A method, device and system for fast response to claim automatic review

Abstract

This application discloses a method, apparatus, and system for rapid response and automatic review of claims, addressing the problem that computer systems cannot automatically and rapidly respond to claims events triggering the generation of payout calculation results. The method includes: receiving a claims case identifier; acquiring its multi-dimensional risk characteristic data; determining whether the case belongs to a low-risk category that can be automatically processed through a risk stratification model based on a configurable rule set; if so, then executing intelligent claims processing and integrated risk control in parallel, i.e., determining the payout amount based on policy and claim information matching rules, while simultaneously comparing the case data with a dynamically configured risk rule set in real time; if the risk control passes, automatically outputting a review instruction. This invention achieves full-process automation of life insurance claims processing from risk stratification and intelligent claims processing to real-time risk control, significantly improving processing efficiency and accuracy while strictly ensuring risk and compliance.

Description

Technical Field

[0001] This application relates to the field of insurance science and technology, and in particular to a method, apparatus and system for rapid response to automatic claims review. Background Technology

[0002] In recent years, with the rapid development of fintech, commercial insurance companies have increasingly matured their automation technologies in the field of medical expense reimbursement claims (hereinafter referred to as "medical insurance"). Through technologies such as rule engines and image recognition, medical insurance claims can now achieve payouts in seconds, and the overall case processing time can be reduced to within half a day, significantly improving customer experience and operational efficiency.

[0003] However, in stark contrast, the automation process for commercial life insurance claims (hereinafter referred to as "life insurance," mainly referring to cases where payment is contingent upon death or total disability) is severely lagging. Although life insurance claims typically account for less than 3% of an insurance company's total claims volume, the manual labor hours required for their review account for nearly 30% of the daily claims review process, with the industry average payout time exceeding 7 days. Compared to medical insurance, the long processing time and poor customer experience of life insurance claims have become major shortcomings affecting the overall image of commercial insurance claims services.

[0004] The root cause of the aforementioned predicament lies in the inherent complexity and high risk of life insurance claims at the technical level, making it difficult to directly apply the automated models of medical insurance. Specifically: First, the average claim amount is high and the logic is complex: Life insurance payouts often involve large sums of money and require dynamic matching of liabilities across multiple policies, clearing of policy cash value, and complex verification of beneficiary and payee relationships, making the calculation logic far more complex than that of medical expense reimbursement. Second, fraud and compliance risks are prominent: Due to the large payout amounts, the life insurance sector is more prone to moral hazard and insurance fraud, while also requiring strict compliance with anti-money laundering and other requirements, making risk management pressure far greater than in medical insurance. Finally, business rules vary significantly and are highly variable: Business rules differ significantly between different insurance products and regions, and terms are frequently updated, requiring the system to have a high degree of flexibility and configurability.

[0005] Under current technological conditions, the life insurance claims process relies almost entirely on manual review. Reviewers must manually query data from multiple systems, understand complex policy terms, perform tedious calculations, and make experience-based risk assessments. This model is not only inefficient and costly in terms of manpower, but also susceptible to the influence of subjective experience and professional competence, leading to inconsistent processing standards, increased risk of calculation errors, and difficulty in achieving real-time, accurate, and systematic risk interception.

[0006] In the wave of "financial technology", due to the relatively small absolute number of life insurance cases and the more difficult technical challenges to be solved in order to achieve automation, most insurance companies lack sufficient motivation and resources to invest in systematic technological research and development in this field. As a result, life insurance claims have long been in a "technological backwater", and the entire operation process is still in a state of high manual intervention.

[0007] Therefore, there is an urgent need in this field for an innovative technical solution that can fundamentally solve the aforementioned technical obstacles faced by the automated review of life insurance claims, and achieve a qualitative leap in processing efficiency while ensuring controllable risks and compliance, thereby promoting the comprehensive upgrade of insurance claims services towards intelligence and automation. Summary of the Invention

[0008] This application proposes a method, apparatus, and system for rapid response to automatic review of claims, which solves the problem that computer systems cannot automatically and quickly respond to events that trigger claims to generate compensation calculation results.

[0009] In a first aspect, embodiments of this application provide a rapid response method for automatic review of claims, executed by a computer system, comprising the following steps:

[0010] Obtain the claim case identifier;

[0011] Retrieve predefined risk characteristic data from the database associated with the claim case identifier;

[0012] The risk characteristic data is matched with a predefined risk stratification rule set to determine a first judgment result; the first judgment result is used to indicate the processing category of the claim case.

[0013] In response to the first determination result being an automated processing identifier, the following steps are executed:

[0014] The compensation amount is determined based on the case data corresponding to the claim case identifier; the compensation amount is determined by matching the case data with a predefined set of calculation rules; the case data includes policy data and incident information;

[0015] A second judgment result is obtained; the second judgment result is determined by matching the case data with a predefined set of risk rules.

