Financial insurance front-end customer data acquisition and intelligent flow transfer conversion system

Abstract

The invention relates to the technical field of financial science and insurance business customer acquisition, in particular to a financial insurance front-end customer acquisition data acquisition and intelligent flow transfer conversion system, which realizes full-process automatic processing of data acquisition, intelligent flow transfer, data analysis, conversion and feedback optimization through a modular architecture. The system comprises a data acquisition module, an intelligent flow casting module, a data analysis module, a conversion module, a feedback optimization module, a system management module, an interface standardization module, a data governance module and an intelligent decision module, and adopts technologies such as distributed storage, a machine learning algorithm and block chain evidence storage to realize accurate customer acquisition and efficient conversion of financial insurance products. The customer obtaining efficiency and the conversion rate are improved, the operation cost is reduced, and the digital transformation of the financial insurance industry is promoted.

Description

A financial insurance front-end customer acquisition data collection and intelligent lead generation conversion system Technical Field

[0001] This invention relates to the field of financial technology and customer acquisition technology in insurance business, and in particular to a front-end customer acquisition data collection and intelligent lead generation conversion system for financial insurance. Background Technology

[0002] Traditional financial and insurance customer acquisition suffers from problems such as limited data collection, rigid targeting strategies, and low conversion rates. With the development of the digital ecosystem, financial institutions need to build systems that integrate multi-source data and achieve intelligent targeting and dynamic conversion to improve customer acquisition efficiency and conversion rates while reducing operating costs. Summary of the Invention

[0003] The purpose of this invention is to provide a financial insurance front-end customer acquisition data collection and intelligent traffic conversion system. This system achieves fully automated processing of the entire process of data collection, intelligent traffic conversion, data analysis, conversion and feedback optimization through a modular architecture.

[0004] To achieve the aforementioned objectives, the technical solution adopted by this invention is as follows: a financial insurance front-end customer acquisition data collection and intelligent lead generation conversion system. This system includes: a data collection module, which uses a distributed crawler architecture combined with API interfaces to collect user behavior data, device information, geographic location data, social media interaction data, and third-party platform data in real time. Data transmission is end-to-end encrypted using the national cryptographic SM4 encryption algorithm and stored in a distributed data lake built with Hadoop+HBase; an intelligent lead generation module, which constructs user profiles based on machine learning algorithms, including multi-dimensional tags such as demographic attributes, behavioral characteristics, financial preferences, and risk tolerance. It dynamically generates lead generation strategies using a recommendation algorithm combining collaborative filtering and deep learning, supporting A / B testing and multivariate optimization; and a data analysis module, integrating Spark. The MLlib and TensorFlow frameworks are used for data cleaning, feature extraction, model training, and predictive analysis. A risk assessment model is built using random forests and LSTM neural networks, combined with association rule mining algorithms to identify potential user needs. The conversion module, based on data analysis results, performs intelligent insurance product recommendations, policy guidance, online customer service access, and transaction facilitation. A microservice architecture is used for high-concurrency processing, and a dynamic rule engine is supported to adjust conversion strategies. The feedback optimization module uses the ELK stack to monitor traffic delivery effectiveness in real time, employs reinforcement learning algorithms to optimize traffic delivery strategies, and uses a canary release mechanism to gradually update strategies and monitor results. The system management module includes RBAC / ABAC hybrid access control, log auditing with blockchain notarization, system monitoring visualization, and anomaly alerts. The interface standardization module defines RESTful API interface specifications, uses the Swagger framework to automatically generate interface documentation, and supports traffic control and circuit breaking / degradation. The data governance module integrates an automated cleaning rule engine and a machine learning data quality assessment model to achieve standardized data processing and dynamic repair. The intelligent decision-making module integrates a BI visualization platform and deep learning algorithms to achieve real-time monitoring of traffic delivery effectiveness, multi-dimensional analysis, and intelligent decision support.

[0005] Furthermore, the data acquisition module uses the Kafka stream processing engine to realize real-time data entry into the lake, performs real-time data processing through the Flink framework, adopts a cold and hot data tiered storage strategy, stores high-frequency access data in the Redis in-memory database, and archives historical data to the HDFS object storage system.

