Insurance adjustment method and system based on multi-source heterogeneous data
Through the insurance calculation method based on multi-source heterogeneous data, a personalized calculation engine is built to realize a fully automated process from underwriting to claims settlement, solving the problems of low efficiency, high cost and low accuracy of traditional insurance claims settlement, and improving claims efficiency and user satisfaction.
Patent Information
- Application Number
- CN202411661611.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-20
- Publication Date
- 2025-03-14
AI Technical Summary
The traditional insurance claims process is inefficient, high cost and low accuracy, and lacks big data risk judgment and claims model, which makes it difficult to make the judgment reasonable, the time cost is high and the accuracy is low.
The insurance calculation method based on multi-source heterogeneous data is adopted, and a personalized calculation engine is built by abstracting the rules and configuration factors of insurance product liability claims, and a fully automated process from underwriting to claims is realized.
The fully automated process of claims settlement has been realized, which greatly improves claims settlement efficiency, reduces claims settlement costs, and improves user satisfaction.
Smart Images
Figure CN119624665A_ABST
Abstract
Description
Background Art
[0002] Insurance claims settlement under the traditional model requires very professional claims personnel to manually check more than 30 types of claims information one by one, and make liability judgments based on more than a thousand product liabilities and clauses. At the same time, it is necessary to consider various differences such as local medical insurance policies and designated hospitals to complete the calculation of the compensation amount. There are pain points such as low efficiency of claims review, long case time limit, high risk of errors, and excessive reliance on experience. Claims review relies on professional claims personnel, lacks big data risk judgment and claims models, and judges claims risks based on past experience, which makes it difficult to achieve a reasonable scale, high time cost and low accuracy. Summary of the invention
[0003] The present application provides an insurance settlement method and system based on multi-source heterogeneous data, which is used to realize a fully automated process from underwriting to claims settlement, improve claims settlement efficiency, reduce claims settlement costs, and improve user satisfaction.
[0004] In a first aspect, an insurance settlement method based on multi-source heterogeneous data is provided, comprising the following steps:
[0005] Abstract the rule configuration factors of liability claims of insurance products;
[0006] Based on the rule configuration factors, a personalized settlement engine is constructed;
[0007] The personalized settlement engine is used to perform claims settlement and compensation settlement.
[0008] In the above technical scheme, the rule configuration factors of the liability claims of insurance products are abstracted; based on the rule configuration factors, a personalized settlement engine is constructed; the personalized settlement engine is used to settle claims and compensation; by building the intelligent settlement platform, the review of the claims data, the judgment of risk points, and the settlement of claims and compensation compensation are efficiently and batch processed based on medical insurance data; no customer application is required, and no manual intervention in the settlement is required, thus realizing a fully automated process from underwriting to claims settlement, greatly improving claims settlement efficiency, reducing claims settlement costs, and improving user satisfaction.
[0009] In a specific embodiment, it also includes:
[0010] Obtain insurance claims acceptance data;
[0011] Data analysis is performed on the insurance claims acceptance data to obtain processed claims data.
[0012] In a specific embodiment, it also includes:
[0013] Preset the risk liability and insurance rules information.
[0014] In a specific embodiment, it also includes:
[0015] The final compensation result is displayed.
[0016] In a specific implementation scheme, the steps of obtaining insurance claim acceptance data are as follows:
[0017] Summarize and organize the medical insurance data and local medical insurance policies of different regions, dynamically adapt multi-source heterogeneous data through configuration files, identify, clean and verify the medical insurance data, and obtain the insurance acceptance and payment data.
[0018] In a specific implementation scheme, data analysis is performed on the insurance claim acceptance data to obtain the steps for processing the claim data, specifically:
[0019] The insurance knowledge and medical knowledge catalog database is used to perform knowledge matching and information verification on the insurance claim acceptance data to obtain the processed claim data.
[0020] In a specific implementation scheme, the step of presetting the risk liability and insurance rule information specifically includes:
[0021] The deductible days, deductible amount, upper limit of compensation amount or upper limit of compensation days and rate mechanism related to the pre-set insurance plan, and the insurance liability corresponding to the medical insurance data;
[0022] Preset rule information corresponding to different insurance plans.