[0016] If the second judgment result is passed, an approval instruction is output.

[0017] In one embodiment, the risk characteristic data includes at least one of the following: claim amount, time of incident, ownership information, and policy special identification.

[0018] In one embodiment, determining the first judgment result specifically includes the following steps:

[0019] The risk characteristic data is input into a configurable risk stratification model;

[0020] The risk stratification model performs logical judgment operations on the input risk feature data based on the predefined risk stratification rule set.

[0021] Output the first judgment result.

[0022] In one embodiment, matching the case data with the predefined set of calculation rules specifically includes:

[0023] The policy data and incident information are matched with predefined insurance liability rules to determine the applicable insurance liability clauses.

[0024] In one embodiment, determining the compensation amount further includes:

[0025] Define the terms of insurance liability;

[0026] Based on the aforementioned insurance liability terms and the aforementioned case data, execute the corresponding compensation calculation function.

[0027] In one embodiment, the predefined risk rule set includes at least one of the following categories:

[0028] Rules on risk of rights holders, rules on anti-money laundering risks, and rules on characteristics of insurance fraud.

[0029] In one embodiment, the second determination result is determined concurrently with the determination of the compensation amount result.

[0030] In one embodiment, the predefined set of computational rules and / or risk rules is edited and managed through a graphical rule configuration interface. The system converts configuration operations into structured rule description files for storage and supports rule version control and canary releases.

[0031] Secondly, embodiments of this application also provide a rapid response automatic claims review device, used to implement the rapid response automatic claims review method as described in any embodiment of the first aspect, comprising: a receiving module, used to receive a claims case identifier; and further used to obtain predefined risk characteristic data from a database associated with the claims case identifier. A determining module, used to match the risk characteristic data with a predefined risk stratification rule set to determine a first judgment result; the first judgment result is used to indicate the processing category of the claims case. It is also used to determine a compensation amount result based on the case data corresponding to the claims case identifier; the compensation amount result is determined by matching the case data with a predefined calculation rule set; the case data includes policy data and incident information. It is also used to obtain a second judgment result; the second judgment result is determined by matching the case data with a predefined risk rule set. An output module, in response to the second judgment result being passed, outputs a review pass instruction.

[0032] Thirdly, embodiments of this application also provide a rapid response automatic claims review system, comprising: an application server, deployed with the apparatus described in the second aspect embodiment, or configured to execute the method described in any one of the first aspect embodiments; a user terminal, communicatively connected to the application server, used to provide a claims case identification input interface and a review instruction output interface; a rule configuration server, used to provide a graphical rule configuration interface and convert configuration operations into structured rule description files for storage and distribution; and an email server and / or payment gateway, used to receive the review approval instruction and deliver a claims notification or trigger a payment process.

[0033] Fourthly, embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in any one of the embodiments of the first aspect.

[0034] Fifthly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any embodiment of the first aspect.

[0035] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0036] This application's embodiments automate risk stratification and screening of claims cases based on multi-dimensional risk characteristics, identifying cases that meet preset low-risk conditions. Subsequently, the system automatically invokes a rule engine to accurately match insurance liabilities and intelligently calculate compensation amounts for the identified cases. During or after this process, a dynamically configurable risk rule set is further integrated to perform real-time, parallel compliance and risk scanning of the cases. The systematic combination of the above technical means enables the review of life insurance claims, which was originally highly dependent on manual processes and lengthy, to be completed in a coherent and automated process. This fundamentally ensures the effectiveness of risk control while achieving a leapfrog improvement in processing efficiency, accurate and unified compensation results, and optimizing customer service experience and operating costs. Through the above technical solution, the system transforms the originally scattered and rigid business rules into centralized and dynamically configurable technical objects, realizing standardized management, visual configuration, and agile deployment of risk control strategies and compensation logic. This fundamentally solves the technical problems of lagging rule updates and inconsistent execution under the traditional manual model. Attached Figure Description

[0037] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0038] Figure 1 This is a flowchart illustrating a rapid response automatic review method for claims according to an embodiment of this application;

[0039] Figure 2 This is a structural diagram illustrating the insurance liability and compensation rules in an embodiment of this application;

[0040] Figure 3 This is a schematic diagram of the claims process reengineering provided in an embodiment of this application;

[0041] Figure 4 A structural diagram of a rapid response automatic claims review device provided in this application embodiment;

[0042] Figure 5 A structural diagram of a rapid response automatic claims review system provided in this application embodiment;

[0043] Figure 6 A schematic diagram of the server equipment of the rapid response automatic claims review device provided in this application embodiment;

[0044] Figure 7 This is a schematic diagram of the terminal device of the rapid response automatic claims review device provided in the embodiments of this application. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0047] Figure 1 The flowchart of a rapid response automatic review method for claims provided in this application embodiment illustrates the core data processing flow of the automatic review method, including the following steps: Step 110 to Step 170.

