1 Flow chart of step S105 in FIG.

[0052] Figure 7 yes Figure 1 Flow chart of step S106 in FIG.

[0053] Figure 8 is a schematic diagram of the structure of the insurance product recommendation device provided in an embodiment of the present application;

[0054] 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

[0055] 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.

[0056] 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.

[0057] 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.

[0058] Based on this, the embodiments of the present application provide an insurance product recommendation method and device, an electronic device and a storage medium, aiming to improve the efficiency of insurance product recommendations.

[0059] The insurance product recommendation method and device, electronic device and storage medium provided in the embodiments of the present application are specifically described through the following embodiments. First, the insurance product recommendation method in the embodiments of the present application is described.

[0060] The insurance product recommendation method provided in the embodiment of the present application relates to the field of financial technology and intelligent recommendation technology. The insurance product recommendation method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can 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 distributed system composed of multiple physical servers, or 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 the insurance product recommendation method, etc., but is not limited to the above forms.

[0061] 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 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.

[0062] 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 the user's identity or characteristics such as user ID information, insurance request information, and user ID images, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. 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.

[0063] Figure 1 is an optional flow chart of the insurance product recommendation method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S106.

[0064] Step S101, in response to an insurance request from a target user, obtaining a user ID image of the target user.

[0065] Step S102: Perform document recognition on the user document image to obtain user document information.

[0066] Step S103, obtaining the target insurance request information of the target user based on the user certificate information.

[0067] Step S104, identifying the risk level of the target insurance request information to obtain the target user risk level.

[0068] Step S105, obtaining candidate insurance products according to the risk level of the target user, and calculating the risk premium of the candidate insurance products according to the risk level of the target user to obtain a target risk premium.

[0069] Step S106, screening out a target insurance product from candidate insurance products according to the target risk premium and the risk level of the target user, and recommending the target insurance product to the target user.

[0070] Steps S101 to S106 shown in the embodiment of the present application are as follows: by responding to the insurance request of the target user, obtaining a user ID image of the target user; performing ID recognition on the user ID image to obtain user ID information; obtaining the target insurance request information of the target user based on the user ID information; performing risk level identification on the target insurance request information to obtain the risk level of the target user; obtaining candidate insurance products based on the risk level of the target user, and calculating the risk premium for the candidate insurance products based on the risk level of the target user to obtain the target risk premium; screening out the target insurance product from the candidate insurance products based on the target risk premium and the risk level of the target user, and recommending the target insurance product to the target user. The present application first performs ID recognition on a user ID image to obtain user ID information, and obtains target insurance request information of a target user based on the user ID information, and can automatically obtain user ID information and insurance information associated with the ID without manual intervention, thereby facilitating improving the efficiency of subsequent insurance product recommendations; secondly, the risk level of the target insurance request information is identified to obtain the risk level of the target user; candidate insurance products are obtained based on the risk level of the target user, and risk premiums are calculated for the candidate insurance products based on the risk level of the target user to obtain the target risk premium, which can recommend insurance products suitable for the risk status of users with different risk levels, improve user satisfaction, and help provide users with personalized insurance product recommendations and reasonable premium pricing; finally, the target insurance product is screened out from the candidate insurance products based on the target risk premium and the risk level of the target user, and the target insurance product is recommended to the target user, which can achieve accurate recommendation of insurance products and improve the efficiency of insurance product recommendation.

[0071] In step S101 of some embodiments, specifically, the user ID image refers to the ID image of the user who needs to be insured, and the user ID image can be a personal ID image of the user or an ID image of a company associated with the user.

[0072] For example, in a financial scenario, the user ID image may be a user ID card, a business license, or a company registration number.

[0073] Specifically, users can submit insurance requests through the insurance company's online platform, mobile application or physical store, and upload user ID images (such as ID card, business license, etc.) as part of the insurance application, and then obtain user ID images from the online platform, mobile application or physical store.