[0023] In a specific implementation scheme, the steps of using the personalized settlement engine to settle claims and compensation include:
[0024] Based on the processed claims data, the personalized settlement engine is utilized to complete the claims settlement and output the fields required for the claim.
[0025] In a specific implementation scheme, the rule configuration factors include calculation formula factors, constraint factors and special agreements.
[0026] In a second aspect, an insurance settlement system based on multi-source heterogeneous data is provided, comprising:
[0027] The rule configuration factor abstraction module is used to abstract the rule configuration factors of liability claims of insurance products;
[0028] A settlement engine module, used to build a personalized settlement engine based on the rule configuration factors;
[0029] The claims settlement module is used to use the personalized settlement engine to perform claims settlement.
[0030] In the above technical scheme, the rule configuration factors of the liability claims of insurance products are abstracted; based on the rule configuration factors, a personalized settlement engine is constructed; the personalized settlement engine is used to settle claims and compensation; by building the intelligent settlement platform, the review of the claims data, the judgment of risk points, and the settlement of claims and compensation compensation are efficiently and batch processed based on medical insurance data; no customer application is required, and no manual intervention in the settlement is required, thus realizing a fully automated process from underwriting to claims settlement, greatly improving claims settlement efficiency, reducing claims settlement costs, and improving user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS
[0031] Figure 1 A flowchart of an insurance settlement method based on multi-source heterogeneous data provided in an embodiment of the present application;
[0032] Figure 2 A structural block diagram of an insurance settlement system based on multi-source heterogeneous data provided in an embodiment of the present application. DETAILED DESCRIPTION
[0033] The present application is further described in detail below through the accompanying drawings and embodiments. Through these descriptions, the characteristics and advantages of the present application will become clearer and more specific.
[0034] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise noted.
[0035] In addition, the technical features involved in different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.
[0036] To facilitate understanding of the insurance settlement method and system based on multi-source heterogeneous data provided in the embodiment of the present application, its application scenario is first explained. The insurance settlement method and system based on multi-source heterogeneous data provided in the embodiment of the present application are used to realize a fully automated process from underwriting to claims, improve claims efficiency, reduce claims costs, and improve user satisfaction. Insurance settlement and claims under the traditional model require very professional claims personnel to manually check more than 30 types of claims information one by one, and make liability judgments based on more than a thousand product liabilities and clauses. At the same time, it is necessary to consider various differences such as local medical insurance policies and designated hospitals to complete the calculation of the amount of compensation. There are pain points such as low efficiency of claims review, long case time limit, high risk of errors, and excessive judgment based on experience. Claims review relies on professional claims personnel, lacks big data risk judgment and claims model, and judges claims risks based on past experience. It is difficult to achieve a reasonable scale, high time cost and low accuracy. To this end, the embodiment of the present application provides an insurance settlement method and system based on multi-source heterogeneous data to achieve a fully automated process from underwriting to claims, improve claims efficiency, reduce claims costs, and improve user satisfaction. The following is a detailed description of the embodiment in conjunction with specific drawings.
[0037] refer to Figure 1 and Figure 2 , Figure 1 A flowchart of an insurance settlement method based on multi-source heterogeneous data provided in an embodiment of the present application; Figure 2 A structural block diagram of an insurance settlement system based on multi-source heterogeneous data provided in an embodiment of the present application.
[0038] exist Figure 1 In the embodiment of the present application, a method for insurance settlement based on multi-source heterogeneous data is provided, comprising the following steps:
[0039] Abstract the rule configuration factors of liability claims of insurance products;
[0040] Based on the rule configuration factors, a personalized settlement engine is constructed;
[0041] The personalized settlement engine is used to perform claims settlement and compensation settlement.
[0042] In the above technical scheme, the rule configuration factors of the liability claims of insurance products are abstracted; based on the rule configuration factors, a personalized settlement engine is constructed; the personalized settlement engine is used to settle claims and compensation; by building the intelligent settlement platform, the review of the claims data, the judgment of risk points, and the settlement of claims and compensation compensation are efficiently and batch processed based on medical insurance data; no customer application is required, and no manual intervention in the settlement is required, thus realizing a fully automated process from underwriting to claims settlement, greatly improving claims settlement efficiency, reducing claims settlement costs, and improving user satisfaction.
