Personal insurance fraud whole-process prevention monitoring system and method

Abstract

The invention relates to the technical field of insurance information, and discloses a personal insurance fraud full-process prevention monitoring system and method, and the system comprises a data collection and fusion module which is used for extracting data in real time or quasi-real time from business systems of insurance buying, underwriting, preservation, claim settlement and the like in an insurance company, external data such as medical records, credit investigation data, judicial information, internet financial behavior data, black and grey lists shared by other insurance companies and the like are integrated through an API interface, and multi-source heterogeneous data are cleaned, standardized and associated; and the multi-modal risk analysis engine comprises a rule engine layer, a machine learning model layer and a correlation graph analysis layer. A prevention and monitoring system covering a personal insurance full-service process is constructed, and anti-fraud monitoring is preposed to insurance and underwriting links and runs through a claim settlement link to form a closed loop. A traditional afterward response mode is changed, and early warning can be given out and intervention measures can be taken at the early stage of risk occurrence.

Description

Technical Field

[0001] This invention relates to the field of insurance information technology, specifically to a whole-process prevention and monitoring system and method for personal insurance fraud. Background Technology

[0002] In the life insurance sector, insurance fraud is rampant, severely damaging the economic interests of insurance companies and undermining financial order and social credit systems. Traditional anti-fraud methods in life insurance mainly include manual review and automated screening based on rule engines. Manual review relies on the experience and expertise of underwriters and claims personnel. While it can identify some fraudulent activities, it suffers from significant drawbacks such as strong subjectivity, low efficiency, and susceptibility to oversights. Rule engines, on the other hand, screen transactions based on predefined hard rules. Although this improves the efficiency of fraud detection, the rules are noticeably outdated, making it difficult to identify new or complex fraud patterns. Furthermore, it lacks flexibility and struggles to detect anomalies across different stages. Moreover, existing anti-fraud systems mostly respond after the fact during the claims process, a "lock the stable door after the horse has bolted" approach that fails to intervene effectively in the early stages of risk, resulting in insurance companies still incurring substantial unnecessary underwriting and management costs. Simultaneously, data within insurance companies and with external institutions (such as hospitals, police, and credit reporting agencies) is fragmented and isolated, forming information silos that hinder efficient and comprehensive information sharing and cross-validation, further limiting the accuracy and timeliness of fraud detection. These problems make existing anti-fraud measures inadequate in dealing with increasingly complex and covert insurance fraud.

[0003] To address these issues, those skilled in the art propose a comprehensive prevention and monitoring system and method for personal insurance fraud. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a comprehensive prevention and monitoring system and method for personal insurance fraud, which solves the problems of information silos, post-event response, and limited models and rules in personal insurance fraud identification.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a full-process prevention and monitoring system for personal insurance fraud, comprising: Data Acquisition and Fusion Module: Used to extract data from the insurance company's internal business systems such as insurance application, underwriting, policy maintenance, and claims in real time or near real time, and to integrate external data such as medical records, credit data, judicial information, internet finance behavior data, and black and gray lists shared by other insurance companies through API interfaces, and to clean, standardize and correlate multi-source heterogeneous data; The multimodal risk analysis engine comprises a rule engine layer, a machine learning model layer, and a correlation graph analysis layer. The rule engine layer executes predefined hard rules for initial risk screening. The machine learning model layer constructs feature variables based on historical fraud case data and trains a fraud prediction model using ensemble learning algorithms or deep learning models to generate a real-time fraud risk score for each insurance application and claim application. The correlation graph analysis layer constructs a knowledge graph with nodes such as people, policies, hospitals, and claim locations, and uses graph algorithms to identify fraud gangs and hidden relationships. Intelligent Decision and Response Module: Based on the integrated rule triggering results, machine learning risk scores, and graph analysis conclusions, it automatically makes decisions according to the preset strategy matrix and implements differentiated and hierarchical intervention strategies in the insurance, underwriting, and claims processes. In the underwriting process, it raises medical examination requirements, conducts survival investigations, or politely declines coverage. In the claims process, it automatically marks high-risk cases and pushes correlation graphs and anomaly analysis reports to investigators. Full-process output interface: used to connect the output results of each stage of the system with the insurance business process, so as to realize dynamic monitoring and response of the entire process.

