Insurance risk assessment method and device and readable storage medium

Abstract

The invention provides an insurance risk assessment method and device and a readable storage medium, and relates to the technical field of computers, and the method comprises the steps: after a preset reference time point, regularly collecting a monitoring image of a designated region; comparing the monitoring image with a reference image, and detecting and judging whether factors related to the insurance risk exist or not through an image intelligent identification technology; in response to the existence of the factors related to the insurance risk, sending an early warning to a corresponding user, and storing early warning data; and during insurance claim settlement, calculating a final claim settlement proportion according to the factors related to the insurance risk, and taking the final claim settlement proportion and / or the early warning data and / or the monitoring image as a basis for a claim settlement decision. According to the method, the device and the readable storage medium, the problems of insufficient real-time monitoring, low manual identification efficiency and lack of data support of an existing insurance risk assessment method can be solved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an insurance risk assessment method, device and readable storage medium. Background Art

[0002] Insurance risk assessment is a vital part of the insurance industry, which directly affects the insurance company's underwriting decisions, premium pricing and claims processing. With the development of technology, the methods of insurance risk assessment are also constantly evolving. However, the existing technology still has some significant shortcomings in practical applications:

[0003] (1) Lack of real-time monitoring: Currently, many insurance companies rely on post-incident investigations and are unable to monitor the status of insured property in real time. This means that when an accident occurs, the insurance company may not be able to respond quickly, which affects the efficiency and accuracy of claims settlement.

[0004] (2) Low efficiency of manual identification: Traditional risk assessment mainly relies on manual inspection and identification, which is inefficient and prone to errors.

[0005] (3) Lack of data support: Claims decisions often lack sufficient data support, leading to an increase in claims disputes. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide an insurance risk assessment method, device and readable storage medium in view of the above-mentioned deficiencies in the prior art, so as to solve the problems faced by the existing insurance risk assessment methods, such as insufficient real-time monitoring, low manual identification efficiency and lack of data support.

[0007] In a first aspect, the present invention provides an insurance risk assessment method, the method comprising:

[0008] After a preset reference time point, regularly collect monitoring images of the designated area;

[0009] Comparing the monitoring image with a reference image, and detecting and judging whether there are factors related to insurance risks by using image intelligent recognition technology, wherein the reference image is an image in which no factors related to insurance risks exist in the designated area;

[0010] In response to the existence of factors related to insurance risks, an early warning is issued to the corresponding user, and the early warning data is stored;

[0011] When making an insurance claim, the final claim ratio is calculated based on the factors related to the insurance risk, and the final claim ratio and / or early warning data and / or monitoring images are used as the basis for the claim decision.

[0012] Furthermore, the periodic acquisition of surveillance images of a designated area specifically includes:

[0013] Surveillance images of designated areas are collected regularly through surveillance cameras installed inside the building.

[0014] Furthermore, before comparing the monitoring image with the reference image and detecting and determining whether there are factors related to insurance risks by using image intelligent recognition technology, the method further includes:

[0015] A monitoring image at a certain moment is defined as the reference image, and a time point corresponding to the certain moment is set as the reference time point.

[0016] Furthermore, the factors related to insurance risk include risk factors of claim rejection and risk factors of claim reduction, and the detection and determination of whether there are factors related to insurance risk by using image intelligent recognition technology specifically includes:

[0017] Through the use of image intelligent recognition technology, it is determined whether there are risk factors for claim rejection and risk factors for claim reduction. If there are risk factors for claim rejection and / or risk factors for claim reduction, it is determined that there are factors related to insurance risk.

[0018] Furthermore, the final claim settlement ratio is calculated based on the factors related to the insurance risk, specifically including:

[0019] According to the detected claim rejection risk factors and / or claim reduction risk factors, using a claim rejection risk factor model and a claim reduction risk factor model to obtain a claim rejection risk value and a claim reduction risk value;

[0020] The final claim ratio is obtained by using a claim function model according to the claim rejection risk value and the claim reduction risk value.

[0021] Furthermore, in the claim rejection risk factor model, if any claim rejection risk factor is detected, the claim rejection risk value is 0, and if no claim rejection risk factor is detected, the claim rejection risk value is 1;

[0022] The formula of the compensation reduction risk factor model is as follows:

[0023]

[0024] Where P reduce_compensation is the risk reduction value, D i represents the compensation reduction ratio of the i-th compensation reduction risk factor, DF i Indicates whether the i-th compensation reduction risk factor is detected, 1 indicates detection, and 0 indicates non-detection.

