An insurance service information management system based on big data processing technology

Abstract

This invention belongs to the interdisciplinary field of big data processing and insurance service management. It discloses an insurance service information management system based on big data processing technology, comprising a multi-source data access layer, a standardized processing engine, a dynamic rule engine, a full-process service module, a distributed evidence storage module, and an interaction layer. These modules are sequentially connected via a data bus. The standardized processing engine automatically performs data verification, deduplication, and correlation. The dynamic rule engine achieves millisecond-level rule matching. What previously required several days of manual review can now be completed in a short time, significantly reducing manual intervention, lowering labor costs, and avoiding subjective errors in human judgment. Insurance product types, regulatory policies, and market demands are constantly changing. The dynamic rule engine supports visual configuration and real-time updates, allowing staff to adjust underwriting and claims rules according to new insurance product terms and update data verification standards according to regulatory requirements without modifying the system's core code. This significantly improves adaptation speed, meeting the diverse and rapidly iterating business needs of insurance companies and facilitating practical application and operation.

Description

Technical Field

[0001] This invention relates to the field of big data processing and insurance service management, and in particular to an insurance service information management system based on big data processing technology. Background Technology

[0002] With the acceleration of informatization in the insurance industry, the amount of business data of insurance companies has exploded, and the data sources have become increasingly diversified. These include basic customer information (such as identity information and occupational information), policy lifecycle data (such as insurance application, policy activation, and status change records), as well as third-party verification data (such as credit reports, asset certificates, and public utility payment records), and claims material data (such as repair invoices, medical bills, and damage images).

[0003] In practical applications, existing systems lack a unified multi-source data processing mechanism. Data formats from different sources vary significantly (e.g., inconsistent field naming in third-party credit reports, varying resolutions of claim photos, and inconsistent policy data field types), making direct data association impossible. Insurance product types (e.g., auto insurance, critical illness insurance, medical insurance), regulatory policies (e.g., claims review standards, underwriting factor requirements), and market demands are all dynamically changing. However, existing systems' business rules (e.g., underwriting age thresholds, claims repair price ranges, renewal assessment conditions) are often directly embedded in code logic, lacking flexible configuration mechanisms. Insurance business data (especially claims materials, underwriting conclusions, and customer privacy information) involves multiple parties' rights, and its authenticity and security directly affect business compliance. Existing systems often use centralized storage, making data susceptible to tampering or loss, and lacking authoritative evidence preservation mechanisms. In cases of insurance fraud or business disputes, the stored data is difficult to use as legitimate evidence. Summary of the Invention

[0004] One objective of this invention is to provide an insurance service information management system based on big data processing technology.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: an insurance service information management system based on big data processing technology, comprising a multi-source data access layer, a standardized processing engine, a dynamic rule engine, a full-process service module, a distributed evidence storage module, and an interaction layer, wherein each module is connected to the other via a data bus.

[0006] The multi-source data access layer is configured with an API gateway and protocol adaptation interface to synchronously acquire basic insurance customer data, policy lifecycle data, third-party verification data and claims material data. The third-party verification data includes credit reports, asset certificates and public utility payment records.

[0007] The standardized processing engine is used to perform triple preprocessing on the access data: format verification is performed through a preset regular expression library, redundant data is removed based on a hash deduplication algorithm, cross-source data association marking is completed using a preset field mapping table, and standardized data units are generated.

[0008] The dynamic rule engine has a built-in configurable rule library that stores underwriting threshold rules, claims eligibility rules, and renewal assessment rules. It supports receiving manual update instructions through the interaction layer and synchronizing them to the rule library in real time. The rule library uses a tree index structure to achieve millisecond-level rule matching.

[0009] The full-process service module includes an underwriting sub-module, a claims sub-module, and a renewal sub-module, which respectively call the dynamic rule engine to perform rule matching on standardized data units and output underwriting conclusions, claims amount calculation results, and renewal suggestions;

[0010] The distributed evidence storage module adopts a blockchain node architecture to encrypt and store standardized data units and service conclusions, generating evidence storage hash values ​​containing timestamps, and supports connection with the data intellectual property registration system to complete ownership evidence storage.

[0011] The interaction layer provides a visual configuration interface and a result display interface, which are used for rule parameter configuration and service conclusion query, respectively.

[0012] Preferably, the preprocessing flow of the standardized processing engine includes:

[0013] S1. Validate the format of claim materials data using preset field templates and remove scanned data with a resolution of less than 300 DPI.

