Financial insurance premium calculation method and device, computer equipment and storage medium

Abstract

The invention provides a property insurance premium calculation method and device, computer equipment and a storage medium. The method comprises the steps of determining attribute information of related articles of an insurance object and position information of the insurance object; determining target risk levels, corresponding to different risk types, of the insurance object from a multi-dimensional risk database according to the position information; determining a risk score corresponding to the related article according to the attribute information and the target risk levels corresponding to the different risk types; and according to the attribute information of the related article and the risk score, calculating and determining the insurance premium rate of the insurance object. Therefore, the target risk levels, corresponding to the different risk types, of the insurance object can be rapidly determined based on the multi-dimensional risk database, then the risk scores corresponding to the related articles are determined, and the insurance premium rate of the insurance object is finally obtained, so that differential pricing of the personalized insurance object is realized while the claim settlement efficiency is improved to a great extent, and the cost is reduced. And the customer experience is effectively improved.

Description

Technical Field

[0001] This disclosure relates to the field of insurance risk pricing technology, specifically to a method, apparatus, computer equipment, and storage medium for calculating property insurance premiums. Background Technology

[0002] Currently, risk assessment for home and fixture insurance and appliance insurance primarily relies on generic risk models, typically based on historical data and simple regional classifications. Claims processing mainly employs a single-document approach, meaning that after an incident, the customer must provide relevant proof of loss for claims settlement.

[0003] This approach results in lower claims processing efficiency and negatively impacts customer experience. Summary of the Invention

[0004] This disclosure aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, the purpose of this disclosure is to propose a property insurance premium calculation method, device, computer equipment, and storage medium, which can quickly determine the target risk level of the insured object and the corresponding risk type based on a multi-dimensional risk database, thereby determining the risk score corresponding to the relevant items, and finally obtaining the insurance premium rate of the insured object. This can significantly improve claims efficiency while achieving differentiated pricing for personalized insured objects, thereby effectively improving customer experience.

[0006] To achieve the above objectives, the property insurance premium calculation method proposed in the first aspect of this disclosure includes:

[0007] Determine the attribute information of the relevant items of the insured object, as well as the location information of the insured object;

[0008] Based on the location information, the target risk level of the insured object corresponding to different risk types is determined from a multi-dimensional risk database;

[0009] Based on the attribute information and the target risk level corresponding to different risk types, determine the risk score corresponding to the relevant item;

[0010] Based on the attribute information of the relevant items and the risk score, the insurance premium rate for the insured object is calculated and determined.

[0011] To achieve the above objectives, the property insurance premium calculation device proposed in the second aspect of this disclosure includes:

[0012] The first determining module is used to determine the attribute information of the relevant items of the insured object, as well as the location information of the insured object;

[0013] The second determining module is used to determine the target risk level of the insured object corresponding to different risk types from a multi-dimensional risk database based on the location information;

[0014] The third determining module is used to determine the risk score corresponding to the relevant item based on the attribute information and the target risk level corresponding to different risk types;

[0015] The fourth determining module is used to calculate and determine the insurance premium rate of the insured object based on the attribute information of the relevant items and the risk score.

[0016] The computer device proposed in the third aspect of this disclosure includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the property insurance premium calculation method proposed in the first aspect of this disclosure.

[0017] The fourth aspect of this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the property insurance premium calculation method as proposed in the first aspect of this disclosure.

[0018] The fifth aspect of this disclosure provides a computer program product that, when executed by a processor, performs a property insurance premium calculation method as described in the first aspect of this disclosure.

[0019] The property insurance premium calculation method, apparatus, computer equipment, and storage medium disclosed herein determine the attribute information of the relevant items of the insured object, as well as the location information of the insured object; based on the location information, determine the target risk level corresponding to different risk types of the insured object from a multi-dimensional risk database; based on the attribute information and the target risk level corresponding to different risk types, determine the risk score corresponding to the relevant items; and based on the attribute information and risk score of the relevant items, calculate and determine the insurance premium rate of the insured object. Therefore, it is possible to quickly determine the target risk level corresponding to different risk types of the insured object based on a multi-dimensional risk database, thereby determining the risk score corresponding to the relevant items, and finally obtaining the insurance premium rate of the insured object. This significantly improves claims efficiency while enabling differentiated pricing for personalized insured objects, thereby effectively enhancing customer experience.