[0006] Through the above technical solutions, the data acquisition and fusion module integrates multi-source data to provide a comprehensive data foundation for risk analysis; the multimodal risk analysis engine combines rule engine, machine learning model and correlation graph analysis to achieve accurate identification and assessment of fraud risk; the intelligent decision-making and response module automatically makes decisions based on the analysis results and executes corresponding intervention strategies in different business links to effectively reduce fraud risk; and the full-process output interface ensures seamless integration between the system and insurance business processes, enabling dynamic monitoring and response.

[0007] Preferably, the data acquisition and fusion module further includes a data security and privacy protection unit, which is used to encrypt the acquired data and ensure that the use of the data complies with relevant laws, regulations and privacy policies.

[0008] The aforementioned technical solution ensures the security of collected data through encryption, preventing data leakage or unauthorized use. Simultaneously, this unit ensures that data use strictly complies with relevant laws, regulations, and privacy policies, protecting the personal information of policyholders and insured individuals from misuse and safeguarding their privacy rights.

[0009] Preferably, the feature engineering of the machine learning model layer also includes analyzing the social network data of the policyholder and the insured, extracting feature variables such as social network concentration, in order to discover potential fraud gangs or abnormal behavior patterns.

[0010] By employing the aforementioned technical solutions and analyzing the social network data of policyholders and insured individuals, and extracting characteristic variables such as social network concentration, the machine learning model layer can more comprehensively assess fraud risk. Characteristic variables such as social network concentration help reveal potential connections between policyholders and insured individuals, thereby uncovering hidden fraud rings or abnormal behavioral patterns. This not only enhances the accuracy of fraud detection but also improves the system's risk identification capabilities, enabling insurance companies to more effectively prevent and respond to complex and ever-changing fraudulent activities, ensuring the robust operation of their business.

[0011] Preferably, the correlation graph analysis layer can also identify abnormal transaction patterns related to fraudulent behavior, such as frequent policy changes, inconsistencies between the claim location and the insurance location, and use these abnormal patterns as an important basis for risk assessment.

[0012] The aforementioned technical solutions identify abnormal transaction patterns associated with fraudulent activities, such as frequent policy changes and discrepancies between the claim location and the insurance application location, providing crucial information for risk assessment. This analysis can reveal complex relationships and potential fraudulent activities hidden within the data, helping the system more accurately identify high-risk cases, thereby improving the accuracy and efficiency of fraud detection and effectively reducing the fraud risk for insurance companies.

[0013] Preferably, the intelligent decision-making and response module further includes a closed-loop feedback unit, which is used to feed back the final investigation conclusions of all cases to the system, automatically optimize the machine learning model and rule base, and form a self-iterative reinforcement learning closed loop.

[0014] By employing the aforementioned technical solution, the system automatically optimizes its machine learning model and rule base by feeding back the final investigation findings of each case. This mechanism enables the system to continuously adjust and improve its analytical and decision-making capabilities based on actual investigation results, thereby forming a self-iteratory reinforcement learning loop.

[0015] Preferably, the end-to-end output interface also includes a data interface with external regulatory agencies, used to report relevant information on suspected fraud cases to the regulatory agencies and assist them in monitoring and preventing industry risks.

[0016] Through the aforementioned technical solution, the data interface with external regulatory agencies within the end-to-end output interface enables the timely reporting of information related to suspected fraud cases to these agencies. This function helps regulatory agencies monitor fraud dynamics within the industry in real time, strengthening industry risk monitoring and prevention capabilities. Through data sharing with regulatory agencies, insurance companies can better comply with regulatory requirements.