[0025] Furthermore, the final claim settlement ratio is obtained by using a claim settlement function model according to the claim rejection risk value and the claim reduction risk value, and is specifically calculated according to the following formula:

[0026] P final =P0·P claims_rejected ·P reduce_compensation

[0027] Where P final is the final claim ratio, P0 is the initial claim ratio, P claims_rejected is the risk value of claim rejection.

[0028] In a second aspect, the present invention provides an insurance risk assessment device, the device comprising:

[0029] A monitoring image acquisition module is used to regularly acquire monitoring images of a specified area after a preset reference time point;

[0030] a risk factor detection module, connected to the monitoring image acquisition module, for comparing the monitoring image with a reference image, and detecting and judging whether there are factors related to insurance risk by using image intelligent recognition technology, wherein the reference image is an image in which there are no factors related to insurance risk in the specified area;

[0031] An early warning sending storage module, connected to the risk factor detection module, is used to send an early warning to the corresponding user in response to the existence of factors related to insurance risks, and store early warning data;

[0032] The claim decision basis module is connected to the early warning sending storage module, and is used to calculate the final claim ratio based on the factors related to the insurance risk when making an insurance claim, and use the final claim ratio and / or early warning data and / or monitoring images as the basis for the claim decision.

[0033] In a third aspect, the present invention provides an insurance risk assessment device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the insurance risk assessment method described in the first aspect above.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the insurance risk assessment method described in the first aspect is implemented.

[0035] The present invention provides an insurance risk assessment method, device and readable storage medium. First, after a preset reference time point, regularly collect monitoring images of a designated area; then compare the monitoring image with the reference image, and use image intelligent recognition technology to detect and determine whether there are factors related to insurance risks, wherein the reference image is an image in which there are no factors related to insurance risks in the designated area; then, in response to the presence of factors related to insurance risks, issue an early warning to the corresponding user, and store early warning data; and when making an insurance claim, calculate the final claim ratio according to the factors related to insurance risks, and use the final claim ratio and / or early warning data and / or monitoring image as the basis for claim decision. The present invention can achieve real-time monitoring and early warning, timely discover potential risks, and reduce manual intervention by regularly collecting images and performing intelligent recognition. At the same time, through continuous data collection and recording, sufficient data support is provided for claim decisions, which can improve the fairness and transparency of decisions. The existing insurance risk assessment methods solve the problems of insufficient real-time monitoring, low efficiency of manual recognition and lack of data support. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of an insurance risk assessment method according to Embodiment 1 of the present invention;

[0037] Figure 2 This is a schematic diagram of the structure of an insurance risk assessment device according to Embodiment 2 of the present invention;

[0038] Figure 3 This is a schematic diagram of the structure of an insurance risk assessment device according to Example 3 of the present invention. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the technical solution of the present invention, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0040] It should be understood that the specific embodiments and drawings described herein are only used to explain the present invention rather than to limit the present invention.

[0041] It can be understood that, in the absence of conflict, the various embodiments of the present invention and the various features in the embodiments can be combined with each other.

[0042] It can be understood that, for the convenience of description, the drawings of the present invention only show the parts related to the present invention, while the parts irrelevant to the present invention are not shown in the drawings.

[0043] It can be understood that each unit and module involved in the embodiments of the present invention may correspond to only one physical structure, or may be composed of multiple physical structures, or multiple units and modules may be integrated into one physical structure.

[0044] It can be understood that the terms "first", "second", etc. in the embodiments of the present invention are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.

[0045] It can be understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of the present invention may occur in an order different from that marked in the drawings.

[0046] It is understood that the flowcharts and block diagrams of the present invention illustrate the possible architectures, functions, and operations of the systems, devices, equipment, and methods according to the various embodiments of the present invention. Each box in the flowchart or block diagram may represent a unit, module, program segment, or code, which contains executable instructions for implementing the specified functions. Moreover, each box or combination of boxes in the block diagram and flowchart may be implemented by a hardware-based system that implements the specified functions, or may be implemented by a combination of hardware and computer instructions.

[0047] It can be understood that the units and modules involved in the embodiments of the present invention can be implemented by software or hardware. For example, the units and modules can be located in a processor.