[0020] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0022] Figure 1 This is a flowchart illustrating a property insurance premium calculation method proposed in an embodiment of this disclosure;

[0023] Figure 2 This is a flowchart illustrating a property insurance premium calculation method according to another embodiment of this disclosure;

[0024] Figure 3 This is a schematic diagram of the structure of a property insurance premium calculation device according to an embodiment of the present disclosure;

[0025] Figure 4 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. Detailed Implementation

[0026] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are used only to explain this disclosure, and should not be construed as limiting this disclosure. Rather, embodiments of this disclosure include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0027] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0028] Figure 1 This is a flowchart illustrating a property insurance premium calculation method proposed in one embodiment of this disclosure.

[0029] It should be noted that the execution subject of the property insurance premium calculation method in this embodiment is a property insurance premium calculation device. This device can be implemented by software and / or hardware. The device can be configured in a computer device, which may include, but is not limited to, a terminal, a server, etc. For example, the terminal may be a mobile phone, a handheld computer, etc.

[0030] like Figure 1 As shown, the property insurance premium calculation method includes:

[0031] S101: Determine the attribute information of the relevant items of the insured object, as well as the location information of the insured object.

[0032] The subject matter of insurance refers to property and related interests, or human life and body, which are the objects of insurance. For example, the subject matter of car insurance is the vehicle, the subject matter of life insurance is human life, and the subject matter of property insurance refers to houses and their attached decorations, appliances, etc.

[0033] Among them, related items refer to items that are covered by the insurance, such as decorations and home appliances.

[0034] Among them, attribute information can be used to describe the attributes of related items, such as brand, production date, price, materials, place of origin, etc., without any restrictions.

[0035] Location information can be used to indicate the location of the property insured, such as the street or building where the house is located.

[0036] In other words, in this embodiment of the disclosure, the attribute information of the relevant items of the insured object and the location information of the insured object can be determined before calculating the property insurance premium, thereby providing reliable data support for premium calculation.

[0037] S102: Determine the target risk level of the insured object and the corresponding risk type from a multi-dimensional risk database based on location information.

[0038] Among them, the multi-dimensional risk database refers to the amount of data pre-constructed based on different dimensions (such as street blocks, buildings, etc.) to indicate various types of risk conditions of insured objects.

[0039] The types of risks can include, for example, floods, fires, typhoons, and sandstorms, without any restrictions.

[0040] Among them, the target risk level can be used to indicate the risk level of the insured object in a certain risk type, such as high risk, medium risk and low risk.

[0041] Optionally, in some embodiments, when the target risk level exceeds a preset threshold, a target sensor can be configured at a designated location on the insured property based on the risk type. This target sensor is used to sense environmental information corresponding to the risk type. Therefore, corresponding sensors can be configured for high-risk types to promptly detect environmental changes and provide timely warnings, effectively preventing property loss.

[0042] The preset threshold refers to the threshold set for the target risk level. The specific value of the preset threshold can be flexibly adjusted according to the application scenario, and there are no restrictions on it.

[0043] Among them, the target sensor can refer to an environmental parameter sensor configured for the type of risk, such as a water level sensor for floods and a smoke sensor for fires.

[0044] In other words, in this embodiment of the disclosure, after determining the location information of the insured object, the target risk level corresponding to different risk types of the insured object can be directly determined based on the pre-configured multi-dimensional risk database, thereby providing reliable reference information for subsequently determining the risk score corresponding to the relevant items.

[0045] S103: Determine the risk score corresponding to the relevant item based on the attribute information and the target risk level corresponding to different risk types.

[0046] Risk scoring can be used to indicate the overall risk of related items under different risk types.

[0047] For example, in this embodiment of the disclosure, when determining the risk score corresponding to the relevant item based on attribute information and the target risk level corresponding to different risk types, the maximum value of the target risk level corresponding to different risk types may be determined and the maximum value may be used as the risk score.

[0048] In other words, in this embodiment of the disclosure, after determining the target risk level of the insured object and different risk types from the multi-dimensional risk database based on the location information, the risk score corresponding to the relevant items can be determined based on the attribute information and the target risk level corresponding to different risk types, thereby realizing a comprehensive consideration of the impact of different risk types on the relevant items, so as to effectively improve the descriptive accuracy of the obtained risk score.