[0014] S2. Use a sliding window comparison algorithm to remove consecutive and duplicate policy status change records;

[0015] S3. Establish cross-source data association markers based on the dual key fields of ID card number and policy number, and generate standardized units containing 18 unique data identifiers.

[0016] Preferably, the dynamic rule engine includes a rule configuration unit, an index management unit, and a matching execution unit;

[0017] The rule configuration unit supports visual input of underwriting factor weights, which include age, occupation type, credit rating, and asset size.

[0018] The index management unit uses a red-black tree structure to maintain the rule update log, ensuring the ability to backtrack rule versions.

[0019] The matching execution unit triggers rules through precise field matching, without iterative learning or probability prediction.

[0020] Preferably, the distributed evidence storage module uses a symmetric encryption algorithm to encrypt and store the original data, and an asymmetric encryption algorithm to generate an evidence storage hash value. The evidence storage hash value includes a data identifier, a processing timestamp, and node signature information, and supports registration and filing through the data evidence storage interface of the State Intellectual Property Office.

[0021] Preferably, the claims sub-module execution process of the full-process service module includes:

[0022] T1. Call the standardized processing engine to parse the claims material data;

[0023] T2. Match the repair item price range rules and the damage level correspondence rules in the dynamic rule engine;

[0024] T3. Calculate the number of non-conformities and generate a comprehensive audit result. The non-conformities include prices exceeding the range and unnecessary repair items.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0026] (1) This invention automatically completes data verification, deduplication, and association through a standardized processing engine, and achieves millisecond-level rule matching through a dynamic rule engine. The manual review process that originally required several days can now be completed in a short time, greatly reducing manual intervention and lowering labor costs while avoiding subjective errors in human judgment. Insurance product types, regulatory policies, and market demands are constantly changing. The dynamic rule engine supports visual configuration and real-time updates. Staff can adjust underwriting and claims rules according to new insurance product terms and update data verification standards according to regulatory requirements without modifying the core system code. The adaptation speed is increased several times, meeting the diversified and rapidly iterating business needs of insurance companies.

[0027] (2) This invention ensures the authenticity of customer information through third-party data verification. The distributed evidence storage module adopts a blockchain architecture and dual encryption technology, making the data immutable from access to processing results. Each operation has a timestamp and signature record, which can be traced at any time. At the same time, it connects to the evidence storage interface of the State Intellectual Property Office, giving the data legal effect, effectively reducing the risk of insurance fraud, protecting the legitimate rights and interests of insurance companies and customers, and meeting industry compliance requirements. Through the centralized collection of data by the multi-source data access layer, the standardized processing engine establishes cross-source data association, allowing the scattered data to form complete customer, policy, and verification files, ensuring that underwriting, claims, renewal and other links can be based on comprehensive and accurate data decisions, and improving service accuracy.

[0028] (3) The present invention adopts a visual design through the system interaction layer. Operations such as rule configuration and result query do not require professional technical knowledge, and insurance business personnel can operate directly. For example, when adjusting the underwriting weight of a certain occupation, you only need to enter the value on the interface. There is no need to write algorithms or code, which lowers the threshold for using the system, improves the self-operation capability of the business department, and reduces the dependence on the technical department. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the overall structure of the present invention. Detailed Implementation

[0030] The present invention will now be further described in conjunction with specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0031] In the description of this invention, it should be noted that directional terms such as "center," "lateral," "longitudinal," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise" indicate the orientation and positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. They should not be construed as limiting the specific protection scope of this invention.

[0032] It should be noted that the terms "first" and "second" in the specification and claims of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0033] One preferred embodiment of the present invention, such as Figure 1 As shown, an insurance service information management system based on big data processing technology includes a multi-source data access layer, a standardized processing engine, a dynamic rule engine, a full-process service module, a distributed evidence storage module, and an interaction layer. Each module is connected to the system via a data bus.

[0034] The multi-source data access layer is configured with an API gateway and protocol adaptation interface to synchronously obtain basic insurance customer data, policy lifecycle data, third-party verification data and claims material data. The third-party verification data includes credit reports, asset certificates and public utility payment records.

[0035] The standardized processing engine is used to perform triple preprocessing on the accessed data: format validation is performed through a preset regular expression library, redundant data is removed based on a hash deduplication algorithm, cross-source data association marking is completed using a preset field mapping table, and standardized data units are generated.