[0006] Furthermore, the user profile construction in the intelligent traffic delivery module adopts K-means clustering and Apriori association rule algorithm, combined with TF-IDF keyword extraction and Word2Vec word embedding technology to improve the accuracy of user profiles.

[0007] Furthermore, the risk assessment model in the data analysis module adopts an ensemble learning framework, combining random forest, gradient boosting tree and deep neural network, and uses feature selection algorithm to screen key risk factors, and cross-validation and grid search to fine-tune hyperparameters.

[0008] Furthermore, the dynamic rule engine in the conversion module combines the Drools rule engine with a machine learning model to support dynamic configuration and intelligent optimization of conversion strategies, thereby enabling the formulation of differentiated conversion strategies.

[0009] Furthermore, the reinforcement learning algorithm in the feedback optimization module adopts the Q-learning framework, guides the optimization of the policy through the design of the reward function, and realizes the dynamic adjustment of the flow delivery policy by combining Monte Carlo tree search.

[0010] Furthermore, the log auditing in the system management module uses blockchain smart contracts to achieve operation evidence storage, ensures log integrity through the SHA-256 hash algorithm, and verifies operation time by combining timestamp service.

[0011] Furthermore, the interface gateway in the interface standardization module uses the Spring Cloud Gateway framework to implement traffic control and circuit breaking and degradation, and uses Sentinel to implement interface rate limiting and degradation strategies, supporting the parallel operation of multiple version interfaces.

[0012] Furthermore, the BI visualization platform in the intelligent decision-making module adopts a solution combining ECharts and Tableau, supporting real-time monitoring dashboards, multi-dimensional analysis reports, and trend prediction charts, and providing strategic decision support through a data dashboard.

[0013] As an improvement, the beneficial effects of this invention are as follows: This invention achieves fully automated processing of data collection, intelligent data delivery, data analysis, conversion, and feedback optimization through a modular architecture. The system includes a data collection module, an intelligent data delivery module, a data analysis module, a conversion module, a feedback optimization module, a system management module, an interface standardization module, a data governance module, and an intelligent decision-making module. It adopts technologies such as distributed storage, machine learning algorithms, and blockchain notarization to achieve accurate customer acquisition and efficient conversion of financial and insurance products, improve customer acquisition efficiency and conversion rate, reduce operating costs, and promote the digital transformation of the financial and insurance industry. Attached Figure Description

[0014] Figure 1 is a diagram of the overall architecture of a financial insurance front-end customer acquisition data collection and intelligent lead conversion system according to the present invention. Detailed Implementation

[0015] To make the content of this invention easier to understand, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings.

[0016] 1. Data Acquisition Module Implementation: Construct a distributed crawler architecture and API interface gateway, define a standardized data acquisition protocol, and capture user behavior data, device information, geolocation data, social media interaction data, and third-party platform data in real time.

[0017] The national standard SM4 encryption algorithm is used to encrypt the transmitted data end-to-end. The data is then fed into the Hadoop+HBase distributed data lake in real time through the Kafka stream processing engine.

[0018] Implement a tiered storage strategy for hot and cold data. High-frequency access data resides in the Redis in-memory database to achieve sub-second queries, while historical data is archived to the HDFS object storage system.

[0019] An integrated automated cleaning rule engine completes data standardization processing through regular expression matching, missing value imputation, and outlier detection algorithms. A machine learning-based data quality assessment model monitors data consistency, integrity, and accuracy indicators in real time and dynamically triggers the data repair process.

[0020] 2. The intelligent traffic delivery module is implemented by building user profiles based on machine learning algorithms. It adopts K-means clustering and Apriori association rule algorithms, combined with TF-IDF keyword extraction and Word2Vec word embedding technology, to build a multi-dimensional tag system that includes demographic attributes, behavioral characteristics, financial preferences, risk tolerance, etc.

[0021] It employs a recommendation algorithm that combines collaborative filtering and deep learning to dynamically generate traffic delivery strategies based on user profiles, including ad delivery time, channel selection, content recommendation, and budget allocation, and supports A / B testing and multivariate optimization.