[0049] S104: Calculate and determine the insurance premium rate for the insured object based on the attribute information and risk score of the relevant items.

[0050] In this embodiment of the disclosure, when calculating and determining the insurance premium rate of the insured object based on the attribute information and risk score of the relevant items, the premium rate information of the corresponding items can be determined based on the attribute information and risk score of the relevant items, and then the premium rate information of all relevant items can be comprehensively calculated to determine the insurance premium rate of the insured object.

[0051] In this embodiment, the attribute information of the relevant items and the location information of the insured object are determined. Based on the location information, the target risk level corresponding to different risk types for the insured object is determined from a multi-dimensional risk database. Based on the attribute information and the target risk level corresponding to different risk types, the risk score corresponding to the relevant items is determined. Based on the attribute information and risk score of the relevant items, the insurance premium rate for the insured object is calculated and determined. Therefore, based on a multi-dimensional risk database, the target risk level corresponding to different risk types for the insured object can be quickly determined, thereby determining the risk score corresponding to the relevant items and finally obtaining the insurance premium rate for the insured object. This significantly improves claims efficiency while enabling differentiated pricing for personalized insured objects, effectively enhancing the customer experience.

[0052] Figure 2 This is a flowchart illustrating a property insurance premium calculation method proposed in another embodiment of this disclosure.

[0053] like Figure 2 As shown, the property insurance premium calculation method includes:

[0054] S201: Determine the attribute information of the relevant items of the insured object, as well as the location information of the insured object.

[0055] For a detailed description of S201, please refer to the above embodiments, which will not be repeated here.

[0056] S202: Based on location information, determine the reference risk level of the insured object in the candidate risk assessment dimension and corresponding to different risk types from a multi-dimensional risk database.

[0057] Among them, the candidate risk assessment dimensions can be, for example, street-level, community-level, building-level, etc., without any restrictions.

[0058] Among them, the reference risk level can be the risk level corresponding to different risk types determined after risk assessment based on the candidate risk assessment dimensions.

[0059] Optionally, in some embodiments, the multi-dimensional risk database is constructed as follows: Property insurance-related data corresponding to candidate risk assessment dimensions are acquired, including historical claims data, building information, and public facility information; based on the property insurance-related data, sub-databases corresponding to candidate risk assessment dimensions are constructed; and based on the sub-databases corresponding to different candidate risk assessment dimensions, a multi-dimensional risk database is constructed. This effectively improves the comprehensiveness and reliability of the obtained multi-dimensional risk database.

[0060] In other words, in this embodiment of the disclosure, the reference risk level of the insured object in the candidate risk assessment dimension and the corresponding risk type can be determined from the multi-dimensional risk database based on the location information, thereby realizing the separate analysis of the risk situation under different dimensions, and thus providing multi-dimensional data support for the subsequent determination of the target risk level.

[0061] S203: Determine the target risk level based on the risk type and the reference risk level corresponding to different candidate risk assessment dimensions.

[0062] For example, in this embodiment of the disclosure, when determining the target risk level based on the reference risk level corresponding to the risk type and different candidate risk assessment dimensions, the maximum value of the reference risk level corresponding to the risk type and different candidate risk assessment dimensions can be determined, and then the maximum value can be used as the target risk level. Alternatively, corresponding weight coefficients can be configured for different candidate risk assessment dimensions, and then the reference risk levels corresponding to the risk type and different candidate risk assessment dimensions can be weighted and summed to determine the target risk level.

[0063] In other words, in this embodiment of the disclosure, after determining the location information of the insured object, the reference risk level corresponding to different risk types in candidate risk assessment dimensions can be determined from a multi-dimensional risk database based on the location information; and the target risk level is determined based on the reference risk levels corresponding to different candidate risk assessment dimensions and risk types. Therefore, risk information under different dimensions can be comprehensively considered in the process of determining the target risk level, thereby effectively improving the accuracy of the description of the obtained target risk level.

[0064] S204: Determine the risk score corresponding to the relevant item based on the attribute information and the target risk level corresponding to different risk types.

[0065] S205: Calculate and determine the insurance premium rate for the insured object based on the attribute information and risk score of the relevant items.

[0066] For a detailed description of S204 and S205, please refer to the above embodiments, which will not be repeated here.

