End-to-end reinsurance data processing and checking method
Through the end-to-end reinsurance data processing and verification method, the layered data processing and verification are used to use YARN and SparkCore technologies to solve the accuracy and timeliness of traditional reinsurance data processing methods, and efficient data processing and automated early warning are achieved , improves data quality and processing efficiency.
Patent Information
- Application Number
- CN202411155184.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-22
- Publication Date
- 2025-01-17
AI Technical Summary
Traditional reinsurance data processing methods have shortcomings in the accuracy, rationality, timeliness and timeliness of data processing. They often require manual intervention, and the process is unclear and lack effective data verification and problem-solving solutions, resulting in untimely problem discovery. And the positioning is complicated.
The end-to-end reassurance data processing and verification method is adopted, and the data from the core end to the data pre-supply end, the data pre-supply end to the extraction end, the extraction end to the calculation end and the calculation end to the exit end are layered and processed and inspected. Use YARN scheduling engine and SparkCore technology to perform offline processing to improve the accuracy and process clarity of data processing, and realize full-process verification and automatic early warning.
It improves the accuracy, rationality and completeness of re-ensure data processing, promptly discovers and solves problems, saves time and manpower, realizes clarity and automated warning of data processing processes, and improves data quality and processing efficiency.
Smart Images
Figure CN119323480A_ABST
Abstract
Description
Background Art
[0002] As the amount of insurance business data increases and the number of business scenarios increases, traditional reinsurance data processing methods can no longer meet current needs. They are lacking in the accuracy and rationality of data processing, and the timeliness of problem discovery and problem solving. Manual intervention is often required, which is time-consuming and labor-intensive.
[0003] At present, the entire reinsurance data processing process has unclear levels, and there is no relatively clear data verification and problem data processing plan. Problems are not discovered in a timely manner, and locating problem data is cumbersome, time-consuming and labor-intensive. Summary of the invention
[0004] The present application provides an end-to-end reinsurance data processing and verification method, which is used to effectively solve the problem of missed orders through end-to-end data processing and full-process verification, improve the accuracy, rationality and completeness of reinsurance data processing, and improve the timeliness of problem discovery and problem solving.
[0005] The present application provides an end-to-end reinsurance data processing and verification method, comprising the following steps:
[0006] Carry out data processing and data verification on reinsurance data from the core end to the data withholding end;
[0007] Perform data processing and data verification on the reinsurance data from the data withholding end to the data withdrawal end.
[0008] In the above technical scheme, data processing and data verification are performed on the reinsurance data from the core end to the data withholding end; data processing and data verification are performed on the reinsurance data from the data withholding end to the extraction end; the business logic and the reinsurance provision rules and calculation rules are layered to improve the accuracy and rationality of data processing, and the entire process is clearly layered for easy verification.
[0009] In a specific embodiment, it also includes:
[0010] Filter and analyze the reinsurance data from the data withholding end to the data extraction end.
[0011] In a specific embodiment, it also includes:
[0012] Perform data processing and data verification on the reinsurance data from the extraction end to the calculation end.
[0013] In a specific embodiment, it also includes:
[0014] Process the reinsurance data from the calculation end to the export end.
[0015] In a specific implementation scheme, the steps of processing and checking the reinsurance data from the core end to the data withholding end are as follows:
[0016] Obtain the full period data of all business modules from the core business data;
[0017] Regularly re-extract data within a specific period from the core end to generate new withholding end data, and compare this data with the withholding end data of the same period generated last time to verify the newly added, reduced, and updated data; and send warning emails to issue warning reminders for the verified difference data.
[0018] In a specific implementation scheme, the steps of processing and checking the reinsurance data from the data pre-withdrawal end to the withdrawal end are as follows:
[0019] Filter out the data that complies with the reinsurance rules from the full amount of data during the period;
[0020] The difference data between the withholding end and the extraction end is checked synchronously, and early warning is issued in time.
[0021] In a specific implementation scheme, the steps of filtering and analyzing the reinsurance data from the data withholding end to the extraction end are as follows:
[0022] Based on the YARN scheduling engine, SparkCore technology is used to perform labeling using offline processing;
[0023] In the process of generating any data in the filter table, the synchronization mark indicates the reason why the data is filtered out;
[0024] Analyze whether the filtered data is normal.
[0025] In a specific implementation scheme, the steps of processing and checking the reinsurance data from the extraction end to the calculation end are as follows:
[0026] Process the extraction end data to generate calculation end data;
[0027] Check the data integrity of the calculation process.
[0028] In a specific implementation scheme, the steps of processing the reinsurance data from the computing end to the export end are specifically as follows:
[0029] Based on the computing end data, it is processed accordingly and applied to different ports according to the needs of different modules.
