A system to promote insurance industry re-service

Abstract

The present invention discloses a system for promoting insurance industry re-service, which relates to the technical field of software development and mainly solves the problems of slow efficiency in solving customer problems, low efficiency in customer claims application and messy claims information. The system for promoting insurance industry re-service includes a server, a client and a middleware. The long short-term memory network LSTM is used to analyze customer purchase, consultation and claims behavior data to improve the efficiency of solving customer problems. The automated claims model is used to automatically match claim conditions for claim applications submitted by customers, thereby improving the efficiency of customer claims application. The online intelligent customer service is used to realize instant demand communication services, thereby improving the efficiency of preparing customer claims application information.

Description

Technical Field

[0001] The present invention relates to the technical field of software development, and in particular to a system for promoting insurance industry re-service. Background Art

[0002] Insurance services in the traditional sense are mainly carried out around the core of emergency compensation and payment, which mainly includes the insurance company's liability to customers, that is, when an insurance accident occurs, the insurer provides corresponding economic compensation or payment to the insured or beneficiary in accordance with the contract. With the deepening of modern service concepts, the connotation and extension of insurance services have undergone significant changes. Traditional service methods can no longer adapt to modern people's service concepts and cannot provide customers with more comprehensive and detailed protection services. This invention is to solve the problems of slow customer problem solving efficiency, low customer claim application efficiency and messy claim information, and is a re-service system to promote the insurance industry.

[0003] Therefore, the present invention provides a re-service system for the insurance industry, which analyzes customer purchase, consultation and claims behavior data through the long short-term memory network LSTM to improve the efficiency of solving customer problems; automatically matches claim conditions for claim applications submitted by customers through an automated claim model, thereby improving the efficiency of customer claims applications; and realizes instant demand communication services through online intelligent customer service, thereby improving the efficiency of customer claim application information preparation. Summary of the invention

[0004] The purpose of the present invention is to solve the problems of slow customer problem solving efficiency, low customer claim application efficiency and messy claim information in the prior art, and to propose a re-service system to promote the insurance industry.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A system for promoting re-service in the insurance industry, comprising:

[0007] Servers, which are used to store insurance contracts, customer information, and transaction record data, and process insurance business logic;

[0008] Client, used to enable interaction between users and service personnel;

[0009] Middleware, which manages the communication and coordination between different components.

[0010] As a further description of the above technical solution, the server includes:

[0011] Distributed database management system, using HBase distributed database to distribute data on multiple nodes and store redundantly on multiple nodes;

[0012] The data analysis module decomposes the data processing tasks into multiple nodes for processing, and processes and analyzes the data stream in real time;

[0013] Cloud service unit, which implements elastic computing resources through Google Cloud and performs data backup and data recovery;

[0014] Security components monitor system performance in real time and record operation logs, using SSL / TLS to encrypt data transmission.

[0015] As a further description of the above technical solution, the data analysis module integrates the system with the CRM system to achieve accurate customer matching and customer service optimization; the data analysis module uses the long short-term memory network LSTM to analyze customer purchase, consultation and claims behavior data, and evaluates risks based on customer data and historical records to optimize insurance product pricing.

[0016] As a further description of the above technical solution, the application method of the LSTM in the system is: collect historical data from the CRM system, transaction records and customer service log sources, and construct time series features, including purchase frequency and service request time interval; select a multi-layer LSTM structure to handle complex problems in the insurance policy, and define the input dimension of the time series data, and divide the data into a training set, a validation set and a test set for iterative training; adjust the learning rate and network parameters by monitoring the performance of the validation set, and evaluate the model performance on the validation set and the test set; deploy the model, encapsulate the LSTM model as a microservice, and use an API interface to integrate it with the CRM system and the automated claims module in the insurance system; receive new data for real-time prediction, and update the model regularly and on demand based on model performance and business needs.

[0017] As a further description of the above technical solution, the client includes:

[0018] User interface, where customers manage policies, apply for claims, and perform service operations through HTML and React front-end frameworks, interacting with the system and service personnel;

[0019] The claims module automatically matches the claims conditions of the claims applications submitted by customers through an automated claims model to calculate the compensation amount;

[0020] Auxiliary tools allow customers to sign insurance policies online through electronic signature tools; find the nearest insurance agent through map services; and achieve instant demand communication services through online intelligent customer service.