[0048] Embodiment 1:

[0049] This embodiment provides an insurance risk assessment method, such as Figure 1 As shown, the method includes:

[0050] Step S101: After a preset reference time point, regularly collect monitoring images of a designated area.

[0051] Specifically, after a preset reference time point, surveillance images of a designated area are regularly collected through surveillance cameras installed inside the building.

[0052] In this embodiment, the building can be any building that has been insured with property insurance, such as an office building, a warehouse, a production workshop, etc. The purpose of regularly collecting surveillance images of a designated area through the surveillance cameras installed inside the building is achieved by installing surveillance cameras inside the building or obtaining access rights to existing cameras.

[0053] Step S102: Compare the monitoring image with a reference image, and use image intelligent recognition technology to detect and determine whether there are factors related to insurance risks, wherein the reference image is an image in which there are no factors related to insurance risks in the specified area.

[0054] In this embodiment, based on the reference image, image intelligent recognition technology is used to detect and record factors related to insurance risks in the monitoring image, such as piles of objects in fire passages, blocked vents, placement of flammable materials, signs of burning on the wall, signs of water immersion, etc.

[0055] Optionally, before comparing the monitoring image with the reference image and detecting and determining whether there are factors related to insurance risks by using image intelligent recognition technology, the method further includes:

[0056] A monitoring image at a certain moment is defined as the reference image, and a time point corresponding to the certain moment is set as the reference time point.

[0057] In this embodiment, a monitoring image at a certain time is defined as a reference image A (generally, the reference image is collected when the insurance is purchased and before the claim accident occurs), and is set as the reference time point T. After the reference time point T, subsequent images A1 to An are collected at regular intervals and compared with the reference image A.

[0058] Optionally, the factors related to insurance risk include claim rejection risk factors and claim reduction risk factors, and the detection and determination of whether there are factors related to insurance risk by using image intelligent recognition technology specifically includes:

[0059] Through the use of image intelligent recognition technology, it is determined whether there are risk factors for claim rejection and risk factors for claim reduction. If there are risk factors for claim rejection and / or risk factors for claim reduction, it is determined that there are factors related to insurance risk.

[0060] In this embodiment, factors related to insurance risks are further divided into claim rejection risk factors and claim reduction risk factors according to the degree and nature of their impact on insurance claims. Claim rejection risk factors include piled objects in fire passages; claim reduction risk factors include blocked vents and placement of flammable materials.

[0061] Step S103: In response to the existence of factors related to insurance risk, a warning is issued to the corresponding user, and the warning data is stored.

[0062] In this embodiment, based on the recognition results, a warning is issued to the corresponding user (such as the owner) to indicate the existing risk factors; the warning data is stored and synchronized with the insurance company.

[0063] It should be noted that for users, receiving early warnings can help them deal with some hidden dangers and reduce the occurrence of accidents. However, if it is a problem of their own due diligence, even if the hidden dangers are dealt with, the claims may still be reduced when subsequent accidents occur. The insurance company will record these incidents, evaluate the responsible party, and calculate the claim ratio.

[0064] Step S104: When making an insurance claim, the final claim ratio is calculated based on the factors related to the insurance risk, and the final claim ratio and / or early warning data and / or monitoring images are used as the basis for the claim decision.

[0065] In this embodiment, when making an insurance claim, the final claim ratio and / or warning data and / or monitoring images can be used as the basis for claim decision-making to guide the insurance company to determine whether to make a claim or determine the claim ratio.

[0066] It should be noted that the detection of risks and the occurrence of accidents may or may not occur at the same time. When a risk is detected but no accident has occurred: the risk may be due to non-human factors (normal claims can be settled) or human factors (for example: dereliction of duty, operational errors, which are generally judged to be problematic in due diligence. If the insured company subsequently applies for compensation based on other accident reasons, the insurance company will reduce the compensation). When a risk is detected and an accident has occurred, such as a small-scale flooding, regardless of whether the insured company handles the accident, the insurance company can record the incident for subsequent comprehensive assessment of liability and the proportion of the claim.

[0067] Optionally, the calculating of the final claim settlement ratio according to the factors related to the insurance risk specifically includes:

[0068] According to the detected claim rejection risk factors and / or claim reduction risk factors, using a claim rejection risk factor model and a claim reduction risk factor model to obtain a claim rejection risk value and a claim reduction risk value;

[0069] The final claim ratio is obtained by using a claim function model according to the claim rejection risk value and the claim reduction risk value.

