Health insurance automatic quotation system based on big data

Abstract

This invention provides a big data-based automated health insurance pricing system, comprising: a big data platform, which is connected to a data acquisition interface via an API; a data analysis and modeling module connected to the big data platform; a rules engine connected to the big data platform; and a user interface on the big data platform for customers or agents to input information, view quotes, and complete the purchase process. The data analysis and modeling module includes actuarial models and a machine learning algorithm library for risk assessment and pricing. The rules engine stores and executes the business rules, compliance requirements, and underwriting policies for insurance products. This invention enables personalized pricing: it can achieve more reasonable rate pricing based on individual health conditions, rather than relying on traditional large-group average data; and it improves efficiency: the automated process significantly shortens underwriting and pricing time, enhancing the customer experience.

Description

Technical Field

[0001] This invention relates to the field of health insurance system technology, specifically to an automatic health insurance quotation system based on big data. Background Technology

[0002] Health insurance, short for health insurance, refers to insurance provided by insurance companies to cover losses caused by health reasons through methods such as disease insurance, medical insurance, disability income loss insurance, and long-term care insurance. It is regulated by the "Administrative Measures for Health Insurance." Types of health insurance include disease insurance, long-term care insurance, and medical insurance (including reimbursement-type medical insurance and fixed-benefit medical insurance).

[0003] Despite this, with the aging population and rapid urbanization, the disease spectrum is undergoing profound changes, leading to a rapid increase in personalized and diversified health needs among the public. Regrettably, while there is a huge demand from the public for health insurance and health management services covering medical care, illness, nursing care, and disability, the development of commercial health insurance is relatively lagging. Developing differentiated health insurance products tailored to the diverse needs of various groups is an urgent priority for the development of health insurance. Therefore, it is necessary to rely on new technologies to generate differentiated, rapid, and rational health insurance products for different groups with varying needs. Summary of the Invention

[0004] The technical problem solved by this invention is to provide an automatic health insurance quotation system based on big data, so as to solve the problems mentioned in the background art above.

[0005] The technical problem solved by this invention is achieved through the following technical solution: a health insurance automatic quotation system based on big data, comprising: a big data platform, which is connected to a data acquisition interface via an API interface; a data analysis and modeling module connected to the big data platform; a rules engine connected to the big data platform; and a user interface on the big data platform for customers or agents to input information, view quotations, and complete the purchase process. The data analysis and modeling module includes actuarial models and machine learning algorithm libraries for risk assessment and pricing. The rules engine stores and executes business rules, compliance requirements, and underwriting policies for insurance products. The API interface exchanges data with external systems such as hospitals, medical examination centers, wearable devices, and third-party data providers.

[0006] Furthermore, the big data platform is responsible for storing, managing, and processing massive amounts of data, using the Hadoop and Spark technology stack.

[0007] Furthermore, the big data platform collects and integrates data from API interfaces, including user health data, lifestyle data, social data, historical claims records, and economic capacity data.

[0008] Furthermore, the data analysis and modeling module utilizes big data technology to clean, deduplicatize, and standardize the collected unstructured and structured data; it employs machine learning and actuarial statistical models to analyze the data in order to assess the client's individual health risks and potential future medical expenses.

[0009] Furthermore, the rules engine generates personalized insurance rates in real time based on the risk assessment results, combined with the insurance company's product rules and preset profit targets, to achieve dynamic pricing and automatic quotation.

[0010] Furthermore, the big data platform has a data source and a collection layer, which are responsible for collecting data from various internal and external sources. Internal data includes the insurance company's existing CRM system, claims database, and policy management system; external data includes medical and health data such as hospital electronic medical record system (EHR) API and physical examination center data interface; and data from wearable devices, public data, and third-party data.

[0011] Furthermore, the big data platform includes a core data storage and processing platform, which comprises a data lake / warehouse: using Hadoop HDFS or Amazon S3 to store raw and semi-structured data; using a data warehouse to store cleaned structured data; and a data processing engine: using Apache Spark or Apache Flink for batch processing and real-time stream processing.

[0012] Furthermore, the big data platform includes a data analysis and modeling layer, which comprises: data cleaning and preprocessing (standardization, deduplication, missing value handling, feature engineering); actuarial and statistical modeling (traditional actuarial models used to establish basic risk benchmarks); machine learning models (risk assessment model using classification algorithms to predict the probability of a customer's illness); medical expense prediction model using regression algorithms to predict possible future medical expenses); personalized pricing model (dynamically adjusting premiums based on risk scores); and fraud detection model (analyzing data patterns to identify potential fraudulent activities).

