Intelligent insurance business management system and method based on big data

Abstract

The invention relates to an insurance business intelligent management system and method based on big data. The system comprises a data acquisition module, a data storage module, a data analysis module, a business management module and a user interaction module which operate cooperatively. The method comprises the steps of data acquisition and preprocessing, storage and management, analysis and model construction and business process intelligent processing. Multi-source data is collected through a web crawler and an API interface, the data is cleaned by applying a Hash algorithm and a 3 sigma principle, a model is constructed by adopting logistic regression, LSTM and other algorithms, and intelligent underwriting, claim settlement and precision marketing are realized. The insurance service management efficiency is improved, the risk is reduced, and the customer service experience is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of insurance business management, and particularly to an intelligent management system and method for insurance business based on big data. Background Art

[0002] In the development process of the insurance industry, the traditional insurance business management mode faces many severe challenges. With the increasingly fierce market competition and the continuous improvement of customers' requirements for insurance services, the limitations of the traditional management mode become more and more obvious.

[0003] In terms of data processing, insurance companies have accumulated a vast amount of customer information, insurance product data, and market dynamic data. However, due to the lack of an efficient data collection and integration mechanism, these data are scattered in various business processes and systems, forming "data islands". This not only makes it difficult to ensure the consistency and accuracy of data, but also makes it extremely difficult to deeply mine and analyze data. For example, in customer information management, the customer contact information, risk preferences, and other data recorded by different departments may be different, which makes it difficult for insurance companies to comprehensively and accurately understand customer needs and unable to provide precise services for customers.

[0004] From the perspective of business processes, the traditional insurance business processes are cumbersome and rely on manual operations. In the underwriting link, staff need to manually check a large number of paper documents and electronic materials to evaluate the risk status of customers. This process is not only time-consuming and laborious, but also easily affected by human factors, resulting in the difficulty of ensuring the accuracy and fairness of underwriting results. The claims settlement process is also complex. Customers need to submit a large number of supporting materials and go through the review and approval of multiple departments. The claims settlement cycle is long and customer satisfaction is low.

[0005] In terms of customer service, due to the inability to deeply analyze customer data, insurance companies are difficult to achieve personalized services. Most insurance products and services are in a "one-size-fits-all" mode and cannot meet the diverse needs of different customer groups. This leads to low customer loyalty to insurance companies and a gradual decline in market competitiveness.

[0006] In addition, with the rapid development of Internet technology, the insurance industry is facing an urgent need for digital transformation. The traditional insurance business management system cannot be effectively integrated with emerging Internet platforms and technologies and is difficult to adapt to market changes and customer needs.

[0007] In summary, the traditional insurance business management mode has many problems in data processing, business processes, and customer service, which seriously restricts the development of the insurance industry. The intelligent management system and method for insurance business based on big data proposed by the present invention aim to solve these problems and promote the development of the insurance industry towards intelligence and high efficiency. Summary of the Invention

[0008] The object of the present invention is to provide an intelligent management system and method for insurance business based on big data, which can break through the bottleneck of traditional insurance business management and build a highly intelligent and efficient management system. By making full use of big data and artificial intelligence technologies, it comprehensively solves the long-existing problems in the insurance industry in aspects such as data processing, business processes, and customer service, thereby significantly improving the management level of insurance business, enhancing the competitiveness of insurance companies in the market, and providing customers with a better and more personalized insurance service experience.

[0009] To achieve the above object, the present invention is implemented through the following technical solutions: An intelligent management system for insurance business based on big data, comprising:

[0010] A data collection module, which collects multi-source data such as customer information, insurance product information, and market data from various channels, and after the collection is completed, transmits the data to the data storage module;

[0011] A data storage module, which stores the data from the data collection module using distributed database technology and provides a data call interface for the data analysis module, so that the data analysis module can obtain the data for analysis;

[0012] A data analysis module, which uses big data analysis algorithms and artificial intelligence technologies to deeply mine and analyze the stored data called from the data storage module, and feeds back the analysis results to the business management module;

[0013] A business management module, which conducts full-process management of insurance business based on the analysis results provided by the data analysis module, and at the same time feeds back the relevant data requirements in the business process to the data storage module to obtain data to support the business;

[0014] A user interaction module, which provides an operation interface for customers and insurance staff. The business requests and data query operations initiated by users through this module are processed by the business management module, and the business management module feeds back the processing results to the user interaction module for display to users; the user interaction module can also transmit the new data generated by users during the operation process to the data collection module for collection.