[0070] Specifically, in the claim rejection risk factor model, if any claim rejection risk factor is detected, the claim rejection risk value is 0, and if no claim rejection risk factor is detected, the claim rejection risk value is 1;

[0071] The formula of the compensation reduction risk factor model is as follows:

[0072]

[0073] Where P reduce_compensation is the risk reduction value, D i represents the compensation reduction ratio of the i-th compensation reduction risk factor, DF i Indicates whether the i-th compensation reduction risk factor is detected, 1 indicates detection, and 0 indicates non-detection.

[0074] In this embodiment, all the risk factors for claim rejection and claim reduction can be set in advance, wherein once any risk factor for claim rejection is detected, the claim will be rejected, that is, the risk value of claim rejection is 0. i represents the i-th claim rejection risk factor, RF i Indicates whether the i-th claim rejection risk factor is detected (1 means detected, 0 means not detected). The claim rejection risk factor model can be expressed as:

[0075]

[0076] In this embodiment, assuming that the risk factors for compensation reduction are: vent blocking (D1=0.3), flammable material placement (D2=0.2), the compensation reduction risk value is calculated as follows:

[0077] P reduce_compensation =(1-0.3·1)·(1-0.2·1)=0.7·0.8=0.56

[0078] It should be noted that the claim rejection risk factor model and the claim reduction risk factor model are calculated separately. When the corresponding value of the claim rejection risk factor model is 0, the claim reduction risk factor model can also be calculated. This is because the claim reduction risk factor model can also evaluate the due diligence performance of an enterprise and provide a reference for future underwriting and pricing.

[0079] Optionally, the final claim settlement ratio is obtained by using a claim settlement function model according to the claim rejection risk value and the claim reduction risk value, and is specifically calculated according to the following formula:

[0080] P final =P0·P claims_rejected ·P reduce_compensation

[0081] Where P final is the final claim ratio, P0 is the initial claim ratio, P claims_rejected is the risk value of claim rejection.

[0082] In this embodiment, the initial compensation ratio is P0, that is, the compensation ratio without risk factors, which is usually 1 (depending on the design of the insurance product, the compensation ratio may be lower than 100%, such as 80%). The final compensation ratio can be obtained according to the above calculation formula.

[0083] The insurance risk assessment method provided by the present invention first sets a reference image A for insurance risk survey (i.e., a reference image before the insurance claim occurs in the space), and defines the time when image A is collected as the reference time point T, and then collects images A1 to An at regular intervals after the reference time point T, and performs intelligent image recognition and risk recording by comparing the reference image A with a certain image An collected subsequently. At the same time, an insurance claim function model is designed to integrate the risk factors of claim rejection and claim reduction, thereby providing a basis for claim decision-making.

[0084] In a specific embodiment, by installing surveillance cameras inside the building or obtaining access rights to existing cameras, surveillance images inside the building are regularly collected, and image intelligent recognition and analysis are performed to manage and reduce the insurance company's property insurance claims risk. By comparing the baseline image with the subsequent collected images, possible risk factors are identified and recorded, early warnings are provided, and claims decisions are guided. The insurance risk assessment method can specifically include the following steps:

[0085] 1. Surveillance image acquisition: Install surveillance cameras inside the building or obtain access rights to existing cameras to regularly collect surveillance images of designated areas.

[0086] The building may be any building that is insured with property insurance, such as an office building, a warehouse, a production workshop, etc.

[0087] 2. Definition of reference image: Define the monitoring image at a certain moment as reference image A (that is, the reference image before the insurance claim occurs in the space) and set it as the reference time point T.

[0088] 3. Image comparison: After the reference time point T, subsequent images A1 to An are collected regularly and compared with the reference image A.

[0089] 4. Intelligent recognition: Through image intelligent recognition technology, factors related to insurance risks are detected and recorded, such as piles of objects in fire passages, blocked vents, placement of flammable materials, signs of burning on the wall, signs of water immersion, etc.

[0090] Among them, factors related to insurance risks include claim rejection risk factors and claim reduction risk factors. Here, it is necessary to identify claim rejection risk factors and claim reduction risk factors through image intelligent recognition technology.

[0091] 5. Risk warning: Based on the identification results, a warning is issued to the corresponding users (such as owners) to remind them of the existing risk factors; the warning data is stored and synchronized with the insurance company.

[0092] 6. Claims decision support: Use identification records as the basis for claims decisions to guide insurance companies in determining whether to pay a claim or determine the claim ratio.