[0074] For example, user A uploads his ID card image and the associated business license through the insurance company's mobile application, then the ID card image of user A and the associated business license image are collected from the mobile application.

[0075] See also Figure 2In some embodiments, step S102 includes but is not limited to steps S201 to S203:

[0076] Step S201: Use a preset certificate recognition model to perform text area detection on the user certificate image to obtain the certificate text area.

[0077] Step S202, performing character segmentation on the text area to obtain segmented text characters.

[0078] Step S203, performing certificate recognition on the segmented text characters to obtain user certificate information.

[0079] In step S201 of some embodiments, specifically, the document recognition model may be an OCR (Optical Character Recognition) model.

[0080] Specifically, before performing OCR recognition, the user's ID card image needs to be pre-processed by denoising and binarization to improve the recognition accuracy.

[0081] Furthermore, the noise in the user ID image can be removed by Gaussian filtering method, and methods such as Gaussian filtering can be used, and the denoised image can also be binarized by adaptive threshold method to convert the user ID image into an image containing only black and white.

[0082] Specifically, the certificate text area refers to the image area containing the certificate text information.

[0083] Specifically, the contour of the ID text is extracted through canny edge detection, and the text area of ​​the user ID image is segmented according to the geometric features of the ID text (such as size, shape, direction, etc.), that is, text line detection is performed on the user ID image, and the text blocks in each text line are identified, and the text blocks are used as the ID text area.

[0084] For example, in the ID card image of user A, the text blocks containing key information such as user A's name and user A's ID number can be identified through layout analysis.

[0085] In step S202 of some embodiments, specifically, the text block may be segmented into individual characters by a projection analysis method.

[0086] For example, in the ID card image of user A, the name is "Zhang San", and the name is divided into two separate characters "Zhang" and "San".

[0087] In step S203 of some embodiments, specifically, feature representation (such as shape, angle, texture, etc.) is extracted for each segmented text character, and a classifier (such as a support vector machine SVM, a CNN neural network, etc.) is used to match the extracted feature representation with known characters to determine the user ID information.

[0088] For example, for the complete ID card information of user A, the ID card information including the user A's name "Zhang San" and the ID card number "123456789012345678" can be identified through ID card recognition. If the user ID card image of user A is a business license, the unified social credit code of the business license is identified as "91350100M000100Y43".

[0089] In this embodiment, by performing certificate recognition on the segmented text characters to obtain user certificate information, the user certificate information can be automatically extracted without manual intervention, thereby improving the efficiency of user certificate information extraction.

[0090] See also Figure 3 In some embodiments, after step S102, the insurance product recommendation method further includes but is not limited to steps S301 to S303:

[0091] Step S301, performing face recognition on a target user based on user ID information to obtain the target user's face.

[0092] Step S302, verify the authenticity of the user ID information based on the target user's face to obtain legitimate user ID information.

[0093] Step S303, verify the authenticity of the user ID information based on the target user's face to obtain legal user ID information.

[0094] In step S301 of some embodiments, specifically, face detection is first performed on the user ID information to quickly locate the position of the user's face in the user ID image, and the key points of the user's face (such as the positions of the eyes, nose, mouth, etc.) are further located. The key points of the user's face are extracted through LBP (local binary pattern), and the key points of the face are activated through an activation function (such as a Sign function) to obtain the target user's face.

[0095] In step S302 of some embodiments, specifically, the legitimate user certificate information refers to real user certificate information.

[0096] Specifically, check whether the certificate information meets the standard format requirements.

[0097] For example, the user's ID card information usually consists of 18 digits, the first 6 digits are the area code, the next 8 digits are the date of birth, and the last 4 digits are the sequence code and the check code. By verifying that the total length of the ID card number is 18 digits and the date of birth and the check code are reasonable, the user's ID card information is a legitimate user.

[0098] Furthermore, the user's ID information is compared with existing records in the insurance company's database to confirm the consistency of the information.