[0013] Furthermore, the big data platform includes an application service and interaction layer, comprising user interfaces and interfaces for interaction with the system: API Gateway: providing secure API interfaces for front-end applications and external partners to call; Front-end Applications: Customer Portal: allowing customers to fill in information online, obtain quotes, and purchase insurance; Agent Workstation: enabling agents to assist customers in generating quotes and tracking the application process; Underwriter / Actuary Dashboard: monitoring model performance and viewing risk analysis reports; Workflow Automation: automating underwriting and quote approval processes to reduce manual intervention.

[0014] Compared with the prior art, the beneficial effects of the present invention are:

[0015] This invention enables personalized pricing: it allows for more reasonable rate pricing based on individual health conditions, rather than relying on traditional large-group average data.

[0016] Efficiency improvements: Automated processes significantly reduce underwriting and quotation times, enhancing the customer experience.

[0017] Precision Marketing: By deeply analyzing customer data, we help insurance companies accurately reach potential customers and improve conversion rates.

[0018] Risk control: More accurate risk assessment helps insurance companies better manage risk exposure and improve profitability. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0020] To make the technical means, creative features, objectives and effects of the present invention easier to understand, the present invention will be further described below with reference to specific illustrations. In the description of the present invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation", "connection" and "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection between the internal parts of two components.

[0021] Example 1

[0022] like Figure 1As shown, the big data-based automated health insurance pricing system includes: a big data platform, which is connected to a data acquisition interface via an API; a data analysis and modeling module connected to the big data platform; a rules engine connected to the big data platform; and a user interface on the big data platform for customers or agents to input information, view quotes, and complete the purchase process. The data analysis and modeling module includes actuarial models and machine learning algorithm libraries for risk assessment and pricing. The rules engine stores and executes business rules, compliance requirements, and underwriting policies for insurance products. The API interface exchanges data with external systems such as hospitals, health check centers, wearable devices, and third-party data providers. The big data platform is responsible for storing, managing, and processing massive amounts of data, using the Hadoop and Spark technology stack.

[0023] Example 2

[0024] like Figure 1 As shown, the big data-based automated health insurance pricing system includes: a big data platform, which is connected to a data collection interface via an API; a data analysis and modeling module connected to the big data platform; a rules engine connected to the big data platform; and a user interface on the big data platform for customers or agents to input information, view quotes, and complete the purchase process. The data analysis and modeling module includes actuarial models and machine learning algorithm libraries for risk assessment and pricing. The rules engine stores and executes business rules, compliance requirements, and underwriting policies for insurance products. The API interface exchanges data with external systems such as hospitals, medical examination centers, wearable devices, and third-party data providers. The big data platform collects and integrates data from the API interface, including user health data (such as medical examination reports and wearable device data), lifestyle data (such as consumption habits and exercise frequency), social data, historical claims records, and economic capacity data.

[0025] Example 3

[0026] like Figure 1As shown, a big data-based automated health insurance pricing system includes: a big data platform, which is connected to a data collection interface via an API; a data analysis and modeling module connected to the big data platform; a rules engine connected to the big data platform; and a user interface on the big data platform for customers or agents to input information, view quotes, and complete the purchase process. The data analysis and modeling module includes actuarial models and machine learning algorithm libraries for risk assessment and pricing. The rules engine stores and executes business rules, compliance requirements, and underwriting policies for insurance products. The API interface exchanges data with external systems such as hospitals, health check centers, wearable devices, and third-party data providers. The data analysis and modeling module uses big data technology to clean, deduplicate, and standardize the collected unstructured and structured data. The data analysis and modeling module uses machine learning and actuarial statistical models to analyze data to assess customers' individual health risks and potential future medical expenses. The model predicts the probability of contracting various diseases and average treatment costs. Based on the risk assessment results, combined with the insurance company's product rules and preset profit targets, the rules engine generates personalized insurance rates in real time, achieving dynamic pricing and automated quoting.