[0093] Among them, the claim function model is defined as follows:

[0094] In order to describe the claim function model in detail, we divide the model into two parts: the claim rejection risk factor model and the claim reduction risk factor model.

[0095] (1) Claim rejection risk factor model

[0096] Once the risk factor for claim rejection is detected, the claim will be rejected and the output of the claim function will be 0.

[0097] Assume R i represents the i-th claim rejection risk factor, RF i Indicates whether the i-th claim rejection risk factor is detected (1 means detected, 0 means not detected). The claim rejection risk factor model can be expressed as:

[0098]

[0099] (2) Risk factor model for compensation reduction

[0100] The risk factor of reduced compensation will reduce the compensation ratio according to its severity. i represents the compensation reduction ratio of the i-th compensation reduction risk factor (ranging from 0 to 1), DF i Indicates whether the i-th compensation reduction risk factor has been detected (1 means detected, 0 means not detected). Total compensation ratio P 减赔 It can be expressed as:

[0101]

[0102] (3) Overall representation of the claim function model:

[0103] Combining the claim rejection risk factor model with the claim reduction risk factor model, the claim function model can be expressed as:

[0104] Assume that the initial compensation ratio is P0, that is, the compensation ratio in the absence of risk factors, which is usually 1 (depending on the design of the insurance product, the compensation ratio may be lower than 100% as clearly stated at the time of insurance, such as 80%).

[0105] The final claim ratio P 最终 It is expressed as:

[0106] P 最终 =P0·P 拒赔 ·P 减赔

[0107] It should be noted that the claim rejection risk factor model and the claim reduction risk factor model are calculated separately. When the corresponding value of the claim rejection risk factor model is 0, the claim reduction risk factor model can also be calculated. This is because the claim reduction risk factor model can also evaluate the due diligence performance of an enterprise and provide a reference for future underwriting and pricing.

[0108] In another specific embodiment, the following risk factors are assumed:

[0109] Risk factor for claim rejection: piles of objects in fire escape passages (RF1)

[0110] Risk factors for reducing compensation: blocked ventilation openings (D1 = 0.3), placement of flammable materials (D2 = 0.2)

[0111] If a pile of objects is detected in the fire passage (RF1=1), then:

[0112] P 拒赔 =0

[0113] Therefore, the final compensation ratio P 最终 for:

[0114] P 最终 =P0·P 拒赔 ·P 减赔 =1·0·P 减赔 =0

[0115] That means rejecting the claim.

[0116] If no claim rejection risk factors are detected, but blocked ventilation openings and flammable materials are detected (DF1=1, DF2=1), then:

[0117] P 减赔 =(1-0.3·1)·(1-0.2·1)=0.7·0.8=0.56

[0118] Therefore, the final compensation ratio P 最终 for:

[0119] P 最终 =P0·P 减赔 =1·0.56

[0120] That is, the compensation ratio is 56%.

[0121] Through this claim function model, insurance companies can reasonably determine whether to pay a claim and its payment ratio based on the detection results of risk factors, thereby effectively managing and reducing claim risks.

[0122] It should be noted that the insurance risk assessment method provided by the present invention has the following beneficial effects:

[0123] a) Real-time monitoring and early warning: By regularly collecting images and performing intelligent recognition, real-time monitoring and early warning can be achieved to detect potential risks in a timely manner.

[0124] b) Automated identification: Use image intelligent recognition technology to improve the accuracy and efficiency of risk factor identification and reduce manual intervention.

[0125] c) Data-supported claims: Through continuous data collection and recording, sufficient data support is provided for claims decisions, improving the fairness and transparency of decisions.

[0126] The insurance risk assessment method provided by the embodiment of the present invention first collects monitoring images of a designated area regularly after a preset reference time point; then compares the monitoring image with the reference image, and uses image intelligent recognition technology to detect and determine whether there are factors related to insurance risk, wherein the reference image is an image in which there are no factors related to insurance risk in the designated area; then, in response to the presence of factors related to insurance risk, issues an early warning to the corresponding user, and stores early warning data; and when making an insurance claim, calculates the final claim ratio according to the factors related to insurance risk, and uses the final claim ratio and / or early warning data and / or monitoring image as the basis for claim decision. The present invention can achieve real-time monitoring and early warning, timely discover potential risks, and reduce manual intervention by regularly collecting images and performing intelligent recognition. At the same time, through continuous data collection and recording, sufficient data support is provided for claim decision, which can improve the fairness and transparency of decision. The existing insurance risk assessment method solves the problems of insufficient real-time monitoring, low efficiency of manual recognition, and lack of data support.