[0099] For example, if user A already has an insurance record with the insurance company before purchasing insurance, the user's ID information will be compared with the user's ID information stored in the database to ensure that the information matches.

[0100] Furthermore, the authenticity of the user's ID information is verified through official verification channels.

[0101] For example, when the user ID information is the business license of Company B, the unified social credit code of the business license of Company B stored in the official database is obtained through the API interface of the official database. When the business license of Company B exists in the official database, it means that the business license of Company B is legal.

[0102] In step S303 of some embodiments, specifically, since user certificate information has a time limit, it is necessary to detect whether the user certificate information is within the validity period.

[0103] For example, for Company B's business license system, it is necessary to check the validity period of the business license to ensure that the business license has not expired.

[0104] Specifically, when the authenticity and validity of the user certificate information are verified, the valid user certificate information is used as the user certificate information.

[0105] For example, when the certificate information verification of Company B's business license passes, the insurance system will display verification success and allow user A to continue the insurance process; when the certificate information verification of Company B's business license fails, the insurance system will display verification failure and require user A to resubmit the user certificate information until the user certificate information verification succeeds before the insurance process can continue.

[0106] In this embodiment, by performing validity verification on legitimate user certificate information to obtain valid user certificate information, the insurance company can ensure that the user certificate information submitted by the target user is authentic, valid and accurate, thereby reducing the risk of insurance fraud.

[0107] In step S103 of some embodiments, specifically, the target insurance request information refers to the user insurance status information associated with the user ID image, and the target insurance request information includes but is not limited to information such as the user's associated enterprises, enterprise registration place, enterprise address, whether the insurance is purchased in a different place, the user's historical insurance records, and the user's claims records.

[0108] Specifically, the target insurance request information can be obtained from the database of the insurance company.

[0109] For example, if the company associated with user A has previously purchased similar corporate property insurance and has retrieved relevant records, the user's ID number, corporate registration number, social credit code, corporate registration location, corporate address, and whether the insurance was purchased in a different location will be obtained.

[0110] For example, the insurance company uses user A's ID number and the company's unified social credit code as keywords to search the insurance company's customer database. The query results return the insurance records of "User A Technology Co., Ltd." in the past few years, and selects user A's latest insurance request based on the timestamp. The matched insurance request information includes the type of insurance, amount of insurance, premium, etc. previously insured by user A.

[0111] In this embodiment, the target insurance request information of the target user is obtained based on the user ID information. The insurance company can quickly obtain the insurance request information associated with the target user and provide the user with personalized insurance services, thereby facilitating the subsequent improvement of the efficiency of insurance product recommendations.

[0112] See also Figure 4 In some embodiments, step S104 includes but is not limited to steps S401 to S403:

[0113] Step S401, identifying the disaster risk level of the target insurance request information to obtain the disaster risk level.

[0114] Step S402, identifying the industry risk level of the target insurance request information to obtain the industry risk level.

[0115] Step S403, determining the target user risk level according to the disaster risk level and the industry risk level.

[0116] See also Figure 5 In some embodiments, step S401 includes but is not limited to steps S501 to S505:

[0117] Step S501, obtaining historical disaster data of the geographical location where the target insurance request information is located.

[0118] Step S502, obtaining the geographical location coordinates of the historical disaster data, and constructing a disaster layer of the historical disaster data according to the geographical location coordinates.

[0119] Step S503, obtaining historical disaster risk levels of historical disaster data, and constructing a digital disaster risk map according to the historical disaster risk levels and disaster layers.

[0120] Step S504, obtaining target disaster data of the target insurance request information geographical location within a preset time period.

[0121] Step S505: Identify the disaster risk level of the target disaster data according to the digital disaster risk map to obtain the disaster risk level.