[0048] Step 110: Obtain the claim case identifier.

[0049] Receive the first data; the first data contains the claim case identifier.

[0050] The system receives external input, which is encapsulated as first data. This first data must contain a key index, namely a claims case identifier (such as a policy number or system case number), which is used to uniquely associate and retrieve all relevant information for a specific case in all subsequent steps.

[0051] Upon receiving external input containing a claims case identifier, the system uses this identifier as a unique query key to initiate parallel data query requests to multiple underlying databases associated with the business (such as policy databases, customer information databases, and historical claims databases). Through database indexing technology, it quickly locates all data records associated with this identifier, and then extracts, aggregates, and returns predefined field information necessary to constitute the risk characteristic data of this case and subsequent case data from these heterogeneous data sources.

[0052] Step 120: Obtain predefined risk characteristic data from the database associated with the claim case identifier.

[0053] Determine the second data; the second data includes risk characteristic data corresponding to the claim case identifier.

[0054] Based on the claim case identifier received in step 110, the system systematically collects multi-dimensional features related to the case and used for risk probability assessment from the associated business database to form the second data. The risk feature data in the second data consists of multi-dimensional parameters selected after structured analysis of risk factors in life insurance claims business.

[0055] The system constructs a risk profile through comprehensive analysis of multi-dimensional information such as historical data, insurance coverage, business type, claim amount, rights attribution, and policy status.

[0056] For example, the system can be configured to automatically identify and obtain the following key information to assist in judgment: whether the case is a standard case that does not require in-depth investigation; whether the time of the claim in a long-term or short-term insurance policy is within the validity period of the insurance liability; whether the information of the payee is consistent with the designated beneficiary of the policy; whether the time interval from the claim to the report application is within a reasonable range; whether there is any suspicion of duplicate claims by the same insured; whether the policy has any special clauses that affect automatic processing; whether the preliminary estimated amount of claims is lower than the preset threshold for automatic processing; and, transforming the scattered control points in traditional operations, such as death registration verification, preliminary anti-money laundering screening, and internal list comparison, into quantifiable and assessable system risk factor data.

[0057] In one embodiment, the risk characteristic data includes at least one of the following: claim amount, time of incident, ownership information, and policy special identifier.

[0058] The claim amount, as a numerical feature, is a core variable for assessing the potential scale of financial loss.

[0059] The time of the incident, as a temporal feature, is used to determine the validity of the insurance liability period.

[0060] The information regarding ownership of rights serves as a relational feature, linking the legitimate entity responsible for insurance payouts.

[0061] The policy special identifier serves as a flag feature, indicating additional contractual clauses that may exclude automated processing. The selection of these features is based on a structured analysis of risk factors in life insurance claims.

[0062] This process is essentially a preprocessing and feature extraction based on preset rules, which transforms unstructured business information into standardized secondary data.

[0063] Step 130: Match the risk characteristic data with a predefined risk stratification rule set to determine a first judgment result; the first judgment result is used to indicate the processing category of the claim case.

[0064] Based on the second data, a first judgment result is obtained; the first judgment result is used to indicate the processing category of the claim case.

[0065] The system inputs the risk characteristic data into the risk stratification model to determine the risk stratification result. The risk stratification result is used to determine whether the claim case belongs to the category that can be processed automatically.

[0066] The system performs a predetermined analysis on the second data and outputs a first judgment result. This result serves as a classification identifier to determine whether a case can safely enter the fully automated processing channel (i.e., the automated processing category), thereby controlling subsequent process branches.

[0067] In one embodiment, the first judgment result is obtained by processing the second data through a risk stratification model.

[0068] Determining the first judgment result specifically includes the following steps:

[0069] The risk characteristic data is input into a configurable risk stratification model.

[0070] The risk stratification model is a judgment model based on a configurable rule set. Its core is a set of rules that can be dynamically loaded and modified, consisting of Boolean logic (AND, OR, NOT) and comparison operators (>, <, ==, ∈, etc.).

[0071] For example, the model can be configured with rules: claim amount < 1 million yuan, time of incident ∈ insurance liability period, and policy special identifier = none.

[0072] This application's embodiment describes data preparation and model invocation. The system uses previously acquired, structured risk characteristic data (such as claim amount, incident time, etc.) as input parameters and passes them to the model. Internally, the model first maps this data into a standardized multi-dimensional feature vector so that the subsequent rule engine can perform efficient pattern matching.

[0073] The risk stratification model performs logical judgment operations on the input risk feature data based on the predefined risk stratification rule set.

[0074] The embodiments described in this application represent the core reasoning process of the model. The model loads a subset of predefined risk stratification rules that are bound to the attributes of the current case (such as product line, region). These rules together constitute a configurable decision tree or rule set.

