20 results about "Group insurance" patented technology

The invention discloses a group insurance product recommendation algorithm and device based on big data and computer equipment, particularly relates to the field of insurance science and technology, and comprises feature fusion and standardization, space-time adaptation degree calculation, group preference propagation, dynamic demand prediction, multi-objective optimization sorting and feedback self-adaption. According to the method, deep integration of multi-source data is realized through multi-modal feature fusion and encryption standardization, service coverage radius change and policy periodic attenuation characteristics are dynamically captured by using a three-dimensional space-time quantification model, and recommendation timeliness is improved in combination with a group preference propagation mechanism; the innovatively designed double-target Pareto optimization strategy cooperates with enterprise profit and customer utility targets, and a space-time adaptation coefficient and a propagation weight are dynamically adjusted by collecting business indexes in real time, so that a closed-loop feedback system is formed. The technology effectively solves the problems of single data dimension, static rule lagging and benefit imbalance of a traditional method, and has the advantages of privacy protection, dynamic response and multi-target balance.

The invention discloses a group insurance service intelligent renewal reminding and automatic insurance renewal method and system, and relates to the technical field of insurance services. According to the method, the work risk factors are extracted from the historical employee work records through the work risk dimension, the historical group insurance business records are traversed through the work risk factors to output the group insurance business fragments, the group insurance business fragments and the work risk factors are subjected to cyclic matching, and the group insurance-risk reference pairs are output according to the cyclic matching result. Comparing the historical group insurance service records and the historical employee work records with group insurance service fragments, obtaining habitual group insurance service fragments according to a comparison result, matching a group insurance-risk reference pair according to a comparison result of the real-time employee work records and work risk factors, generating a preliminary group insurance renewal scheme according to the habitual group insurance service fragments, and performing group insurance renewal according to the preliminary group insurance renewal scheme. And judging whether the preliminary group insurance renewal scheme is completely matched with the real-time employee work record, and obtaining a group insurance renewal implementation scheme according to a matching result.

The invention relates to the technical field of data processing, and discloses a group insurance policy processing method and device based on multi-modal learning, equipment and a medium. According to the scheme, a first semantic embedding feature of a target field is extracted from a group insurance policy text and image through a multi-modal large model; the second semantic embedding feature is extracted from the system filling text and the target field in the group insurance policy image, and the text and image information is fully fused, so that the key information in the group insurance policy can be captured more comprehensively and accurately. The feature similarity of the first semantic embedding feature and the second semantic embedding feature is calculated through a cross-modal semantic alignment algorithm, so that the semantic difference is recognized more accurately. The difference detection result is generated according to the semantic similarity, the problem field in the group insurance policy is positioned, the auditing efficiency and accuracy are improved, and potential risks are avoided. According to the scheme, claim settlement disputes caused by information errors of group insurance policies in the financial field and the medical field can be reduced, and the insurance service efficiency and quality are improved.

The invention discloses a group insurance underwriting method based on artificial intelligence. The method comprises the steps that S1, enterprise level and employee level multi-source data information submitted by an insurance buying enterprise is acquired and preprocessed; s2, constructing a composite risk feature set of an enterprise level and an employee level based on the data preprocessed in the S1; s3, performing correlation evaluation on the data preprocessed in the S1 based on the composite risk feature set constructed in the S2, screening out high-correlation features higher than a preset threshold value, and generating feature vectors according to the high-correlation features; s4, performing primary review on the feature vectors generated in the S3, and screening insurance policies which are obviously compliant or illegal; s5, constructing a multi-model fusion engine for risk prediction by adopting a Stacking integrated learning architecture; and S6, carrying out risk scoring on the insurance policy screened and compliant in the S4 by adopting the multi-model fusion engine constructed in the S5, and outputting a final underwriting conclusion. According to the method, multi-dimensional analysis is carried out on the underwriting data, and the risk assessment report is generated regularly, so that risk grading is carried out on insurance enterprises and customers.