[0122] In step S501 of some embodiments, specifically, the historical disaster data includes earthquakes, floods, typhoons, droughts, topography, soil types, vegetation coverage, the number of disasters that occurred in history, intensity data, etc., and environmental factors related to the geographical location (such as earthquake zones, flood areas, typhoon paths, etc.) that occurred in the geographical location where the target insurance request information is located within a historical time period.

[0123] Specifically, historical disaster data can be obtained from government agencies, research institutes or historical archives.

[0124] In step S502 of some embodiments, specifically, the geographical location coordinates refer to the geographical space location coordinates when the historical disaster occurred.

[0125] Specifically, the disaster layer refers to the risk map graphics generated by GIS technology, and the disaster layer is used to describe the spatial map graphics of historical disasters.

[0126] Specifically, by establishing a unified spatial reference coordinate system, ensuring that all historical disaster data are located in the spatial reference coordinate system, different layers (such as earthquake layer, flood layer, typhoon layer, etc.) are created according to different disaster types.

[0127] In this embodiment, a disaster layer of historical disaster data is constructed according to geographic location coordinates, which can combine historical disaster data with geographic location to achieve spatial visualization of disaster distribution, help intuitively understand the spatial distribution characteristics of disasters, and facilitate subsequent improvement of the efficiency of disaster risk level identification.

[0128] In step S503 of some embodiments, specifically, the historical disaster risk level of the geographical location where the target insurance information is located can be determined based on the number of historical disasters that occurred and the losses experienced in the past.

[0129] Specifically, the historical disaster risk level can be obtained through the following formula:

[0130] N=a*log(1+n)+b*log(1+e)

[0131] Among them, N represents the historical disaster risk level, n represents the number of historical disasters, e represents the historical economic losses, and a and b represent weight coefficients.

[0132] For example, in the past year, there were 5 typhoons, and the historical economic loss was 1 million yuan. a is 0.5, b is 0.3, and the historical disaster risk level is 4.8. Since 4.8 is within the historical disaster risk level threshold of 3.5-5, the historical disaster risk level is high.

[0133] Specifically, through the spatial analysis function of GIS, different disaster risk levels should be mapped to corresponding disaster layers, and the disaster risk levels should be represented on the corresponding disaster layers in the form of different numbers or different colors to form a visual disaster digital risk map.

[0134] In step S504 of some embodiments, specifically, the target disaster data refers to disaster situations that may have occurred in the last six months or the last year.

[0135] Specifically, satellite remote sensing data can be used to monitor tropical cyclone activities in the sea areas near the geographical location of the target insurance request information, predict possible typhoon paths and impact ranges, and update the predicted typhoon paths and impact ranges in real time to the digital disaster risk map to adjust the typhoon risk level.

[0136] In step S505 of some embodiments, specifically, the typhoon path, wind speed, rainfall and other data of the geographical location where the target insurance request information is located are monitored through satellite remote sensing data, and typhoon simulation results are obtained through a meteorological monitoring network and satellite remote sensing data. The wind speed and rainfall in the simulation results are used to determine the typhoon hazard index, and the typhoon vulnerability of buildings, infrastructure and population is further analyzed based on the geographical location, and the typhoon risk level is determined based on the hazard index and typhoon vulnerability.

[0137] In this embodiment, the disaster risk level of the target disaster data is identified based on the digital disaster risk map, and historical and real-time disaster data can be combined to conduct a comprehensive assessment of the disaster risk level in a specific area, thereby improving the accuracy and timeliness of the disaster risk assessment, facilitating the subsequent formulation of reasonable insurance premiums, and thus improving the accuracy of insurance product recommendations.

[0138] In step S402 of some embodiments, specifically, the industry risk level is determined based on industry risk factors specific to the industry (such as explosion risk in the chemical industry, data leakage risk in the IT industry, etc.).

[0139] Specifically, historical disaster data of the industry can be obtained, including the number of industry accidents, number of deaths, economic losses, etc., and the industry digital risk map can be used to identify the risk level of industry disaster data within a preset time period.