[0075] The model internally iterates through the rule set using the aforementioned feature vectors. For example, it executes a compound rule: IF (claim amount < threshold X) AND (policy special identifier = false) AND (equity attribution verification result = true) THEN Execute action A. This rule is a judgment logic composed of Boolean expressions and comparison operators.

[0076] The rule engine matches and evaluates the input feature vectors against the conditional parts of each rule. This process is transparent and interpretable because each judgment corresponds to a clear business rule. By iterating through and evaluating the feature vectors, the model transforms data into logical conclusions.

[0077] Output the first judgment result.

[0078] Based on the combined result of the logical judgment operation, the model generates and outputs a definite classification label.

[0079] When all the conditions of the aforementioned example rules are met, the model outputs a low-risk classification label (i.e., the first category label). If not, it may output a high-risk label or switch to manual labeling.

[0080] The first judgment result output (such as the first category identifier) ​​is directly used to indicate the processing category of the claim case (such as automatic processing).

[0081] The advantages of this design are: the entire decision-making process is rule-based, transparent and explainable, and easy to audit and comply with; the rule set is configurable and can be agilely adjusted and updated as business strategies, regional policies or risk appetite change without the need to refactor the system or model.

[0082] It should be further noted that the risk stratification model supports differentiated configurations based on region and strategy. The system maintains a set of dynamically loadable strategy configuration files (e.g., JSON or XML format). When processing a case, the system loads the corresponding subset of rules and risk threshold parameters in real time based on the case attributes (such as the branch office ID and product line ID), achieving dynamic context adaptation of the risk control strategy.

[0083] It should be noted that the construction and optimization of the risk stratification model is an ongoing process. In practice, through continuous learning and verification from historical claims data, it is possible to accurately screen out case types that are allowed to be processed automatically under the premise of controllable risk, and to design and dynamically adjust exclusive risk screening rule sets for these low-risk case categories. Step 140: In response to the first judgment result being an automated processing identifier, the following steps are executed:

[0084] In response to the first judgment result being a first type of identifier, third data is determined; the third data is the case data corresponding to the claim case identifier, including policy data and incident information.

[0085] When step 130 determines that the case belongs to the category that can be processed automatically (i.e., the first category identifier), the system immediately triggers the subsequent precise calculation sub-process.

[0086] The system queries and extracts the source data set used for actuarial and logical judgments, i.e., the third data, based on the claims case identifier. This data mainly includes structured policy data (such as type of insurance, sum insured, cash value, payment period, etc.) and incident information (such as cause of death, time, place and relevant supporting information).

[0087] Step 150: Determine the compensation amount result based on the case data corresponding to the claim case identifier; the compensation amount result is determined by matching the case data with a predefined set of calculation rules.

[0088] Obtain the compensation amount result; the compensation amount result is determined by matching the third data with a predefined set of calculation rules.

[0089] The system processes the third data determined in step 140 to obtain the compensation amount. This result is determined by matching and calculating the third data against a predefined set of calculation rules.

[0090] like Figure 2 As shown, the calculation rule set is a structured, executable set of rules pre-converted from complex insurance liabilities and payment conditions. The predefined calculation rule set is stored in the rule database as rule objects. Each rule object contains a condition part and an action part. The condition part defines the matching logic with each field of the policy data and claim information (e.g., Insurance Type = Whole Life Insurance AND Cause of Claim = Death Due to Illness); the action part is associated with a unique payout calculation function identifier. The rule matching engine takes the third-party data as input facts and performs pattern matching with the condition parts in the rule database. The matching process is as follows: the rule engine sequentially takes the third-party data as input facts and evaluates them against the condition parts of each rule. For rules that evaluate to true, the associated calculation function is executed. Through the above rule matching process, the system automatically determines the applicable insurance liability clauses and triggers the corresponding calculation functions, thereby automatically calculating the payable amount based on the specific third-party data.

[0091] Rule matching is the process of searching for rules that satisfy all preconditions in a predefined rule space; calculating the payout amount is the process of performing mathematical or logical operations associated with that rule.

[0092] For example, a matched rule might trigger a calculation function that takes the policy’s basic sum assured and current cash value as input and outputs the larger of the two as the payout amount.

[0093] In one embodiment, the matching calculation process specifically includes: matching the policy data and incident information with predefined insurance liability rules to determine the applicable insurance liability clauses. In another embodiment, calculating the compensation amount specifically includes: determining the insurance liability clauses; and executing the corresponding compensation calculation function based on the insurance liability clauses and the policy data and incident information. This embodiment clarifies the dependency of the compensation amount calculation on the aforementioned liability determination steps and points out that the calculation behavior is essentially calling a specific function or algorithm uniquely bound to the liability clauses.

[0094] Step 160: Obtain the second judgment result; the second judgment result is determined by matching the case data corresponding to the claim case identifier with the predefined risk rule set.