[0021] As a further description of the above technical solution, the working implementation method of the automated claims model is: automatically classify and evaluate the risk level of claims requests through machine learning and historical claims cases; automatically determine claim eligibility and compensation amount by applying insurance terms and policies; automatically execute claim conditions through the blockchain platform, and the smart contract automatically executes the payment process according to the claim conditions to ensure transaction security and transparency; automatically read and convert image information in claims documents into processable text through the convolutional neural network CNN; call the API interface to integrate with external data sources of the system, verify the accuracy of the information, and quickly process claims payments.

[0022] As a further description of the above technical solution, the online intelligent customer service combines optical character recognition (OCR) and natural language processing (NLP) to automatically identify and classify claims documents and extract key information, automatically compare the information submitted by the customer with the requirement list, and remind the customer of missing and incorrectly formatted documents; and provide information submission channels and communication channels based on customer preferences, and automatically notify customers of the progress of claims.

[0023] As a further description of the above technical solution, the middleware implements data transmission between the client and the server through the MQTT protocol, and uses SMS to push real-time data information. The MQTT protocol integrates the mobile application system to achieve real-time communication with the server, push notifications and message updates.

[0024] Compared with the prior art, the present invention provides a system for promoting insurance industry re-service, which has the following beneficial effects.

[0025] 1. The present invention analyzes the behavior data of customers' purchase, consultation and claims through the long short-term memory network (LSTM) to improve the efficiency of solving customer problems.

[0026] 2. The present invention automatically matches claim conditions for claim applications submitted by customers through an automated claim model, thereby improving the efficiency of customer claim applications.

[0027] 3. The present invention realizes instant demand communication service through online intelligent customer service, thereby improving the efficiency of customers in preparing claim application materials.

[0028] Other advantages, objectives and features of the present invention will be described in part in the following description; and in part, will be apparent to those skilled in the art based on an examination of the following; or, may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to understand and grasp the technical solution more intuitively and clearly, when describing the embodiments of the present invention or the prior art, drawings are often used to supplement and illustrate. It should be noted that the drawings are only one way of expressing the embodiments of the present invention or the prior art. In fact, the technical solution can also have other implementation methods and changes, which all fall within the protection scope of the present invention. Therefore, technicians can design other drawings as needed to implement the technical solution of the present invention, among which,

[0030] Figure 1 It is a schematic diagram of the overall structure of the present invention.

[0031] Figure 2 It is a schematic diagram of the business process of the system of the present invention.

[0032] Figure 3 It is a schematic diagram of the structure of the server of the present invention.

[0033] Figure 4 It is a schematic diagram of the structure of the client of the present invention.

[0034] Figure 5 This is a process step diagram of the automated claims settlement model of the present invention. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of this invention through the accompanying drawings in the embodiments of this invention. Obviously, the described embodiments are only part of the embodiments of this invention, not all of the embodiments. At the same time, the description of well-known structures and technologies is omitted in the following description to avoid unnecessary confusion of the concepts of the present invention.

[0036] like Figure 1-Figure 5 As shown, a system for promoting insurance industry re-service includes:

[0037] Servers, which are used to store insurance contracts, customer information, and transaction record data, and process insurance business logic;

[0038] Client, used to enable interaction between users and service personnel;

[0039] Middleware, which manages the communication and coordination between different components.

[0040] In a specific embodiment, the server includes:

[0041] The distributed database management system uses the HBase distributed database to distribute data on multiple nodes and store them redundantly on multiple nodes. In the server architecture, the distributed database management system is the core of the system and uses the HBase distributed database. It not only evenly distributes massive data on multiple nodes in the cluster to achieve efficient data storage and access, but also backs up key information on multiple nodes through data redundancy strategies to ensure high data availability and persistence. Even in the face of node failures, it can quickly recover and ensure business continuity. The data analysis module decomposes data processing tasks to multiple nodes for processing and performs real-time processing and analysis of data streams. The data analysis module decomposes complex data processing tasks and distributes them to each node of the cluster for parallel processing, which greatly improves the efficiency and speed of data processing. At the same time, this module also has powerful real-time data stream processing capabilities, can analyze data in real time, and timely understand the patterns and trends behind the data, providing strong support for decision-making. The cloud service unit realizes elastic computing resources through Google Cloud, and performs data backup and data recovery; it realizes on-demand expansion of computing resources, and can ensure stable operation of the system regardless of how the business volume fluctuates. In addition, Google Cloud's cloud storage service provides reliable protection for data backup and recovery. Even in extreme cases, data can be quickly restored, reducing business interruption time and enhancing the overall resilience of the system. Security components monitor system performance in real time and record operation logs, using SSL / TLS to encrypt data transmission. By monitoring system performance in real time, the security component can promptly discover and warn of potential performance bottlenecks to ensure the healthy operation of the system. At the same time, the recording of operation logs provides an important basis for troubleshooting, ensuring the rapid location and resolution of problems. At the data transmission level, SSL / TLS encryption technology is used to build an encrypted channel for data transmission, effectively preventing data leakage and tampering during transmission and ensuring data security.