[0017] Preferably, the system also includes a user interface for displaying fraud risk analysis results, providing visual correlation graphs and evidence chains, and assisting insurance business personnel and investigators in decision-making and investigation.

[0018] The aforementioned technical solutions provide insurance agents and investigators with powerful decision support and investigative tools by intuitively displaying fraud risk analysis results, visualized correlation graphs, and evidence chains. This not only improves the efficiency of fraud identification and handling but also enhances agents' understanding and analysis of complex data, enabling them to make decisions more quickly and accurately, effectively improving the insurance company's risk management level and operational efficiency.

[0019] Preferably, the system further includes an anomaly detection and early warning unit, which is used to monitor the system's operating status and data anomalies in real time, promptly detect and warn of possible system failures or data tampering, and ensure the stable operation of the system and the reliability of the data.

[0020] Through the aforementioned technical solution, the anomaly detection and early warning unit can promptly detect and warn of potential system failures or data tampering by monitoring the system's operating status and data anomalies in real time. This function ensures the stable operation of the system and the reliability of the data, effectively preventing external attacks and internal errors, maintaining the normal operation of the system and the integrity of the data, and providing strong support for the sound operation of insurance business.

[0021] A method for preventing and monitoring personal insurance fraud throughout the entire process includes the following steps: Data collection and integration: Data is extracted in real time or near real time from the insurance company's internal business systems such as insurance application, underwriting, policy maintenance, and claims settlement. External data such as medical records, credit data, judicial information, internet finance behavior data, and black and gray lists shared by other insurance companies are integrated through API interfaces. Multi-source heterogeneous data is cleaned, standardized, and correlated to build a 360° panoramic data lake centered on the individual. Risk Analysis: Multimodal fraud risk analysis is performed on the collected and fused data, including rule engine analysis, machine learning model analysis, and correlation graph analysis, to comprehensively derive fraud risk assessment results; Decision-making and response: Based on the fraud risk assessment results, the system automatically makes decisions and implements corresponding intervention measures in the insurance application, underwriting, and claims processes. For example, in the insurance application process, it prompts sales personnel to strengthen their disclosure obligations or triggers supplementary questionnaires; in the underwriting process, it suggests raising medical examination requirements, conducting survival investigations, or politely declining to underwrite; and in the claims process, it automatically marks high-risk cases and pushes correlation graphs and anomaly analysis reports to investigators. Feedback and Optimization: The final investigation conclusions of all cases are fed back to the system, automatically optimizing the machine learning model and rule base, forming a self-iteratory reinforcement learning closed loop.

[0022] By integrating multi-source data and conducting in-depth analysis, the aforementioned technical solution enables accurate identification and effective intervention of fraud risks. It not only improves the efficiency and accuracy of fraud detection but also continuously optimizes system performance through a closed-loop feedback mechanism, thereby reducing potential losses for insurance companies and ensuring the stable operation of insurance business.

[0023] This invention provides a comprehensive prevention and monitoring system and method for personal insurance fraud. It has the following beneficial effects: 1. This invention constructs a prevention and monitoring system covering the entire life insurance business process, placing anti-fraud monitoring forward to the underwriting and application stages and extending it to the claims process, forming a closed loop. This changes the traditional reactive approach, enabling early warnings and interventions at an early stage of risk, such as suspicious underwriting, through measures like strengthening disclosure obligations and raising medical examination requirements. This proactive intervention effectively avoids insurance companies incurring significant unnecessary underwriting and management costs due to fraudulent activities, while also preventing fraudsters from continuing their fraudulent activities. It reduces fraud risk at its source, significantly minimizes potential losses for insurance companies, and protects their economic interests.