[0015] It should be noted that for the data collection module: This module has powerful data scraping capabilities. It can not only obtain data from various business systems within the insurance company, such as customer relationship management systems, policy management systems, etc., but also collect external data such as macroeconomic data, industry dynamics data, and competitor data by cooperating with third-party data platforms. Advanced data collection technologies, such as web crawlers, API interface docking, etc., are used to ensure the real-time and comprehensiveness of the data. At the same time, preliminary format conversion and standardization processing are performed on the collected data for subsequent data storage and analysis.

[0016] Data storage module: Based on a distributed storage architecture, such as the Hadoop Distributed File System (HDFS), combined with a NoSQL database (such as MongoDB) and a relational database (such as MySQL), it realizes the efficient storage and management of massive data. Classifies and stores data according to the type, usage frequency, and importance of the data to ensure data security and scalability. Guarantees data integrity and reliability through data backup and recovery mechanisms.

[0017] Data analysis module: Integrates a variety of big data analysis algorithms and artificial intelligence technologies, such as decision tree and neural network algorithms in machine learning, as well as convolutional neural network and recurrent neural network in deep learning. Utilizes these technologies to deeply mine and analyze the data stored in the data storage module. On the one hand, constructs customer portraits to comprehensively depict customers from multiple dimensions such as basic information, consumption behavior, and risk preferences; on the other hand, establishes risk assessment models, claim prediction models, etc., providing data support and decision-making basis for all aspects of the insurance business.

[0018] Business management module: Driven by the results of the data analysis module as the core, it realizes the intelligent management of the entire insurance business process. In the underwriting link, through the intelligent underwriting system, it automatically reads the customer's risk assessment results and insurance product rules, quickly and accurately completes the underwriting decision, greatly shortening the underwriting time and improving the accuracy and fairness of underwriting. In the claims settlement link, the intelligent claims settlement system automatically matches the claims settlement rules according to the claims settlement case data and customer information, realizes fast claims settlement processing, and at the same time, through the analysis of claims settlement data, timely discovers potential fraud risks. In the marketing link, based on the results of precise marketing analysis, it customizes personalized insurance product recommendation plans for different customer groups to improve the customer's purchase conversion rate.

[0019] User interaction module: Provides an intuitive and convenient operation interface for customers and insurance staff. Customers can query insurance product information, apply for insurance online, submit claims settlement applications, etc. anytime and anywhere through the mobile application or web platform. At the same time, the system provides personalized service recommendations and information reminders according to the customer's historical behavior and preferences. Insurance staff can perform operations such as business management, viewing data analysis results, and communicating with customers through this module, improving work efficiency and service quality.

[0020] Further, as an improvement of the technical solution of the present invention, the data analysis module uses machine learning algorithms to construct a customer risk assessment model.

[0021] Further, as an improvement of the technical solution of the present invention, the business management module includes corresponding processes for intelligent underwriting, intelligent claims settlement, and precise marketing.

[0022] As a further improvement of the technical solution of the present invention, the data acquisition module sets the frequency, source and acquisition rules of data acquisition.

[0023] As a further improvement of the technical solution of the present invention, the system is deployed in a server cluster of an insurance company and is configured with high-performance server hardware and big data processing software.

[0024] As a further improvement of the technical solution of the present invention, a method for intelligent management of insurance business based on big data comprises the following steps:

[0025] Collect internal and external data in real time through the data acquisition module, and clean and pre-process the data;

[0026] storing the processed data in a data storage module;

[0027] The data analysis module analyzes the data based on the preset analysis model and algorithm;

[0028] Underwriters use the intelligent underwriting system to complete underwriting based on customer risk assessment results and insurance product rules;

[0029] Claims adjusters complete claims processing through the intelligent claims system based on claims case data and customer information;

[0030] Insurance companies push personalized insurance products and services to customers based on the results of precision marketing analysis.

[0031] It should be noted that in the above method steps:

[0032] Data collection and preprocessing: Start the data collection module and collect data from internal and external data sources in real time according to the preset collection frequency and rules. Clean the collected data to remove duplicate data, erroneous data and abnormal data, and standardize the data to unify the data format and coding rules. For example, standardize and classify the customer's age, gender, occupation and other information for subsequent data analysis.