[0095] The system synchronously executes embedded risk control to obtain a second judgment result.

[0096] This result was determined by comparing and matching various types of case data associated with claim case identifiers with a predefined set of risk rules. This mechanism constructs an intelligent prevention and control safety net.

[0097] The risk rule set integrates and executes multiple types of risk control rules in real time, such as rights holder risk rules, anti-money laundering (AML) risk rules, and insurance fraud feature rules mined from historical fraud data. The system also integrates a real-time risk scanning service that monitors the case processing pipeline and compares case data with a dynamically updated high-risk feature pattern library (such as multiple related claims within a short period, combinations of specific regions and hospitals, etc.).

[0098] In one embodiment, the predefined risk rule set includes at least one of the following categories of rules: beneficiary risk rules, anti-money laundering risk rules, and insurance fraud characteristic rules. Beneficiary risk rules typically involve verifying the legal relationship between the beneficiary and the insured, and the compliance of the insurance purchase motivation; anti-money laundering risk rules monitor transaction patterns and fund flows in accordance with financial regulatory requirements; and insurance fraud characteristic rules set up identification logic based on known fraud patterns.

[0099] The second judgment result is the ruling on the risk scan. The logic is that the second judgment result is determined to be "passed" only when the comparison results of all risk checkpoints do not trigger alarms.

[0100] In one embodiment, the determination of the compensation amount in step 150 and the determination of the second judgment result in step 160 are executed concurrently. This concurrent execution method embodies the reengineering of the traditional linear workflow. In system implementation, this is achieved by creating and scheduling two independent threads (or asynchronous tasks). After step 140, the main process synchronously calls the claims service thread and the risk scanning service thread. After the two threads have completed execution, they return the results to the main process through a thread synchronization mechanism (such as CountDownLatch) or a message queue. After receiving the results from both threads, the main process executes step 170. Specifically, the comparison of the relevant data of the claims case with the preset risk judgment conditions is executed concurrently during the calculation of the compensation amount. Concurrent execution means that the two processing threads overlap in time, may share system resources and proceed independently. Its technical advantage lies in reducing the total waiting time of the process and improving the overall throughput.

[0101] Step 170: In response to the second judgment result being passed, output the approval instruction.

[0102] When the second judgment result obtained in step 160 is clearly approved, the system automatically generates and outputs a structured approval instruction. This instruction can directly trigger subsequent financial payment processes and notify relevant parties. The generated instruction includes at least a case identifier, approval status, and approved amount, used to drive downstream systems to execute payment and service notifications.

[0103] Thus, through steps 110 to 170, this application has achieved fully automated intelligent processing of a life insurance claim from acceptance to the generation of a settlement instruction. A closed loop of risk screening, intelligent claims calculation, and risk review has been completed without human intervention.

[0104] To support high-concurrency operations and full regional coverage, this technical solution can be implemented based on a distributed microservice architecture. The risk stratification service, rule engine service, and risk control scanning service can be deployed as independent microservices, communicating through an API gateway. Case data flows between services as event messages, and different regional rule policies are dynamically loaded through a configuration center, achieving high availability and elastic scalability. Under this architecture, each provincial branch can encapsulate its localized claims business rules and risk policies through the graphical rule configuration interface and publish them to the central configuration center. When processing cases, the system automatically retrieves and loads the corresponding rule package from the configuration center based on the case's location information, thus achieving a technical unification of centralized control by the head office and the individual needs of branch offices.

[0105] The system also includes a rule configuration and management module, which provides a graphical rule configuration interface. This allows business administrators to intuitively define or modify risk rules and calculation rules by dragging and dropping logic components (AND / OR / NOT), setting threshold sliders, and so on. The backend serializes this visual configuration into a specific rule description language (such as Drools rule language) and stores it in the rule database, supporting rule version control and canary releases.

[0106] Figure 4 A structural diagram of a rapid response automatic claims review device provided in this application embodiment, used to implement the rapid response automatic claims review method as described in any embodiment of the first aspect, includes:

[0107] The receiving module 401 is used to receive the claim case identifier; it is also used to obtain predefined risk characteristic data from the database associated with the claim case identifier, as described in steps 110-120 of the embodiment.

[0108] The determination module 402 is used to match the risk feature data with a predefined risk stratification rule set to determine a first judgment result; the first judgment result is used to indicate the processing category of the claim case, as described in step 130 of the embodiment.

[0109] It is also used to determine the compensation amount result based on the case data corresponding to the claim case identifier; the compensation amount result is determined by matching the case data with a predefined set of calculation rules; the case data includes policy data and accident information, as described in step 150 of the embodiment.

[0110] It is also used to obtain a second judgment result; the second judgment result is determined by matching the case data with a predefined set of risk rules, as described in step 160 of the embodiment.

[0111] The output module 403, in response to the second judgment result being passed, outputs an approval instruction, as described in step 170 of the embodiment.