[0030] In a specific implementation scheme, SparkCore technology is used based on the YARN scheduling engine to process data using offline processing;
[0031] Based on the YARN scheduling engine, SparkCore technology is used to perform data verification using offline processing. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] Figure 1 A flowchart of an end-to-end reinsurance data processing and verification method provided in an embodiment of the present application;
[0033] Figure 2 A schematic diagram of the structure of the reinsurance data processing and verification model provided in an embodiment of the present application. DETAILED DESCRIPTION
[0034] 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.
[0035] 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.
[0036] 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.
[0037] To facilitate understanding of the end-to-end reinsurance data processing and verification method provided in the embodiment of the present application, its application scenario is first explained. The end-to-end reinsurance data processing and verification method provided in the embodiment of the present application is used to effectively solve the problem of missed orders through end-to-end data processing and full-process verification, improve the accuracy, rationality, and completeness of reinsurance data processing, and improve the timeliness of problem discovery and the timeliness of problem solving. With the increase in the amount of insurance business data and the increase in business scenarios, the traditional reinsurance data processing method can no longer meet current needs. It is lacking in the accuracy and rationality of data processing, the timeliness of problem discovery, and the timeliness of problem solving. It often requires manual intervention, which is time-consuming and labor-intensive. At present, the hierarchy of the entire reinsurance data processing process is unclear, and there is no relatively clear data verification and problem data processing solution. Problems are not discovered in a timely manner, and locating problem data is also cumbersome, time-consuming and labor-intensive. To this end, the embodiment of the present application provides an end-to-end reinsurance data processing and verification method, which effectively solves the problem of missed orders through end-to-end data processing and full-process verification, improves the accuracy, rationality, and completeness of reinsurance data processing, and improves the timeliness of problem discovery and problem solving. The following is a detailed description of the embodiment in conjunction with specific drawings.
[0038] refer to Figure 1 and Figure 2 , Figure 1 A flowchart of an end-to-end reinsurance data processing and verification method provided in an embodiment of the present application; Figure 2A schematic diagram of the structure of the reinsurance data processing and verification model provided in an embodiment of the present application.
[0039] exist Figure 1 In the embodiment of the present application, an end-to-end reinsurance data processing and verification method is provided, comprising the following steps:
[0040] Carry out data processing and data verification on reinsurance data from the core end to the data withholding end;
[0041] Perform data processing and data verification on the reinsurance data from the data withholding end to the data withdrawal end.
[0042] In the above technical scheme, data processing and data verification are performed on the reinsurance data from the core end to the data withholding end; data processing and data verification are performed on the reinsurance data from the data withholding end to the extraction end; the business logic and the reinsurance provision rules and calculation rules are layered to improve the accuracy and rationality of data processing, and the entire process is clearly layered for easy verification.
[0043] Specifically, the end-to-end reinsurance data processing and verification method can achieve the following effects through end-to-end data processing and data verification at the core end, data pre-withdrawal end, extraction end, and calculation end:
[0044] 1. The data processing process is clear and not redundant;
[0045] 2. System verification saves time and manpower;
[0046] 3. Problems such as missed orders and inaccurate data can be discovered in a timely manner;
[0047] 4. Quickly and accurately locate problem nodes;
[0048] 5. Automatically perform attribution analysis on filtered data.
[0049] refer to Figure 1 and Figure 2 , in a specific implementation scheme, further comprising:
[0050] Filter and analyze the reinsurance data from the data withholding end to the data extraction end.
[0051] refer to Figure 1 and Figure 2 , in a specific implementation scheme, further comprising:
[0052] Perform data processing and data verification on the reinsurance data from the extraction end to the calculation end.
[0053] refer to Figure 1 and Figure 2 , in a specific implementation scheme, further comprising:
[0054] Process the reinsurance data from the calculation end to the export end.
[0055] refer to Figure 1 and Figure 2 In a specific implementation plan, the steps of processing and checking the reinsurance data from the core end to the data withholding end are as follows:
[0056] Obtain the full period data of all business modules from the core business data;
[0057] Regularly re-extract data within a specific period from the core end to generate new withholding end data, and compare this data with the withholding end data of the same period generated last time to verify the newly added, reduced, and updated data; and send warning emails to issue warning reminders for the verified difference data.
[0058] Specifically, the steps of processing and checking the reinsurance data from the core end to the data withholding end include:
[0059] 1. Data processing: By using SparkSQL technology based on the YARN scheduling engine, offline processing is used to obtain data from the core to reinsurance. The full data of the first period, renewal, renewal, preservation, claims and other business modules are obtained from a large amount of business data on the core end (including individual insurance core, group insurance core, individual insurance core, etc.). This process does not involve the processing, screening, and elimination logic of reinsurance-related rules, and retains the full amount of direct insurance business data during the period.