[0022] We implement reinforcement learning algorithms to optimize the feed strategy, use the Q-learning framework to design a reward function to guide the optimization direction, combine Monte Carlo tree search to achieve dynamic adjustment of the strategy, and gradually verify and promote the optimal strategy through a canary release mechanism.

[0023] 3. The data analysis module integrates Spark MLlib and TensorFlow frameworks to achieve data cleaning, feature extraction, model training, and predictive analysis. A risk assessment model is built using random forests and LSTM neural networks, combined with association rule mining algorithms to identify potential user needs.

[0024] Key risk factors are screened using feature selection algorithms, and hyperparameters are tuned using cross-validation and grid search to improve model prediction accuracy. An ensemble learning framework is implemented, combining random forests, gradient boosting trees, and deep neural networks to construct a multi-model fusion system.

[0025] 4. The conversion module executes conversion operations based on data analysis results, including intelligent insurance product recommendations, policy application guidance, online customer service integration, and transaction facilitation. A microservice architecture is used to achieve high concurrency processing, and a dynamic rule engine is supported to adjust conversion strategies.

[0026] The dynamic rule engine combines the Drools rule engine with machine learning models to achieve dynamic configuration and intelligent optimization of conversion strategies. It supports the formulation of differentiated conversion strategies based on customer profiles and enables rapid iteration and flexible adjustment of conversion rules through the rule engine.

[0027] 5. The feedback optimization module is implemented by using the ELK technology stack to achieve real-time monitoring of the traffic delivery effect, employing the isolated forest algorithm to detect abnormal click behavior, combining reinforcement learning algorithms to optimize the traffic delivery strategy, and gradually updating the strategy and monitoring the effect through a canary release mechanism.

[0028] We implement the Q-learning reinforcement learning framework, guide the optimization direction of the policy through the design of the reward function, and realize the dynamic adjustment of the flow strategy by combining Monte Carlo tree search. We compare the effects of different strategies through A / B testing and select the optimal strategy for promotion.

[0029] 6. System Management Module Implementation: A hybrid RBAC / ABAC access control model is constructed, combined with LDAP unified identity authentication to achieve multi-level access management. Log auditing and blockchain-based evidence storage are implemented, using the SHA-256 hash algorithm to ensure log integrity and combining it with timestamp services to verify operation times.

[0030] The system is visualized through Prometheus+Grafana, which monitors resource utilization and interface response time in real time. Threshold alarms and intelligent prediction algorithms provide early warning of faults, and automatic scaling mechanisms ensure high system availability.

[0031] 7. Standardized Interface Module Implementation: Defines RESTful API interface specifications, uses the Swagger framework to automatically generate interface documentation, implements traffic control and circuit breaking / defeat through an interface gateway, and supports parallel operation and canary release of multiple version interfaces.

[0032] Implement the Spring Cloud Gateway framework to control API traffic, use Sentinel to implement API rate limiting and degradation strategies, support the parallel operation of multiple API versions, and provide interactive API documentation through Swagger UI.

[0033] 8. The data governance module integrates an automated cleaning rule engine, using regular expression matching, missing value imputation, and outlier detection algorithms to complete data standardization. A machine learning-based data quality assessment model monitors data consistency, integrity, and accuracy metrics in real time, dynamically triggering data repair processes.

[0034] 9. The intelligent decision-making module integrates a BI visualization platform with deep learning algorithms to achieve real-time monitoring of traffic flow effects, multi-dimensional analysis, trend prediction, and intelligent decision support. Through a visualization solution combining ECharts and Tableau, it constructs real-time monitoring dashboards, multi-dimensional analysis reports, and trend prediction charts. This data dashboard enables management to have real-time control over the overall business picture and provide strategic decision support.