[0140] For example, if the insured address of the target insurance request information has experienced several major chemical accidents in history, and the industry risk level of the insured address is classified as Class A (extremely dangerous), it can be predicted that the insured address will face a higher risk of chemical accidents in the future.

[0141] In step S403 of some embodiments, specifically, the target user risk level refers to the natural risk of the insured geographical location where the insured user is located and the risk level of the industry itself.

[0142] Specifically, the risk index corresponding to the disaster risk level and the risk index corresponding to the industry risk level are weighted and summed to obtain the target user risk index, and the target user risk index is compared with a preset risk threshold to determine the final target user risk level.

[0143] For example, the disaster risk index is 4.8, the corresponding weight is 0.6, the industry risk index is 0.2, and the corresponding weight is 0.4. The target user risk index is 4.8x0.6+0.2x0.4=2.96. Since 2.96 belongs to the medium risk threshold between 2-3.5, the target user risk level is medium risk level.

[0144] In this embodiment, the risk level of the target user is determined based on the disaster risk level and the industry risk level. The disaster risk level and the industry risk level can be combined to conduct a more comprehensive risk assessment of the target user to obtain a comprehensive risk level, which is convenient for subsequent insurance companies to conduct more reasonable insurance pricing based on the comprehensive risk level, so as to quickly locate the corresponding target insurance price products for the user, thereby improving the efficiency of insurance product recommendations.

[0145] In an optional embodiment of the present application, if the target user's risk level is low risk, the approval chain is triggered, and the target user's access approval is carried out through institutional underwriting; if the target user's risk level is medium risk, the approval chain is triggered, and the target user's underwriting approval is carried out through institutional underwriting and the headquarters; if the target user's risk level is high risk: the risk pricing logic is triggered, and the plan premium is adjusted according to the risk factor to determine the final target risk premium. Among them, the high risk level in the price adjustment also involves three levels: mild high risk, moderate high risk and severe high risk, so as to achieve accurate regulation of risk premiums.

[0146] See also Figure 6 In some embodiments, step S105 includes but is not limited to steps S601 to S603:

[0147] Step S601, obtaining historical claims data and historical product premiums of candidate insurance products.

[0148] Step S602, determining the historical claims data of the candidate insurance products based on the historical claims data and historical product premiums.

[0149] Step S603, obtaining the basic product premium of the candidate insurance product, calculating the risk premium of the candidate insurance product according to the basic product premium, historical claims data and the target user risk level, and obtaining the target risk premium.

[0150] In step S601 of some embodiments, specifically, the historical claim data refers to claim data of candidate insurance products with a risk level similar to that of the user enterprise, including the number of historical claims, historical claim amounts, etc.

[0151] Specifically, historical product premiums refer to the historical premium income of candidate insurance products with a risk level similar to that of the user enterprise.

[0152] In step S602 of some embodiments, specifically, the historical claims data may be represented by historical claims ratios of candidate insurance products with risk levels similar to that of the user's enterprise.

[0153] Specifically, the total amount of historical claims is obtained from the historical claims data, the total historical premium income is obtained from the historical product premiums, and the ratio of the total amount of historical claims to the total historical premium income is used as the historical claims data of the candidate insurance products.

[0154] In step S603 of some embodiments, specifically, the basic product premium refers to the basic premium income of the candidate insurance product.

[0155] Specifically, the target risk premium refers to the final premium determined based on the target user's risk level premium.

[0156] Specifically, the expected claim ratio of the candidate insurance product can be determined based on historical claim data, and the risk level factor can be determined based on the risk level of the target user.

[0157] Furthermore, the expected claim ratio of the product can be calculated through the historical claim ratio x(1+α), where α is the claim adjustment factor, which reflects the expected changes in future claim trends.

[0158] Specifically, the target risk premium can be determined by the following formula:

[0159] P=P 0 x(1+R)xβ

[0160] Where P represents the target risk premium, P 0represents the basic product premium, R represents the expected claim ratio of the product, and β represents the risk level factor.