[0112] Furthermore, the receiving module includes a first receiving unit for receiving a claim case identifier; and is also used to obtain predefined risk characteristic data from the database associated with the claim case identifier.

[0113] The determining module includes a first determining unit, used to match the risk characteristic data with a predefined risk stratification rule set to determine a first judgment result; the first judgment result is used to indicate the processing category of the claim case.

[0114] Furthermore, the first determining unit integrates a risk stratification model based on a configurable rule set, used to perform the risk stratification judgment described in step 130. The internal implementation of this first determining unit can be further refined as follows:

[0115] Data acquisition subunit: used to acquire risk characteristic data from the receiving module.

[0116] Feature processing subunit: used to standardize risk feature data and combine it into feature vectors.

[0117] Model execution subunit: It has a built-in risk stratification model based on a configurable rule set, loads a rule subset that matches the attributes of the current case, performs logical judgment on the feature vector, and outputs the first judgment result.

[0118] The risk stratification model integrated within the first determining unit specifically includes:

[0119] Rule set storage unit: Stores a set of risk judgment rules based on Boolean logic and comparison operators.

[0120] Feature adapter: Maps the input risk feature data into a standardized feature vector.

[0121] Configurable inference engine: Loads a subset of rules bound to the current case context and iterates through the feature vectors to evaluate them.

[0122] Policy loading interface: Provides an interface with an external configuration center for dynamically loading subsets of differentiated rules.

[0123] It also includes a second determining unit, used to determine the compensation amount result based on the case data corresponding to the claim case identifier; the compensation amount result is determined by matching the case data with a predefined set of calculation rules; the case data includes policy data and incident information.

[0124] It also includes a third determining unit, which is further used to obtain a second judgment result; the second judgment result is determined by matching the case data with a predefined set of risk rules.

[0125] The output module includes a first output unit, which is used to output an approval instruction in response to the second judgment result being approved.

[0126] In one embodiment, the receiving module further includes a second receiving unit for determining the risk characteristic data, wherein the risk characteristic data includes at least one of the following: claim amount, time of incident, ownership information, and policy special identifier.

[0127] The above embodiments specifically define the constituent elements of the risk characteristic data in step 120.

[0128] In one embodiment, the first determining unit in the determining module includes a logically integrated risk stratification model for obtaining a first judgment result; the risk stratification model is a judgment model based on a configurable rule set.

[0129] The above embodiments specifically define the implementation method of the generation model of the first judgment result in step 130.

[0130] In one embodiment, the second determining unit in the determining module, in the process of obtaining the compensation amount result, matches the case data with a predefined set of calculation rules, specifically including: matching the policy data and accident information with predefined insurance liability rules to determine the applicable insurance liability clauses.

[0131] The above embodiment specifically defines the liability determination operation in the compensation amount matching calculation process described in step 150.

[0132] In one embodiment, the second determining unit in the determining module further includes, in the process of obtaining the compensation amount result, executing the corresponding compensation calculation function according to the determined insurance liability terms and the case data.

[0133] The above embodiment specifically defines the calculation execution operation in the compensation amount calculation process described in step 150.

[0134] In one embodiment, the determining module further includes a fourth determining unit, wherein the predefined risk rule set used includes at least one of the following types of rules: rights holder risk rules, anti-money laundering risk rules, and insurance fraud feature rules.

[0135] These rule sets can be configured and managed in a differentiated and dynamic manner based on business type, amount range, regional policies, etc. The system integrates a real-time risk scanning service, which compares case data with a dynamically updated high-risk feature pattern library.

[0136] The above embodiments specifically define the categories of the risk rule set described in step 160.

[0137] In one embodiment, the determining module further includes a fifth determining unit, specifically used to obtain a second determination result, and the obtaining process is executed concurrently with the process by which the second determining unit obtains the compensation amount result.

[0138] Concurrent execution is achieved by creating and scheduling two independent threads or asynchronous tasks. After step 140, the main process synchronously calls the calculation service thread and the risk scanning service thread. After both threads complete execution, the results are returned through a thread synchronization mechanism or message broker. This fifth determining unit corresponds to the specific implementation of the concurrency control method, including three steps: task parsing and dispatching, parallel execution, and result synchronization and decision-making.

[0139] The above embodiments specifically define the concurrent relationship in time between the process of obtaining the second judgment result and the process of obtaining the compensation amount result, thereby realizing parallel processing of the process.

[0140] In one embodiment, the system further includes a rule configuration management module. This module provides a graphical rule configuration interface, allowing authorized users to define or modify risk rules and calculation rules by dragging and dropping logic components, setting thresholds, and other methods. The backend serializes this configuration into a structured rule description file and stores it in a rule repository, and supports version control and canary release of rules, thereby enabling the iteration and secure deployment of business rules.