[0035] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A front-end customer acquisition data collection and intelligent lead generation conversion system for financial insurance, characterized in that, The system includes: a data acquisition module, which uses a distributed crawler architecture combined with API interfaces to collect user behavior data, device information, geolocation data, social media interaction data, and third-party platform data in real time. Data transmission is end-to-end encrypted using the national cryptographic standard SM4 and stored in a distributed data lake built on Hadoop + HBase; an intelligent traffic delivery module, which constructs user profiles based on machine learning algorithms, including multi-dimensional tags such as demographic attributes, behavioral characteristics, financial preferences, and risk tolerance. It dynamically generates traffic delivery strategies using a recommendation algorithm combining collaborative filtering and deep learning, supporting A / B testing and multivariate optimization; and a data analysis module, integrating Spark. The MLlib and TensorFlow frameworks are used for data cleaning, feature extraction, model training, and predictive analysis. A risk assessment model is built using random forests and LSTM neural networks, combined with association rule mining algorithms to identify potential user needs. The conversion module, based on data analysis results, performs intelligent insurance product recommendations, policy guidance, online customer service access, and transaction facilitation. A microservice architecture is used for high-concurrency processing, and a dynamic rule engine is supported to adjust conversion strategies. The feedback optimization module uses the ELK stack to monitor traffic delivery effectiveness in real time, employs reinforcement learning algorithms to optimize traffic delivery strategies, and uses a canary release mechanism to gradually update strategies and monitor results. The system management module includes RBAC / ABAC hybrid access control, log auditing with blockchain notarization, system monitoring visualization, and anomaly alerts. The interface standardization module defines RESTful API interface specifications, uses the Swagger framework to automatically generate interface documentation, and supports traffic control and circuit breaking / degradation. The data governance module integrates an automated cleaning rule engine and a machine learning data quality assessment model to achieve standardized data processing and dynamic repair. The intelligent decision-making module integrates a BI visualization platform and deep learning algorithms to achieve real-time monitoring of traffic delivery effectiveness, multi-dimensional analysis, and intelligent decision support.

2. The financial insurance front-end customer acquisition data collection and intelligent lead generation conversion system according to claim 1, characterized in that, The data acquisition module uses the Kafka stream processing engine to realize real-time data entry into the lake, performs real-time data processing through the Flink framework, adopts a cold and hot data tiered storage strategy, stores high-frequency access data in the Redis in-memory database, and archives historical data to the HDFS object storage system.

3. The financial insurance front-end customer acquisition data collection and intelligent lead generation conversion system according to claim 1, characterized in that, The user profile construction in the intelligent traffic delivery module adopts K-means clustering and Apriori association rule algorithm, combined with TF-IDF keyword extraction and Word2Vec word embedding technology to improve the accuracy of user profiles.

4. The financial insurance front-end customer acquisition data collection and intelligent lead generation conversion system according to claim 1, characterized in that, The risk assessment model in the data analysis module adopts an ensemble learning framework, combining random forest, gradient boosting tree and deep neural network. It uses feature selection algorithm to screen key risk factors and cross-validation and grid search to fine-tune hyperparameters.

5. The financial insurance front-end customer acquisition data collection and intelligent lead generation conversion system according to claim 1, characterized in that, The dynamic rule engine in the conversion module combines the Drools rule engine with a machine learning model to support dynamic configuration and intelligent optimization of conversion strategies, enabling the formulation of differentiated conversion strategies.

6. The financial insurance front-end customer acquisition data collection and intelligent lead generation conversion system according to claim 1, characterized in that, The reinforcement learning algorithm in the feedback optimization module adopts the Q-learning framework, which guides the optimization of the policy through the design of the reward function and combines Monte Carlo tree search to realize the dynamic adjustment of the flow delivery policy.

7. The financial insurance front-end customer acquisition data collection and intelligent lead generation conversion system according to claim 1, characterized in that, The log auditing in the system management module uses blockchain smart contracts to achieve operation evidence storage, ensures log integrity through the SHA-256 hash algorithm, and verifies operation time by combining timestamp service.

8. The financial insurance front-end customer acquisition data collection and intelligent lead generation conversion system according to claim 1, characterized in that, The interface standardization module uses the Spring Cloud Gateway framework to implement traffic control and circuit breaking / degradation, and Sentinel to implement interface rate limiting and degradation strategies, supporting the parallel operation of multiple version interfaces.

9. The financial insurance front-end customer acquisition data collection and intelligent lead generation conversion system according to claim 1, characterized in that, The BI visualization platform in the intelligent decision-making module adopts a solution combining ECharts and Tableau, supporting real-time monitoring dashboards, multi-dimensional analysis reports, and trend prediction charts, and providing strategic decision support through a data dashboard.

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