[0024] 2. This invention integrates multiple technologies, including rule engines, machine learning models, and correlation graph analysis, to construct a multimodal risk analysis engine. The rule engine can quickly and clearly screen known fraud patterns; the machine learning model, based on a large amount of historical fraud case data, is trained through complex feature engineering and advanced algorithms to adaptively discover hidden and novel fraud patterns; and correlation graph analysis can uncover hidden organized fraud networks and abnormal cross-stage relationships. This multimodal fusion analysis framework utilizes expert experience while fully leveraging the self-learning and adaptive capabilities of artificial intelligence, significantly improving the accuracy and coverage of fraud identification.

[0025] 3. This invention constructs a continuously updated dynamic personal risk profile based on multi-source internal and external data, capable of reflecting the risk status of policyholders and insured individuals in real time. With the continuous development of the insurance business and the rapid evolution of fraud methods, the system, through a closed-loop feedback mechanism, feeds back the final investigation conclusions of all cases to the system, automatically optimizing the machine learning model and rule base, forming a self-iterable reinforcement learning closed loop. This dynamic updating and self-learning capability enables the system to continuously adapt to new fraud patterns and regional differences, maintaining a keen awareness and effective identification ability against fraudulent activities. Attached Figure Description

[0026] Figure 1 This is the overall flowchart of the present invention; Figure 2 This is a flowchart illustrating the decision-making and response process of the present invention. Detailed Implementation

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0028] Please see the appendix Figure 1 - Appendix Figure 2 This invention provides a comprehensive prevention and monitoring system for personal insurance fraud, comprising: The data acquisition and fusion module is used to extract data from the insurance company's internal business systems such as underwriting, policy maintenance, and claims in real time or near real time. It integrates external data such as medical records, credit data, judicial information, internet finance behavior data, and blacklists and graylists shared by other insurance companies through API interfaces. The module cleans, standardizes, and correlates multi-source heterogeneous data. The data acquisition and fusion module also includes a data security and privacy protection unit, which encrypts the collected data and ensures that the use of the data complies with relevant laws, regulations, and privacy policies.

[0029] Specifically, the data acquisition and fusion module is the foundation of the entire system. It is responsible for collecting and integrating data from multiple sources to provide comprehensive and accurate data support for subsequent risk analysis and decision-making. Specifically, this module first extracts data in real-time or near real-time from the insurance company's internal business systems, including underwriting, policy maintenance, and claims processing. This data covers all aspects of the insurance business and is crucial information for assessing risk and identifying fraudulent activities. Simultaneously, to gain a more comprehensive understanding of the risk status of policyholders and insured individuals, this module also integrates various external data through API interfaces. This external data includes medical records to verify the policyholder's health status; credit data to assess the policyholder's credit risk; legal information to identify potential legal risks; internet finance behavior data to analyze the policyholder's financial behavior patterns; and blacklists and graylists shared by other insurance companies to identify known fraud suspects or high-risk individuals. After collection, this multi-source, heterogeneous data needs to be cleaned, standardized, and correlated. Data cleaning removes erroneous, duplicate, or incomplete data to ensure data quality; standardization converts data from different sources into a unified format for easier subsequent analysis and processing; and correlation integrates data from different systems to build a complete user profile and risk view. The data security and privacy protection unit is responsible for encrypting the collected data to ensure its security during transmission and storage. It also ensures that data use complies with relevant laws, regulations, and privacy policies, protecting the personal information of policyholders and insured persons from disclosure or misuse. Through these measures, the data acquisition and fusion module not only provides the system with rich data resources but also guarantees the secure and compliant use of data.

