Insurance claim settlement intelligent processing method and device based on alignment modeling

Abstract

This invention discloses an intelligent insurance claims processing method and apparatus based on alignment modeling, belonging to the field of intelligent insurance claims processing technology. The method includes: acquiring claims application information; integrating and standardizing multi-source data of the claims application information to obtain structured claims feature data; matching the structured claims feature data with pre-stored claims guidance principles to obtain principle matching results and associated risk factors; performing dynamic weight adjustment and fraud risk assessment based on the principle matching results and the risk factors to generate a comprehensive claims decision result; generating decision explanation information based on the comprehensive claims decision result, and outputting a claims processing report containing the decision explanation information. This invention can achieve dynamic alignment between claims business rules and AI processing behavior, effectively solving the problems of rigid and inconsistent decision-making, improving fraud identification accuracy and processing efficiency, and enhancing the transparency of the decision-making process.

Description

Technical Field

[0001] This invention relates to the field of intelligent insurance claims processing technology, and in particular to an intelligent insurance claims processing method and apparatus based on alignment modeling. Background Technology

[0002] Insurance claims processing, as a core component of insurance business, is widely used in risk management and customer service. With the development of artificial intelligence technology, related technologies typically employ the collaborative operation of rule engines and workflow systems to construct automated claims processing systems. Specifically, this system covers the entire process from claim acceptance to payout decisions, including key aspects such as predefined process configuration, static rule-driven processes, and traditional OCR document recognition, achieving digital processing of basic business operations.

[0003] However, existing claims processing methods rely on static rule configurations without dynamic adaptation mechanisms. This can lead to delayed rule updates or difficulty in identifying complex fraud patterns, impacting the accuracy and timeliness of decisions. Furthermore, traditional solutions lack transparency and consistency in decision-making, resulting in significant discrepancies in decisions among different reviewers. This reduces customer trust and increases compliance risks, severely hindering the intelligent development of insurance technology services and the improvement of user experience. Summary of the Invention

[0004] The main objective of this invention is to provide an intelligent insurance claims processing method based on alignment modeling.

[0005] Another objective of this invention is to propose an intelligent insurance claims processing device based on alignment modeling.

[0006] The third objective of this invention is to provide an electronic device.

[0007] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0008] To achieve the above objectives, a first aspect of the present invention proposes an intelligent insurance claims processing method based on alignment modeling, comprising:

[0009] Obtain claims application information, integrate and standardize multi-source data on the claims application information to obtain structured claims feature data; The structured claims feature data is matched with the pre-stored claims guidelines to obtain the matching results and associated risk factors. Based on the principle matching results and the aforementioned risk factors, dynamic weight adjustments and fraud risk assessments are performed to generate comprehensive claims decision results. Based on the comprehensive claims decision results, decision explanation information is generated, and a claims processing report containing the decision explanation information is output.

[0010] Optionally, the process includes obtaining claim application information, integrating and standardizing multi-source data to obtain structured claim feature data, and also includes: The system retrieves multi-source claims information of the target object in batches through a standardized API interface, and performs classification and labeling of the multi-source claims information into structured data and unstructured materials to obtain a classified claims data set. The unstructured materials in the categorized claims data set are processed jointly using VL large model OCR technology and large language model technology to generate a text data stream with semantic tags; Based on preset data quality control rules, integrity checks and outlier detection are performed on text data streams and structured data to obtain structured claims feature data.

[0011] Optionally, the structured claims feature data can be matched with pre-stored claims guidelines to obtain the matching results and associated risk factors, including: Load pre-stored claims guidelines, each of which adopts a seven-tuple structure including risk factors, triggering conditions, processing actions, decision weights, compliance levels, scope of application, and confidence thresholds; Perform conditional semantic matching between claim feature data and triggering conditions in the claim guidelines, and perform business scenario filtering based on insurance product type and claim amount; In accordance with regulatory requirements and company policies, the matched principles are stratified by risk level, and the matching results and associated risk factors are generated by comprehensively considering semantic similarity and historical success rate.