[0036] The dynamic rule engine has a built-in configurable rule library that stores underwriting threshold rules, claims eligibility rules, and renewal assessment rules. It supports receiving manual update instructions through the interaction layer and synchronizing them to the rule library in real time. The rule library uses a tree index structure to achieve millisecond-level rule matching.

[0037] The full-process service module includes an underwriting sub-module, a claims sub-module, and a renewal sub-module. Each module calls a dynamic rule engine to perform rule matching on standardized data units and outputs underwriting conclusions, claims amount calculation results, and renewal suggestions.

[0038] The distributed evidence storage module adopts a blockchain node architecture to encrypt and store standardized data units and service conclusions, generating evidence storage hash values ​​containing timestamps, and supports integration with the data intellectual property registration system to complete ownership evidence storage.

[0039] The interaction layer provides a visual configuration interface and a result display interface, which are used for rule parameter configuration and service conclusion query, respectively.

[0040] The preprocessing flow of the standardized processing engine includes:

[0041] S1. Validate the format of claim materials data using preset field templates and remove scanned data with a resolution of less than 300 DPI.

[0042] S2. Use a sliding window comparison algorithm to remove consecutive and duplicate policy status change records;

[0043] S3. Establish cross-source data association markers based on the dual key fields of ID card number and policy number, and generate standardized units containing 18 unique data identifiers.

[0044] The dynamic rule engine includes a rule configuration unit, an index management unit, and a matching execution unit;

[0045] The rule configuration unit supports visual input of underwriting factor weights, which include age, occupation type, credit rating, and asset size.

[0046] The index management unit uses a red-black tree structure to maintain the rule update log, ensuring the ability to backtrack rule versions.

[0047] The matching execution unit triggers rules through precise field matching, without iterative learning or probability prediction.

[0048] The distributed evidence storage module uses a symmetric encryption algorithm to encrypt and store the original data, and an asymmetric encryption algorithm to generate evidence storage hash values. The evidence storage hash values ​​contain data identifiers, processing timestamps, and node signature information, and support registration and filing through the State Intellectual Property Office's data evidence storage interface.

[0049] The claims sub-module execution process of the end-to-end service module includes:

[0050] T1. Call the standardized processing engine to parse the claims material data;

[0051] T2. Match the repair item price range rules and the damage level correspondence rules in the dynamic rule engine;

[0052] T3. Calculate the number of non-conformities and generate a comprehensive audit result. Non-conformities include prices exceeding the range and unnecessary repair items.

[0053] Working principle:

[0054] In use, the system centrally acquires various insurance-related data, including basic customer information, policy lifecycle records, third-party verification data, and claims materials, through the API gateway and protocol adaptation interface of the multi-source data access layer, acting like a data transfer station. Regardless of whether this data comes from the insurance company's internal system, third-party partner platforms, or customer uploads, it can be received by the system through a unified interface, avoiding the problem of data being scattered across different platforms and unable to communicate. The system performs triple purification of the accessed data, which is often disorganized; it checks data compliance according to preset templates, such as removing blurry claims photos with a resolution lower than 300 DPI, ensuring accurate information identification during subsequent review; it deletes consecutive and duplicate invalid data through a sliding window comparison algorithm, reducing system processing pressure; and it uses ID number and policy number as dual key fields to uniformly identify data from different sources, such as binding customer credit records to corresponding policies, creating a linked archive of scattered data and providing complete evidence for subsequent review. The dynamic rule engine is the core of the system's decision-making, with built-in manually configured insurance business rules. The rule base uses a tree-like index structure, similar to a book catalog, allowing for quick location of the corresponding rule.

[0055] Staff can directly adjust rule parameters through the visual interface of the interactive layer. Updated rules are logged using a red-black tree structure, supporting retrospective updates for subsequent versions. The underwriting, claims, and renewal sub-modules of the full-process service module call the rule engine to match standardized data. The underwriting sub-module matches the eligibility rules for age, occupation, and credit history; the claims sub-module matches the compensation rules for repair item prices and damage levels; and the renewal sub-module matches the renewal suggestion rules for policy performance records and customer payment status. The matching process does not involve any automatic optimization or fuzzy judgment; it is entirely based on precise field comparison to output results.