[0030] Multimodal risk analysis engine: includes a rule engine layer, a machine learning model layer, and a correlation graph analysis layer; the rule engine layer executes predefined hard rules for initial risk screening; the machine learning model layer constructs feature variables based on historical fraud case data, uses ensemble learning algorithms or deep learning models to train fraud prediction models, and generates real-time fraud risk scores for each insurance application and claim application; the correlation graph analysis layer constructs a knowledge graph with nodes such as people, policies, hospitals, and claim locations, and uses graph algorithms to identify fraud gangs and hidden relationships. Specifically, by integrating multiple analytical techniques, a comprehensive assessment of insurance fraud risk is achieved. This engine consists of three main layers: a rules engine layer, a machine learning model layer, and a correlation graph analysis layer. The rules engine layer performs initial data screening based on predefined hard rules. These rules, based on industry experience and known fraud patterns, can quickly identify obvious signs of fraud. The machine learning model layer uses historical fraud case data to construct feature variables and employs ensemble learning algorithms or deep learning models to train fraud prediction models. These models can learn complex patterns and relationships in the data, thereby generating real-time fraud risk scores for each insurance application and claim, providing a quantitative basis for risk assessment.

[0031] Feature engineering in the machine learning model layer also includes analyzing the social network data of policyholders and insured persons to extract feature variables such as social network concentration in order to discover potential fraud gangs or abnormal behavior patterns.

[0032] Specifically, the system collects and analyzes social network information related to policyholders and insured individuals. This may include their social media activities, interactions with others, and the composition of their social circles. Through in-depth analysis of this social network data, the system can extract characteristic variables such as social network concentration. Social network concentration reflects an individual's centrality within their social network—that is, how many other individuals they are connected to, and the strength and nature of these connections. In the context of insurance fraud, a high social network concentration indicates that the individual is part of a tightly connected social group.

[0033] The correlation graph analysis layer can also identify abnormal transaction patterns related to fraudulent activities, such as frequent policy changes and inconsistencies between the claim location and the insurance location, and use these abnormal patterns as an important basis for risk assessment.

[0034] Specifically, a knowledge graph is constructed to identify abnormal transaction patterns related to fraudulent activities. The knowledge graph uses nodes such as people, policies, hospitals, and claim locations, and edges such as insured relationships, kinship relationships, medical treatment relationships, and geographical location associations, forming a complex network structure. By employing graph algorithms, the system can identify abnormal transaction patterns. For example, frequent policy changes may indicate that the policyholder is attempting to circumvent the insurance company's scrutiny or seeking loopholes between different insurance companies to obtain improper benefits. Inconsistencies between the claim location and the insured's place of insurance may suggest that the insured is deliberately creating false claim scenarios or that an insurance fraud gang is conducting organized fraudulent activities in different regions.

[0035] Intelligent Decision and Response Module: Based on the integrated rule triggering results, machine learning risk scores, and graph analysis conclusions, it automatically makes decisions according to the preset strategy matrix and implements differentiated and hierarchical intervention strategies in the insurance, underwriting, and claims processes. In the underwriting process, it raises medical examination requirements, conducts survival investigations, or politely declines coverage. In the claims process, it automatically marks high-risk cases and pushes correlation graphs and anomaly analysis reports to investigators. Specifically, by comprehensively analyzing the results of the rule engine layer, machine learning model layer, and correlation graph analysis layer, the system automatically makes decisions based on a preset strategy matrix and executes corresponding intervention strategies at different stages of the insurance business. In the underwriting stage, if the risk assessment results indicate a high risk, the system will automatically prompt for increased medical examination requirements, conduct a survival investigation, or even refuse coverage, thereby reducing fraud risk at the source. In the claims stage, for cases marked as high-risk, the system will automatically mark the case as suspicious and push detailed information such as correlation graphs and anomaly analysis reports to professional investigators so that they can conduct investigations and handle cases more quickly and accurately. This differentiated and tiered intervention strategy not only improves the efficiency of fraud identification but also ensures the smoothness and compliance of the insurance business process, effectively protecting the interests of insurance companies.

[0036] The intelligent decision-making and response module also includes a closed-loop feedback unit, which is used to feed back the final investigation conclusions of all cases to the system, automatically optimize the machine learning model and rule base, and form a self-iterative reinforcement learning closed loop.

[0037] End-to-end output interface: This interface connects the output results of each stage of the system with the insurance business process, enabling dynamic monitoring and response throughout the entire process. The end-to-end output interface also includes a data interface with external regulatory agencies, used to report information on suspected fraud cases to these agencies, assisting them in monitoring and preventing industry risks.