[0012] Optionally, based on the principle matching results and the aforementioned risk factors, dynamic weight adjustments and fraud risk assessments are performed to generate a comprehensive claims decision, including: The initial weights of each risk factor are set based on regulatory requirements and industry standards, and the weight configuration is optimized based on historical claims data and fraud results. The weight configuration is individually adjusted for specific customer groups or product types, and the effect of the weight configuration is monitored in real time and automatically fine-tuned and optimized. We collect data on claim application materials and behavioral patterns through multi-source fraud signal collection channels, and use data mining techniques to discover hidden patterns and abnormal indicators. Based on the analysis results, fraud patterns are identified and classified. The analysis results are compared with known fraud patterns and abnormal behaviors that deviate from the normal pattern are identified. The system integrates all analysis results to generate a comprehensive claims decision, including the determination of the compensation decision, suggestions for the processing procedure, and the rationale for the decision.

[0013] Optionally, decision explanation information is generated based on the comprehensive claims decision results, and a claims processing report containing the decision explanation information is output, including: Record the choices and reasons for each decision-making node in detail and mark the key information and evidence that affect the decision, generating a complete timeline of the decision-making process; The decision-making basis is visualized in the form of charts and flowcharts, and the decision results are given easy-to-understand explanations and multilingual support text. Provides specific guidance and contact information for decision appeals, and generates a claims processing report that includes explanations of the decisions and visual displays of the results.

[0014] To achieve the above objectives, a second aspect of the present invention provides an intelligent insurance claims processing device based on alignment modeling, comprising: The preprocessing module is used to acquire claims application information, integrate and standardize the claims application information from multiple sources, and obtain structured claims feature data. The matching module is used to match structured claims feature data with pre-stored claims guidelines to obtain the matching results and associated risk factors. The assessment module is used to dynamically adjust the weights and assess fraud risk based on the principle matching results and the risk factors, and generate a comprehensive claims decision result. The processing module is used to generate decision explanation information based on the comprehensive claims decision results, and output a claims processing report containing the decision explanation information.

[0015] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0016] To achieve the above objectives, a third aspect of this application provides an electronic device, including a processor and a memory; wherein the processor runs a program corresponding to the executable program code stored in the memory, in order to implement the intelligent insurance claims processing method based on alignment modeling as described in the first aspect embodiment.

[0017] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements the intelligent insurance claims processing method based on alignment modeling as described in the first aspect embodiment.

[0018] The embodiments of the present invention have the following beneficial effects: This invention enables precise alignment between claims processing rules and AI processing behavior, effectively solving the problems of rigid decision-making and insufficient fraud detection, and significantly improving the accuracy, consistency, transparency and efficiency of claims processing. Attached Figure Description

[0019] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating an intelligent insurance claims processing method based on alignment modeling, provided in an embodiment of the present invention; Figure 2 An architecture diagram of an intelligent insurance claims processing system based on alignment modeling is provided for an embodiment of the present invention. Figure 3 This is a schematic diagram of the claims alignment principle matching algorithm provided in an embodiment of the present invention; Figure 4 A schematic diagram of the intelligent fraud detection algorithm provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the intelligent claims decision generation algorithm provided in an embodiment of the present invention; Figure 6 This is a structural diagram of an intelligent insurance claims processing device based on alignment modeling, provided in an embodiment of the present invention. Detailed Implementation

[0020] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] The following description, with reference to the accompanying drawings, describes an intelligent insurance claims processing method and apparatus based on alignment modeling, according to embodiments of the present invention.

[0023] Example 1 This invention provides an intelligent insurance claims processing method based on alignment modeling, such as... Figure 1 As shown, the method includes the following steps: Step 1: Obtain claim application information, integrate and standardize multi-source data of claim application information to obtain structured claim feature data.

[0024] In order to obtain claims application information and obtain structured claims feature data through multi-source data integration and standardization, this application relies on system architecture and related modules and algorithms to implement this process to support subsequent claims decisions.

[0025] In the embodiments of this application, such as Figure 2 As shown, the intelligent insurance claims processing system proposed in this application adopts a five-layer vertical architecture design. Each layer has clearly defined responsibilities, and modules collaborate efficiently. The vertical data flow provides stable architectural support for obtaining claims application information and subsequent multi-source data integration and standardized processing. In this embodiment, the first layer, the user interaction layer, includes a claims application interface, mobile application, and API interfaces, used to receive claims application information submitted by customers. It also supports third-party system integration, providing an entry point for obtaining multi-source claims application information. The fifth layer, the data storage layer, includes a claims case database, used to store various types of claims-related data, providing a data foundation for multi-source data integration.