[0056] The underwriting submodule provides "approved / rejected" results, the claims submodule calculates the specific payout amount, and the renewal submodule generates opinions such as "recommend renewal / adjust premium / not renew." These results, along with corresponding standardized data, are synchronously transmitted to the distributed evidence storage module. The original information is stored using a symmetric encryption algorithm (to protect data privacy), and then an asymmetric encryption algorithm is used to generate an evidence hash value containing "data ID + processing timestamp + node signature," essentially stamping the data with an "unalterable seal." Simultaneously, the system supports integration with the State Intellectual Property Office's data evidence storage interface to complete ownership registration, ensuring that the data can serve as legal evidence in case of future disputes. The system features two main features: a rule configuration interface that allows staff to visually adjust underwriting factor weights and claims rule details without writing code; and a results display interface that provides a clear view of underwriting conclusions, claims amounts, and evidence records. Adjusted rules are synchronized to the dynamic rule engine in real time, allowing the system to quickly adapt to changes in different insurance products or regulatory policies without requiring a reconstruction of the core system architecture.

[0057] The basic principles, main features, and advantages of this invention have been described above. Those skilled in the art should understand that this invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made without departing from the spirit and scope of the invention, and all such changes and modifications fall within the scope of the invention as claimed. The scope of protection claimed by this invention is defined by the appended claims and their equivalents.

Claims

1. An insurance service information management system based on big data processing technology, characterized in that, It includes a multi-source data access layer, a standardized processing engine, a dynamic rule engine, a full-process service module, a distributed evidence storage module, and an interaction layer. Each module is connected to the other via a data bus. The multi-source data access layer is configured with an API gateway and protocol adaptation interface to synchronously acquire basic insurance customer data, policy lifecycle data, third-party verification data and claims material data. The third-party verification data includes credit reports, asset certificates and public utility payment records. The standardized processing engine is used to perform triple preprocessing on the access data: format verification is performed through a preset regular expression library, redundant data is removed based on a hash deduplication algorithm, cross-source data association marking is completed using a preset field mapping table, and standardized data units are generated. The dynamic rule engine has a built-in configurable rule library that stores underwriting threshold rules, claims eligibility rules, and renewal assessment rules. It supports receiving manual update instructions through the interaction layer and synchronizing them to the rule library in real time. The rule library uses a tree index structure to achieve millisecond-level rule matching. The full-process service module includes an underwriting sub-module, a claims sub-module, and a renewal sub-module, which respectively call the dynamic rule engine to perform rule matching on standardized data units and output underwriting conclusions, claims amount calculation results, and renewal suggestions; The distributed evidence storage module adopts a blockchain node architecture to encrypt and store standardized data units and service conclusions, generating evidence storage hash values ​​containing timestamps, and supports connection with the data intellectual property registration system to complete ownership evidence storage. The interaction layer provides a visual configuration interface and a result display interface, which are used for rule parameter configuration and service conclusion query, respectively.

2. The insurance service information management system based on big data processing technology as described in claim 1, characterized in that: The preprocessing flow of the standardized processing engine includes: S1. Validate the format of claim materials data using preset field templates and remove scanned data with a resolution of less than 300 DPI. S2. Use a sliding window comparison algorithm to remove consecutive and duplicate policy status change records; S3. Establish cross-source data association markers based on the dual key fields of ID card number and policy number, and generate standardized units containing 18 unique data identifiers.

3. The insurance service information management system based on big data processing technology as described in claim 1, characterized in that: The dynamic rule engine includes a rule configuration unit, an index management unit, and a matching execution unit; The rule configuration unit supports visual input of underwriting factor weights, which include age, occupation type, credit rating, and asset size. The index management unit uses a red-black tree structure to maintain the rule update log, ensuring the ability to backtrack rule versions. The matching execution unit triggers rules through precise field matching, without iterative learning or probability prediction.

4. The insurance service information management system based on big data processing technology as described in claim 1, characterized in that: The distributed evidence storage module uses a symmetric encryption algorithm to encrypt and store the original data, and an asymmetric encryption algorithm to generate an evidence storage hash value. The evidence storage hash value contains data identifier, processing timestamp, and node signature information, and supports registration and filing through the data evidence storage interface of the State Intellectual Property Office.

5. The insurance service information management system based on big data processing technology as described in claim 1, characterized in that: The execution process of the claims sub-module of the full-process service module includes: T1. Call the standardized processing engine to parse the claims material data; T2. Match the repair item price range rules and the damage level correspondence rules in the dynamic rule engine; T3. Calculate the number of non-conformities and generate a comprehensive audit result. The non-conformities include prices exceeding the range and unnecessary repair items.

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