The application discloses a group insurance product recommendation algorithm and device based on big data and a computer device, and particularly relates to the field of insurance technology, and comprises feature fusion and standardization, space-time adaptation degree calculation, group preference propagation, dynamic demand prediction, multi-objective optimization sorting and feedback self-adaptation.The application realizes deep integration of multi-source data through multi-modal feature fusion and encryption standardization, dynamically captures service coverage radius change and policy period attenuation characteristics by using a three-dimensional space-time quantization model, improves recommendation timeliness in combination with a group preference propagation mechanism, cooperates enterprise profit and customer utility targets by means of an innovatively designed double-target Pareto optimization strategy, dynamically adjusts space-time adaptation coefficients and propagation weights by means of real-time collection of business indexes, and forms a closed-loop feedback system.The technology effectively solves the problems of single data dimension, static rule lag and interest imbalance in traditional methods, and has the advantages of privacy protection, dynamic response and multi-objective balance.

The invention provides an automatic processing method and device for life insurance counter business, and relates to the technical field of control systems.The method comprises the steps that a multi-mode identity verification module authenticates the identity of a salesman, and after the identity passes, group insurance policy information under the name of the salesman is inquired and displayed; the OCR recognition and intelligent classification module scans and recognizes security or claim settlement application materials and automatically classifies the materials, and structured data is generated and presented to a confirmation interface; a two-channel mechanism of local code scanning authentication or background short message notification auditing is selected according to service types, and an application is automatically submitted to a core system after auditing is completed; polling verification is carried out on the application, if verification fails, verification is carried out again at the interval of 48 hours, and manual intervention is triggered after more than 10 times of verification fails; after the audit is passed, the unified output platform generates and prints a delivery order, and meanwhile, a service result is notified through a short message. According to the life insurance counter service automatic processing method, full-process automation of preservation and claim settlement services is realized, the processing efficiency is improved, and the manual intervention and operation error rate is reduced.

The invention relates to the technical field of intelligent decision making, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses an insurance data control value analysis method, device and equipment and a medium. Classifying the historical insurance data into discrete data and regression data according to a data type analysis result; dividing the discrete data into a plurality of groups of classified batch data according to equal data volume; extracting explicit features and implicit features of the classified batch data to obtain feature data; calculating a warning parameter of the feature data, and performing feature code matching on the feature data one by one according to the warning parameter to obtain a matching identifier; and carrying out weighted averaging processing on the regression data to obtain a first control value. And by dividing data category distribution and accurately analyzing a control value result, clearer guidance is provided for subsequent business decision-making.

The invention specifically relates to the field of data analysis, and discloses an intelligent machine account data reminding, analysis and management system for group insurance businesses, which comprises a data entry module, a data updating module, a data analysis module, a system management module, an intelligent reminding module and a man-machine interaction module, and is characterized in that the data entry module is used for entering group insurance business data; the data updating module automatically updates the standing book state; the data analysis module is used for performing multi-dimensional analysis on group insurance business data and calculating a risk assessment index, a business performance assessment index and a business stability index; the system management module is used for managing and adjusting group insurance services; the intelligent reminding module is used for sending a reminding notice; the man-machine interaction module is used for providing a man-machine interaction interface According to the invention, compliance check is carried out through the data entry module, and multi-dimensional analysis is carried out on group insurance business data through the data analysis module, so that the operation cost and the management difficulty are effectively reduced, and the flexibility and convenience of work are improved.

The invention discloses an intelligent bidirectional scoring and management system for group insurance salesmen and channels, and particularly relates to the technical field of digital management, and the system comprises a bidirectional scoring module which carries out the scoring of the salesmen and channels through a bidirectional evaluation interface disposed at a salesman mobile terminal and a channel management system. The method comprises the following steps: acquiring resource support scores of salesmen for channels and service capability scores of the channels for the salesmen in real time, introducing a Nash equilibrium game algorithm to eliminate malicious score deviation, obtaining bidirectional score data based on a time attenuation factor and data normalization, and constructing a bidirectional mapping relationship with a multi-modal data acquisition module according to a target service; according to the invention, the bidirectional scoring data, the business operation layer data, the interaction communication layer data, the external environment data and the compliance risk control layer data are analyzed and processed, and the two layers jointly output the salesman-channel matching degree and trigger the intelligent strategy, so that the automatic intelligent matching of the cooperation relationship is realized, the matching efficiency and precision are improved, and the user experience is improved. And efficient resource configuration is promoted.