[0042] In a specific embodiment, the data analysis module integrates the system with the CRM system to achieve accurate customer matching and customer service optimization; the data analysis module uses the long short-term memory network LSTM to analyze the behavioral data of customer purchases, consultations and claims, and evaluates risks based on customer data and historical records to optimize the pricing of insurance products. By analyzing customer data and historical records, the data analysis module can intelligently evaluate potential risks, provide a scientific basis for the pricing of insurance products, ensure that the pricing of each insurance policy can reflect the real risk, and achieve a fair and reasonable pricing strategy. In addition, based on a deep understanding of customer behavior, the data analysis module provides more personalized insurance solutions to meet the specific needs of different customer groups and improve customer satisfaction and loyalty. The data analysis module can continuously learn and self-optimize. With the continuous accumulation of data, its analysis capabilities will be increasingly enhanced, providing customers with more accurate and efficient services. Through this module, it is possible to predict the possible future behavior of customers, plan service strategies in advance, ensure that customers can respond quickly when they need it, and provide intimate and timely support, so as to stand out in the fiercely competitive market and establish lasting customer relationships.

[0043] In a specific embodiment, the application method of LSTM in promoting the re-service system of the insurance industry is as follows: first, historical data is collected from the CRM system, transaction records and customer service log sources, and time series features are constructed, including purchase frequency and service request time interval. Data collection and preprocessing are implemented based on the LSTM model. Customer interaction data extracted from the CRM system, financial information in transaction records, and detailed communication details in customer service logs jointly construct a multi-dimensional time series data set. After cleaning and normalization, these data are converted into key features such as purchase frequency and service request time interval, providing a rich source of information for model training. Subsequently, a multi-layer LSTM structure is selected to handle complex problems in the insurance policy, and the input dimensions of the time series data are defined, and the data is divided into a training set, a validation set and a test set for iterative training. Selecting a multi-layer LSTM structure is the key to handling complex problems. This structure can capture the long-term dependencies involved in the insurance policy, including the periodicity of customer purchasing behavior, seasonal fluctuations in service demand, etc. By defining the input dimensions of the time series data and dividing the historical data into a training set, a validation set and a test set, the model can be continuously optimized in iterative training to learn the patterns and laws in the data. The learning rate and network parameters are adjusted by monitoring the performance of the validation set, and the model performance is evaluated on the validation set and test set. During the model training process, the performance of the validation set is a weather vane for tuning. By monitoring the loss changes and accuracy on the validation set, the learning rate and network parameters can be dynamically adjusted to ensure that the model has good generalization ability when facing new data. The performance evaluation on the validation set and test set further verifies the stability and reliability of the model. The model is deployed, the LSTM model is encapsulated as a microservice, and the API interface is used to integrate with the CRM system and the automated claims module in the insurance system. The model deployment stage is a manifestation of the deep integration of technology and business. The CRM system and the automated claims module can call the model in real time, predict new data, and quickly respond to customer needs and market changes. At the same time, according to the real-time performance of the model and business needs, the model is updated regularly and on demand to ensure the continuous optimization and iteration of the system in a dynamic environment. In short, the application of LSTM in the insurance system not only improves the depth and breadth of data processing, but also realizes the intelligence and automation of business processes through real-time prediction and model update.