[0026] In this embodiment of the application, claim application information is obtained through the information collection module of the system. The claim application information comes from multiple data sources, including structured data such as basic information of the insured, insurance product information, and historical claim records obtained from the customer basic information database; unstructured materials such as accident reports, medical certificates, and photos obtained from claim material sources; and auxiliary information such as hospital records, repair records, and court records obtained from external data sources, to ensure the comprehensiveness of the claim application information.

[0027] In this embodiment, the claims alignment modeler has a multi-source data fusion function, which can integrate the above-mentioned multi-source claims application information, summarize and merge claims application information from different data sources and of different types, break down data barriers, realize the unified collection of multi-source data, and lay the foundation for subsequent standardized processing.

[0028] In the embodiments of this application, such as Figure 3 As shown, the integrated claim application information is standardized through the information collection and preprocessing module of the intelligent decision engine and the stage 1 of the claim alignment principle matching algorithm. In this embodiment, the standardization process specifically includes data cleaning, data integrity checking, outlier detection, duplicate data removal, and data normalization. For unstructured accident reports, medical certificates, and other materials, VL large-model OCR technology and large language model NLP technology are used to extract key information and convert it into a processable data format, ensuring the integrity and consistency of the claim application information and eliminating data format differences and invalid data interference.

[0029] In this embodiment, through stage 2 of the claims alignment principle matching algorithm, deep feature extraction is performed on the standardized claims application information. For text data, a pre-trained insurance domain language model is used for semantic encoding; for numerical data, standardization and normalization are performed; for image data, computer vision technology is used to extract relevant features; and for time series data, time series analysis technology is used to extract trend features. Finally, all features are converted into structured claims feature data of a unified dimension, providing reliable and effective data support for subsequent claims-related decision analysis.

[0030] In this embodiment of the application, the acquisition of claims application information, integration of multi-source data and standardized processing are realized through a system layered architecture, related functional modules and algorithms, which lays the foundation for obtaining the principle matching results and associated risk factors.

[0031] Step 2: Match the structured claims feature data with the pre-stored claims guidelines to obtain the matching results and associated risk factors.

[0032] In order to obtain accurate principle matching results and associated risk factors, this application conducts multi-dimensional intelligent matching and evaluation of structured claims feature data and pre-stored claims guidelines.

[0033] In this embodiment of the application, the structured claims feature data obtained after the aforementioned multi-source data integration and standardization process is precisely matched with the claims guidance principles pre-stored in the system, and finally the principle matching result and the risk factors associated with the matching result are obtained, providing core matching basis for subsequent claims decisions.

[0034] In this embodiment, an intelligent decision engine performs intelligent risk factor matching. This matching algorithm, based on a dual evaluation mechanism of semantic similarity and business rules, ensures the accuracy and relevance of the matching results. In this embodiment, conditional semantic matching is one of the core steps in the matching process. This application calculates the semantic similarity between the currently acquired structured claims feature data and the triggering conditions set in the claims guidelines. Through precise semantic analysis, it determines the degree of fit between the two, avoiding matching errors caused by semantic bias.

[0035] In this embodiment, during the business scenario filtering stage, the application uses multiple key dimensions such as insurance product type, claim amount, and customer group to perform scenario-based screening of the matching process. This ensures that the matching claim guidelines are highly compatible with the specific business scenario of the current claim application, excluding inapplicable guidelines and improving matching efficiency. In this embodiment, during the risk level stratification stage, the application strictly follows regulatory requirements and company policies to manage the risks involved in the matching process in a tiered manner, clarifying the control standards for different risk levels and providing a basis for subsequent risk management. In this embodiment, during the matching degree scoring stage, the application comprehensively considers multiple factors such as semantic similarity, historical matching success rate, and business importance to quantitatively score each matching operation, intuitively reflecting the degree of matching between structured claim feature data and claim guidelines.