[0033] Data storage and management: Store preprocessed data in the data storage module, and select the appropriate storage method and storage location according to the classification and characteristics of the data. Establish a data index and metadata management mechanism to facilitate rapid query and call of data. Back up and archive data regularly to ensure data security and traceability.

[0034] Data Analysis and Model Building: The data analysis module selects appropriate analysis algorithms and models according to the requirements and goals of the insurance business. It trains and optimizes the models using historical data, and improves the accuracy and generalization ability of the models through methods such as cross-validation and parameter adjustment. For example, when building a customer risk assessment model, it trains the model to identify the characteristics and behavior patterns of high-risk customers through a large amount of historical customer data and risk event data.

[0035] Intelligent Processing of Business Processes: In the underwriting stage, underwriters log in to the business management module, and the system automatically calls the results of the customer risk assessment model and insurance product rules to provide underwriters with underwriting suggestions and decision-making basis. Underwriters can make manual interventions and adjustments according to the actual situation to ensure the rationality of the underwriting results. In the claims settlement stage, claims adjusters can quickly obtain relevant information and customer data of the claims settlement cases through the intelligent claims settlement system. The system automatically matches the claims settlement rules and calculates the claims settlement amount to achieve fast claims settlement. In the marketing stage, the insurance company formulates personalized marketing plans according to the results of precise marketing analysis and pushes personalized insurance product and service information to customers through the user interaction module.

[0036] Further, as an improvement of the technical solution of the present invention, the cleaning and preprocessing of data cover removing duplicate data and correcting incorrect data.

[0037] Further, as an improvement of the technical solution of the present invention, the data analysis module uses association rule mining to analyze the insurance demand preferences of customers.

[0038] Further, as an improvement of the technical solution of the present invention, the models in the data analysis module are regularly evaluated and optimized, and the model parameters and algorithms are adjusted according to new data and business requirements.

[0039] Further, as an improvement of the technical solution of the present invention, insurance staff log in to the system through the user interaction module for business processing, and customers access the system through the mobile terminal or web terminal to handle business.

[0040] The present invention has the following beneficial effects:

[0041] Improve Data Processing Capability: Through an efficient data collection and integration mechanism, break data islands, achieve centralized management and sharing of data. Improve the consistency and accuracy of data, and provide reliable data support for data analysis and business decision-making. Use big data analysis technology to deeply explore data value and provide data-driven for the innovation and development of the insurance business.

[0042] Optimize business processes: Achieve the automation and intelligence of the entire insurance business process, reduce manual intervention, and improve business processing efficiency. Shorten the underwriting and claims settlement cycles, reduce operating costs, and improve the profitability of insurance companies. Through intelligent risk assessment and fraud detection, reduce the risks of insurance business and ensure the stable operation of insurance companies.

[0043] Enhance the customer service experience: Based on customer portraits and personalized recommendations, provide customers with accurate insurance products and services to meet their diverse needs. Improve customer satisfaction and loyalty, and enhance the market competitiveness of insurance companies. Through a convenient user interface and online service platform, provide customers with 24 / 7 uninterrupted services and enhance the customer service experience. Description of the Drawings

[0044] Other features, objectives, and advantages of the present invention will become more apparent by reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0045] Figure 1 is the system architecture diagram of the embodiment of the present invention;

[0046] Figure 2 is the data flow framework diagram of the embodiment of the present invention. Detailed Embodiments

[0047] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. Here, the schematic embodiments and descriptions of the present invention are used to explain the present invention, but not to limit the present invention.

[0048] It should be noted that all directional indications (such as up, down, left, right, front, back, upper end, lower end, top, bottom...) in the embodiments of the present invention are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly.

[0049] In the present invention, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral body; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components or the interaction relationship between two components, unless otherwise clearly limited. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0050] In addition, in the present invention, descriptions such as "first", "second", etc. are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second" may explicitly or implicitly include at least one such feature. Additionally, the technical solutions between various embodiments may be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0051] The present invention will be further described in detail below with reference to the accompanying drawings.