[0027] Example 4

[0028] like Figure 1 As shown, the big data-based automated health insurance pricing system includes: a big data platform, which is connected to a data acquisition interface via an API; a data analysis and modeling module connected to the big data platform; a rules engine connected to the big data platform; and a user interface on the big data platform for customers or agents to input information, view quotes, and complete the purchase process. The data analysis and modeling module includes actuarial models and machine learning algorithm libraries for risk assessment and pricing. The rules engine stores and executes business rules, compliance requirements, and underwriting policies for insurance products. The API interface exchanges data with external systems such as hospitals, health check centers, wearable devices, and third-party data providers. The big data platform has a data source and an acquisition layer, which are responsible for collecting data from various internal and external sources. Internal data includes the insurance company's existing CRM system, claims database, and policy management system. External data includes medical and health data such as hospital electronic medical record (EHR) system APIs and health check center data interfaces.

[0029] Example 5

[0030] like Figure 1As shown, the big data-based automated health insurance pricing system includes: a big data platform, which is connected to a data collection interface via an API; a data analysis and modeling module connected to the big data platform; a rules engine connected to the big data platform; and a user interface on the big data platform for customers or agents to input information, view quotes, and complete the purchase process. The data analysis and modeling module includes actuarial models and machine learning algorithm libraries for risk assessment and pricing. The rules engine stores and executes business rules, compliance requirements, and underwriting policies for insurance products. The API interface exchanges data with external systems such as hospitals, health check centers, wearable devices, and third-party data providers. The data source and collection layer also collects data from wearable devices: health data (steps, heart rate, sleep quality) from smart devices such as Fitbit and Apple Watch; public data: macro-level health statistics released by the government, regional medical cost levels, and disease incidence data; and third-party data: financial data from credit reporting agencies and social media data (subject to compliance).

[0031] Example 6

[0032] like Figure 1 As shown, the health insurance automated pricing system based on big data includes: a big data platform, which is connected to a data acquisition interface via an API interface; a data analysis and modeling module connected to the big data platform; a rules engine connected to the big data platform; and a user interface on the big data platform for customers or agents to input information, view quotes, and complete the purchase process. The data analysis and modeling module includes actuarial models and machine learning algorithm libraries for risk assessment and pricing. The rules engine stores and executes business rules, compliance requirements, and underwriting policies for insurance products. The API interface exchanges data with external systems such as hospitals, medical examination centers, wearable devices, and third-party data providers. The big data platform has a core data storage and processing platform, which includes a data lake / warehouse: using Hadoop HDFS or Amazon S3 to store raw and semi-structured data; and using a data warehouse (such as Snowflake, Google BigQuery) to store cleaned structured data. The data processing engine uses Apache Spark or Apache Flink for batch processing and real-time stream processing. Databases: Use NoSQL databases (such as MongoDB, Cassandra) to store unstructured data; use relational databases (such as PostgreSQL, MySQL) to store customer files and policy information.

[0033] Example 7

[0034] like Figure 1 As shown, the health insurance automatic pricing system based on big data includes: a big data platform, which is connected to a data acquisition interface via an API interface; a data analysis and modeling module connected to the big data platform; a rule engine connected to the big data platform; and a user interface on the big data platform for customers or agents to input information, view quotes, and complete the purchase process. The data analysis and modeling module includes actuarial models and machine learning algorithm libraries for risk assessment and pricing. The rule engine stores and executes business rules, compliance requirements, and underwriting policies for insurance products. The API interface exchanges data with external systems such as hospitals, medical examination centers, wearable devices, and third-party data providers. The big data platform has a data analysis and model layer, which includes data cleaning and preprocessing: standardization, deduplication, missing value handling, and feature engineering. Actuarial and statistical modeling: traditional actuarial models are used to establish basic risk benchmarks. Machine learning models: risk assessment model: using classification algorithms (such as logistic regression, random forest, gradient boosting tree XGBoost) to predict the probability of a customer's illness. Medical expense prediction model: using regression algorithms to predict possible future medical expenses. Personalized pricing model: dynamically adjusting premiums based on risk scores. Fraud Detection Model: Analyzes data patterns to identify potential fraudulent activities. The big data platform includes an application service and interaction layer, comprising user interfaces and interfaces for system interaction: API Gateway: Provides secure API interfaces for front-end applications and external partners. Front-end Applications: Customer Portal: Customers fill in information online, obtain quotes, and purchase insurance. Agent Workstation: Agents assist customers in generating quotes and tracking the application process. Underwriter / Actuary Dashboard: Monitors model performance and views risk analysis reports. Workflow Automation: Automates underwriting and quote approval processes, reducing manual intervention.