[0036] In this embodiment, the matching evaluation process of claims alignment principles is further improved through stage 3 of the claims alignment principle matching algorithm. In this embodiment, the system iterates through all available claims alignment principles, performing a comprehensive matching calculation between the risk factors and triggering conditions in each principle and the current structured claims feature data. Specifically, this matching calculation process includes: calculating the semantic similarity between the current structured claims features and the triggering conditions of the claims alignment principles; comprehensively evaluating the matching degree of the business scenario; fully considering relevant factors such as the historical execution success rate of the claims alignment principle; finally generating a comprehensive matching score; combining this score to determine the final principle matching result; and extracting the risk factors associated with the result to ensure that the matching result is scientific and reliable, providing strong support for subsequent claims decisions.

[0037] In this embodiment of the application, risk factor intelligent matching and claims guidance principle matching evaluation are used to obtain principle matching results and associated risk factors, which lays the foundation for generating comprehensive claims decision results in the future.

[0038] Step 3: Based on the principle matching results and the risk factors, perform dynamic weight adjustment and fraud risk assessment to generate a comprehensive claims decision result.

[0039] In order to generate accurate and reliable comprehensive claims decision results, this application dynamically adjusts the weights of the principle matching results and risk factors and conducts fraud risk assessment.

[0040] In this embodiment, based on the principle matching results and corresponding risk factors, dynamic weight adjustment and fraud risk assessment are performed to ultimately generate a comprehensive claims decision. In this embodiment, dynamic weight calculation is achieved through a weight calculation engine within the intelligent decision engine. This engine determines the contribution of each risk factor to the final claims decision, specifically including initializing basic weights based on regulatory requirements and industry standards, optimizing weight configuration based on historical claims data and fraud detection results, performing individualized weight fine-tuning for specific customer groups or insurance product types, and real-time monitoring and automatic adjustment of the weight configuration effect. In this embodiment, the application generates intelligent claims decisions by integrating various analysis results through a decision generation engine. Based on the comprehensive evaluation results, the compensation amount and method are determined, and processing flow suggestions including normal claims, supplementary materials, and claim rejection are generated. Detailed explanations and justifications are provided for each claims decision, and the confidence and reliability of the decision results are evaluated.

[0041] In the embodiments of this application, such as Figure 4 As shown, an intelligent fraud detector identifies and issues early warnings for fraudulent claims in real time, improving the accuracy and security of claims processing. In this embodiment, the intelligent fraud detector acquires fraud-related information through multi-source fraud signal collection, including abnormal pattern analysis of claims application materials, abnormal pattern monitoring of claims application behavior, analysis of abnormal correlations between customers and claims, and verification of the authenticity of claims information through external data sources. In this embodiment, fraud identification is achieved through a fraud pattern recognition algorithm. Fraud-related feature vectors are extracted from claims data, unsupervised learning methods are used for anomaly detection, supervised learning methods are used for classification and identification of suspected fraud, and multiple ensemble learning algorithms are combined to improve fraud detection accuracy. In this embodiment, a risk level assessment mechanism calculates a fraud risk score for each claims application, converting the score into high, medium, and low risk levels. The risk judgment threshold is dynamically adjusted according to actual business conditions, and the confidence level of the fraud risk judgment results is assessed. In this embodiment of the application, the warning level is divided into four levels: red, orange, yellow and blue, according to the degree of impact of fraud risk. The application automatically generates a warning report containing risk description, analysis basis and response suggestions, and pushes it to different business departments and positions in a personalized manner. The warning information and warning level are updated in real time through a dynamic update mechanism.

[0042] In this embodiment, dynamic weight adjustment and decision generation are completed through stage 4 of the claims alignment principle matching algorithm. Based on the matched claims guidance principles, the system dynamically adjusts the weights of each risk factor, considering regulatory requirements, historical data, business strategies, and other factors. This is then combined with all analytical data, including fraud risk assessment results, to generate the final claims decision, including payment decisions, processing recommendations, and risk warnings, along with detailed explanations of the decisions. In this embodiment, the application utilizes an intelligent fraud detection algorithm to achieve full-process fraud identification and warning. This involves sequentially executing steps such as multi-source fraud signal acquisition, fraud feature engineering and analysis, fraud pattern identification and classification, risk level assessment and warning, and warning information generation and push notifications. This comprehensively identifies various types of claims fraud and promptly pushes risk alerts. In this embodiment, the application uses an intelligent claims decision generation algorithm to complete the entire process from basic claims assessment to final claims decision output. This involves sequentially executing steps such as basic claims assessment, composite risk factor analysis, dynamic weight calculation and adjustment, comprehensive decision scoring, and claims decision logic application, resulting in a scientific, standardized, and interpretable claims decision.