[0052] Refer to Figure 1 and Figure 2 , system deployment and environment setup:

[0053] In the core computer room of the insurance company, deploy a high-performance server cluster, adopt a cloud computing architecture such as OpenStack to achieve flexible allocation and management of resources. According to the performance requirements of each module of the system, allocate sufficient network bandwidth and computing resources for the data collection module to ensure that it can efficiently capture data from internal and external data sources. For the data storage module, configure multiple distributed storage servers, build a Hadoop Distributed File System (HDFS), and combine it with MongoDB and MySQL databases. For example, in HDFS, the replication factor of data blocks is set to 3 to improve data fault tolerance. The calculation formula is: replication factor = fault tolerance coefficient * (mean time between failures / data recovery time), where the fault tolerance coefficient is determined according to actual business requirements and data importance, and the value range is usually between 2 and 4.

[0054] Install and configure big data processing software and tools such as Hadoop, Spark, TensorFlow, etc. In the Spark environment, set reasonable parallelism and memory allocation parameters to improve the efficiency of data processing and analysis. For example, according to the number of CPU cores and memory size of the server, calculate the appropriate parallelism through the formula: parallelism = number of CPU cores * memory usage rate / memory requirement per single task to ensure that tasks can run efficiently.

[0055] Data collection and preprocessing:

[0056] The data collection module obtains macroeconomic data and industry dynamic data from major financial websites, industry forums, etc. according to the preset collection plan through web crawler technology. Connect to third-party data platforms using API interfaces to obtain product information and market share data of competitors. At the same time, extract customer information and policy data from the insurance company's internal customer relationship management system, policy management system, etc.

[0057] In the data cleaning phase, data cleaning algorithms such as rule-based cleaning algorithms are used to remove duplicate data. For the judgment of duplicate data, a hash algorithm can be adopted. By calculating the hash value of the data record, it can be determined whether there is duplication. The formula is: Hash value = Hash(field1 + field2 +... + fieldn), where field1 to fieldn are the key fields in the data record. For incorrect data and abnormal data, by establishing a data quality rule library, such as the age field must be within a reasonable range (1 - 120 years old), and using anomaly detection algorithms such as the 3σ principle based on statistics for identification and processing.

[0058] In data standardization processing, for numerical data, a normalization method such as min-max normalization is adopted. The formula is: where x is the original data x min and x max are the minimum and maximum values in the dataset respectively, and the data is mapped to the interval [0, 1] to facilitate subsequent data analysis and model training.

[0059] Data storage and management:

[0060] According to the type and usage frequency of the data, structured data is stored in a MySQL relational database to support transaction processing and complex queries. Semi-structured and unstructured data, such as customers' text evaluations, image materials, etc., are stored in MongoDB. In HDFS, hierarchical storage is performed according to the importance and usage frequency of the data. Hot data is stored on high-speed storage media, and cold data is stored on low-cost storage media.

[0061] Data indexes are established. For the MySQL database, B-Tree indexes and hash indexes are used to improve data query efficiency. In MongoDB, its built-in index mechanism is utilized to create single-field indexes, composite indexes, etc. according to query requirements. At the same time, a metadata management system is established to record information such as the source of the data, collection time, data format, data meaning, etc., to facilitate data management and use.

[0062] Data analysis and model construction:

[0063] When constructing a customer risk assessment model, a logistic regression algorithm is adopted. The formula is: where P(Y = 1|X) represents the probability that a customer has a risk event (Y = 1) given the feature X = (x1, x2,..., x n ), and β1, β2,..., β n are the parameters of the model. Through a large amount of historical customer data and risk event data, the gradient descent algorithm is used for parameter estimation and model training.

[0064] In the claims prediction model, the recurrent neural network (RNN) in deep learning is used, such as the long short-term memory network (LSTM). The core formulas of LSTM include the forget gate f t , the input gate i t , the output gate o t and the memory cell C t update formulas:

[0065] f t = σ(W f ·[h t-1 , x t +b f )

[0066] i t = σ(W i ·[h t-1 , x t +b i )

[0067] o t = σ(W o ·[h t-1 , x t +b o )

[0068]

[0069] h t = o t ☉tanh(C t )

[0070] where σ is the sigmoid activation function, W f , W i , W o , W C are weight matrices, b f , b i , b o , b C are bias vectors, h t-1 is the hidden state at the previous moment, x t is the input at the current moment, and ⊙ represents element-wise multiplication. By training the LSTM model, the time series features and patterns in the claims case data are learned to predict the likelihood and amount of claims.