[0127] Embodiment 2:

[0128] like Figure 2 As shown, this embodiment provides an insurance risk assessment device, which is used to execute the above-mentioned insurance risk assessment method, including:

[0129] The monitoring image acquisition module 11 is used to regularly acquire monitoring images of a specified area after a preset reference time point;

[0130] The risk factor detection module 12 is connected to the monitoring image acquisition module 11 and is used to compare the monitoring image with a reference image and detect and determine whether there are factors related to insurance risk by using image intelligent recognition technology, wherein the reference image is an image in which there are no factors related to insurance risk in the specified area;

[0131] An early warning sending and storage module 13, connected to the risk factor detection module 12, is used to send an early warning to a corresponding user in response to the presence of factors related to insurance risk, and store early warning data;

[0132] The claim decision basis module 14 is connected to the warning sending storage module 13, and is used to calculate the final claim ratio according to the factors related to the insurance risk when making an insurance claim, and use the final claim ratio and / or warning data and / or monitoring images as the basis for the claim decision.

[0133] Optionally, the monitoring image acquisition module 11 includes:

[0134] The periodic collection unit is used to periodically collect monitoring images of a designated area through monitoring cameras installed inside the building.

[0135] Optionally, the device further comprises:

[0136] The reference image definition module is used to define the monitoring image at a certain moment as the reference image, and set the time point corresponding to the certain moment as the reference time point.

[0137] Optionally, the factors related to insurance risk include claim rejection risk factors and claim reduction risk factors, and the risk factor detection module 12 includes:

[0138] The detection and judgment unit is used to detect and judge whether there are risk factors for claim rejection and risk factors for claim reduction through image intelligent recognition technology. If there are risk factors for claim rejection and / or risk factors for claim reduction, it is determined that there are factors related to insurance risk.

[0139] Optionally, the claim decision basis module 14 includes:

[0140] A first obtaining unit is used to obtain a claim rejection risk value and a claim reduction risk value by using a claim rejection risk factor model and a claim reduction risk factor model according to the detected claim rejection risk factor and / or claim reduction risk factor;

[0141] The second obtaining unit is used to obtain the final claim ratio by using a claim function model according to the claim rejection risk value and the claim reduction risk value.

[0142] Optionally, in the claim rejection risk factor model, if any claim rejection risk factor is detected, the claim rejection risk value is 0, and if no claim rejection risk factor is detected, the claim rejection risk value is 1;

[0143] The formula of the compensation reduction risk factor model is as follows:

[0144]

[0145] Where P reduce_compensation is the risk reduction value, D i represents the compensation reduction ratio of the i-th compensation reduction risk factor, DF iIndicates whether the i-th compensation reduction risk factor is detected, 1 indicates detection, and 0 indicates non-detection.

[0146] Optionally, the final claim settlement ratio is calculated specifically according to the following formula:

[0147] P final =P0·P claims_rejected ·P reduce_compensation

[0148] Where P final is the final claim ratio, P0 is the initial claim ratio, P claims_rejected is the risk value of claim rejection.

[0149] Embodiment 3:

[0150] refer to Figure 3 This embodiment provides an insurance risk assessment device, including a memory 21 and a processor 22. The memory 21 stores a computer program, and the processor 22 is configured to run the computer program to execute the insurance risk assessment method in Example 1.

[0151] The memory 21 is connected to the processor 22. The memory 21 may be a flash memory, a read-only memory or other memory. The processor 22 may be a central processing unit or a single-chip microcomputer.

[0152] Embodiment 4:

[0153] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the insurance risk assessment method in the above-mentioned embodiment 1 is implemented.

[0154] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable read only memory), flash memory or other memory technology, CD-ROM (Compact Disc Read-Only Memory), digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer.