[0044] In a specific embodiment, the client includes: a user interface, where customers manage policies, apply for claims, and perform demand services through HTML and React front-end frameworks, and interact with the system and service personnel, ensuring a smooth and intuitive interface, making policy management, claim applications, and various demand service operations easy; customers can manage their own policies, apply for claims, and seek services in this interface, and the interaction with the system and service personnel becomes direct and efficient. The claims module automatically matches the claim conditions for the claim applications submitted by customers through an automated claims model to calculate the amount of compensation; this process not only greatly shortens the claims cycle and reduces the tediousness of manual review, but also ensures the fairness and transparency of the claims results. Customers only need to submit the necessary information and wait for the claims results, saving time and worry. Auxiliary tools: customers can sign the insurance policy online through the electronic signature tool. The use of the electronic signature tool makes the online signing of the insurance policy natural and convenient. Customers can sign the insurance policy anytime, anywhere, using any device, which greatly improves the application efficiency. The nearest insurance agent can be found through the map service. The integration of the map service provides convenience for users. Customers can easily find the nearest insurance agent through precise positioning. Whether it is for consultation or business processing, they can quickly find the right help. Through online intelligent customer service, instant demand communication services are realized. Customers can communicate with the intelligent customer service at any time, whether it is for information query or problem solving, they can get instant response. Intelligent customer service can not only provide 24-hour uninterrupted service, but also provide personalized and accurate answers according to the user's language habits and needs, making every communication efficient and considerate.

[0045] In a specific embodiment, the working implementation method of the automated claims model is as follows: first, intelligent risk grading is performed, and the risk level of claims requests is automatically classified and evaluated through machine learning and historical claims cases. Through deep learning machine learning algorithms, the automated claims model can analyze massive historical claims cases and learn from them the potential risk patterns of claims requests. This process not only includes the preliminary classification of cases, but also involves a detailed assessment of risk levels. The model will automatically give a risk score based on multi-dimensional information such as the complexity of the case, the amount involved, and the similarity with past cases, providing an important basis for subsequent claims processing; then, the claim eligibility and compensation amount are automatically judged, and the insurance terms and policies are applied to automatically judge the claim eligibility and compensation amount. Based on detailed insurance terms and policies, the automated claims model can quickly determine whether the claim request is eligible and automatically calculate the compensation amount. This process involves in-depth analysis and application of the terms to ensure that every compensation strictly complies with the insurance contract, which not only protects the legitimate rights and interests of the insurance company, but also guarantees the reasonable demands of the insured; then, the claim conditions are automatically executed through the blockchain platform, and the smart contract automatically executes the payment process according to the claim conditions to ensure transaction security and transparency. Once the claim conditions are met, the smart contract will automatically trigger the payment process without human intervention, ensuring the security and transparency of the transaction. This mechanism not only improves efficiency, but also greatly enhances customers' trust in the claims process; then, the image information is automatically read and converted, and the image information in the claim document is automatically read and converted into processable text through the convolutional neural network CNN. The automated claims model can automatically read the image information in the claim document, including pictures of key documents such as identity cards and invoices, and accurately convert them into processable text data. This function greatly reduces manual input errors and speeds up information processing. Finally, external data source integration and information verification are performed, and the API interface is called to integrate with the system's external data source to verify the accuracy of the information and quickly process claims payments. The automated claims model can be seamlessly integrated with data sources outside the system, including credit assessments and medical records, to verify the accuracy of information. This integration mechanism ensures the authenticity and reliability of claims information, avoids fraud, and speeds up the processing of claims payments.

[0046] In a specific embodiment, the online intelligent customer service combines optical character recognition (OCR) and self-recognition language processing (NLP) to automatically identify and classify claim documents and extract key information, automatically compare the information submitted by the customer with the list of requirements, and remind the customer of missing and incorrectly formatted documents; and provide information submission channels and communication channels according to customer preferences, and automatically notify the customer of the progress of the claim. When the claim document is uploaded, the system starts quickly, automatically identifies and finely classifies each document, and accurately extracts key information, including accident details, loss amount, time and place, etc., to ensure comprehensive capture of information. Subsequently, the intelligent system automatically compares the information submitted by the customer with the list of claims requirements, quickly identifies missing or incorrectly formatted documents, and promptly reminds the customer to supplement or correct them through the customer's preferred communication channels, including SMS, email, and instant messaging software, to avoid delays and inconveniences in the claim process. Furthermore, the system flexibly provides a variety of information submission channels and communication methods according to the different preferences of each customer, whether it is online upload, email sending or mobile APP submission, it ensures the rapid transmission and efficient processing of information. At the same time, the intelligent customer service system tracks the progress of claims in real time. Once there is an update, it will automatically notify the customer through the channel preferred by the customer, so that the customer can grasp the status of the claim at any time and enjoy worry-free claims. The intelligent operation of the online intelligent customer service not only greatly simplifies the claims process and improves the efficiency of claims, but also provides personalized, efficient and convenient claims services with a customer-centric approach.