[0043] In this embodiment, complex claims business rules are decomposed into fine-grained atomic-level guiding principles through claims alignment modeling technology, and a dynamic risk factor weight adjustment mechanism is established to achieve real-time alignment and adaptive adjustment of claims processing behavior. In this embodiment, a multi-channel fraud signal collection and intelligent analysis mechanism is constructed using intelligent fraud detection technology, and an automatic identification and classification algorithm for emerging fraud patterns is established to achieve personalized push and dynamic updates of fraud risk warnings. In this embodiment, a complete evaluation system is constructed using multi-dimensional claims assessment technology, encompassing basic assessment, risk analysis, weight optimization, and decision generation, to achieve multi-level intelligent analysis and confidence assessment of claims decisions. In this embodiment, the four core components of this application—the claims alignment modeler, intelligent decision engine, intelligent fraud detector, and decision interpreter—form a deep collaborative working mechanism, jointly constituting a closed-loop intelligent processing system, realizing intelligent processing throughout the entire process from claims application acceptance to decision interpretation.

[0044] In this embodiment, the system can be efficiently integrated with existing claims systems. It connects to core business systems, claims management systems, imaging systems, and third-party data interfaces such as hospitals, repair shops, and courts through standard interfaces, supporting real-time data synchronization, historical data import and migration, and data quality monitoring. In this embodiment, the system adopts a microservice architecture and cloud-native design. Each core component can be deployed independently and elastically scaled, supporting containerized deployment, multi-cloud environment deployment, and automatic scaling. It possesses high availability, disaster recovery capabilities, and a comprehensive monitoring and logging system.

[0045] In this embodiment of the application, a comprehensive claims decision is obtained through dynamic weight adjustment, fraud risk assessment and intelligent decision generation, which lays the foundation for the subsequent output of claims processing reports.

[0046] Step 4: Generate decision explanation information based on the comprehensive claims decision results, and output a claims processing report containing the decision explanation information.

[0047] To enhance the transparency and explainability of claims decisions, this application performs transparency processing on claims decisions and generates traceable and visual explanations of the decisions.

[0048] In this embodiment, an intelligent decision-making engine is used to process claims decisions with transparency, ensuring that the claims decision-making process is explainable and traceable. This transparency processing includes tracing the complete decision-making path, recording key decision points, visualizing the basis for the decisions, generating corresponding explanatory text for the decision results, and providing relevant guidance for customers to appeal the decisions.

[0049] In this embodiment, a decision interpreter provides clear and explainable explanations for claims decisions, further enhancing decision transparency and customer trust. In this embodiment, the decision interpreter traces the decision-making process, meticulously recording the selection and rationale for each decision node, marking key information and evidence influencing the decision, generating a complete timeline of the decision-making process, and recording the responsible party for each decision stage. In this embodiment, the decision interpreter visualizes the decision basis, generating an intuitive decision flowchart, displaying the impact weight of each risk factor on the decision, and generating case comparison analysis charts and related indicator trend analysis charts. In this embodiment, the decision interpreter generates concise decision summaries and detailed explanations of the reasons for the decision results, transforming technical jargon into easily understandable expressions, and supporting the generation of multilingual explanatory text. In this embodiment, the decision interpreter provides customers with an explanation of the appeal process, a list of required appeal materials, reminders of key appeal deadlines, and corresponding contact information and consultation channels.

[0050] In the embodiments of this application, such as Figure 5 The system demonstrates that stage 6 of the intelligent claims decision generation algorithm performs decision transparency processing and outputs the final claims decision result. In this embodiment, the system performs compliance checks on the decision result to ensure that the decision complies with regulatory requirements and company policies, generates visualized decision basis and explanations, provides decision appeal guidance, and generates a complete claims decision report.

[0051] In this embodiment of the application, an interpretable generation mechanism for claims decisions is established through decision transparency generation technology. This enables the automatic sorting and visualization of decision-making basis, complete traceability and recording of the decision-making process, enhances customer trust in claims decisions, facilitates business personnel's understanding and application of system suggestions, and supports regulatory inspections and customer appeal handling.