[0038] Specifically, the main function of the end-to-end output interface is to seamlessly integrate the output results of each stage of the system with the insurance business process, ensuring dynamic monitoring and real-time response throughout the entire insurance business process. Through this interface, the system can promptly feed back risk assessment results, the implementation status of intervention measures, and other key information to each stage of the insurance business, such as underwriting, insurance application, and claims settlement, thereby achieving effective management and risk control of the entire insurance business process. In addition, the end-to-end output interface also includes a data interface with external regulatory agencies for reporting relevant information on suspected fraud cases to these agencies.

[0039] The system also includes a user interface to display fraud risk analysis results, provide visual correlation graphs and evidence chains, and assist insurance agents and investigators in decision-making and investigation.

[0040] The system also includes an anomaly detection and early warning unit, which is used to monitor the system's operating status and data anomalies in real time, promptly detect and warn of possible system failures or data tampering, and ensure the stable operation of the system and the reliability of the data.

[0041] Specifically, the user interface intuitively displays fraud risk analysis results, presenting correlation graphs and evidence chains through visualization technology. This enables insurance agents and investigators to quickly understand the fraud risks behind complex data, thereby making more effective decisions and conducting investigations. Simultaneously, the system's built-in anomaly detection and early warning unit can monitor system operation status and data anomalies in real time, promptly detecting and warning of potential system failures or data tampering.

[0042] A method for preventing and monitoring personal insurance fraud throughout the entire process includes the following steps: Data collection and integration: Data is extracted in real time or near real time from the insurance company's internal business systems such as insurance application, underwriting, policy maintenance, and claims settlement. External data such as medical records, credit data, judicial information, internet finance behavior data, and black and gray lists shared by other insurance companies are integrated through API interfaces. Multi-source heterogeneous data is cleaned, standardized, and correlated to build a 360° panoramic data lake centered on the individual. Risk Analysis: Multimodal fraud risk analysis is performed on the collected and fused data, including rule engine analysis, machine learning model analysis, and correlation graph analysis, to comprehensively derive fraud risk assessment results; Decision-making and response: Based on the fraud risk assessment results, the system automatically makes decisions and implements corresponding intervention measures in the insurance application, underwriting, and claims processes. For example, in the insurance application process, it prompts sales personnel to strengthen their disclosure obligations or triggers supplementary questionnaires; in the underwriting process, it suggests raising medical examination requirements, conducting survival investigations, or politely declining to underwrite; and in the claims process, it automatically marks high-risk cases and pushes correlation graphs and anomaly analysis reports to investigators. Feedback and Optimization: The final investigation conclusions of all cases are fed back to the system, automatically optimizing the machine learning model and rule base, forming a self-iteratory reinforcement learning closed loop.

[0043] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A comprehensive prevention and monitoring system for personal insurance fraud, characterized in that, include: Data Acquisition and Fusion Module: Used to extract data from the insurance company's internal business systems such as insurance application, underwriting, policy maintenance, and claims in real time or near real time, and to integrate external data such as medical records, credit data, judicial information, internet finance behavior data, and black and gray lists shared by other insurance companies through API interfaces, and to clean, standardize and correlate multi-source heterogeneous data; The multimodal risk analysis engine comprises a rule engine layer, a machine learning model layer, and a correlation graph analysis layer. The rule engine layer executes predefined hard rules for initial risk screening. The machine learning model layer constructs feature variables based on historical fraud case data and trains a fraud prediction model using ensemble learning algorithms or deep learning models to generate a real-time fraud risk score for each insurance application and claim application. The correlation graph analysis layer constructs a knowledge graph with nodes such as people, policies, hospitals, and claim locations, and uses graph algorithms to identify fraud gangs and hidden relationships. Intelligent Decision and Response Module: Based on the integrated rule triggering results, machine learning risk scores, and graph analysis conclusions, it automatically makes decisions according to the preset strategy matrix and implements differentiated and hierarchical intervention strategies in the insurance, underwriting, and claims processes. In the underwriting process, it raises medical examination requirements, conducts survival investigations, or politely declines coverage. In the claims process, it automatically marks high-risk cases and pushes correlation graphs and anomaly analysis reports to investigators. Full-process output interface: used to connect the output results of each stage of the system with the insurance business process, so as to realize dynamic monitoring and response of the entire process.