The invention relates to the technical field of group insurance risk early warning, in particular to a group insurance risk grading early warning method and system based on big data, and the method comprises the following steps: obtaining group members corresponding to a group insurance and risk features for risk assessment, and building an association relationship between the group members and the risk features, the association relationship comprises a feature disk, a member disk and a plurality of docking chains; acquiring risk features corresponding to each group member in the insurance group to obtain a current risk feature set; monitoring a member change event of the insured group; when a member change event occurs in the insured group, updating a current risk feature set of the group based on the association relationship; inputting the updated current risk feature set into a risk assessment model, and generating an updated risk level and early warning information of the insured group; and according to the updated risk level, a corresponding risk early warning response action is triggered, so that the accuracy and timeliness of an evaluation result can be improved.

The present application provides a group insurance type granularity claims ratio prediction method and related equipment. First, the enterprise insurance type is used as the granularity to indicate that the smallest unit of data is the insurance type, and there is no need to obtain overly detailed data, which provides convenience for subsequent data use. Secondly, cleaning up dirty data can improve the accuracy and efficiency of claims ratio prediction. Then, a binary classification model group is used to predict the claims ratio, which frees the staff from the high-intensity labor brought by manual prediction, and there is no need to use complex algorithms to confirm the specific value of the claims ratio. It is only necessary to judge the position of the predicted claims ratio in the preset distribution range, which reduces the difficulty of claims ratio prediction. Finally, the sub-interval label corresponding to the target sub-interval is output as the prediction result, which achieves a certain degree of confidentiality and makes the prediction result more intuitive.

The invention relates to the technical field of insurance risk assessment, and discloses a group insurance policy claim settlement high-risk time sequence early warning and analysis method based on SQL, and the method comprises the following steps: a data collection and preprocessing module which collects basic data related to group insurance policy claim settlement from a plurality of business databases at regular intervals, comprising but not limited to insurance policy information, claim settlement case details and group member information, the collected data is cleaned and standardized to ensure the quality and consistency of the data, and a reliable data basis is provided for subsequent analysis. And the time sequence feature extraction module is used for extracting key time sequence features related to the group insurance policy claim risk based on the preprocessed data by using an SQL query statement. Through constructing the group insurance policy claim settlement high-risk time sequence early warning and analysis method based on the SQL, analysis of the group insurance policy claim settlement risk is realized, and at the same time, the powerful data processing and query functions of the SQL language are utilized to show a significant efficiency advantage in the aspects of group insurance policy claim settlement risk management and analysis.

The invention provides a group insurance dynamic pricing method based on multi-source data fusion. The group insurance dynamic pricing method comprises the following steps that S1, a data access module accesses and obtains data; s2, performing feature extraction and fusion on the desensitized and aggregated data by a feature extraction and fusion module; s3, performing dynamic pricing calculation on the group risk portrait vector by a dynamic pricing calculation engine; and S4, the quotation generation and feedback module generates a final quotation scheme and feeds back the final quotation scheme to the approval operation interface. The invention also provides a group insurance dynamic pricing model system based on multi-source data fusion. The system comprises a data access module, a feature extraction and fusion module, a dynamic pricing calculation engine and a quotation generation and feedback module. According to the pricing system model and method provided by the invention, objective, scientific and sufficient underwriting risk assessment is carried out on the group short-term insurance service according to the previous compensation condition of the service and the compensation condition of the same kind of service in the pricing link, the compensation condition is reasonably predicted, and the underwriting risk can be effectively prevented.

The invention discloses a unitized construction method for a life insurance core business system, and relates to the technical field of computers, and the method comprises the steps: dividing the life insurance core business system into independent system units such as a product unit, a contract unit, a preservation unit, a claim settlement unit, a group insurance core unit, an insurance policy management unit and a universal center, and each unit comprises an exclusive database, an operation environment and a code library; a standardized interface protocol containing a unified data format, HTTPS transmission and request response flow is defined, and it is guaranteed that interaction is efficient and accurate; each unit is realized by a micro-service architecture, and faults are isolated by means of service registration discovery, load balancing and a fusing mechanism; the system integrates DevOps and AIOPS systems, realizes continuous delivery through automation, predicts faults through machine learning, and visually manages micro-services through a unified operation and maintenance monitoring platform. According to the invention, the expansibility and flexibility of the life insurance core service system can be improved, the maintenance cost is reduced, and the complex service processing capability is enhanced.