[0052] In this embodiment of the application, an intelligent decision engine, a decision interpreter, and related algorithms and technologies are used to achieve transparent processing, traceability and explainability of claims decisions, and provision of appeal guidance, thereby improving decision transparency and customer trust.

[0053] This application provides three detailed embodiments: Specific Implementation Example 1: Intelligent Processing of Car Insurance Claims.

[0054] Application scenario: An insurance company handles claims for vehicle collision accidents.

[0055] Technical implementation process: 1. Customer reporting: Customers upload photos and related materials of the accident through a mobile application.

[0056] 2. Intelligent material recognition: The system automatically recognizes information such as accident photos, driver's licenses, and vehicle registration certificates.

[0057] 3. Matching of claims settlement principles: Risk factors: accident severity, vehicle value, driver's record; Triggering conditions: Loss amount > 5000 yuan AND involving a third party AND first police report; Action taken: Initiate in-depth review process + fraud risk assessment.

[0058] 4. Intelligent Fraud Detection: Identify abnormal patterns: The accident photos do not match the time of the report; Risk level: Medium risk (7.2 points / 10 points); Warning level: Orange alert, manual review required.

[0059] 5. Generation of decision-making transparency: Reasoning for the decision: "Based on the analysis of accident photos and historical records, the system recommends manual review to ensure the accuracy of the claim." Visualization: Accident analysis charts and risk assessment graphs.

[0060] Specific Implementation Example 2: Intelligent Processing of Health Insurance Claims.

[0061] Application scenario: Intelligent review of critical illness insurance claim applications.

[0062] Technical implementation process: 1. Claim application receipt: Customers submit medical diagnosis reports, expense lists, and other materials.

[0063] 2. Intelligent Analysis of Medical Materials: OCR recognizes the content of medical reports; NLP analysis of disease diagnosis and treatment plans; Verify the authenticity of medical institutions.

[0064] 3. Underwriting alignment principle matching: Risk factors: disease type, treatment cost, past medical history, hospital level; Triggering conditions: Critical illness claim AND medical expenses > 100,000 AND age > 50 years old; Action taken: Initiate expert consultation process + medical authenticity verification.

[0065] 4. Multi-dimensional compliance checks: Regulatory compliance: Check whether it complies with the relevant provisions of the Insurance Law; Business rule: Verify whether it complies with the company's critical illness insurance terms; Medical standards: Checking the compliance of medical practices.

[0066] 5. Intelligent decision generation: Compensation decision: Normal compensation of 500,000 yuan; Recommended course of action: Normal procedure, no additional review required; Decision confidence level: 92.3%.

[0067] Specific Implementation Example 3: Intelligent Anti-Fraud for Property Insurance Claims.

[0068] Application scenario: Fraud detection in corporate property insurance claims.

[0069] Technical implementation process: 1. Claims Application Analysis: The company applied for property damage compensation of 5 million yuan.

[0070] 2. Multi-source data fusion: Corporate financial data: financial statements and tax records for the past three years; Industry data: Claims statistics of companies in the same industry; External verification: business registration information, judicial records.

[0071] 3. Anomaly pattern recognition: Anomaly in timing: The claim application date differs from the date of the disaster by two months; Abnormal amount: The proportion of claims amount to the company's assets is too high (80%); Abnormal behavior: The company has seen an abnormal increase in the frequency of claims over the past two years.

[0072] 4. Fraud Risk Assessment: Overall risk score: 8.7 out of 10 (high risk); Risk level: Red. Fraud probability: 78.3%.

[0073] 5. Early warning and response mechanism: Automatic blocking: Suspends the claims process; Human intervention: Initiate an anti-fraud investigation; Evidence collection: Automatically collect relevant evidence materials.

[0074] Example 2 This invention provides an intelligent insurance claims processing device based on alignment modeling, such as... Figure 6 As shown, the device includes: The preprocessing module 100 is used to acquire claim application information, integrate and standardize the claim application information from multiple sources, and obtain structured claim feature data. The matching module 200 is used to match structured claims feature data with pre-stored claims guidelines to obtain the matching results and associated risk factors. The assessment module 300 is used to dynamically adjust the weights and assess fraud risk based on the principle matching results and the risk factors, and generate a comprehensive claims decision result. The processing module 400 is used to generate decision explanation information based on the comprehensive claims decision results and output a claims processing report containing the decision explanation information.