2. The personal insurance fraud prevention and monitoring system according to claim 1, characterized in that, The data acquisition and fusion module also includes a data security and privacy protection unit, which is used to encrypt the acquired data and ensure that the use of the data complies with relevant laws, regulations and privacy policies.

3. The personal insurance fraud prevention and monitoring system according to claim 1, characterized in that, The feature engineering of the machine learning model layer also includes analyzing the social network data of policyholders and insured persons, extracting feature variables such as social network concentration, in order to discover potential fraud gangs or abnormal behavior patterns.

4. The personal insurance fraud prevention and monitoring system according to claim 1, characterized in that, The correlation graph analysis layer can also identify abnormal transaction patterns related to fraudulent activities, such as frequent policy changes and inconsistencies between the claim location and the insurance location, and use these abnormal patterns as an important basis for risk assessment.

5. The personal insurance fraud prevention and monitoring system according to claim 1, characterized in that, The intelligent decision-making and response module also includes a closed-loop feedback unit, which is used to feed back the final investigation conclusions of all cases to the system, automatically optimize the machine learning model and rule base, and form a self-iterative reinforcement learning closed loop.

6. The personal insurance fraud prevention and monitoring system according to claim 1, characterized in that, The end-to-end output interface also includes a data interface with external regulatory agencies, used to report relevant information on suspected fraud cases to the regulatory agencies and assist them in monitoring and preventing industry risks.

7. The personal insurance fraud prevention and monitoring system according to claim 1, characterized in that, The system also includes a user interface for displaying fraud risk analysis results, providing visual correlation graphs and evidence chains, and assisting insurance agents and investigators in decision-making and investigation.

8. A full-process prevention and monitoring system for personal insurance fraud according to claim 1, characterized in that, The system also includes an anomaly detection and early warning unit, which is used to monitor the system's operating status and data anomalies in real time, promptly detect and warn of possible system failures or data tampering, and ensure the stable operation of the system and the reliability of the data.

9. A method for preventing and monitoring personal insurance fraud throughout the entire process, comprising a personal insurance fraud prevention and monitoring system according to any one of claims 1-8, characterized in that, Includes the following steps: Data collection and integration: Data is extracted in real time or near real time from the insurance company's internal business systems such as insurance application, underwriting, policy maintenance, and claims settlement. External data such as medical records, credit data, judicial information, internet finance behavior data, and black and gray lists shared by other insurance companies are integrated through API interfaces. Multi-source heterogeneous data is cleaned, standardized, and correlated to build a 360° panoramic data lake centered on the individual. Risk Analysis: Multimodal fraud risk analysis is performed on the collected and fused data, including rule engine analysis, machine learning model analysis, and correlation graph analysis, to comprehensively derive fraud risk assessment results; Decision-making and response: Based on the fraud risk assessment results, the system automatically makes decisions and implements corresponding intervention measures in the insurance application, underwriting, and claims processes. For example, in the insurance application process, it prompts sales personnel to strengthen their disclosure obligations or triggers supplementary questionnaires; in the underwriting process, it suggests raising medical examination requirements, conducting survival investigations, or politely declining to underwrite; and in the claims process, it automatically marks high-risk cases and pushes correlation graphs and anomaly analysis reports to investigators. Feedback and Optimization: The final investigation conclusions of all cases are fed back to the system, automatically optimizing the machine learning model and rule base, forming a self-iteratory reinforcement learning closed loop.

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