[0067] In this embodiment, the reference risk level of the insured object in different candidate risk assessment dimensions and risk types is determined from a multi-dimensional risk database based on location information; the target risk level is then determined based on the reference risk level corresponding to the risk type and different candidate risk assessment dimensions. Therefore, risk information under different dimensions can be comprehensively considered in the process of determining the target risk level, thereby effectively improving the accuracy of the obtained target risk level description.

[0068] Optionally, in some embodiments, image information of the relevant items provided by the policyholder can also be obtained; based on the image information, the verification result of the insured object is determined; and if the verification result is satisfactory, the insured object is underwritten. This ensures the reliability of the underwriting process.

[0069] The verification results can be used to indicate whether the insured object meets the insurance requirements.

[0070] Optionally, in some embodiments, when the inspection result indicates the existence of damaged items, agreement information corresponding to the damaged items can be generated. This agreement information is used to indicate the liability allocation information for the damaged items. Therefore, liability allocation for damaged items can be achieved before underwriting, effectively avoiding disputes.

[0071] In summary, this disclosure, by establishing a multi-dimensional risk database and combining risk warning functions with differentiated pricing strategies, achieves accurate risk assessment, proactive prevention, and efficient claims processing for home and fixture insurance and electrical appliance insurance. This mainly includes the following:

[0072] 1. Multi-dimensional risk database module

[0073] This database is used to establish and maintain a risk database for housing and its associated furnishings and appliances, organized by region, neighborhood, and building. The database includes, but is not limited to: historical claims data (such as the frequency, type, and amount of loss from incidents like pipe ruptures, appliance damage, and fires), building information (year and structure), and the status of public utilities (power supply quality, water supply network status), etc. Data sources include historical claims cases, public utility data, and third-party data collaborations.

[0074] 2. Risk Assessment and Intelligent Pricing Module

[0075] When a customer applies for home insurance through the front end, the system automatically matches the information of the house address entered by the customer in a multi-dimensional risk database to obtain the risk level of the community and building where the house is located.

[0076] The system calculates the risk score of the property based on a preset risk assessment model and information about the property itself (such as its renovation status and appliance brands).

[0077] Based on the risk score, the system automatically calculates and generates differentiated insurance rates, achieving precise pricing with "one price per household".

[0078] 3. Risk Warning and Intervention Module

[0079] Risk identification: The system continuously monitors the risk database to identify high-risk communities or buildings (such as areas with frequent pipe ruptures or unstable power supply).

[0080] Early warning equipment deployment: For identified high-risk targets, the system can automatically trigger risk intervention procedures. For example, it may offer customers free or recommend the installation of Internet of Things (IoT) devices such as water level sensors and smoke detectors. These devices are connected to the company's early warning platform.

[0081] Proactive early warning: When the IoT device detects an anomaly (such as an abnormal rise in water level or excessive smoke concentration), it will immediately send an early warning notification to the insurance company's back-end and the insured's mobile APP / mini-program, so that both parties can take timely measures to prevent or reduce losses.

[0082] 4. Intelligent label verification and underwriting module

[0083] The system guides customers through the verification process via mobile apps, Alipay mini-programs, and WeChat mini-programs. It provides a standardized verification checklist, requiring customers to upload photos or videos of key areas of the home (such as the kitchen, bathroom, and old appliances). The system has built-in image recognition and content verification functions to automatically review the uploaded materials. If obvious damage or incomplete information is found, the system will mark it as "verification damaged" and prompt the customer to supplement the information or sign a relevant disclaimer. Only verification results that pass the system's verification can proceed to the underwriting stage.

[0084] 5. Front-end application and interaction module

[0085] The system provides services through multiple channels, including an app, Alipay mini-program, and WeChat mini-program. The front-end interface offers the following functions: First, user-friendly operation: It provides a user-friendly interface to guide customers through operations such as insurance application, product verification, policy viewing, receiving alerts, and claims filing. Second, operation prompts and verification: During user operations, the system provides clear step-by-step prompts. After the user submits information, the system automatically verifies it (e.g., image quality, information completeness, description reasonableness). If the quality is poor or the information is incomplete, the system will prompt the user to repeat the operation or supplement the information.