[0043] Preferably, the settlement engine is combined with a big data computing engine to obtain a system for processing insurance claims data and performing complex calculations and analysis. Such a system combines a variety of technologies and algorithms, including machine learning, data mining, natural language processing, etc., to provide more efficient, accurate and intelligent settlement services.
[0044] In a specific embodiment, it also includes:
[0045] Obtain insurance claims acceptance data;
[0046] Data analysis is performed on the insurance claims acceptance data to obtain processed claims data.
[0047] In a specific embodiment, it also includes:
[0048] Preset the risk liability and insurance rules information.
[0049] In a specific embodiment, it also includes:
[0050] The final compensation result is displayed.
[0051] In a specific implementation scheme, the steps of obtaining insurance claim acceptance data are as follows:
[0052] Summarize and organize the medical insurance data and local medical insurance policies of different regions, dynamically adapt multi-source heterogeneous data through configuration files, identify, clean and verify the medical insurance data, and obtain the insurance acceptance and payment data.
[0053] In a specific implementation scheme, data analysis is performed on the insurance claim acceptance data to obtain the steps for processing the claim data, specifically:
[0054] The insurance knowledge and medical knowledge catalog database is used to perform knowledge matching and information verification on the insurance claim acceptance data to obtain the processed claim data.
[0055] In a specific implementation scheme, the step of presetting the risk liability and insurance rule information specifically includes:
[0056] The deductible days, deductible amount, upper limit of compensation amount or upper limit of compensation days and rate mechanism related to the pre-set insurance plan, and the insurance liability corresponding to the medical insurance data;
[0057] Preset rule information corresponding to different insurance plans.
[0058] In a specific implementation scheme, the steps of using the personalized settlement engine to settle claims and compensation include:
[0059] Based on the processed claims data, the personalized settlement engine is utilized to complete the claims settlement and output the fields required for the claim.
[0060] In a specific implementation scheme, the rule configuration factors include calculation formula factors, constraint factors and special agreements.
[0061] Specifically, the insurance settlement method based on multi-source heterogeneous data includes the following steps:
[0062] Step 1: Summarize and organize the medical insurance data and local medical insurance policies of different regions. The system dynamically adapts multi-source heterogeneous data through configuration files, and intelligently identifies, cleans, and verifies medical insurance data to meet the data needs for insurance claims acceptance.
[0063] Step 2: Built-in insurance knowledge and medical knowledge directory database, during the file upload process, the medical insurance data, insurance data and other information will be subjected to knowledge matching, information verification and other data analysis processes.
[0064] Step 3: Preset the insurance plan-related data such as deductible days, deductible, upper limit of compensation amount or upper limit of compensation days and rate mechanism, as well as the insurance liability corresponding to the medical insurance data identified by the knowledge graph.
[0065] Step 4: Preset constraint information, special agreements and other rule information corresponding to different insurance plans.
[0066] Step 5: Abstract the product liability claims rule configuration factors and assemble different personalized settlement engines according to the insurance plan for claims settlement.
[0067] Step 6: Based on the actual insurance claims scenario, assign operating permissions of different companies to corresponding operators.
[0068] Step 7: Operators collect medical insurance data that needs to be settled through different channels and upload it to the platform. The system matches it to the specific insurance plan, reviews the payment information, determines the risk points, and filters out abnormal data.
[0069] Step 8: The payable data enters the settlement engine, which parallelizes the computing tasks, completes the claim settlement and outputs the fields required for the claim, and enters the automated payment process.
[0070] Step 9. The final compensation results are presented to the operating personnel in the form of charts, dashboards, etc., so that they can intuitively understand the data and conduct more in-depth analysis and decision-making.
[0071] In the above technical solution, new technologies such as big data and computing engines are integrated into insurance claims and services, and intelligent technology is used to replace manual claims operations to achieve the transformation from traditional claims to intelligent claims. By building this intelligent settlement platform, the review of claims data, risk point judgment, and claims settlement can be efficiently and batch processed based on medical insurance data. No customer application is required, and no manual intervention is required for settlement. The fully automated process from underwriting to claims is realized, which greatly improves claims efficiency, reduces claims costs, and improves user satisfaction. Innovation in claims services can also enhance product value, enhance core market competitiveness, and allow claims customers to have more protection and health solutions, improve customer experience and satisfaction, and also gain higher profits, better customer reputation, and more loyal customers.