[0161] For example, suppose user A's technology company wants to purchase a corporate property insurance policy. The insurance company determines the basic product premium to be 10,000 yuan and the expected product claim ratio to be 0.8 by analyzing historical data and industry standards. Since the technology company is located in an earthquake-prone area and belongs to a high-risk industry, the risk level factor is 1.5, and the target risk premium is: 10,000x(1+0.8)x1.5=42,000 yuan.

[0162] In this embodiment, the risk premium of the candidate insurance products is calculated based on the basic product premium, historical claims data and the target user's risk level to obtain the target risk premium, so that the insurance company can ensure that the premium collected covers the expected claims expenditure, and reasonably adjust the premium according to the risk level of the user enterprise, thereby ensuring the fairness and rationality of the premium and improving the accuracy of subsequent insurance product recommendations.

[0163] See also Figure 7 In some embodiments, step S106 includes but is not limited to steps S701 to S702:

[0164] Step S701, scoring the candidate insurance products according to a preset scoring function, target risk premium and target user risk level to obtain the candidate product scores.

[0165] Step S702: Select the candidate insurance product with the highest score from the candidate product scores as the target insurance product.

[0166] In step S701 of some embodiments, specifically, the scoring function is used to evaluate the matching degree of each candidate insurance product with the target risk premium and risk level.

[0167] Specifically, the premium suitability score, risk suitability score and product coverage suitability score are obtained, and different scoring weights are matched to each score. The premium suitability score, risk suitability score and product coverage suitability score are summed based on the scoring weights to obtain the candidate product score.

[0168] For example, an insurance company has three candidate insurance products J, Q, and K. These three candidate insurance products have different premiums, coverage, and risk adaptability. Among them, product J has high premiums and high coverage, which is suitable for mildly high-risk enterprises; product Q has medium premiums and medium coverage, which is suitable for moderately high-risk enterprises; product C has low premiums and basic coverage, which is suitable for severely high-risk enterprises. The matching degree of each product is calculated by using the scoring function: Score(J,14,000,2)=0.8; Score(Q,14,000,2)=0.9; Score(K,14,000,2)=0.7. Among them, 14000 is the target risk premium, and 2 is the target user risk level.

[0169] In this embodiment, the candidate insurance products are scored according to a preset scoring function, target risk premium and target user risk level to obtain a candidate product score. The scoring function can quantify multiple factors, which helps to objectively evaluate the pros and cons of the candidate insurance products. The scoring process is transparent, so that the target can clearly understand the comparison of different insurance products and select the most appropriate insurance product, thereby improving the accuracy of insurance product recommendations.

[0170] In step S702 of some embodiments, specifically, it can be determined that product Q has the highest matching degree based on the candidate product scores, the candidate insurance product Q is used as the target insurance product, and the insurance product Q is recommended to the target user.

[0171] The embodiment of the present application obtains a user ID image of the target user in response to the insurance request of the target user; performs ID recognition on the user ID image to obtain user ID information; obtains target insurance request information of the target user based on the user ID information; performs risk level recognition on the target insurance request information to obtain the risk level of the target user; obtains candidate insurance products based on the risk level of the target user, and calculates risk premiums for the candidate insurance products based on the risk level of the target user to obtain a target risk premium; screens out a target insurance product from the candidate insurance products based on the target risk premium and the risk level of the target user, and recommends the target insurance product to the target user. The present application first performs ID recognition on a user ID image to obtain user ID information, and obtains target insurance request information of a target user based on the user ID information, and can automatically obtain user ID information and insurance information associated with the ID without manual intervention, thereby facilitating improving the efficiency of subsequent insurance product recommendations; secondly, the risk level of the target insurance request information is identified to obtain the risk level of the target user; candidate insurance products are obtained based on the risk level of the target user, and risk premiums are calculated for the candidate insurance products based on the risk level of the target user to obtain the target risk premium, which can recommend insurance products suitable for the risk status of users with different risk levels, improve user satisfaction, and help provide users with personalized insurance product recommendations and reasonable premium pricing; finally, the target insurance product is screened out from the candidate insurance products based on the target risk premium and the risk level of the target user, and the target insurance product is recommended to the target user, which can achieve accurate recommendation of insurance products and improve the efficiency of insurance product recommendation.