The application discloses a kind of group insurance business personnel and channel intelligent two-way scoring and management system, specifically related to digital management technical field, including two-way scoring module, through the two-way evaluation interface of business personnel mobile terminal and channel management system deployment, real-time acquisition business personnel resource support score to channel and channel service ability score to business personnel, introduce Nash equilibrium game algorithm to eliminate malicious scoring deviation, and obtain two-way scoring data based on time decay factor and data normalization, and according to target business and multi-modal data acquisition module constructs two-way mapping relationship;The application is analyzed and processed by two-way scoring data and business operation layer data, interactive communication layer data, external environment data and compliance risk control layer data, and the business personnel-channel matching degree is outputted and intelligent strategy is triggered, the automatic intelligent matching of cooperative relationship is realized, the matching efficiency and accuracy are improved, and resource efficient configuration is promoted.

The invention belongs to the technical field of data processing, is suitable for financial scenes and medical scenes, and discloses a group insurance policy data processing method and device, equipment and a medium, and the method comprises the steps: obtaining to-be-processed group insurance policy data, and carrying out the multi-thread task decomposition, generating a plurality of to-be-processed sub-tasks corresponding to the to-be-processed group insurance policy data; based on a consistent Hash algorithm, distributing each to-be-processed sub-task to each computing node of a distributed cluster; the resource utilization rate of each computing node is monitored in real time based on a resource monitor, and the monitored resource utilization rate is synchronized to a load balancer; the load balancer evaluates the load condition of each computing node according to the response delay and the error rate, and dynamically adjusts the flow distribution of each computing node according to the evaluation result; and asynchronous processing is carried out on the sub-tasks in each computing node after the flow distribution is adjusted, and the asynchronously processed group insurance policy data is generated, so that the processing efficiency of the group insurance policy data is effectively improved.

The invention discloses a group insurance renewal data intelligent comparison method and system, and mainly relates to the technical field of application of big data analysis in insurance business. Comprising the following steps that multi-dimensional data of group insurance personnel is acquired, and the multi-dimensional data comprises basic information, historical insurance renewal records, non-insurance renewal reasons, occupational types, health states and claim settlement records; preprocessing the multi-dimensional data, including data cleaning, duplicate removal, formatting and data desensitization, and constructing a three-level label system; based on the preprocessed data, performing insurance renewal behavior analysis by using a machine learning model, including training an insurance renewal trend analysis model, calculating an insurance renewal influence coefficient and generating an insurance renewal prediction result; and according to an analysis result, outputting an insurance renewal rate report, attribute analysis of the non-insurance-renewal personnel and a service strategy suggestion. The method has the beneficial effects that dynamic prediction of group insurance renewal behaviors, accurate tracing of multi-dimensional attributes and automatic generation of personalized service strategies are realized.

The invention provides a group insurance customized product intelligent combination recommendation system based on a knowledge graph, and relates to the technical field of knowledge graph construction and intelligent recommendation, and the system comprises a knowledge graph construction module, a group demand analysis module, an intelligent combination recommendation module, a dynamic updating module, and an optional family guarantee linkage and health management closed loop module. A group insurance knowledge graph is constructed by integrating multi-source heterogeneous data, enterprise demands are structurally analyzed to generate demand portraits, a customized product combination scheme is generated in combination with knowledge reasoning and a combination optimization algorithm, and system suitability is maintained through dynamic updating; the knowledge graph dimension is expanded through an optional module, and personal and family guarantee deep adaptation and health management closed loop are achieved. According to the system, the problems that a traditional recommendation system is insufficient in association modeling, poor in product combination pertinence and opaque in recommendation logic are solved, the accuracy, transparency and dynamic adaptation capability of a recommendation scheme are improved, and the enterprise credibility and landing efficiency are improved.

The invention relates to the technical field of insurance business management, in particular to a group insurance salesman portrait construction method and device and computer equipment, and the method comprises the steps: firstly constructing a salesman portrait standard database, receiving and verifying a salesman information data template file, and obtaining salesman information data passing verification; then performing multi-dimensional feature extraction and comprehensive analysis on the salesman information data to obtain a salesman portrait comprehensive feature score, and generating a salesman portrait construction result according to the salesman portrait comprehensive feature score; and finally, monitoring through a system monitoring technology and a salesman portrait comprehensive feature score, and triggering an update feedback mechanism according to a monitoring result. Through a feature extraction technology, multi-dimensional feature extraction is carried out on received and verified salesman information data, an accurate and comprehensive salesman portrait is constructed, and an insurance company is helped to deeply understand the features and advantages of salesmen.