[0047] In a specific embodiment, the middleware realizes the data transmission between the client and the server through the MQTT protocol, ensuring that the data transmission between the client and the server is both rapid and reliable. The integration of the MQTT protocol not only realizes the real-time communication between the mobile application system and the server, but also greatly optimizes the resource consumption, so that the data transmission is still smooth even under limited network conditions. After the client and the server are connected, through the MQTT protocol, the client can subscribe to a specific topic. Once there is a new message or data update on the server, the real-time information will be pushed to the client through these subscribed topics to ensure that the user can receive the latest notifications and messages in the first place. In order to further enhance the user experience, the middleware also uses SMS service as a push channel for real-time data information. When the network environment is unstable or the device does not support the MQTT protocol, the SMS push can be used as an effective supplement to ensure that important information can be delivered to the user in a timely and accurate manner. Whether it is account changes, transaction confirmations, or system notifications, users will not miss any important information through the SMS push. The MQTT protocol integrates the mobile application system to realize real-time communication with the server, push notifications and message updates.

[0048] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0049] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

Claims

1. A system for promoting re-service in the insurance industry, comprising: Servers, which are used to store insurance contracts, customer information, and transaction record data, and process insurance business logic; Client, used to enable interaction between users and service personnel; Middleware, which manages the communication and coordination between different components.

2. A system for promoting insurance industry re-service according to claim 1, characterized in that: The server comprises: Distributed database management system, using HBase distributed database to distribute data on multiple nodes and store redundantly on multiple nodes; The data analysis module decomposes the data processing tasks into multiple nodes for processing, and processes and analyzes the data stream in real time; Cloud service unit, which implements elastic computing resources through Google Cloud and performs data backup and data recovery; Security components monitor system performance in real time and record operation logs, using SSL / TLS to encrypt data transmission.

3. A system for promoting insurance industry re-service according to claim 2, characterized in that: The data analysis module integrates the system with the CRM system to achieve accurate customer matching and customer service optimization; the data analysis module uses the long short-term memory network LSTM to analyze customer purchase, consultation and claims behavior data, and evaluates risks based on customer data and historical records to optimize insurance product pricing.

4. A system for promoting insurance industry re-service according to claim 3, characterized in that: The LSTM application method in the system is as follows: historical data is collected from the CRM system, transaction records and customer service log sources, and time series features are constructed, including purchase frequency and service request time interval; a multi-layer LSTM structure is selected to handle complex problems in insurance policies, and the input dimension of time series data is defined, and the data is divided into a training set, a validation set and a test set for iterative training; Adjust the learning rate and network parameters by monitoring the performance of the validation set, and evaluate the model performance on the validation set and test set; deploy the model, encapsulate the LSTM model as a microservice, and use the API interface to integrate it with the CRM system and automated claims module in the insurance system; receive new data for real-time prediction, and update the model regularly and on demand based on model performance and business needs.

5. A system for promoting insurance industry re-service according to claim 1, characterized in that: The client comprises: User interface, where customers manage policies, apply for claims, and perform service operations through HTML and React front-end frameworks, interacting with the system and service personnel; The claims module automatically matches the claims conditions of the claims applications submitted by customers through an automated claims model to calculate the compensation amount; Auxiliary tools allow customers to sign insurance policies online through electronic signature tools; find the nearest insurance agent through map services; and achieve instant demand communication services through online intelligent customer service.

6. A system for promoting insurance industry re-service according to claim 5, characterized in that: The working implementation method of the automated claims model is as follows: automatically classify and evaluate the risk level of claims through machine learning and historical claims cases; automatically determine the eligibility and amount of claims by applying insurance terms and policies; automatically execute claims conditions through the blockchain platform, and the smart contract automatically executes the payment process according to the claims conditions to ensure transaction security and transparency; automatically read and convert image information in claims documents into processable text through convolutional neural networks (CNN); Call API interfaces to integrate with external data sources, verify the accuracy of information, and quickly process claims payments.

7. A system for promoting insurance industry re-service according to claim 5, characterized in that: The online intelligent customer service combines optical character recognition (OCR) and natural language processing (NLP) to automatically identify and classify claims documents and extract key information, automatically compare the information submitted by the customer with the requirement list, and remind the customer of missing and incorrectly formatted documents; and provide information submission channels and communication channels based on customer preferences, and automatically notify customers of the progress of claims.

8. A system for promoting insurance industry re-service according to claim 1, characterized in that: The middleware realizes data transmission between the client and the server through the MQTT protocol, and uses SMS to push real-time data information. The MQTT protocol integrates the mobile application system to realize real-time communication with the server, push notifications and message updates.

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