[0172] See also Figure 8 The embodiment of the present application further provides an insurance product recommendation device, which can implement the above-mentioned insurance product recommendation method, and the device includes:

[0173] The certificate image acquisition module is used to obtain the user certificate image of the target user in response to the insurance request of the target user;

[0174] The certificate recognition module is used to perform certificate recognition on the user's certificate image to obtain the user's certificate information;

[0175] An insurance information acquisition module is used to acquire target insurance request information of a target user based on user certificate information;

[0176] The risk level identification module is used to identify the risk level of the target insurance request information and obtain the risk level of the target user;

[0177] The premium calculation module is used to obtain candidate insurance products according to the risk level of the target user, and calculate the risk premium of the candidate insurance products according to the risk level of the target user to obtain the target risk premium;

[0178] The insurance product recommendation module is used to screen out target insurance products from candidate insurance products based on target risk premium and target user risk level, and recommend the target insurance products to target users.

[0179] The specific implementation of the insurance product recommendation device is basically the same as the specific implementation of the above-mentioned insurance product recommendation method, and will not be repeated here.

[0180] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned insurance product recommendation method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0181] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0182] 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;

[0183] 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 processing systems and other applications. When the technical solution provided in the embodiments 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 product recommendation method of the embodiment of this application;

[0184] Input / output interface 903, used to implement information input and output;

[0185] 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, WI FI, Bluetooth, etc.);

[0186] 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);

[0187] 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 .

[0188] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above-mentioned insurance product recommendation method is implemented.

[0189] 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.

[0190] The insurance product recommendation method, insurance product recommendation device, electronic device and storage medium provided in the embodiments of the present application obtain a user ID image of the target user in response to the insurance request of the target user; perform ID recognition on the user ID image to obtain user ID information; obtain target insurance request information of the target user based on the user ID information; perform risk level recognition on the target insurance request information to obtain the risk level of the target user; obtain candidate insurance products based on the risk level of the target user, and calculate the risk premium for the candidate insurance products based on the risk level of the target user to obtain the target risk premium; screen out the target insurance product from the candidate insurance products based on the target risk premium and the risk level of the target user, and recommend the target insurance product to the target user. The present application first performs ID recognition on a user ID image to obtain user ID information, and obtains target insurance request information of a target user based on the user ID information, and can automatically obtain user ID information and insurance information associated with the ID without manual intervention, thereby facilitating improving the efficiency of subsequent insurance product recommendations; secondly, the risk level of the target insurance request information is identified to obtain the risk level of the target user; candidate insurance products are obtained based on the risk level of the target user, and risk premiums are calculated for the candidate insurance products based on the risk level of the target user to obtain the target risk premium, which can recommend insurance products suitable for the risk status of users with different risk levels, improve user satisfaction, and help provide users with personalized insurance product recommendations and reasonable premium pricing; finally, the target insurance product is screened out from the candidate insurance products based on the target risk premium and the risk level of the target user, and the target insurance product is recommended to the target user, which can achieve accurate recommendation of insurance products and improve the efficiency of insurance product recommendation.

[0191] 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.

[0192] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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. 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.

[0198] 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.

[0199] 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.

[0200] 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 is essentially 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.

[0201] The preferred embodiments of the present application are described above with reference to the accompanying drawings, but the scope of the rights of the present application 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 application should be within the scope of the rights of the present application.