[0086] 6. Risk Assessment and Pricing Module

[0087] (1) Risk scoring: Based on the above information, a machine learning model (such as random forest, gradient boosting tree, etc.) is used to score the risk of each house. The higher the score, the greater the risk of the house.

[0088] (2) Risk classification: Houses are classified into different risk levels (such as low risk, medium risk, and high risk) according to risk scores to facilitate subsequent pricing.

[0089] (3) Risk Pricing: A base premium is determined based on the value and type of the property. The base premium is adjusted based on the risk level determined by the risk assessment module. For example, higher-risk properties require higher premiums. The premium is also fine-tuned by considering factors such as the policyholder's credit score and prior claims history. Finally, the final premium is calculated by considering all these factors.

[0090] (4) Model Training and Optimization: Continuously collect historical data, including housing information, geographical location, policyholder information, and claims records. Clean, process, and extract features from the data to facilitate model training. Select a suitable machine learning model for training, such as random forest or gradient boosting tree. Use methods such as cross-validation to evaluate the model and ensure its accuracy and stability. Optimize the model based on the evaluation results to improve its predictive ability.

[0091] Based on the above embodiments, this disclosure can achieve at least the following technical effects:

[0092] (I) A system and method are proposed to construct a risk database for houses and their appurtenances with multi-dimensional precision based on region, community, and building, and to be used for differentiated pricing of home insurance. This achieves refined and precise risk assessment, enabling insurance pricing to more accurately reflect the actual risk level of the insured object, solving the problem of the crudeness of traditional pricing models, and providing insurance companies with a more scientific basis for pricing.

[0093] (II) Risk sensitivity and early warning functions, including pricing adjustments for high-risk targets, provision of monitoring equipment and anomaly notification mechanisms, and systems and methods for proactive risk early warning and intervention through the deployment of Internet of Things (IoT) devices. This extends insurance services from "post-event compensation" to "pre-event prevention," effectively reducing the probability and severity of high-risk accidents through proactive risk intervention, significantly improving the insurance company's payout ratio, and achieving proactive risk management.

[0094] (III) Automatic underwriting verification and premium calculation functions based on multi-dimensional risk assessment. This improves the efficiency and accuracy of underwriting verification, reduces errors caused by manual operation, lowers operating costs, and enables rapid response to the underwriting needs of individual customers.

[0095] (iv) An intelligent verification and underwriting process integrating image recognition and content verification functions, including automatic marking of damaged insured items and association with exclusion agreements. This standardizes the verification process, improves verification efficiency and information quality, clarifies liability for damage to the insured item before underwriting, and effectively reduces the risk of subsequent claims disputes through pre-emptive risk screening and exclusion agreements, thereby enhancing the compliance and robustness of underwriting business and protecting the legitimate rights and interests of both the insurer and the insured.

[0096] (v) Supports a program architecture that runs on multiple platforms such as apps and mini-programs. This greatly improves the convenience of customer operation, expands service coverage, better meets the usage habits of individual customers, and is conducive to attracting and retaining individual customers. Through systematic guidance and verification, the quality of input data is ensured, providing a reliable data foundation for subsequent intelligent risk assessment and pricing.

[0097] (vi) An interactive system with operation prompts and content verification functions. This improves the standardization and accuracy of customer operations, reduces business processing delays and errors caused by improper operation, and enhances the overall quality and efficiency of business processing.

[0098] Figure 3 This is a schematic diagram of the structure of a property insurance premium calculation device according to an embodiment of the present disclosure.

[0099] like Figure 3 As shown, the property insurance premium calculation device 30 includes:

[0100] The first determining module 301 is used to determine the attribute information of the relevant items of the insured object, as well as the location information of the insured object;

[0101] The second determining module 302 is used to determine the target risk level of the insured object and different risk types from a multi-dimensional risk database based on the location information;

[0102] The third determination module 303 is used to determine the risk score corresponding to the relevant items based on the attribute information and the target risk level corresponding to different risk types.

[0103] The fourth determination module 304 is used to calculate and determine the insurance premium rate of the insured object based on the attribute information and risk score of the relevant items.

[0104] It should be noted that the foregoing explanation of the property insurance premium calculation method also applies to the property insurance premium calculation device of this embodiment, and will not be repeated here.