[0141] The rule configuration management module includes a front-end interaction layer, a rule transformation and storage layer, and a rule lifecycle management layer. Users can intuitively build or modify rules by dragging and dropping logic components and setting threshold sliders. This module communicates with the determination module and is used to define and maintain the risk stratification rule set used by the first determination unit, the calculation rule set used by the second determination unit, and the risk rule set used by the third determination unit.

[0142] Figure 5 This is a schematic diagram of an application scenario system according to an embodiment of this application. This embodiment corresponds to the rapid response automatic claims review system described in the third aspect of this application. The system includes:

[0143] User terminal 700 communicates with application server 600-1 and is used to provide an input interface for claims case identification and an output interface for review instructions.

[0144] Application server 600-1 is deployed with a fast-response automatic review device for claims as described in the second aspect of the present application, or is configured to execute the method described in any one of the first aspects of the present application, as described in steps 110 to 160.

[0145] The rule configuration server 600-2 communicates with the application server 600-1 to provide a graphical rule configuration interface and to convert configuration operations into structured rule description files for storage and distribution.

[0146] The mail server and / or payment gateway 600-3 are communicatively connected to the application server 600-1, and are used to receive the approval instruction and deliver the compensation notification or trigger the payment process.

[0147] The user terminal, application server, database server, rule configuration server, mail server, and payment gateway described in this application include physical servers, virtual servers, cloud servers, personal computers, smartphones, tablets, or distributed clusters consisting of multiple devices.

[0148] This application contains several embodiments. Figure 1 The corresponding text describes the flow of the method described in the first aspect; Figure 2 This is a structural diagram illustrating the insurance liability and compensation rules. Figure 3 A schematic diagram of the claims processing workflow reengineering; Figure 4 To review the device structure diagram; Figure 5 This is a schematic diagram illustrating the application scenario of the third aspect system described in this embodiment; Figure 6 and 7 An embodiment of the internal structure diagram of the server equipment and terminal equipment that make up the third aspect system is described.

[0149] Figure 6 This is a schematic diagram of the server equipment of the rapid response automatic claims review device provided in an embodiment of this application. Figure 6 As shown in the embodiments of this application, the server-side device is used to implement the rapid response automatic review method for claims as described in any embodiment of this application. The server-side device is used to perform, for example... Figure 4 The functions described in steps 110-160 are servers or server clusters, including interconnected application servers 600-1, rule configuration servers 600-2, mail servers and / or payment gateways 600-3.

[0150] Risk feature database 604 is connected to the application server and is used to store the risk feature data.

[0151] The policy database 605 is connected to the application server and is used to store the policy data and incident information.

[0152] The rule configuration server 606 communicates with the application server, has a built-in rule library, provides a graphical rule configuration interface, and converts configuration operations into structured rule description files for storage and distribution.

[0153] A standalone server device 600 includes at least one processor 601, a memory 602, and a communication interface 603. The various components of the server device 600 are coupled together via a bus system 604. The bus system 604 is used to enable communication between these components.

[0154] The communication interface 603 is used for data communication with external devices. The memory 602 stores executable modules or data structures. In this embodiment, the memory 602 stores a computer program for executing any of the foregoing method embodiments of this application. The processor 601 reads and executes the computer program in the memory 602 to control the electronic device 600 to perform the automatic review method steps as described above.

[0155] Figure 7 This is a schematic diagram of the terminal device of the rapid response claims automatic review apparatus provided in an embodiment of this application. The terminal device 700 includes at least one processor 701, a memory 702, a user interface 703, and at least one network interface 704. The various components of the terminal device 700 are coupled together via a bus system. The user interface 703 includes a display, keyboard, or touchscreen, etc., and is used to run an automated underwriting assessment interactive interface. The memory 702 stores a computer program, which, when executed by the processor 701, is used to implement the steps performed by the terminal device in the rapid response claims automatic review method as described in any embodiment of this application. The network interface 704 is used to enable communication with a server.

[0156] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0157] Therefore, this application also proposes a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the methods described in any embodiment of this application.

[0158] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0159] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0160] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0161] Furthermore, this application also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method described in any embodiment of this application. The processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor.

[0162] In a typical configuration, the electronic or computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory. Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0163] Furthermore, the aforementioned methods, apparatus, and hardware can be collectively implemented into a complete, rapid-response automated claims review system. This system can employ methods such as... Figure 6 The hardware foundation shown is built upon a distributed microservices architecture at the software level, and mainly includes the following core service modules that work together:

[0164] Rule configuration and management service: Provides a graphical interface for business personnel to define and maintain claims calculation rules and risk rules, and stores the rules in a structured manner in a central rule base, supporting rule version control and canary release.

[0165] Risk Stratification Service: As an independent microservice, it receives claim case identifiers, determines the risk level of a case based on multidimensional risk characteristic data obtained from multiple data sources and configurable rule sets, and outputs instructions on whether the case should enter an automatic or manual processing flow.