[0071] Intelligent processing of business processes:

[0072] During the underwriting stage, the underwriters log in to the business management module, and the system automatically calls the results of the customer risk assessment model and the insurance product rules. For example, according to the customer's risk probability P and the risk threshold T of the insurance product, if P ≤ T, the underwriting is automatically passed; if P > T, it enters the manual review process, and the underwriters make judgments and decisions based on specific circumstances.

[0073] During the claims settlement stage, the claims adjusters input the relevant information of the claims settlement case through the intelligent claims settlement system, and the system automatically obtains the customer information and claims settlement rules from the data storage module. According to the results of the claims settlement prediction model and in combination with the claims settlement rules, the claims settlement amount is calculated. For example, the claims settlement amount = the basic compensation amount * the claims settlement adjustment coefficient, where the claims settlement adjustment coefficient is calculated through the claims settlement prediction model based on factors such as the customer's historical claims settlement records and risk levels.

[0074] During the marketing stage, according to the results of the precision marketing analysis, the customers are segmented according to different characteristics and needs, such as age, gender, income level, risk preference, etc. For different customer groups, personalized insurance product recommendation plans are formulated. Through the user interaction module, personalized insurance products and service information are pushed to the customers to improve the purchase conversion rate of the customers.

[0075] Actual case:

[0076] System deployment and environment setup:

[0077] A large insurance company deployed a cluster consisting of 10 high-performance servers in its core computer room, adopting the OpenStack cloud computing architecture. It allocated a network bandwidth of 10 Gbps for the data collection module to ensure that it can quickly crawl data from major financial information websites and industry databases. In terms of data storage, it built a Hadoop distributed file system (HDFS) and configured 5 distributed storage servers. For example, considering the importance and real-time requirements of insurance business data, the fault tolerance coefficient was set to 3. According to the company's past average data recovery time of 2 hours and average time between failures of 1000 hours, the number of data block replicas was calculated by the formula as 3 * (1000 / 2) = 1500, and the actual number of replicas set was 3 to balance the storage cost and fault tolerance requirements. At the same time, it combined the MySQL database to store structured business data, such as policy details and customer basic information; and used MongoDB to store unstructured data, such as customer complaint texts and scanned medical examination reports.

[0078] In terms of software configuration, big data processing tools such as Hadoop, Spark, and TensorFlow are installed. Taking the Spark environment as an example, the company's server is equipped with a 64-core CPU and 512GB of memory. After testing, the average memory requirement for a single data processing task is 4GB, and the memory utilization rate is set at 80%. Through formula calculation, the parallelism is 64 * 0.8 / 4 = 12.8, and after rounding up, it is 13. In actual operation, the data processing efficiency has increased by 30% compared to before.

[0079] Data collection and preprocessing:

[0080] The data collection module follows the preset collection plan at 2 am every day and uses web crawler technology to obtain macroeconomic indicators from well-known financial websites, such as GDP growth rate and interest rate fluctuation data; it collects the latest developments in the insurance industry and consumer feedback from industry forums. It connects to a third-party data platform through an API interface to obtain data on the terms, prices, and market share of newly launched insurance products by competitors. At the same time, it extracts customer contact information and purchase history from the company's internal customer relationship management system and obtains data such as the effective time and insured amount of policies from the policy management system.

[0081] In the data cleaning stage, a rule-based cleaning algorithm is used. For example, it is set that the ID number must be 18 digits and comply with the verification rules to remove incorrect data. The hash algorithm is used to judge duplicate data. For example, when processing customer information, hash values are calculated for keyword fields such as customer name, ID number, and contact information. Once when importing a batch of new customer data, it was found through the hash algorithm that 50 records were duplicates of the existing data and were promptly cleaned up. For abnormal data, the 3σ principle is used for identification. When processing customer age data, it was found that one record had an age of 200 years, which was clearly outside the reasonable range (1 - 120 years). After verification, it was an input error and was corrected.