Claims

1. A method for recommending insurance products, characterized in that: The method comprises: In response to an insurance request from a target user, obtaining a user ID image of the target user; Performing document recognition on the user document image to obtain user document information; Acquire target insurance request information of the target user based on the user certificate information; Identify the risk level of the target insurance request information to obtain the target user risk level; Acquire candidate insurance products according to the target user risk level, and calculate risk premiums for the candidate insurance products according to the target user risk level to obtain target risk premiums; A target insurance product is screened out from the candidate insurance products according to the target risk premium and the risk level of the target user, and the target insurance product is recommended to the target user.

2. The method according to claim 1, characterized in that The step of identifying the risk level of the target insurance request information to obtain the target user risk level includes: Identifying the disaster risk level of the target insurance request information to obtain a disaster risk level; Identify the industry risk level of the target insurance request information to obtain the industry risk level; The target user risk level is determined according to the disaster risk level and the industry risk level.

3. The method according to claim 2, characterized in that The step of identifying the disaster risk level of the target insurance request information to obtain the disaster risk level includes: Obtaining historical disaster data of the geographical location of the target insurance request information; Acquire the geographical location coordinates of the historical disaster data, and construct a disaster layer of the historical disaster data according to the geographical location coordinates; Acquire the historical disaster risk level of the historical disaster data, and construct a digital disaster risk map according to the historical disaster risk level and the disaster layer; Acquire target disaster data of the geographical location within a preset time period; The disaster risk level of the target disaster data is identified according to the disaster digital risk map to obtain the disaster risk level.

4. The method according to claim 1, characterized in that: The step of calculating the risk premium of the candidate insurance product according to the risk level of the target user to obtain a target risk premium includes: Obtaining historical claims data and historical product premiums of the candidate insurance products; Determine the historical claim data of the candidate insurance product based on the historical claim data and the historical product premiums; The basic product premium of the candidate insurance product is obtained, and the risk premium of the candidate insurance product is calculated according to the basic product premium, the historical claims data and the target user risk level to obtain the target risk premium.

5. The method according to claim 1, characterized in that The step of performing certificate recognition on the user certificate image to obtain user certificate information includes: Using a preset certificate recognition model to perform text area detection on the user certificate image to obtain the certificate text area; Performing character segmentation on the text area to obtain segmented text characters; Perform certificate recognition on the segmented text characters to obtain the user certificate information.

6. The method according to any one of claims 1 to 5, characterized in that: The step of selecting a target insurance product from the candidate insurance products according to the target risk premium and the target user risk level includes: Scoring the candidate insurance products according to a preset scoring function, the target risk premium and the target user risk level to obtain a candidate product score; The candidate insurance product with the highest score is selected from the candidate product scores as the target insurance product.

7. The method according to any one of claims 1 to 5, characterized in that: After performing document recognition on the user document image to obtain user document information, the method further includes: Performing face recognition on the target user based on the user ID information to obtain the target user's face; Performing authenticity verification on the user ID information according to the target user's face to obtain legitimate user ID information; The legal user certificate information is verified for validity to obtain valid user certificate information, and the valid user certificate information is used as the user certificate information.

8. An insurance product recommendation device, characterized in that: The device comprises: The certificate image acquisition module is used to obtain the user certificate image of the target user in response to the insurance request of the target user; The certificate recognition module is used to perform certificate recognition on the user certificate image to obtain user certificate information; An insurance information acquisition module, used to acquire the target insurance request information of the target user based on the user certificate information; A risk level identification module is used to identify the risk level of the target insurance request information and obtain the risk level of the target user; A premium calculation module, used to obtain candidate insurance products according to the risk level of the target user, and calculate the risk premium of the candidate insurance products according to the risk level of the target user to obtain a target risk premium; The insurance product recommendation module is used to screen out a target insurance product from the candidate insurance products according to the target risk premium and the risk level of the target user, and recommend the target insurance product to the target user.

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 product recommendation method according to 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 product recommendation method according to any one of claims 1 to 7 is implemented.

Images