[0155] In summary, the insurance risk assessment method, device and readable storage medium provided by the embodiment of the present invention first collect monitoring images of the specified area regularly after the preset reference time point; then compare the monitoring image with the reference image, and detect and determine whether there are factors related to insurance risk through image intelligent recognition technology, wherein the reference image is an image in which there are no factors related to insurance risk in the specified area; then in response to the existence of factors related to insurance risk, issue an early warning to the corresponding user and store early warning data; and when making an insurance claim, calculate the final claim ratio according to the factors related to insurance risk, and use the final claim ratio and / or early warning data and / or monitoring image as the basis for claim decision. The present invention can realize real-time monitoring and early warning by regularly collecting images and performing intelligent recognition, timely discover potential risks, and reduce manual intervention. At the same time, through continuous data collection and recording, sufficient data support is provided for claim decision, which can improve the fairness and transparency of decision. The existing insurance risk assessment method solves the problems of insufficient real-time monitoring, low efficiency of manual recognition and lack of data support.

[0156] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present invention, but the present invention is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. An insurance risk assessment method, characterized in that: The method comprises: After a preset reference time point, regularly collect monitoring images of the designated area; Comparing the monitoring image with a reference image, and detecting and judging whether there are factors related to insurance risks by using image intelligent recognition technology, wherein the reference image is an image in which no factors related to insurance risks exist within the specified area; In response to the existence of factors related to insurance risks, an early warning is issued to the corresponding user, and the early warning data is stored; When making an insurance claim, the final claim ratio is calculated based on the factors related to the insurance risk, and the final claim ratio and / or early warning data and / or monitoring images are used as the basis for the claim decision.

2. The method according to claim 1, characterized in that The periodic acquisition of monitoring images of the designated area specifically includes: Surveillance images of designated areas are collected regularly through surveillance cameras installed inside the building.

3. The method according to claim 1, characterized in that Before comparing the monitoring image with the reference image and detecting and determining whether there are factors related to insurance risks by using image intelligent recognition technology, the method further includes: A monitoring image at a certain moment is defined as the reference image, and a time point corresponding to the certain moment is set as the reference time point.

4. The method according to claim 1, characterized in that The factors related to insurance risk include the risk factors of claim rejection and the risk factors of claim reduction. The detection and determination of whether there are factors related to insurance risk by using the image intelligent recognition technology specifically include: Through the use of intelligent image recognition technology, it is determined whether there are risk factors for claim rejection and risk factors for claim reduction. If there are risk factors for claim rejection and / or risk factors for claim reduction, it is determined that there are factors related to insurance risk.

5. The method according to claim 4, characterized in that The final claim ratio is calculated based on the factors related to the insurance risk, specifically including: According to the detected claim rejection risk factors and / or claim reduction risk factors, using a claim rejection risk factor model and a claim reduction risk factor model to obtain a claim rejection risk value and a claim reduction risk value; The final claim ratio is obtained by using a claim function model according to the claim rejection risk value and the claim reduction risk value.

6. The method according to claim 5, characterized in that In the claim rejection risk factor model, if any claim rejection risk factor is detected, the claim rejection risk value is 0, and if no claim rejection risk factor is detected, the claim rejection risk value is 1; The formula of the compensation reduction risk factor model is as follows: Where P reduce_compensation is the risk reduction value, D i represents the compensation reduction ratio of the i-th compensation reduction risk factor, DF i Indicates whether the i-th compensation reduction risk factor is detected, 1 indicates detection, and 0 indicates non-detection.

7. The method according to claim 6, characterized in that The final claim settlement ratio is obtained by using a claim settlement function model according to the claim rejection risk value and the claim reduction risk value, and is specifically calculated according to the following formula: P final =P0·P claims_rejected ·P reduce_compensation Where P final is the final claim ratio, P0 is the initial claim ratio, P claims_rejected is the risk value of claim rejection.

8. An insurance risk assessment device, characterized in that: The device comprises: A monitoring image acquisition module is used to regularly acquire monitoring images of a specified area after a preset reference time point; a risk factor detection module, connected to the monitoring image acquisition module, for comparing the monitoring image with a reference image, and detecting and judging whether there are factors related to insurance risk by using image intelligent recognition technology, wherein the reference image is an image in which there are no factors related to insurance risk in the specified area; An early warning sending storage module, connected to the risk factor detection module, is used to send an early warning to the corresponding user in response to the existence of factors related to insurance risks, and store early warning data; The claim decision basis module is connected to the early warning sending storage module, and is used to calculate the final claim ratio based on the factors related to the insurance risk when making an insurance claim, and use the final claim ratio and / or early warning data and / or monitoring images as the basis for the claim decision.

9. An insurance risk assessment device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the insurance risk assessment method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the insurance risk assessment method according to any one of claims 1 to 7 is implemented.

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