[0060] 2. Data verification: By using SparkCore technology based on the YARN scheduling engine and using offline processing to perform data verification, data within a specific period is re-extracted from the core end regularly to generate new withholding end data, and this data is fully compared with the withholding end data of the same period generated last time, and the newly added, reduced, and updated data are verified. In addition, the function of sending early warning emails is developed by connecting to the email interface to warn the difference data verified, and problems are discovered and solved in time. For example: In July 2024, the core end data from January to June 2024 is extracted to generate a new version of the withholding end data, and this data is fully compared with the withholding end data generated in January, February, March, April, May, and June each month before. The verified difference data is stored in an early warning table, and the early warning function developed is used to warn the business and IT. This function can clearly know the range of the difference data, which not only saves the time of querying and locating data, but also can timely discover the problem of missing orders.
[0061] In a specific implementation scheme, the steps of processing and checking the reinsurance data from the data pre-withdrawal end to the data withdrawal end are as follows:
[0062] Filter out the data that complies with the reinsurance rules from the full amount of data during the period;
[0063] The difference data between the withholding end and the extraction end is checked synchronously, and early warning is issued in time.
[0064] Specifically, the steps of processing and checking the reinsurance data from the data withholding end to the withdrawal end specifically include:
[0065] 1. Data processing: Based on the YARN scheduling engine, SparkCore technology is used to process the withholding end data offline to generate extraction end data. The withholding end data is processed according to reinsurance-related rules. Through logical screening such as liability extension, premium calculation, preservation classification, and historical ceding matching, the extraction end data is generated, that is, data that meets the reinsurance rules is screened out from the full amount of data during the period.
[0066] 2. Data verification: By using SparkCore technology based on the YARN scheduling engine and using offline processing to perform data verification, in the process of generating the extraction end data, the data that does not meet the rules (that is, the filtered data) is stored in another filter table, so as to synchronously verify the difference data from the pre-withdrawal end to the extraction end and issue a timely warning.
[0067] In a specific implementation scheme, the steps of filtering and analyzing the reinsurance data from the data withholding end to the extraction end are specifically as follows:
[0068] Based on the YARN scheduling engine, SparkCore technology is used to perform labeling using offline processing;
[0069] In the process of generating any data in the filter table, the synchronization mark indicates the reason why the data is filtered out;
[0070] Analyze whether the filtered data is normal.
[0071] Specifically, by using SparkCore technology based on the YARN scheduling engine and using offline processing to do labeling, in the process of generating each piece of data in the filter table, the reason for the data being filtered out is synchronously marked. This allows further analysis of whether the filtered data is normal. Common reasons for being filtered out include: 1. Normal filtering (conditional filtering, filtering by reinsurance basic configuration rules, such as products, liabilities, etc.); 2. Abnormal filtering (failure to associate with information such as the policyholder, the insured, and liabilities, basic data abnormalities, etc.); 3. Other reasons (operation and maintenance intervention is required). The relevant reasons will also be continuously updated and improved in business practice. By labeling and analyzing the filtered data, problems can be discovered in a timely manner, and the data processing logic can be corrected in a timely manner, which is helpful for establishing a data compensation mechanism and improving the quality of basic data.
[0072] In a specific implementation scheme, the steps of processing and checking the reinsurance data from the extraction end to the calculation end are as follows:
[0073] Process the extraction end data to generate calculation end data;
[0074] Check the data integrity of the calculation process.
[0075] Specifically, the steps of processing and checking the reinsurance data from the extraction end to the calculation end include:
[0076] 1. Data processing: Based on the YARN scheduling engine, SparkCore technology is used to process the extraction end data offline to generate computing end data. Based on the extraction end data, the computing end data is generated through solution matching, company expansion, reserves, risk coverage, retention, ceded premiums, amortized compensation, commissions, value-added tax, etc. In principle, although the amount of data will be expanded from the extraction end to the computing end due to solution configuration, no filtered data will be generated, and both ends are consistent.
[0077] 2. Data verification: Based on the YARN scheduling engine, SparkCore technology is used to perform data verification in an offline processing mode to check the data integrity of the calculation process. 1. From the overall perspective: Verify whether the policy quantities at both ends are consistent and whether the product-level summary amounts are equal. 2. From the details: Perform a two-way comparison, from the extraction end to the calculation end, and from the calculation end to the extraction end, and compare the detailed data in both directions to see if they are consistent. Save the difference data in another verification table and issue an early warning.