[0166] Intelligent claims processing engine service: For cases determined to be low-risk by the risk stratification service, the corresponding insurance liability calculation rule set is automatically loaded from the rule base to complete the accurate calculation of insurance liability matching and compensation amount.

[0167] Integrated risk control scanning service: runs concurrently with the intelligent calculation engine service, and calls multiple risk rule sets such as anti-money laundering and fraud feature recognition in real time to conduct a full-dimensional risk scan of the case.

[0168] Process orchestration and API gateway service: As the unified access point and process hub of the system, it receives external claims requests, automatically routes cases to the corresponding processing pipeline (automatic or manual) based on the output results of the risk stratification service, and coordinates the calls and data exchanges between various microservices.

[0169] Configuration Center: Centrally manages the differentiated rules, strategies, and parameters of various branches and products, enabling dynamic loading of business strategies and unified nationwide control.

[0170] The implementation of this system can integrate the methods, devices and hardware innovations disclosed in this application into a highly available, scalable, and nationwide-differentiated strategy-supporting automated life insurance claims operation platform.

[0171] The computer-readable storage medium includes permanent and non-permanent, removable and non-removable media, which can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0172] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0173] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0174] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical, technical, and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0175] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for rapid response and automatic review of claims, characterized in that, Includes the following steps: Obtain the claim case identifier; Retrieve predefined risk characteristic data from the database associated with the claim case identifier; The risk characteristic data is matched with a predefined risk stratification rule set to determine a first judgment result; the first judgment result is used to indicate the processing category of the claim case. In response to the first determination result being an automated processing identifier, the following steps are executed: The compensation amount is determined based on the case data corresponding to the claim case identifier; the compensation amount is determined by matching the case data with a predefined set of calculation rules; the case data includes policy data and incident information; A second judgment result is obtained; the second judgment result is determined by matching the case data with a predefined set of risk rules. If the second judgment result is passed, an approval instruction is output.

2. The rapid response automatic review method for claims as described in claim 1, characterized in that, The risk characteristic data includes at least one of the following: claim amount, time of incident, ownership information, and policy special identification.

3. The rapid response automatic review method for claims as described in claim 1, characterized in that, Determining the first judgment result specifically includes the following steps: The risk characteristic data is input into a configurable risk stratification model; The risk stratification model performs logical judgment operations on the input risk feature data based on the predefined risk stratification rule set. Output the first judgment result.

4. The rapid response automatic review method for claims as described in claim 1, characterized in that, Matching the case data with the predefined set of calculation rules specifically includes: The policy data and incident information are matched with predefined insurance liability rules to determine the applicable insurance liability clauses.

5. The rapid response automatic review method for claims as described in claim 1, characterized in that, The determination of the compensation amount also includes: Define the terms of insurance liability; Based on the aforementioned insurance liability terms and the aforementioned case data, execute the corresponding compensation calculation function.

6. The rapid response automatic review method for claims as described in claim 1, characterized in that, The predefined set of risk rules includes at least one of the following categories: Rules on risk of rights holders, rules on anti-money laundering risks, and rules on characteristics of insurance fraud.

7. The rapid response automatic review method for claims as described in claim 1, characterized in that, The second judgment result is determined concurrently with the result of determining the compensation amount.

8. The rapid response automatic review method for claims as described in claim 1, characterized in that, The predefined set of calculation rules and / or risk rules is edited and managed through a graphical rule configuration interface; The system converts configuration operations into structured rule description files for storage and supports rule version control and canary release.

9. A rapid response automatic claims review device, used to implement the rapid response automatic claims review method as described in any one of claims 1 to 8, characterized in that, Include: The receiving module is used to receive claim case identifiers; it is also used to obtain predefined risk characteristic data from the database associated with the claim case identifiers. The determination module is used to match the risk characteristic data with a predefined risk stratification rule set to determine a first judgment result; the first judgment result is used to indicate the processing category of the claim case. It is also used to determine the compensation amount based on the case data corresponding to the claim case identifier; the compensation amount is determined by matching the case data with a predefined set of calculation rules; the case data includes policy data and incident information; It is also used to obtain a second judgment result; the second judgment result is determined by matching the case data with a predefined set of risk rules. The output module, in response to the second judgment result being passed, outputs an approval instruction.

10. A rapid response automatic claims review system, characterized in that, Include: An application server, deployed with the apparatus as described in claim 9, or configured to perform the method as described in any one of claims 1 to 8; The user terminal is connected to the application server and is used to provide an input interface for claims case identification and an output interface for review instructions. The rule configuration server provides a graphical rule configuration interface and converts configuration operations into structured rule description files for storage and distribution. A mail server and / or payment gateway are used to receive the approval instruction and deliver compensation notifications or trigger payment processes.

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