[0082] In data standardization processing, the min-max normalization method is used for the numerical data of the customer's premium payment amount. The minimum historical premium payment amount of the company is 100 yuan, and the maximum is 100,000 yuan. A certain customer actually paid a premium of 5,000 yuan. Through the formula It is mapped to the [0, 1] interval to facilitate the subsequent construction of a customer value evaluation model.

[0083] Data storage and management:

[0084] According to the data type and usage frequency, the insurance company stores structured data such as policy transaction records and claim details in a MySQL database to support complex transaction processing, such as fund accounting in the claim settlement process and policy status changes. Semi-structured and unstructured data such as customers' claim descriptions and health disclosure forms are stored in MongoDB. In HDFS, hot data such as recent policy data and consultation records of popular insurance products are stored on high-speed solid-state drives, while cold data such as archived data of expired policies from years ago are stored on large-capacity mechanical hard drives.

[0085] To improve data query efficiency, in the MySQL database, a B-Tree index is established for the policy number field, which increases the speed of querying policy information by 80% according to the policy number; a hash index is established for the customer ID number to accelerate the retrieval of customer information. In MongoDB, according to the query requirements, a single-field index is created for customer complaint data by complaint time to facilitate quick query of complaint records in different time periods; a composite index is created for claim case data by claim amount and claim time to improve the efficiency of complex queries. At the same time, a metadata management system is established to record in detail that the source of GDP data collected from financial websites is the official website of the National Bureau of Statistics, the collection time is the 15th of each month, the data format is numeric, and the meaning is the quarterly growth rate of the gross domestic product, etc., to facilitate data management and use.

[0086] Data Analysis and Model Building:

[0087] When building a customer risk assessment model, the logistic regression algorithm is adopted. The company has collected 1 million pieces of historical customer data and corresponding risk event records, and uses the gradient descent algorithm for parameter estimation and model training. For example, when evaluating the risk of a new customer, the customer's age, occupation, health status, past claim records, etc. are used as features X=(x1, x2, x3, x4) and input into the model. By calculation, the probability of the customer having a risk event is P(Y = 1|X) = 0.3. If the risk threshold T of the insurance product is 0.4, it is initially judged that the customer's risk is within an acceptable range.

[0088] In the claim prediction model, the long short-term memory network (LSTM) is used. The company has collected claim case data for the past 5 years, including time series features such as claim time, claim amount, and accident cause. By training the LSTM model, the patterns in the claim data are learned. In a car insurance claim prediction, when the relevant information of a claim case is input, the model predicts the claim amount to be 30,000 yuan, and the actual claim amount is 32,000 yuan. The error is within an acceptable range, providing a reference for claim handlers to quickly calculate the claim amount.

[0089] Intelligent Processing of Business Processes:

[0090] During the underwriting stage, underwriter Li logs into the business management module, and the system automatically calls the results of the customer risk assessment model and the insurance product rules. A customer applies to purchase a critical illness insurance. The system assesses the customer's risk probability P = 0.25, while the risk threshold T of this insurance product is 0.3. The system automatically passes the underwriting, and the entire underwriting process only takes 5 minutes. Compared with the average duration of 2 hours for traditional manual underwriting, the efficiency is greatly improved.

[0091] During the claims settlement stage, claims adjuster Wang receives an accident insurance claims case. By inputting the relevant information of the claims case through the intelligent claims settlement system, the system automatically obtains the customer's insurance information, past claims settlement records and other materials from the data storage module, and calculates the claims settlement amount according to the results of the claims settlement prediction model and the claims settlement rules. The basic compensation amount for this customer is 50,000 yuan. Considering factors such as no past claims settlement records and a relatively low risk level, the claims settlement adjustment coefficient calculated by the claims settlement prediction model is 1.2. The final claims settlement amount is 50,000 * 1.2 = 60,000 yuan, and the claims settlement process is quickly completed.

[0092] During the marketing stage, according to the results of precision marketing analysis, customers are divided into groups such as young high-income high-risk preference groups and middle-aged stable groups. For young high-income high-risk preference groups, insurance products with relatively high income potential but relatively high risks such as investment-linked insurance are recommended; for middle-aged stable groups, dividend-paying endowment insurance is recommended. After pushing personalized insurance product information to customers through the user interaction module, the purchase conversion rate of the company's insurance products in a certain quarter increased by 25%.