[0072] exist Figure 2 In the embodiment of the present application, an insurance settlement system based on multi-source heterogeneous data is provided, including:
[0073] The rule configuration factor abstraction module is used to abstract the rule configuration factors of liability claims of insurance products;
[0074] A settlement engine module, used to build a personalized settlement engine based on the rule configuration factors;
[0075] The claims settlement module is used to use the personalized settlement engine to perform claims settlement.
[0076] In the above technical scheme, the rule configuration factors of the liability claims of insurance products are abstracted; based on the rule configuration factors, a personalized settlement engine is constructed; the personalized settlement engine is used to settle claims and compensation; by building the intelligent settlement platform, the review of the claims data, the judgment of risk points, and the settlement of claims and compensation compensation are efficiently and batch processed based on medical insurance data; no customer application is required, and no manual intervention in the settlement is required, thus realizing a fully automated process from underwriting to claims settlement, greatly improving claims settlement efficiency, reducing claims settlement costs, and improving user satisfaction.
[0077] Those skilled in the art will appreciate that the present application may be implemented as a system, method or computer program product.
[0078] Therefore, the present disclosure may be specifically implemented in the following forms, namely: it may be completely hardware, it may be completely software (including firmware, resident software, microcode, etc.), or it may be a combination of hardware and software, generally referred to herein as a "circuit", "module" or "system". In addition, in some embodiments, the present application may also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium may contain computer-readable program code.
[0079] Any combination of one or more computer-readable media can be used. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.
[0080] Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art can change, modify, replace and modify the above embodiments within the scope of the present application. On this basis, a variety of replacements and improvements can be made to the present application, all of which fall within the scope of protection of the present application.
Claims
1. An insurance settlement method based on multi-source heterogeneous data, characterized in that: The following steps are involved: Abstract the rule configuration factors of liability claims of insurance products; Based on the rule configuration factors, a personalized settlement engine is constructed; The personalized settlement engine is used to perform claims settlement and compensation settlement.
2. The insurance adjustment method based on multi-source heterogeneous data according to claim 1, characterized in that: Also includes: Obtain insurance claims acceptance data; Data analysis is performed on the insurance claims acceptance data to obtain processed claims data.
3. The insurance adjustment method based on multi-source heterogeneous data according to claim 2, characterized in that: Also includes: Preset the risk liability and insurance rules information.
4. The insurance adjustment method based on multi-source heterogeneous data according to claim 3, characterized in that: Also includes: The final compensation result is displayed.
5. The insurance adjustment method based on multi-source heterogeneous data according to claim 4, characterized in that: The steps to obtain insurance claims data are as follows: Summarize and organize the medical insurance data and local medical insurance policies of different regions, dynamically adapt multi-source heterogeneous data through configuration files, identify, clean and verify the medical insurance data, and obtain the insurance acceptance and payment data.
6. The insurance adjustment method based on multi-source heterogeneous data according to claim 5, characterized in that: The data of the insurance claims acceptance is analyzed to obtain the steps for processing the claims data, which are specifically: The insurance knowledge and medical knowledge catalog database is used to perform knowledge matching and information verification on the insurance claim acceptance data to obtain the processed claim data.
7. The insurance adjustment method based on multi-source heterogeneous data according to claim 6, characterized in that: The steps for presetting the risk liability and insurance rule information specifically include: The deductible days, deductible amount, upper limit of compensation amount or upper limit of compensation days and rate mechanism related to the pre-set insurance plan, and the insurance liability corresponding to the medical insurance data; Preset rule information corresponding to different insurance plans.
8. The insurance adjustment method based on multi-source heterogeneous data according to claim 7, characterized in that: The steps of using the personalized settlement engine to settle claims and compensation specifically include: Based on the processed claims data, the personalized settlement engine is utilized to complete the claims settlement and output the fields required for the claim.
9. The insurance adjustment method based on multi-source heterogeneous data according to claim 8, characterized in that: The rule configuration factors include calculation formula factors, constraint factors and special agreements.
10. An insurance adjustment system based on multi-source heterogeneous data, characterized in that: include: The rule configuration factor abstraction module is used to abstract the rule configuration factors of liability claims of insurance products; A settlement engine module, used to build a personalized settlement engine based on the rule configuration factors; The claims settlement module is used to use the personalized settlement engine to perform claims settlement.