[0078] In a specific implementation scheme, the steps of processing the reinsurance data from the computing end to the export end are specifically as follows:
[0079] Based on the data from the computing end, it is processed accordingly according to the needs of different modules and applied to different ports, including financial certificate preparation, bill confirmation, and regulatory reporting.
[0080] In a specific implementation scheme, SparkCore technology is used based on the YARN scheduling engine to process data using offline processing;
[0081] Based on the YARN scheduling engine, SparkCore technology is used to perform data verification using offline processing.
[0082] In the above technical solution, the advantages of this application are:
[0083] 1. The data processing process is clear and not redundant;
[0084] In traditional data processing, the process of claiming data mixes business logic with reinsurance rules, which not only makes it impossible to verify data, but also makes it impossible to quickly and accurately locate abnormalities in the generated data. The data processing method developed this time layers business logic, reinsurance claiming rules, and calculation rules to improve the accuracy and rationality of data processing, and the entire process is clear and easy to verify.
[0085] 2. Automatically perform attribution analysis of filtered data;
[0086] In the process of data processing from the pre-withdrawal end to the extraction end, the attribution analysis of the filtered data can be automatically performed. Through labeling, it is clear at a glance which data has been filtered out and what the corresponding reasons are. There is no need for manual verification and analysis one by one, which not only improves the timeliness of problem discovery and problem solving, but also helps to establish a data compensation mechanism and improve the quality of basic data.
[0087] 3. Automatic warning of system inspection saves time and manpower;
[0088] The traditional method has no automatic verification and early warning functions, and can only rely on manual self-discovery of problems. The system verification function developed this time realizes the full-process verification between multiple end-to-ends, and through automatic early warning, abnormal data can be discovered in time, saving time and manpower for manual verification.
[0089] Those skilled in the art will appreciate that the present application may be implemented as a system, method or computer program product.
[0090] 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.
[0091] 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.
[0092] 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 end-to-end reinsurance data processing and verification method, characterized in that: The following steps are involved: Carry out data processing and data verification on reinsurance data from the core end to the data withholding end; Perform data processing and data verification on the reinsurance data from the data withholding end to the data withdrawal end.
2. The end-to-end reinsurance data processing and verification method according to claim 1, characterized in that: Also includes: Filter and analyze the reinsurance data from the data withholding end to the data extraction end.
3. The end-to-end reinsurance data processing and verification method according to claim 2, characterized in that: Also includes: Perform data processing and data verification on the reinsurance data from the extraction end to the calculation end.
4. The end-to-end reinsurance data processing and verification method according to claim 3, characterized in that: Also includes: Process the reinsurance data from the calculation end to the export end.
5. The end-to-end reinsurance data processing and verification method according to claim 4, characterized in that: The steps for data processing and data verification of reinsurance data from the core end to the data withholding end are as follows: Obtain the full period data of all business modules from the core business data; Regularly re-extract data within a specific period from the core end to generate new withholding end data, and compare this data with the withholding end data of the same period generated last time to check for new, reduced, and updated data; And by sending early warning emails, early warning reminders are issued for the verified difference data.
6. The end-to-end reinsurance data processing and verification method according to claim 5, characterized in that: The steps for processing and checking the reinsurance data from the data withholding end to the withdrawal end are as follows: Filter out the data that complies with the reinsurance rules from the full amount of data during the period; The difference data between the withholding end and the extraction end is checked synchronously, and early warning is issued in time.
7. The end-to-end reinsurance data processing and verification method according to claim 6, characterized in that: The steps for filtering and analyzing the reinsurance data from the data withholding end to the extraction end are as follows: Based on the YARN scheduling engine, SparkCore technology is used to perform labeling using offline processing; In the process of generating any data in the filter table, the synchronization mark indicates the reason why the data is filtered out; Analyze whether the filtered data is normal.
8. The end-to-end reinsurance data processing and verification method according to claim 7, characterized in that: The steps for data processing and data verification of reinsurance data from the extraction end to the calculation end are as follows: Process the extraction end data to generate calculation end data; Check the data integrity of the calculation process.
9. The end-to-end reinsurance data processing and verification method according to claim 8, characterized in that: The steps for processing reinsurance data from the calculation end to the export end are as follows: Based on the computing end data, it is processed accordingly and applied to different ports according to the needs of different modules.
10. The end-to-end reinsurance data processing and verification method according to claim 9, characterized in that: Based on the YARN scheduling engine, SparkCore technology is used to process data using offline processing; Based on the YARN scheduling engine, SparkCore technology is used to perform data verification using offline processing.