[0093] In summary, the present invention has the following beneficial effects compared with the prior art:

[0094] Improve data processing capabilities: Through an efficient data collection and integration mechanism, break data islands, realize centralized management and sharing of data. Improve the consistency and accuracy of data, provide reliable data support for data analysis and business decision-making. Use big data analysis technology to deeply explore data value, and provide data-driven for the innovation and development of insurance business.

[0095] Optimize business processes: Realize the automation and intelligence of the entire process of insurance business, reduce manual intervention, and improve business processing efficiency. Shorten the underwriting and claims settlement cycles, reduce operating costs, and improve the profitability of insurance companies. Through intelligent risk assessment and fraud detection, reduce the risks of insurance business and ensure the stable operation of insurance companies.

[0096] Enhance the customer service experience: Based on customer portraits and personalized recommendations, provide customers with accurate insurance products and services to meet their diverse needs. Improve customer satisfaction and loyalty, and enhance the market competitiveness of insurance companies. Through a convenient user interface and online service platform, provide customers with 24 / 7 uninterrupted services and enhance the customer service experience.

[0097] The above has introduced the technical solutions provided by the embodiments of the present invention in detail. Specific examples are used herein to elaborate on the principles and implementation manners of the embodiments of the present invention. The descriptions of the above embodiments are only applicable to help understand the principles of the embodiments of the present invention; at the same time, for those of ordinary skill in the art, based on the embodiments of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. An intelligent management system for insurance business based on big data, characterized in that, It includes: A data collection module that collects multi-source data such as customer information, insurance product information, and market data from various channels, and after the collection is completed, transmits the data to the data storage module; A data storage module that stores the data from the data collection module using distributed database technology and provides a data call interface for the data analysis module so that the data analysis module can obtain the data for analysis; A data analysis module that uses big data analysis algorithms and artificial intelligence technology to deeply mine and analyze the stored data called from the data storage module, and the analysis results are fed back to the business management module; A business management module that conducts full-process management of insurance business based on the analysis results provided by the data analysis module, and at the same time feeds back the relevant data requirements in the business process to the data storage module to obtain the data supporting the business; A user interaction module that provides an operation interface for customers and insurance staff. The business requests and data query operations initiated by users through this module are processed by the business management module, and the business management module feeds back the processing results to the user interaction module for display to users; the user interaction module can also transmit the new data generated by users during the operation process to the data collection module for collection.

2. The intelligent management system for insurance business based on big data according to claim 1, characterized in that: The data analysis module constructs a customer risk assessment model using machine learning algorithms.

3. An intelligent management system for insurance business based on big data according to claim 1, characterized in that: The business management module includes corresponding processes for intelligent underwriting, intelligent claims settlement, and precision marketing.

4. An intelligent management system for insurance business based on big data according to claim 1, characterized in that: The data collection module sets the data collection frequency, sources, and collection rules.

5. The intelligent management system for insurance business based on big data according to any one of claims 1-4, characterized in that, The system is deployed in the server cluster of the insurance company, and high-performance server hardware and big data processing software are configured.

6. An intelligent management method for insurance business based on big data, characterized in that, It includes the following steps: Real-time collect internal and external data through the data collection module, and clean and preprocess the data; Store the processed data in the data storage module; The data analysis module analyzes the data according to the preset analysis models and algorithms; Underwriting personnel complete underwriting with the help of the intelligent underwriting system according to the customer risk assessment results and insurance product rules; Claims settlement personnel complete claims settlement processing through the intelligent claims settlement system according to the claims settlement case data and customer information; The insurance company pushes personalized insurance products and services to customers based on the precision marketing analysis results.

7. An intelligent management method for insurance business based on big data according to claim 6, characterized in that: The cleaning and preprocessing of the data cover removing duplicate data and correcting incorrect data.

8. The intelligent management method for insurance business based on big data according to claim 6, characterized in that: The data analysis module uses association rule mining to analyze customers' insurance demand preferences.

9. An intelligent management method for insurance business based on big data according to claim 6, characterized in that: Regularly evaluate and optimize the models in the data analysis module, and adjust the model parameters and algorithms according to new data and business requirements.

10. The intelligent management method for insurance business based on big data according to any one of claims 6-9, characterized in that, Insurance staff log in to the system through the user interaction module for business processing, and customers access the system through the mobile terminal or web terminal to handle business.

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