[0075] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0076] Example 3 To implement the methods of the above embodiments, the present invention also provides an electronic device, which includes a memory and a processor; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the various steps of the methods described above.

[0077] Example 4 To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.

[0078] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0079] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0080] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A smart insurance claims processing method based on alignment modeling, characterized in that, include: Obtain claims application information, integrate and standardize multi-source data on the claims application information to obtain structured claims feature data; The structured claims feature data is matched with the pre-stored claims guidelines to obtain the matching results and associated risk factors. Based on the principle matching results and the aforementioned risk factors, dynamic weight adjustments and fraud risk assessments are performed to generate comprehensive claims decision results. Based on the comprehensive claims decision results, decision explanation information is generated, and a claims processing report containing the decision explanation information is output.

2. The method according to claim 1, characterized in that, The process of obtaining claim application information, integrating and standardizing multi-source data to obtain structured claim feature data, also includes: The system retrieves multi-source claims information of the target object in batches through a standardized API interface, and performs classification and labeling of the multi-source claims information into structured data and unstructured materials to obtain a classified claims data set. The unstructured materials in the categorized claims data set are processed jointly using VL large model OCR technology and large language model technology to generate a text data stream with semantic tags; Based on preset data quality control rules, integrity checks and outlier detection are performed on text data streams and structured data to obtain structured claims feature data.

3. The method according to claim 1, characterized in that, The process of matching structured claims feature data with pre-stored claims guidelines to obtain the matching results and associated risk factors also includes: Load pre-stored claims guidelines, each of which adopts a seven-tuple structure including risk factors, triggering conditions, processing actions, decision weights, compliance levels, scope of application, and confidence thresholds; Perform conditional semantic matching between claim feature data and triggering conditions in the claim guidelines, and perform business scenario filtering based on insurance product type and claim amount; In accordance with regulatory requirements and company policies, the matched principles are stratified by risk level, and the matching results and associated risk factors are generated by comprehensively considering semantic similarity and historical success rate.

4. The method according to claim 1, characterized in that, The dynamic weight adjustment and fraud risk assessment based on the principle matching results and the risk factors generate a comprehensive claims decision, including: The initial weights of each risk factor are set based on regulatory requirements and industry standards, and the weight configuration is optimized based on historical claims data and fraud results. The weight configuration is individually adjusted for specific customer groups or product types, and the effect of the weight configuration is monitored in real time and automatically fine-tuned and optimized. We collect data on claim application materials and behavioral patterns through multi-source fraud signal collection channels, and use data mining techniques to discover hidden patterns and abnormal indicators. Based on the analysis results, fraud patterns are identified and classified. The analysis results are compared with known fraud patterns and abnormal behaviors that deviate from the normal pattern are identified. The system integrates all analysis results to generate a comprehensive claims decision, including the determination of the compensation decision, suggestions for the processing procedure, and the rationale for the decision.

5. The method according to claim 1, characterized in that, The step of generating decision interpretation information based on the comprehensive claims decision results and outputting a claims processing report containing the decision interpretation information further includes: Record the choices and reasons for each decision-making node in detail and mark the key information and evidence that affect the decision, generating a complete timeline of the decision-making process; The decision-making basis is visualized in the form of charts and flowcharts, and the decision results are given easy-to-understand explanations and multilingual support text. Provides specific guidance and contact information for decision appeals, and generates a claims processing report that includes explanations of the decisions and visual displays of the results.

6. An intelligent insurance claims processing device based on alignment modeling, characterized in that, include: The preprocessing module is used to acquire claims application information, integrate and standardize the claims application information from multiple sources, and obtain structured claims feature data. The matching module is used to match structured claims feature data with pre-stored claims guidelines to obtain the matching results and associated risk factors. The assessment module is used to dynamically adjust the weights and assess fraud risk based on the principle matching results and the risk factors, and generate a comprehensive claims decision result. The processing module is used to generate decision explanation information based on the comprehensive claims decision results, and output a claims processing report containing the decision explanation information.

7. An electronic device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method as described in any one of claims 1-5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-5.

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