[0035] The system achieves automatic quotation through the following steps:

[0036] Requirements analysis and compliance review: Clarify business requirements and gain a thorough understanding of local health insurance regulations (such as data privacy and fairness).

[0037] Data platform setup: Building a stable data lake and data processing infrastructure.

[0038] Data Acquisition and Integration: Develop data interfaces (APIs) and automation tools to achieve automated data acquisition.

[0039] Model Development and Training: Perform data cleaning and feature engineering to develop and train risk assessment and pricing models.

[0040] Rule engine configuration: Write all business rules (such as age restrictions, exclusion of existing medical history, etc.) into the rule engine.

[0041] System integration and testing: Integrate the analysis model with the front-end application and back-end service to conduct comprehensive end-to-end testing.

[0042] Deployment and monitoring: Deploy the system to the production environment and continuously monitor model performance and system stability.

[0043] Continuous optimization: Regularly retrain and optimize the model using new data to adapt to market changes and new health trends.

[0044] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A big data based health insurance automatic quoting system, comprising: The big data platform is characterized in that: the big data platform is linked with a data collection interface through an API interface, a data analysis and modeling module is connected on the big data platform, a rule engine is also connected on the big data platform, a user interaction interface is provided on the big data platform, which is a front-end application for customers or agents to input information, view quotes and complete the purchase process, the data analysis and modeling module includes actuarial models and machine learning algorithm libraries for risk assessment and pricing; the rule engine stores and executes business rules, compliance requirements and underwriting policies of insurance products; the API interface exchanges data with external systems such as hospitals, medical centers, wearable devices and third-party data providers.

2. The big data based health insurance automated quoting system of claim 1, wherein: The big data platform is responsible for storing, managing and processing massive data using Hadoop and Spark technology stacks.

3. The big data based health insurance automated quoting system of claim 1, wherein: The big data platform collects and integrates data from the API interface, including user health data, lifestyle data, social data, historical claims records and economic ability data.

4. The big data based health insurance automated quoting system of claim 1, wherein: The data analysis and modeling module uses big data technology to clean, deduplicate and standardize the collected unstructured and structured data; machine learning and actuarial statistical models are used to analyze the data to assess the individual health risks and future possible medical expenses of customers.

5. The big data based health insurance automated quoting system of claim 1, wherein: The rule engine generates personalized insurance rates in real time based on risk assessment results, product rules and preset profit targets of insurance companies, achieving dynamic pricing and automatic quoting.

6. The big data based health insurance automated quoting system of claim 1, wherein: The big data platform has a data source and collection layer, which is responsible for collecting data from various internal and external sources. Internal data: existing CRM systems, claims databases and policy management systems of insurance companies; external data: medical health data: hospital electronic medical record system (EHR) API, medical center data interface; collect wearable device data, public data and third-party data.

7. The big data based health insurance automated quoting system of claim 1, wherein: The big data platform has a core data storage and processing platform, which includes a data lake / storage: using Hadoop HDFS or Amazon S3 to store raw and semi-structured data; using a data warehouse to store cleaned structured data; Data processing engine: using Apache Spark or Apache Flink for batch processing and real-time stream processing.

8. The big data based health insurance automated quoting system of claim 1, wherein: The big data platform has a data analysis and modeling layer, which includes data cleaning and preprocessing: standardization, deduplication, missing value processing and feature engineering; actuarial and statistical modeling: traditional actuarial models for establishing basic risk benchmarks; machine learning models: risk assessment models: using classification algorithms to predict the probability of customers getting sick; medical expense prediction models: using regression algorithms to predict future possible medical expenses; personalized pricing models: dynamically adjusting premiums based on risk scores; fraud detection models: analyzing data patterns to identify potential fraudulent behavior.

9. The big data based health insurance automated quoting system of claim 1, wherein: The big data platform is provided with an application service and an interaction layer, including an interface and an interface for user and system interaction: an API gateway: providing a safe API interface for calling by a front-end application and an external partner; a front-end application: a customer portal: a customer fills in information online, obtains a quotation and purchases insurance; an agent workstation: an agent assists a customer to generate a quotation, tracks an application process; an underwriter / actuary dashboard: monitoring model performance, viewing risk analysis reports; Workflow automation: automated underwriting, quotation approval process, reducing manual intervention.

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