[0105] In this embodiment, the attribute information of the relevant items and the location information of the insured object are determined. Based on the location information, the target risk level corresponding to different risk types for the insured object is determined from a multi-dimensional risk database. Based on the attribute information and the target risk level corresponding to different risk types, the risk score corresponding to the relevant items is determined. Based on the attribute information and risk score of the relevant items, the insurance premium rate for the insured object is calculated and determined. Therefore, based on a multi-dimensional risk database, the target risk level corresponding to different risk types for the insured object can be quickly determined, thereby determining the risk score corresponding to the relevant items and finally obtaining the insurance premium rate for the insured object. This significantly improves claims efficiency while enabling differentiated pricing for personalized insured objects, effectively enhancing the customer experience.

[0106] Figure 4 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. Figure 4 The computer device 12 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0107] like Figure 4 As shown, the computer device 12 is represented in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and a bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0108] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0109] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0110] Memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 4 Not shown; usually referred to as a "hard drive".

[0111] although Figure 4 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a Compact Disc Read-Only Memory (CD-ROM), a Digital Video Disc Read-Only Memory (DVD-ROM), or other optical media). In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0112] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0113] Computer device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable human interaction with the computer device 12, and / or with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, computer device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0114] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the property insurance premium calculation method mentioned in the foregoing embodiments.

[0115] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements the property insurance premium calculation method proposed in the foregoing embodiments of this disclosure.

[0116] To implement the above embodiments, this disclosure also proposes a computer program product that, when the instruction processor in the computer program product is executed, performs the property insurance premium calculation method as proposed in the foregoing embodiments of this disclosure.

[0117] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this disclosure all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0118] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0119] This disclosure is intended to provide implementation schemes for users to selectively prevent the use or access to their personal information data. Specifically, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.

[0120] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0121] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0122] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0123] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0124] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0125] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0126] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0127] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method for calculating property insurance premiums, characterized in that, include: Determine the attribute information of the relevant items of the insured object, as well as the location information of the insured object; Based on the location information, the target risk level of the insured object corresponding to different risk types is determined from a multi-dimensional risk database; Based on the attribute information and the target risk level corresponding to different risk types, determine the risk score corresponding to the relevant item; Based on the attribute information of the relevant items and the risk score, the insurance premium rate for the insured object is calculated and determined.

2. The method as described in claim 1, characterized in that, The step of determining the target risk level of the insured object corresponding to different risk types from a multi-dimensional risk database based on the location information includes: Based on the location information, the reference risk level of the insured object in the candidate risk assessment dimension and corresponding to different risk types is determined from the multi-dimensional risk database; The target risk level is determined based on the risk type and the reference risk level corresponding to different candidate risk assessment dimensions.

3. The method as described in claim 2, characterized in that, The multi-dimensional risk database is constructed based on the following method: Obtain property insurance-related data corresponding to the candidate risk assessment dimensions, wherein the property insurance-related data includes: historical claims data, building information, and public facility information; Based on the property insurance-related data, a sub-database corresponding to the candidate risk assessment dimensions is constructed. The multi-dimensional risk database is constructed based on the sub-databases corresponding to different candidate risk assessment dimensions.

4. The method as described in claim 1, characterized in that, The method further includes: When the target risk level is greater than a preset threshold, a target sensor is configured at a designated location of the insured object based on the risk type, wherein the target sensor is used to sense environmental information corresponding to the risk type.

5. The method as described in claim 1, characterized in that, The method further includes: Obtain image information of the relevant items provided by the policyholder; Based on the image information, the verification result of the insured object is determined; When the inspection result is satisfactory, the insured object is insured.

6. The method as described in claim 5, characterized in that, The method further includes: When the inspection result indicates the existence of damaged items, agreement information corresponding to the damaged items is generated, wherein the agreement information is used to indicate the responsibility allocation information for the damaged items.

7. A property insurance premium calculation device, characterized in that, include: The first determining module is used to determine the attribute information of the relevant items of the insured object, as well as the location information of the insured object; The second determining module is used to determine the target risk level of the insured object corresponding to different risk types from a multi-dimensional risk database based on the location information; The third determining module is used to determine the risk score corresponding to the relevant item based on the attribute information and the target risk level corresponding to different risk types; The fourth determining module is used to calculate and determine the insurance premium rate of the insured object based on the attribute information of the relevant items and the risk score.

8. A computer device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, in, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

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